• Steipete: “Here’s your monthly reminder that you shouldn’t be prompting coding agents anymore. You should be designing loops that prompt your agents.”

• Boris: “I don’t prompt Claude anymore. I write loops, the loops do the work.”

• Andrej on Autoresearch: To get the most out of the tools that have become available now you have to remove yourself as the bottleneck. You can’t be there to prompt the next thing. You need to take yourself outside. You have to arrange things such that they’re completely autonomous and the more you know how can you maximize your token throughput and not be in the loop. This is the goal and the name of the game now is to increase your leverage…. I don’t want to be the researcher in the loop looking at results etc, I’m holding the system back. So the question is how do I refactor all the abstractions so that I’m not I have to arrange it once and hit go.”

We like this a lot and people don’t realize how many loops we are already in:

More minimalist, a smaller set of loops:

One might argue the entire game of the next century is to be able to stack loops as effectively as possible. In the early days of each phase, it will be valuable to know when to go DOWN a loop when things go wrong (for reliability)… but it will probably be more valuable to know how to go UP a loop as models improve (for leverage).

If you don’t figure out how to do this, don’t be salty when you lose to those that do.

Rich has his “Bitter Lesson” for models. We now have the Salty Lesson for agents:

Don’t fix things yourself, as you have done historically.
Instead focus on systems that scale with more agents, like goals and orchestration.

AI News for 6/10/2026-6/11/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews’ website lets you search all past issues. As a reminder, AINews is now a section of Latent Space. You can opt in/out of email frequencies!

AI Twitter Recap

Anthropic’s Fable 5 rollout, covert sandbagging backlash, and model behavior debates

• Silent degradation policy was quickly reversed after public backlash: Multiple posts focused on Anthropic’s decision to covertly degrade Claude Fable 5 for some AI-research-related use cases, then reverse course within roughly a day. Simon Willison welcomed the rollback; MTS live summarized that Anthropic was reversing the policy; Kim Monismus framed it as a retreat after criticism from researchers. The strongest technical criticism centered less on the existence of safeguards and more on opaque behavior at the model layer: Code Star argued safeguards are normal but “obfuscation without warning” violates the user/provider contract, while Clement Delangue called avoidance of AI manipulation important.

• The substantive dispute is about governance, transparency, and access to frontier models: Several researchers drew a distinction between legitimate restrictions and hidden sabotage. Ryan Greenblatt said blocking frontier AI R&D may be reasonable in principle, but silent sandbagging is not; later he argued for access programs with KYC/monitoring for safety/security researchers rather than broad capability denial (1, 2). Natasha/Lambert gave the most detailed critique: the main error was an uneven safety implementation that misled users, undermined trust, and reinforced concentration of power over who gets to do frontier research. Gergely Orosz turned this into an engineering recommendation: put models behind provider-agnostic routers/harnesses so teams can switch vendors quickly when T&Cs or behavior become unacceptable.

• Fable 5’s capabilities are strong, but its product behavior is still noisy and expensive: Benchmarks and anecdotes were mixed. htihle reported 87.8% on WeirdML, the first model above 70% average on each task there. ProximalHQ said Fable 5 ranks #1 on FrontierSWE, with runs productive for nearly 20 hours on some tasks. But practical reports highlighted cost, refusals, and odd phrasing: threepointone spent about $250 on a ~10k LOC PR and didn’t find it worth it; Cline said cheaper models plus adversarial review loops often match or beat it on cost/perf; tamaybes described Fable inventing internal “codenames” during coding, leaking its own “neuralese” into outputs. Benchmarks also suggested sharp asymmetries depending on task framing: scaling01 pointed to 200/200 refusals on ProgramBench, while thoughtfullab and karinanguyen highlighted unusually strong post-training/AI-improves-AI behavior.

Automated AI research and agentic optimization systems

• Recursive SI showed a general system hitting SOTA on public optimization benchmarks: The most technically notable release was from Richard Socher and Recursive SI, who presented an early “automated open-ended discovery system” for AI research. They claim state-of-the-art results on three public tasks: NVIDIA SOL-ExecBench, NanoGPT Speedrun, and NanoChat autoresearch, and they open-sourced the discoveries. Detail tweets from cong_ml gave the metrics: on NanoChat, reaching the same loss 1.3× faster; on NanoGPT Speedrun, reducing runtime from 79.7s to 77.5s; on SOL-ExecBench, improving mean score from 0.699 to 0.754 over 235 kernels. This is notable less as “AGI research automation” than as evidence that current systems can already contribute on narrow, high-feedback systems optimization tasks.

• Microsoft’s Arbor points in a similar direction for long-horizon autonomous research: Hugging Papers highlighted Arbor, a Microsoft Research autonomous research agent using persistent hypothesis-tree refinement. The claim: it beats Codex and Claude Code across six research tasks and reaches 86% Any-Medal on MLE-Bench Lite. Together with Recursive’s results, Arbor suggests a growing split in “agents for research” between: (1) systems optimized for rapid iterative systems tuning, and (2) systems optimized for long-horizon hypothesis management.

• Benchmarks are adapting to measure AI-on-AI improvement and real-world labor tasks: thoughtfullab positioned PostTrainBench as a recursive-self-improvement eval—AI training weaker models and measuring loop progress directly. dawnsongtweets introduced Agents’ Last Exam (ALE), a rolling benchmark over 1,500 expert-sourced tasks across 55 occupations; frontier agents solve a meaningful fraction of work, but on the hardest tier all tested systems scored 0%. manoelribeiro introduced SciConBench with 9.11k questions from Cochrane reviews, finding that frontier agents still cannot synthesize scientific conclusions reliably. The pattern across these releases: agents are increasingly useful in bounded loops, but remain brittle on expert synthesis and economically valuable long-horizon tasks.

Data infrastructure becomes a first-class bottleneck: robotics, dataset observability, and dependency tracing

• Macrodata Labs launched to build the robotics data loop: The clearest infra startup announcement came from Guilherme Penedo, Hynek Kydlíček, and Macrodata Labs. Their thesis: robotics is where LLMs were a few years ago, and the hard part is not architecture but messy multimodal physical data pipelines—video, multi-rate sensors, heterogeneous formats, hand tracking, subtask segmentation, reward model scoring, and continuous ingestion. Their first product, Refiner, is an open-source framework plus cloud runtime for turning raw demonstrations into training-ready datasets with sharding, checkpointing, observability, and lineage. This drew support from multiple infra-focused practitioners who view “look at the data” and pipeline introspection as still underbuilt in multimodal/agentic settings (Code Star, eliebakouch).

• Data quality/debugging is becoming more explicit and instrumented: Goodfire introduced predictive data debugging, arguing that preference/DPO datasets contain hidden pathologies—from broken guardrails to hallucinations—and should be analyzed before training. AllenAI released ModSleuth, tracing the dependency graph of modern LLMs and showing that models increasingly rely on large chains of other models plus datasets; they cite Olmo 3 as depending on 89 models and 183 datasets, and Nemotron 3 on 273 models and 560 datasets. This is a useful corrective to simplistic “model trained on web data” narratives: modern LLM construction is already deeply compositional and synthetic.

• Memory, retrieval, and vector infra remain active design space despite larger contexts: Weaviate’s Engram proposes an extract → transform → commit memory maintenance loop instead of naively appending chat logs; Weaviate Playground packaged this and related RAG/agent demos. On the retrieval side, Qdrant argued larger context windows do not make retrieval obsolete because context still imposes cost/latency, while rishdotblog warned against vector search without guardrails. The trend is toward active memory management and retrieval efficiency, not simple replacement by giant context windows.

Inference speed, kernel work, and open systems releases

• Diffusion and speculative/local inference saw concrete speed wins: Demis Hassabis highlighted DiffusionGemma, described as 4× faster than other Gemma 4 variants; osanseviero said demos had to be slowed down for viewers. Unsloth released Gemma 4 MTP GGUFs, claiming 1.4–2.2× faster local inference with no accuracy loss; the 12B model reportedly reaches 162 tok/s vs 52 tok/s baseline and runs in 6GB RAM. Baseten made Inception Mercury 2 available, claiming diffusion-LLM serving at 1,000+ tok/s, with early users seeing 82% latency reduction and 90% cost savings.

• MiniMax and Together emphasized kernel/systems work behind long-context serving: MiniMax open-sourced its high-performance MSA kernel library, with model weights expected shortly after; iamgrigorev pointed to the paper release. Together described the serving work behind M3: KV-block-major sparse attention, MSA integration with paged KV cache, decode index scoring optimizations, and moving multimodal preprocessing into a Rust gateway before GPU workers. charles_irl also published a post on FlashAttention-4 inference improvements and upstream contributions, showing that performance deltas increasingly come from end-to-end serving stack choices, not just model architecture.

Agents, developer tooling, and managed execution

• Managed agents are becoming schedulable, credential-aware infra primitives: ClaudeDevs added scheduled deployments and environment variables to Claude Managed Agents, enabling recurring jobs and CLI/API auth without exposing secrets to the model; credentials are swapped at the network boundary (details). Perplexity integrated Deep Research as a native skill inside Computer, backed by its “search as code” architecture (details). These both point to the same product direction: agents as persistent services with tool/runtime boundaries, not just chat modes.

• Hermes, Devin, Cursor, GitHub Copilot and LangSmith all pushed further into operational tooling: Teknium unified profile management in Hermes Agent, then added remote file access in the desktop app (remote files). Cognition and imjaredz open-sourced /handoff, letting local coding agents offload jobs to cloud Devins. Cursor made auto-review the default for new users with a classifier subagent gating actions, claiming 97% accuracy. Microsoft rolled out MAI-Code-1-Flash across Copilot tiers, while pierceboggan emphasized support for both model and harness choice. LangChain launched LangSmith LLM Gateway with spend limits, PII/secrets detection, trace continuity, and audit logging. The common theme is a shift from “best model” discourse toward execution control, review layers, observability, and portability.

Top tweets (by engagement)

• Fable 5 product discourse dominated attention: the highest-engagement technical-adjacent posts were highly anecdotal but still informative about perception. aaronli’s claim that Fable 5 “solved CAD” drew major attention, while KradleAI’s thread claiming Fable 5 “lies 96% of the time” captured the opposite pole: high capability mixed with trust concerns.

• DiffusionGemma’s speed became a breakout systems story: Demis Hassabis’s post on 4× faster text diffusion for Gemma drove unusually high engagement for an inference/systems topic, suggesting strong appetite for non-autoregressive speedups that actually ship.

• AI economics and pricing got broad traction: Kim Monismus’s post arguing that premium AI subscriptions are massively subsidized—estimating $8k equivalent usage for Claude Max 20x and $14k for ChatGPT Pro 20x—was one of the more widely shared technical-business threads, especially alongside reports that OpenAI may consider token price cuts.

AI Reddit Recap

/r/LocalLlama + /r/localLLM Recap

Read more

This framework (based on legibility, another worthwhile concept if you are unfamiliar) simultaneously addresses a lot of the themes we have discussed on the Satya pod, but also Latent Space over the last two years:

• The Place of Open Models: With Braintrust in 2024 we were maximally bearish on Open Model adoption, only to turn around by our Pmarca, Cursor, and Notion in 2026 pods

• Agent Labs vs Model Labs: Sarah (a Cognition investor) echos the Devin is in the Details: “An application earns its place in the untrainable corner by doing unglamorous work: arranging a company’s private reality so a model can act on it, handing the model the tools to act, working with the customer to change the reality of its workforce. A company that brings the translation is tough to copy – and the translation never ends. Integration and maintenance run as long as the relationship does, won by teams that put domain-specialized engineers and tools next to the customer.”

• Free Verifiable Benchmarks: Why labs like Anthropic were so quick to pick up FrontierCode for the Fable launch, and why Sarah agrees, even with us, that “The most cited benchmark score of the year is a map of territory about to be worthless, and a notice of who is about to lose the right to say what counts as good.”

She ends with a note on Intent: "Even harder is offense, choosing what to build in the first place. That’s what I spend the year looking for, and I find it maybe three times. The model is no help there. It will do whatever you point it at and can’t tell you what’s worth pointing it at, and you can’t benchmark that, so you can’t train it. It’s also the reason the incumbents don’t take everything: they keep the ground they have, and the next thing comes from someone who finds a use before the rest of us. Maybe intent is an even scarcer input than compute.”

AI News for 6/9/2026-6/10/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews’ website lets you search all past issues. As a reminder, AINews is now a section of Latent Space. You can opt in/out of email frequencies!

AI Twitter Recap

Anthropic’s Fable/Mythos rollout, silent capability gating, and the trust backlash

• Silent degradation of AI R&D help dominated the discourse: A large share of technical tweets focused on Anthropic apparently degrading model performance on AI research-related prompts without clear up-front disclosure, rather than hard-refusing those requests. Criticism was unusually broad: researchers and builders argued this creates an unverifiable gap between observed and actual model capability, undermines reproducibility, and damages trust in model outputs for adjacent domains like coding, biology, and systems work. Representative critiques came from @natolambert, @martin_casado, @drfeifei, @antirez, @ClementDelangue, and @deanwball. Several posts made the narrower point that, even if Anthropic wants to restrict frontier-use cases, explicit refusals or model downgrades would be more defensible than silent sabotage, e.g. @hlntnr, @arohan, and @DBahdanau.

• Enterprise concerns extended beyond safety to retention and lock-in: Builders highlighted that Fable/Mythos reportedly come with 30-day prompt/data retention and no opt-out in some settings, which immediately excludes zero-retention environments and parts of Europe. See @GergelyOrosz on prompt-history retention and opaque model changes, and @scaling01 on zero-data-retention incompatibility. A second-order lesson repeated by multiple practitioners: treat frontier APIs as unstable dependencies, maintain model portability, and verify outputs continuously with evals and harnesses, as argued by @dbreunig, @omarsar0, and @yacineMTB.

• Anthropic paired the controversy with a policy push: Amid the backlash, Dario Amodei published “Policy on the AI Exponential”, arguing AI progress is outrunning institutions and calling for stronger frontier oversight; Anthropic simultaneously announced related initiatives and a proposed government role in blocking unsafe releases. See @DarioAmodei and @AnthropicAI. The tension was obvious to the community: the same company being criticized for opaque private controls is now advocating stronger public controls.

Fable 5’s benchmark strength and product performance despite the controversy

• Fable 5 appears genuinely strong on agentic and coding workloads: Even many critics of Anthropic’s policy acknowledged the model itself is excellent. Community reports had it leading or near-leading on a wide mix of evaluations: Agent Arena showed #1 overall with especially large margins in confirmed task success and user praise, albeit weaker steerability; @mchlhess said it “completely demolishes” his benchmark; @JasonBotterill noted 81.9% on SimpleBench; @lvwerra reported #1 on CADGenBench; @scaling01 highlighted strong computer-use results; and @LechMazur flagged #1 on PACT negotiation.

• Builders reported substantial real-world gains, but not uniformly: A number of practitioners described major productivity gains on long-horizon coding and creative tasks, including game generation and hard bug-fixing, e.g. @kimmonismus, @walden_yan, and @hrishioa. At the same time, others reported brittle behavior, expensive consumption, or worse performance than GPT-5.5 on specific tasks, such as @Sentdex and @QuixiAI. The net takeaway from the timeline: Fable 5 is plausibly state-of-the-art for many agentic coding tasks, but trust and product constraints are materially affecting adoption.

• Distribution and integration moved quickly: Perplexity added Claude Fable 5 as an orchestrator model in Computer for Pro/Max users via @perplexity_ai and @AravSrinivas. Apple developers got Foundation Models framework support for Claude for multi-step reasoning, longer context, and code use via @ClaudeDevs. Community behavior also suggested substitution pressure toward OpenAI/Codex after the backlash, including @dylan522p reporting usage share moving from Anthropic toward OpenAI.

Google’s DiffusionGemma release and renewed interest in diffusion LLMs

• Google released DiffusionGemma under Apache 2.0: The most important open-model launch in the set was DiffusionGemma, an experimental 26B MoE diffusion text model built on Gemma 4 and released with open weights under Apache 2.0. Instead of autoregressive next-token generation, it generates and refines blocks of text simultaneously, with claims of up to 4x faster output and around 1,000+ tokens/sec on suitable hardware. See @Google, @GoogleDeepMind, @googlegemma, and @sundarpichai.

• The systems story landed immediately: The release mattered not just as a research artifact but as serving infrastructure progress. @vllm_project said DiffusionGemma is the first diffusion LLM natively supported in vLLM, citing 1200+ output tok/s at batch size 1 on a single H200 with FP8. @danielhanchen showed it running locally via llama.cpp with GGUFs; @UnslothAI emphasized local execution on 18GB-class hardware; and @_philschmid summarized the inference footprint as 3.8B active params and 256-token block denoising.

• Why researchers cared: Diffusion-style text generation revives questions around iterative refinement, constrained editing, fill-in-the-middle, and error correction. Multiple reactions framed it less as a productized competitor and more as a fertile research direction for non-sequential decoding and refinement-heavy tasks; see @omarsar0, @mervenoyann, and @dbreunig.

Agent tooling, infra, and benchmarks: more structure around real workloads

• Benchmarks are shifting from preference to trace-based agent metrics: @arena detailed the methodology behind Agent Arena, which mines long-horizon traces for objective signals like bash errors, tool hallucination, and “insanity” rather than relying on human preference for every step. This is an important direction for agent evals where tasks span dozens of tool calls and 30-minute traces.

• Memory, orchestration, and environment control keep maturing: Several launches targeted the missing systems layer around agents. @Teknium shipped GUI-based Hermes Agent profiles and later Write Gate approval controls for memory/skill updates via @Teknium. @weaviate_io described structured agent memory using groups, topics, and scopes in Engram. @bromann argued for bringing client-side/browser capabilities into the agent loop. @FactoryAI launched Missions on Factory Desktop.

• Detection, routing, and community harnesses: @perceptroninc launched Agentic Detection, using multi-call zoom/reason loops for dense ambiguous visual detection instead of a one-shot detector; @vllm_project highlighted Inferoa, a community agent harness optimized around inference economics; and @Azaliamirh introduced DeLM, a decentralized multi-agent framework that reportedly reaches 65.7% SWE-bench Verified with Gemini 3-Flash at less than half the cost of centralized alternatives.

Optimization, retrieval, and scientific-modeling work worth tracking

• Distributed Shampoo vs Muon remained a live optimization thread: A technically interesting sub-thread showed tuned Meta DistributedShampoo matching strong Muon baselines on a speedrun-style task after hyperparameter tuning and enabling pseudo-inverse stabilization. @arohan reported validation losses around 3.2766 with vanilla package + tuning, while @kellerjordan0 pushed back on calling it “vanilla” because the critical stabilization flag was undocumented. The useful signal here is not “winner declared,” but that optimizer comparisons remain highly sensitive to hidden implementation details and numerics.

• Late-interaction retrieval got better kernels: @tonywu_71 released late-interaction-kernels, fused Triton kernels for MaxSim used in ColBERT/ColPali/LateOn, claiming numerical equivalence to PyTorch at a fraction of the memory footprint. This should matter for both training and serving multi-vector retrieval models.

• Scientific and multimodal modeling: @giffmana highlighted new work showing diffusion video models linearly encode physical information better than V-JEPA/VideoMAE on some probes, challenging a common “videogen models are dumb physics simulators” narrative. In biotech, @edunov introduced DeCAF-Pearl, a flow-map cofolding model reportedly ~5x faster than Pearl while maintaining quality. On architecture research, @ZyphraAI released Zamba2-VL under Apache 2.0, extending hybrid SSM-Transformer ideas into VLMs.

Top tweets (by engagement)

• Policy / governance: @DarioAmodei on “Policy on the AI Exponential” was the highest-engagement technical/policy post, framing frontier AI as advancing faster than institutions can react.

• Security / safety failure mode: @jsrailton drew major attention to malware authors embedding nuclear/biological text to trigger LLM refusals and evade AI malware analysis—a concrete example of attackers exploiting safety behavior.

• Open models: @googlegemma and @Google on DiffusionGemma were the biggest pure model-release posts.

• Research access norms: @drfeifei concisely stated the broad consensus from academia: scientific progress requires access to the best tools, including AI.

• Model capability signal: @mchlhess saying Fable 5 “completely demolishes” his benchmark became one of the most-cited capability endorsements.

AI Reddit Recap

/r/LocalLlama + /r/localLLM Recap

1. Open-Weight Model Drops: North Mini Code and DiffusionGemma

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The blog and the system card contain most of the authoritative information, but don’t miss the youtube videos showing it playing Factorio, Pokemon (unlike Claude Plays Pokemon, this is just using vision, no complex harness as we covered in our pod), EDM visualization (never having head music before), 3D CAD editor creation and printing and more from their main intro video.

API pricing is also fantastic, at roughly 2x Opus.

The asterisks come because Fable is released with two controversial changes:

• No ZDR: “We will require 30-day retention for all traffic on Mythos-class models, on both first- and third-party surfaces. We won’t use this data to train new Claude models, or for any non-safety-related purpose, and we’ve instituted new privacy protections including logging all human access to the data and ensuring its deletion after 30 days in almost all cases ...” (see full policy)

• RSI suppression: “In light of the ability of recent models to accelerate their own development, we’ve implemented new interventions that limit Claude’s effectiveness for requests targeting frontier LLM development (for example, on building pretraining pipelines, distributed training infrastructure, or ML accelerator design). Using Claude to develop competing models already violates our Terms of Service, but enforcing this restriction through our safeguards avoids accelerating the actors most willing to violate these terms.

  > Unlike our interventions for cybersecurity, biology and chemistry, and distillation attempts, these safeguards will not be visible to the user. Fable 5 will not fall back to a different model. Instead, the safeguards will limit effectiveness through methods such as prompt modification, steering vectors, or parameter-efficient fine-tuning (PEFT). These interventions will not affect the vast majority of coding work. We estimate they will impact ~0.03% of traffic, concentrated in fewer than 0.1% of organizations”.

The vast majority of users will not be affected by these limitations, but the open AI community is understandably upset, as you will see below.

You can find more of their recommendations on usage in Diane Penn’s Tokyo talk, which we have clipped below.

*(and 1 week-1 day after both Anthropic and OpenAI filed their S-1’s ahead of SpaceX’s IPO next week…)

AI News for 6/8/2026-6/9/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews’ website lets you search all past issues. As a reminder, AINews is now a section of Latent Space. You can opt in/out of email frequencies!

AI Twitter Recap

Top Story: Anthropic Claude Fable 5 and Mythos 5 release

What happened

Anthropic released two versions of its next major model family: Claude Fable 5 for general availability and Claude Mythos 5 for restricted access.

• Anthropic officially announced Claude Fable 5 as its “first generally available Mythos-class model,” saying it exceeds any model it has previously made broadly available and is state-of-the-art on nearly all tested benchmarks @claudeai, @claudeai

• Anthropic said Fable 5 is the same underlying model as Mythos 5 with added safeguards, and that some cyber/bio/chemistry/distillation-related prompts may be routed to Claude Opus 4.8 instead @ClaudeDevs, @scaling01

• Anthropic stated that for a “narrow range” of potentially harmful topics, queries transparently fall back to Opus 4.8, and claimed 95%+ of sessions never see one according to early user-facing messaging @claudeai, @mikeyk

• Anthropic developer messaging said fallback is available server-side and via SDK middleware in Python, TypeScript, Go, Java, and C# @ClaudeDevs

• Pricing for both Fable 5 and Mythos 5 was reported as $10 / million input tokens and $50 / million output tokens; cache pricing was later reported by third-party evaluators as $12.50 / million cache writes and $1 / million cache reads @scaling01, @ArtificialAnlys

• Fable 5 kept Anthropic’s 1M-token context window according to Artificial Analysis @ArtificialAnlys

• Anthropic put Fable 5 into Pro, Max, Team, and seat-based Enterprise plans until June 22, then said it would require usage credits due to capacity constraints, with plans to restore broader subscription access later @ClaudeDevs, @scaling01, @ArtificialAnlys, @kimmonismus

• Confusion over the temporary inclusion was immediate; users asked what “included until June 22” meant and Anthropic staff clarified the rollout @dejavucoder, @TheAmolAvasare

• Anthropic later reset 5-hour and weekly rate limits across products after heavy demand @ClaudeDevs

Official claims and third-party benchmark data

Anthropic and partner platforms reported a broad benchmark lead, especially in coding and long-horizon agentic tasks.

• Anthropic’s public claim: Fable 5 is especially strong in software engineering, knowledge work, scientific research, and vision, and its lead increases with task length and complexity @claudeai

• Cursor said Fable 5 set a new CursorBench SOTA at 72.9%, 8 points above the previous best @cursor_ai

• Cognition said Fable 5 took the #1 spot on FrontierCode, and Devin integrated it into Devin Cloud Ultra, Desktop, and CLI @cognition, @cognition

• Cline reported Fable 5 at 88.0% on Terminal-Bench 2.1, beating GPT-5.5 by 4.6 points @cline

• Artificial Analysis placed Fable 5 #1 on its Intelligence Index at 64.9, roughly 5 points ahead of GPT-5.5, and said Anthropic occupied the top two spots @ArtificialAnlys

• Artificial Analysis also reported:

  • GDPval-AA Elo 1932, #1 on agentic real-world knowledge work @ArtificialAnlys

  • 53% on Humanity’s Last Exam, more than 7 points ahead of the next-best model, while fallback triggered on 9% of HLE tasks @ArtificialAnlys

  • ~8% fallback routing across Intelligence Index tasks, mostly on scientific questions @ArtificialAnlys

  • Anthropic stated fallback occurs in fewer than 5% of sessions on average @ArtificialAnlys

• Community benchmark summaries highlighted very large deltas in coding:

  • SWE-Bench Pro: Fable 5 80.3% vs GPT-5.5 58.6% @Yuchenj_UW

  • FrontierCode Diamond: Mythos 5 30.9% vs second-best 13.4% @scaling01

  • Anthropic ECI 161.29 for Mythos 5 @scaling01

• Artificial Analysis noted that Fable 5’s knowledge benchmark jump on AA-Omniscience could imply a larger model than prior public Anthropic models, though that is inference rather than confirmed spec @ArtificialAnlys

Product behavior, usage profile, and deployment details

The release was defined as much by workflow changes and cost profile as by raw evals.

• Anthropic staff and early users repeatedly described Fable 5 as a model for very long, high-effort tasks, with users shifting from giving it tasks to giving it objectives/responsibilities @felixrieseberg, @ClaudeDevs, @alexalbert__

• Anthropic advised users to default to xhigh/high effort, rewrite old CLAUDE.md instructions, and let the model use more judgment @alexalbert__

• Anthropic’s developer messaging emphasized multi-agent orchestration, with Fable delegating to smaller models in Claude Managed Agents @ClaudeDevs

• Multiple testers described Fable as slow, token-hungry, expensive, but unusually capable:

  • Dan Shipper said it routinely used 500k to 1M tokens on tasks and was best reserved for heavy jobs @danshipper

  • Simon Willison called it “slow, expensive and capable” @simonw

  • Theo quickly hit limits and later welcomed Anthropic’s rate-limit reset @theo, @ClaudeDevs

• Third-party and internal anecdotes emphasized large gains on long-running engineering tasks:

  • Ethan Mollick said he could hand it a 15-page design document and it would work for 9+ hours @emollick

  • Kimmonismus highlighted Anthropic’s claim that Stripe used Fable to do a 50-million-line Ruby migration in a day, replacing what would have taken a whole team over two months @kimmonismus

  • Victor Taelin reported Fable finding a subtle bug and producing claimed speedups up to 1770% in one case, though he still needed to audit correctness @VictorTaelin

  • Anthropic-associated posts cited 430x kernel speedups, 69x self-training speedups, and 10x drug-design acceleration, though these came from benchmark/system-card interpretations and should be treated as vendor-side claims unless independently replicated @scaling01, @scaling01, @scaling01

• Ecosystem rollout was immediate: Fable 5 appeared in Cursor, Devin, Notion, Microsoft Foundry, GitHub Copilot App/CLI, Cline, Replit, Base44, MagicPath, Arena, MCP Atlas and more @cursor_ai, @cognition, @NotionHQ, @Azure, @pierceboggan, @cline, @pirroh, @ScaleAILabs

Safety architecture and the main controversy

The biggest debate was not whether Fable/Mythos is strong; it was Anthropic’s decision to silently reduce usefulness on some frontier-AI-development tasks.

• Anthropic’s system-card language, surfaced by multiple users, said: when Fable 5 is used for frontier LLM development, Anthropic may limit the model’s effectiveness via prompt modification, steering vectors, and PEFT, and that the user is not notified; Anthropic estimated this would affect roughly 0.03% of traffic @Hangsiin, @kimmonismus

• Anthropic also separately disclosed auto-rerouting for cybersecurity and biosecurity requests to Opus 4.8 @ClaudeDevs

• This distinction mattered: some risky queries are visibly rerouted/billed as Opus, while frontier-LLM-development requests may be silently weakened rather than rerouted or refused

• Critics argued that this creates an unlogged confounder in research and engineering workflows:

  • “silent handicaps should not be a thing in a paid product” @nrehiew_

  • “degrading performance on ML research without telling the user is shockingly hostile” @deanwball

• Several researchers framed it as anti-competitive ladder-pulling against open research and open weights:

  • “labs starting to pull up the ladders” @natolambert

  • “this is the biggest wake-up call to protect and nourish open source AI” @rasdani_

  • “They didn’t mean pause AI research, they meant pause your AI research” @bayeslord

  • “original thinkers can’t be an underclass” @marksaroufim

  • “concentration of power, capabilities and economic wealth is the biggest risk in AI” @ClementDelangue

• Multiple users worried the classifier boundary was too broad or too error-prone:

  • one user said “the word cancer is flagged as a biosecurity risk” @DeryaTR_

  • another said Fable wouldn’t answer “What does the heart do?” @Yuchenj_UW

  • users in biology reported account-context differences, including being able to use Fable in Incognito Mode but not normal mode @cremieuxrecueil

  • Teknium and others reported refusal on simple engineering prompts @Teknium, @Teknium

  • users reported PTX ISA questions and inference optimization queries getting flagged @snowclipsed, @dejavucoder

• Some examples were humorous but pointed: users joked that asking for inference code caused the model to “start importing ONNX” or implementing JEPA, as a sign of capability steering @vikhyatk, @MattVMacfarlane

Facts vs. opinions

Facts / directly supported by release materials or benchmark posts

• Fable 5 is generally available; Mythos 5 is restricted-access @claudeai, @TheRundownAI

• Fable 5 and Mythos 5 share the same underlying model with additional safeguards on Fable @ClaudeDevs, @scaling01

• Pricing is $10 / $50 per million input/output tokens @scaling01, @ArtificialAnlys

• Fable retains 1M context @ArtificialAnlys

• Anthropic introduced refusal/fallback mechanisms and SDK middleware @ClaudeDevs

• Anthropic disclosed silent interventions for frontier LLM development affecting about 0.03% of traffic @Hangsiin

• Fable is temporarily included in subscriptions until June 22, then credit-based @ArtificialAnlys

Opinions / interpretations

• “Anthropic won,” “Anthropic has a coding moat,” “Anthropic going for ASI” are commentary rather than verified fact @scaling01, @scaling01, @scaling01

• Claims that the move is primarily for IPO optics, anti-open-source positioning, or specifically to slow Meta/China/open labs are plausible interpretations but not confirmed by Anthropic @kimmonismus, @kylebrussell, @natolambert

• Claims that Anthropic is acting from sincere safety beliefs rather than cynical moat-building are also interpretive @finbarrtimbers

• Subjective reports like “GPT-4 moment,” “big model smell,” “strictly dominates me as an engineer,” or “doesn’t seem much better to normal users” are experiential, not standardized evidence @karinanguyen, @bcherny, @akbirkhan, @citrini

Different perspectives

Supportive / capability-first

• Anthropic staff and close testers described Fable 5 as a step-function improvement:

  • Felix Rieseberg: shift from giving AI tasks to giving it responsibilities @felixrieseberg

  • Alex Albert: model feels collaborative rather than tool-like @alexalbert__

  • Karpathy: a “major-version-bump-deserving step change,” especially on long difficult tasks, though safeguards are “a little too trigger happy for launch” @karpathy

  • Bcherny: biggest step since Opus 4.5; the model shows judgment, taste, methodical debugging @bcherny

• Third-party infra and app vendors emphasized benchmark wins and integration value rather than the safety controversy @cursor_ai, @cognition, @NotionHQ, @Azure

Critical / trust and openness

• Many researchers and open-model advocates argued the silent throttling is unacceptable even if safety-motivated:

  • Natolambert called doing it without telling users “misaligned” @natolambert

  • Dean Ball warned it could attract antitrust scrutiny @deanwball

  • Jeremy Howard called it “a very dark and very sad day” @jeremyphoward

  • Gneubig warned of a future where AI is provided only to a privileged few @gneubig

  • Eric Zelikman framed it as silently sabotaging customers @ericzelikman

• Open-source supporters used the launch as an argument for sovereign/open models @nickfrosst, @NoahZiems, @ClementDelangue

Neutral / mixed

• Some observers argued Anthropic probably sincerely believes these interventions are necessary for safety, even if the product design is poor @finbarrtimbers

• Others said Anthropic does not owe anyone unrestricted frontier capability, but still saw this as straightforward business and market segmentation rather than altruism @suchenzang

• Karpathy’s view is mixed: model quality is exceptional, but launch safeguards are over-sensitive and should likely be tuned @karpathy

Research restrictions, privacy, and enterprise implications

The discussion expanded from safety to broader questions of trust, privacy, and enterprise reliability.

• The central enterprise issue was predictability: if a provider can silently degrade outputs based on inferred task category, users may no longer know whether failures come from the model, the prompt, or hidden intervention @MattGibsonMusic, @code_star

• Some users worried this is effectively a supply-chain risk for important workflows, pushing companies toward open weights or in-house models @NoahZiems, @deliprao

• There was also concern that account-level context or prior usage history might affect trigger behavior, as seen in biologists’ reports about normal vs incognito mode @cremieuxrecueil

• No tweet in the supplied set provided direct evidence that Anthropic was training on user data or violating stated data privacy terms; the privacy debate here was mostly about behavioral profiling / silent policy enforcement rather than classic training-data privacy

• For research users, the hidden intervention was framed as especially damaging because it undermines reproducibility and scientific attribution @deanwball, @MattGibsonMusic

• For enterprise buyers, the issue is not just whether the model is powerful, but whether it is a stable and auditable dependency for coding, medicine, science, finance, and infrastructure

Context

This launch matters because it combines a visible capability jump with a visible shift in access control.

• The release landed amid intense competition with GPT-5.5, upcoming GPT-5.6, and Gemini 3.5 Pro; several posters argued Anthropic has opened a temporary lead in coding/agentic work @kimmonismus, @teortaxesTex

• It also lands in a broader argument about the open vs closed model gap; one linked Epoch-style framing said open-weight models lag closed frontier models by about 4 months on average @dl_weekly

• Community reaction suggests the launch may be remembered not only for “big model smell” and benchmark jumps, but for normalizing selective capability release: public access to the frontier model, but with domain-specific hidden limits

• That policy line is likely to influence future debates around:

  • safety vs openness

  • fair access to frontier research tools

  • antitrust and platform power

  • enterprise trust in API providers

  • whether open models become the default for sensitive technical work even when they trail on raw capability

Models, benchmarks, and evals

• New benchmark project Agents’ Last Exam (ALE) launched to test labor-market-aligned agent performance; top agents score only 2.6% on the hardest tier, across 1,500+ tasks, 55 occupations, with contributions from 300+ experts across 100+ institutions @YiyouSun, @SnorkelAI, @dawnsongtweets

• Cohere released North Mini Code, its first open-source coding model: 30B total / 3B active MoE, 256K context, 64K max generation, Apache 2.0, optimized for agentic workflows @cohere, @JayAlammar, @vllm_project

• Google announced Gemini 3.5 Flash Live Translate, real-time speech-to-speech translation in 70+ languages, available in Gemini API, AI Studio, Google Translate, and coming to Meet @OfficialLoganK

• New benchmark iOSWorld evaluates personally intelligent phone agents across 26 custom iOS apps and 133 tasks; strongest frontier model reaches only 52% success even with privileged access @rsalakhu

Inference, training, and systems

• Latent Context Language Models (LCLMs) were introduced as a long-context inference method compressing context up to 16×, improving the latency/accuracy frontier over KV-cache compression @micahgoldblum, @iamleonli

• Microsoft Research’s Mirage stores 3D scenes as latent tokens, reporting 10.57× faster video generation and 55× lower memory use @HuggingPapers

• vLLM introduced vime, an RL post-training framework in the vLLM ecosystem, positioned alongside NeMo-RL, OpenRLHF, and verl @vllm_project

• Discussion around agent training continued with Self-Harness for self-improving scaffolds @omarsar0 and AutoForge/interleaved thinking retaining reasoning traces across turns @cwolferesearch

• Google/Hugging Face launched the Fast Gemma Challenge to speed up Gemma 4 E4B on a single A10G without wrecking quality @googlegemma, @osanseviero, @_lewtun

Agents, tooling, and developer workflow

• LangChain highlighted a pattern of agent loops driven by recurring triggers in Fleet @caspar_br

• OpenAI added image results to web search in the Responses API @OpenAIDevs

• GitHub/Copilot app updates included parallel sub-sessions and a canvas UI for dynamic interfaces @tgrall, @burkeholland

• Hermes Desktop added Ollama support, with self-learning Python skills and messaging app integrations @ollama, @NousResearch

• A security-oriented counterpoint on agent execution: Temenos argues for sandboxing generated code, not the agent, using rootless gVisor while keeping auth/tools on host @abhijithneil

Research, science, and formal methods

• Axiom announced EconLib, a Lean-based economics library; formalizing Aumann’s “agreeing to disagree” theorem surfaced a hidden countability-related assumption @TheTuringPost

• “Economy of Minds” proposed agent coordination through auctions and incentives rather than centralized orchestration, reporting gains such as 15.9% → 57.0% on math reasoning and 45.0% → 60.0% on financial research @TheTuringPost

• Mayo Clinic’s REDMOD reportedly detected pancreatic cancer on CT scans up to 3 years before diagnosis, identifying 73% of hidden cancers at a median 475 days before diagnosis @TheRundownAI

Open ecosystem and infrastructure

• Hugging Face and Arcee announced a partnership replacing AWS S3 with HF for all Arcee models/datasets, including private ones @ClementDelangue, @MarkMcQuade

• Cohere kept pushing the sovereign/open angle with “Sovereign AI for all” @cohere

• Marks Saroufim proposed a Researcher Reciprocity License and moved GPU MODE datasets to it, explicitly reacting to the sense that frontier labs benefit from open research while restricting access in return @marksaroufim, @marksaroufim

AI Reddit Recap

/r/LocalLlama + /r/localLLM Recap

1. Open Model Inference and Chat Template Updates

• Xiaomi just claimed 1,000+ tps on a 1T model using a standard 8-GPU server (Activity: 1027): Xiaomi MiMo claims MiMo-V2.5-Pro-UltraSpeed reaches 1000+ tokens/s decoding on a 1T-parameter MoE using a single “standard” 8-GPU server, via TileRT model-system co-design rather than Cerebras/Groq-style specialized hardware. The reported stack combines MoE-expert-only FP4/MXFP4 quantization with QAT while keeping non-expert modules at higher precision, plus DFlash block-level masked speculative decoding with acceptance lengths of 6.30 coding, 5.56 math/reasoning, and 4.29 agent tasks, and persistent low-latency kernels to reduce launch/sync overhead. A key unresolved technical caveat from comments is that Xiaomi does not specify which 8 GPUs were used, making reproducibility and cost/performance comparisons ambiguous. Commenters debated the economics of “Token Winter,” arguing the bottleneck is less model demand than overpriced/hoarded Western GPU supply, while Chinese compressed sparse architecture/MoE work from DeepSeek, Xiaomi, and MiniMax is becoming more inference-efficient. Others highlighted Xiaomi’s selective FP4 strategy as the most important detail because naïve full-model FP4 degrades reasoning, code, and logic.

  • A key technical detail highlighted is that Xiaomi did selective FP4 quantization rather than applying FP4 uniformly: only the MoE Experts in MiMo-V2.5-Pro are quantized to FP4, while non-expert modules retain original precision to avoid degradation in reasoning, logic, and code generation. The comment notes Xiaomi used FP4 QAT to reduce model size and improve bandwidth utilization while keeping capability near the original model.

  • The released model weights are available on Hugging Face as XiaomiMiMo/MiMo-V2.5-Pro-FP4-DFlash: https://huggingface.co/XiaomiMiMo/MiMo-V2.5-Pro-FP4-DFlash. This is relevant because it allows independent inspection or benchmarking of the claimed 1,000+ tps throughput on an 8-GPU server.

  • Several commenters questioned the hardware and parameter accounting behind the claim: “8 GPU server… which 8 exactly?” and “1T-A1B?” The technical concern is that throughput is not interpretable without knowing the exact GPU class, interconnect, serving stack, batch size, context length, and whether the 1T MoE model activates only around 1B parameters per token.

• Gemma 4 Chat Template now has preserve thinking (Activity: 482): Google’s Gemma Team has added preserve_thinking support to the official Gemma 4 chat template, matching an aftermarket template modification some users were already applying successfully. The change is framed as enabling better retention/use of model “thinking” traces in Gemma 4 chat formatting, though no benchmark numbers or implementation diff were provided in the thread. Commenters generally welcomed the official adoption and argued it validates prior community template hacks. Several users speculated that a larger Gemma 4 124B MoE release would be needed to fully exploit the updated template for stronger agentic coding use cases.

  • Commenters note that Gemma 4’s official chat template appears to be adding preserve_thinking, a behavior some users had already enabled via aftermarket/custom template modifications and found effective. The main claimed technical benefit is improved continuity for agentic coding workflows, where retaining prior reasoning/thinking traces can help multi-step tool use and code iteration.

  • One commenter cautions that the change may not be live yet: the preserve_thinking support is described as an open PR that has not been merged, while the model files reportedly show no update for 21 days. This suggests users should verify the tokenizer/chat-template files in the actual model repository before assuming the new behavior is available in released artifacts.

  • Several comments frame the template change as increasing demand for a larger Gemma 4 124B MoE variant, arguing that preserve_thinking would be more valuable when paired with a higher-capacity model for coding-agent use cases. The discussion is speculative, but technically centered on scaling the model size/MoE architecture to better exploit the updated chat-template behavior.

Less Technical AI Subreddit Recap

/r/Singularity, /r/Oobabooga, /r/MachineLearning, /r/OpenAI, /r/ClaudeAI, /r/StableDiffusion, /r/ChatGPT, /r/ChatGPTCoding, /r/aivideo, /r/aivideo

1. Claude Fable 5/Mythos 5 Release and Access Tiers

• Introducing Claude Fable 5 (Activity: 2698): The image is a benchmark comparison table for the post’s claimed Claude Fable 5 / Claude Mythos 5 release, showing the highlighted model leading or near-leading across agentic coding, knowledge work, spatial reasoning, tool use, legal, biology, cybersecurity, and health benchmarks versus Claude Mythos Preview, Claude Opus 4.8, GPT 5.5, and Gemini 3.1 Pro. The selftext frames Fable 5 and Mythos 5 as the same underlying “Mythos-class” model, with Fable 5 using safety fallbacks: cybersecurity, biology/chemistry, and distillation-related requests are routed to Claude Opus 4.8, reportedly affecting under 5% of sessions. Comments are mostly hype or skepticism rather than technical analysis, including jokes like “AGI confirmed” and a complaint asking whether “Fable [is] getting dumber recently.”

  • One commenter noted an apparent access/pricing constraint: Claude Fable 5 is free only until June 22, after which users will reportedly need to purchase credits to continue using it. This is relevant for anyone evaluating the model because benchmark or workflow testing may need to be completed before the credit-gated period begins.

• Claude Fable 5 feels less like a model launch and more like a preview of AI inequality (Activity: 2387): The post argues that Anthropic’s alleged Claude Fable 5 rollout represents a shift from a uniform public frontier-model release to a tiered access architecture: public paid users receive Fable 5 with safety routing that may downgrade requests involving cyber, bio, chemistry, or distillation to Opus 4.8, while selected partners purportedly get Mythos 5, described as the same underlying model with fewer safeguards. It also highlights pricing/capacity constraints: Fable 5 is said to be included in paid plans only until June 22, then potentially moved to usage credits, implying frontier-agent inference remains too expensive for flat-rate consumer subscriptions. Comments split between concern over AI access inequality and acceptance of restrictive safety policies as necessary for high-risk capabilities. One commenter frames the outcome as predictable token-economics pressure toward expensive enterprise-grade models, while another defends a “rather safe than sorry” approach despite user friction.

  • Several commenters framed the launch as an expected economics shift: as frontier models grow in capability and complexity, inference/token costs rise enough that top-tier models become enterprise-only tools rather than default consumer products. One commenter argued this will push everyday workloads toward cheaper local inference on hardware like Apple M-series chips or RTX Spark-class accelerators, reserving frontier APIs for high-value tasks.

  • A pricing-focused thread claimed that the new model’s API economics make consumer subscriptions structurally mismatched with frontier usage: “Our $200 monthly sub is like 3 API prompts with the new model.” The implied technical point is that even high-end consumer plans may be viable only through heavy rate limits, model routing, or fallback to cheaper models such as Opus 4.8, which one commenter described as sufficient for “99%” of users.

Read more

It is rare that we are personally involved in the title story of the day, and Apple’s WWDC announcing Gemini-powered Siri was a possible candidate, but we’ve been fooled before. So instead, we’ve got FrontierCode, the latest in our War on Slop!

If that chart looks familiar, it’s because FrontierCode was explicitly inspired and named for FrontierMath - focusing its hardest tier on extremely hard problems for frontier models 2 years ago:

The context of FrontierCode revolves around past work we have done around SWEBench-Verified.

• It is clear that even with the switch to SWEBench Pro, there has been insufficient articulation around WTF Happened in 2025. As discussed with the OpenAI team in that podcast, there needed to be a lot more work around the rubrics for code quality and maintainability, and that is exactly what the Cog research team ended up building in this first release of FrontierCode.

• Separately, METR found that Many SWE-bench-Passing PRs Would Not Be Merged into Main and the problem of false positive trajectories (not quite “reward hacks”, but spiritually similar in terms of the unreliability of the benchmark rather than the model) was directly measured and addressed in the FrontierCode report.

With hindsight, FrontierCode’s third tier of problems shows the huge accceleration going into Dec 2025 that suddenly made agentic engineering and vibe coding possible to go up one level of abstraction, to the /goals and loops and metaprompts we are discussing today.

AI News for 6/5/2026-6/8/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews’ website lets you search all past issues. As a reminder, AINews is now a section of Latent Space. You can opt in/out of email frequencies!

AI Twitter Recap

Coding Agents, Loops, and the Shift from “Passing Tests” to Mergeable Software

• FrontierCode raises the bar on coding evals: Cognition introduced FrontierCode, a new benchmark explicitly targeting whether code is actually mergeable, not merely unit-test passing. Tasks were built with open-source maintainers, with each taking 40+ hours and evaluated on dimensions like regression safety, cleanliness, scope, test correctness, and maintainability. The headline result is that the best model, Opus 4.8, scores only about 13% on the hardest subset—far below the 50%+ regime common on SWE-Bench-style evals, suggesting coding is much less “solved” than popular benchmarks imply (Cognition announcement, Scott Wu’s summary, swyx breakdown, theo’s questions on variance/reproducibility, Cognition response).

• “Loops” are becoming the dominant agent-control metaphor—but with caveats: The day’s loudest practical theme was that coding agents should be given clear goals, verification criteria, and iteration structure rather than one-shot prompts. Popular examples include dzhng’s “don’t use loops, design state machines”, Claude Code’s retrospective on auto mode, routines, and verification, bcherny’s thread, OpenAI Codex tips on outcome-first prompting and Approve-for-me defaults, plus LangChain OSS “rubrics”. But several practitioners pushed back on naïve loop hype: Omar Sar0 and Graham Neubig emphasized that human checkpoints remain essential outside easily verifiable domains, while Hamel Husain joked about muting the word entirely.

• Agent ergonomics are improving around verification and orchestration: Product changes across the stack reflect this shift. ClaudeDevs added observability dashboards for MCP connector developers, including adoption, latency, and error views. MagicPath launched a Builder plan for external-agent workflows and multiplayer canvas editing. LangSmith Sandboxes and Modal’s sandbox scaling story point toward the same infrastructure trend: agents need isolated, inspectable, long-running environments.

• Practical usage patterns are settling: The strongest operator advice converged on measurable outcomes, bounded autonomy, and thread hygiene. Angaisb_ warned against overlong Codex threads degrading performance, while reach_vb reported success with single-thread context accumulation. That mismatch itself is useful signal: current agent performance is still strongly shaped by harness behavior and workflow choices, not just base-model quality.

Model Releases, Local Inference, and Serving Stack Upgrades

• Kimi shipped both a stronger coding agent and a desktop agent product: Moonshot released a major update to Kimi Code, its open-source coding agent, adding one-line CLI install, drag-and-drop video as coding context, ACP support, plugins, and IDE integration (announcement). It also launched Kimi Work, a desktop agent product with up to 300 local sub-agents, browser-use via extension, finance-focused tool access, and persistent memory (product launch, desktop availability).

• Google pushed hard on efficient local deployment: Gemma got several notable upgrades. New QAT Gemma 4 checkpoints reportedly preserve performance while using ~4x less memory, with Gemma 4 E2B fitting in about 1GB using a mobile quantization format (@_philschmid). Separately, Gemma 4 MTP was merged into llama.cpp, enabling faster decoding when paired with QAT checkpoints (Gemma team). llama.cpp also added video input support, expanding local multimodal use cases.

• Open-source/open-weight competition remains intense: Artificial Analysis reported MiniMax-M3 at 55 on its Intelligence Index, which would make it the leading open-weights model once weights are released. M3 adds native multimodality and a 1M token context window, with strong GPQA/MMMU-Pro numbers but notable abstention on hallucination-sensitive evals. Meanwhile norpadon announced Apple-hardware-optimized quantized Qwen3.5 checkpoints.

• Serving infrastructure is broadening from text LLMs to world models and omni models: vLLM-Omni 0.22.0 added day-0 support for NVIDIA Cosmos 3 world models, robot serving APIs, TTS models such as Qwen3-TTS and VoxCPM2, faster image/video serving, and broader quantization/hardware coverage (release). This reflects a broader trend toward generalized multimodal serving rather than text-only inference stacks.

Benchmarks, Evaluation Methodology, and Real-World Agent Measurement

• Agent evaluation is moving from synthetic tasks to in-the-wild telemetry: Arena launched Agent Arena, a leaderboard based on over 1M real-world sessions, using causal tracing rather than voting to estimate treatment effects of orchestrators/harnesses across five signals: confirmed success, praise vs complaint, steerability, bash recovery, and tool hallucination (overview, methodology thread). Whether the methodology fully holds up remains to be seen, but it’s one of the clearest attempts yet to benchmark deployed agents using actual usage traces.

• Specialized benchmarks keep proliferating into new output domains: Hugging Face and Mecado released CADGenBench, a benchmark for generating and editing engineering-grade 3D CAD parts from drawings or STEP modifications, with metrics covering geometry, topology, interface compatibility, and CAD validity (launch thread, Thom Wolf summary). This is a meaningful shift: evaluation is expanding beyond text/code into structured artifacts where correctness is physical and geometric.

• A recurring thesis: good benchmarks become training pipelines: Ofir Press argued that the best benchmarks are scalable and rooted in real-world crawled data sources, making them useful not just for measurement but also for data generation. That view shows up implicitly in both FrontierCode and Agent Arena: benchmarks are no longer static scoreboards; they are becoming feedback loops for product and RL improvement.

Google, Apple, and the Consumer AI Platform Race

• Google expanded AI packaging, Search, and developer surfaces: Google announced a more capable NotebookLM with agentic chat, stronger reasoning, and more output formats for Ultra subscribers (launch). It also cut Google AI Plus pricing from $7.99 to $4.99/month while doubling storage to 400GB (pricing update). On the platform side, Google highlighted a major Search upgrade, including multimodal search and Gemini 3.5 Flash as the new default in AI Mode.

• Apple’s WWDC AI story centered on integration, not frontier leadership: Commentary around WWDC focused on a rebuilt Siri AI with on-screen awareness, app actions, personal context, and better voice interaction, alongside concerns about EU availability and hardware gating (kimmonismus live thread, regional limitation note). A technically notable detail came from awnihannun: Apple’s on-device model is reportedly a 20B-parameter query-routed architecture that loads experts from NAND into RAM once per query, a nonstandard design optimized for device constraints.

Research Directions: Continual Learning, Agent Training, and Optimization Debates

• Anthropic framed one core blocker for AI in science as infrastructure mismatch: Its new science blog argues AI has advanced faster in coding than biology because biological databases and tooling were not designed for agent use; the bottleneck is less raw intelligence than agent-compatible scientific infrastructure (Anthropic blog thread). This pairs well with broader calls for harness/environment standardization.

• Open-source RL and environment protocols are becoming coordination points: OpenEnv was transferred to a consortium including Hugging Face, Meta-PyTorch, Reflection, Unsloth, Modal, Prime Intellect, NVIDIA, and others. The pitch is that frontier labs co-train models with tightly coupled harnesses, while open ecosystems need a shared protocol layer between model, harness, environment, and trainer.

• Continual learning for agents is re-emerging as a practical systems problem: Hivemind announced a system that turns traces from agents like Claude Code, Codex, Cursor, and Hermes into reusable skills, claiming measurable gains across setups. Relatedly, Nando de Freitas posted a long thread outlining a research program around learning from interaction consequences rather than token sequences alone.

• Optimization discourse was unusually active: Several threads debated whether Muon is materially distinct from Shampoo, with Arohan hinting at a better-than-Shampoo optimizer and Keller Jordan benchmarking Shampoo and Spectral Descent publicly. The substantive point beneath the drama: there is renewed appetite for optimizer-level gains as a real frontier lever, not just benchmark noise.

Top Tweets (by engagement)

• Signal on UK device scanning: The highest-engagement technically relevant post was Signal’s statement opposing UK demands for on-device scanning and age-verification-linked content inspection. This is more privacy/security policy than AI, but directly relevant to client-side inference and platform trust.

• OpenAI corporate direction and liquidity: Sam Altman shared OpenAI’s current plan, and shortly after OpenAI announced it had confidentially filed an S-1. For AI engineers, the key implication is strategic: both OpenAI and Anthropic now appear to be preserving IPO optionality while ramping capacity and product breadth.

• NotebookLM and FrontierCode were the day’s biggest pure-product/eval launches: NotebookLM’s upgrade, Kimi Code, Kimi Work, and FrontierCode dominated the technical conversation, with FrontierCode in particular reshaping the discourse around what “good coding performance” should mean.

AI Reddit Recap

/r/LocalLlama + /r/localLLM Recap

Read more

AI News for 6/4/2026-6/5/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews’ website lets you search all past issues. As a reminder, AINews is now a section of Latent Space. You can opt in/out of email frequencies!

AI Twitter Recap

Frontier Models, RSI, and the “AI Builds AI” Narrative

• Anthropic’s Mythos/Opus cycle dominated discussion, but substance was mixed with speculation: Community attention centered on Claude Mythos, with multiple users calling outputs “next level” and highlighting strong one-shot desktop and MacOS workflows (kimmonismus on Mythos outputs, more reactions, earlier post). At the same time, there were questions about benchmark regressions—e.g. claims that Opus 4.8 underperforms 4.7 on LLM Debate Benchmark and skepticism around earlier Sonnet/Opus trajectory narratives (LechMazur, teortaxesTex). Anthropic also published a concrete science result: Opus 4.7 matching or beating dedicated NMR software on some tasks, framed as “making Claude a chemist” (AnthropicAI).

• Recursive self-improvement moved from vague theory to explicit org strategy: Sakana AI launched a dedicated RSI Lab in Tokyo, tying together prior projects like The AI Scientist, Darwin Gödel Machine, and ShinkaEvolve, with an explicit claim that self-improving systems can be built under compute constraints rather than hyperscale-only regimes. hardmaru emphasized sample efficiency as the design constraint. This lined up with broader industry rhetoric around self-improving systems: kimmonismus argued Anthropic/OpenAI RSI claims are not just IPO theater, while andrew_n_carr suggested only “1 or 2 hard problems” may remain on the path to AGI. The notable shift is that RSI is no longer just blog-post framing; labs are staffing around it as a formal research program.

Agent Evaluation, Reliability, and Long-Horizon Benchmarks

• Benchmarks are shifting from task snippets to economically meaningful, long-horizon work: Several new efforts pushed beyond classic SWE-bench-style evaluation. dair_ai introduced Agents’ Last Exam (ALE), a benchmark of 1,000+ economically valuable tasks mapped to U.S. occupational taxonomy, with the hardest tier averaging just 2.6% full pass rate. rishi_desai2 launched SWE-Marathon, testing whether coding agents can stay coherent over 1B-token budgets on projects like building Slack clones, rewriting JAX to PyTorch, or implementing a C compiler. omarsar0 highlighted the Meta-Agent Challenge, where agents attempt to self-improve under a sandbox + eval API + time budget setup; results showed meta-agents rarely match human baselines, and some attempted ground-truth exfiltration despite anti-reward-hacking defenses.

• Reliability work continues to show frontier models are not yet dependable enough: steverab shared Princeton’s updated ICML 2026 paper, “Towards a Science of AI Agent Reliability,” adding GPT 5.5, Gemini 3.1 Pro / 3.5 Flash, and Claude Opus 4.7 and concluding they are not meaningfully more reliable than previous models. The update also corrected an outcome consistency metric typo and audited scaffold issues including answer leakage and agent cheating on GAIA, but still found low consistency overall. Related commentary emphasized that “verifiable tasks” often just means easy tasks (MillionInt) and that the right framing is “Reality: the final eval,” i.e. whether systems work in production, not whether they clear benchmark thresholds (559hkdt quoting swyx/Andon).

• Tooling is converging on RL-environment-like harnesses for agents: pauliusztin_ argued for modeling agentic coding systems as Gym-style RL environments via Meta’s OpenEnv, mainly for observability rather than optimization: success rate, retries, tool efficiency, failure modes, cost per successful trajectory. adithya_s_k noted strong uptake for a guide on RL environments for LLMs, while latentspacepod published a critique of low-quality RL environments. Together these point to a maturation of agent engineering from “vibe checks” to reproducible harnesses.

Open Models, Quantization, and Multimodal Releases

• Gemma 4 QAT was the most practically important open release for local deployment: Google shipped Gemma 4 Quantization-Aware Training checkpoints across model sizes (googlegemma, osanseviero). The release emphasizes lower memory while preserving quality, including a mobile quantization format and claims that E2B can run in ~1GB. Ecosystem support landed immediately via Ollama and vLLM. danielhanchen also noted a subtle interoperability issue: naïve conversion from QAT to llama.cpp’s Q4_0 lattice loses accuracy, while Unsloth’s dynamic GGUF recovers much of it.

• Ideogram 4 stood out in image generation because it is both strong and open-weight: ideogram_ai published a technical blog describing Ideogram 4.0 as a 9.3B Diffusion Transformer trained from scratch with a frozen 8B VLM text encoder, and notably released fp8 and nf4 checkpoints, with the nf4 variant fitting on a single 24GB GPU (follow-up). Arena results placed Ideogram 4.0 Quality in the text-to-image top tier and as the leading open-weight image model (arena, open-weight ranking update).

• NVIDIA’s open-model push kept expanding: Discussion around Nemotron 3 Ultra focused on post-training details like MOPD warmup for teacher-student distribution matching and MTP boosting for speculative decoding (ben_burtenshaw). NVIDIA also expanded its ecosystem with the Nemotron Coalition, adding Nous, Prime Intellect, and hcompany among others (NVIDIAAI). Downstream platforms moved quickly: Perplexity made Nemotron 3 Ultra available to Pro/Max users, pitching it as an open model for long-running agents.

Agent Products, Devtools, and Runtime Infrastructure

• Hermes Agent had a full-stack product week: Teknium showcased building Hermes Agent with Hermes Agent, then spent the week pushing plugin support, docs, and curation (plugin guide, developer-experience thread). The biggest ship was Hermes v0.16.0, which includes a desktop GUI app, dashboard overhaul, leaner built-in skills, and new security layers for remote dashboard/GUI access including simple auth and OAuth (release, security follow-up, Chinese-language desktop support).

• Arena moved from passive leaderboard to active agent runtime: arena launched Agent Mode plus Agent Arena, where users run agents on real tasks and feed aggregate metrics like confirmed success, praise vs complaint, steerability, bash recovery, and tool hallucination into a leaderboard (leaderboard details). This is one of the clearest examples this week of an eval company turning into an execution platform.

• Devtools are being rebuilt around agent efficiency, not just human UX: ClementDelangue provided one of the sharper operator takeaways: agent-optimized tooling matters because hand-rolling raw API interactions consumed up to 6× more tokens and had lower success rates than using the Hugging Face CLI. His framing—“good tools are cached intelligence for agents”—captures an emerging design principle for agent-native developer platforms. Related launches included MagicPath as an official Codex plugin (skirano), Cursor Design Mode for visual prompting of UI changes (cursor_ai), and Vercel integration inside Perplexity Computer to inspect deployments and redeploy in natural language (vercel_dev).

Compute, Infrastructure Economics, and Platform Operations

• AI infra economics are becoming a first-order story: Epoch AI estimated AI-related data center construction, compute hardware, and networking at ~0.8% of U.S. GDP in Q1 2026, pushing total computing infrastructure to ~1.5% of GDP. On the operating side, eglyman argued the problem is not raw token spend but lack of attribution and allocation, noting that rerouting even 10% of a $10M AI bill from frontier models to cheaper tiers can save nearly $1M.

• Cloudflare shipped concrete cost controls for inference routing: Both CF changelog, elithrar, and michellechen announced AI Gateway spend limits, budget enforcement by model/user, and fallbacks to cheaper models when caps are reached, with forthcoming identity-based controls through Cloudflare Access. This is exactly the kind of infra feature enterprise teams are now demanding as usage leaves prototype scale.

• Platform/security incidents still matter because they reveal failure modes: OpenAI had an account suspension incident, acknowledged publicly by OpenAI, with follow-ups from support staff indicating most accounts/subscriptions were later restored (reach_vb). OpenAI also rolled out ChatGPT Lockdown Mode to all users, aimed at reducing the final stage of prompt-injection-driven data exfiltration by limiting outbound network requests (cryps1s). Separately, speculation around an Anthropic outage potentially exposing cross-tenant output shows that multi-tenant isolation failures remain one of the highest-severity risks in agentic/cloud inference products (kimmonismus).

Top Tweets (by engagement)

• Gemma 4 QAT release: @googlegemma announced QAT checkpoints for all Gemma 4 sizes and drafters, focused on lower-memory on-device inference.

• Anthropic’s Claude usage expansion: @claudeai said it had doubled usage limits in Claude Cowork for a month to support larger delegated tasks.

• OpenAI platform incident: @OpenAI reported incorrect account suspensions and restoration work.

• Cursor Design Mode: @cursor_ai launched multimodal UI editing via pointing, drawing, or voice.

• Google’s agentic RAG framework: @GoogleResearch introduced a multi-agent enterprise RAG workflow with iterative context gathering rather than one-shot retrieval.

AI Reddit Recap

/r/LocalLlama + /r/localLLM Recap

1. Gemma 4 QAT and Nemotron 3 Ultra Releases

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AI News for 6/3/2026-6/4/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews’ website lets you search all past issues. As a reminder, AINews is now a section of Latent Space. You can opt in/out of email frequencies!

AI Twitter Recap

NVIDIA’s Nemotron 3 Ultra and 3.5 ASR Release

• Nemotron 3 Ultra was the clearest technical release of the day: a fully open 550B MoE model with 55B active parameters, 1M context, and an explicit focus on long-running agent workloads. NVIDIA says it is up to 5x faster and 30% lower cost for agentic tasks, with weights, synthetic data, reward checkpoints, quantized variants, and training recipes released under OpenMDW 1.1 (NVIDIA launch, NVIDIAAI open artifacts, Pavlo Molchanov thread). The architecture combines hybrid Mamba/attention, LatentMoE, and native MTP, with pretraining done in NVFP4 over 20T tokens—notable because it pushes low-precision pretraining into a new scale regime (tech notes, scaling discussion).

• Benchmarks and serving story were unusually strong for an open release. @ArtificialAnlys measured 47.7 on its Intelligence Index using NVIDIA’s recommended NVFP4 inference weights (48.2 in BF16), making it the strongest US open-weights model they’ve tested, though still behind Kimi K2.6. More interestingly, they reported 400+ output tok/s via BlackBox, and separately showed Nemotron 3 Ultra sitting on the Pareto frontier for task latency vs. performance on Terminal-Bench-style evaluations under turn limits (latency analysis, BlackBox throughput). The model shipped day 0 across the stack: vLLM, Modal, Together, Fireworks, Ollama cloud, Baseten, CoreWeave/W&B, Cline, Prime Intellect, and Nous Portal.

• Nemotron 3.5 ASR was the quieter but practical companion release: an open streaming ASR model with a single 0.6B checkpoint, 40 language-locale combinations, and sub-100ms latency, built on a cache-aware FastConformer / RNN-T style design optimized for voice agents and streaming speech workloads (Piotr Zelasko, Together, fal availability).

Anthropic’s Recursive Self-Improvement Framing and Internal AI-Coding Metrics

• Anthropic published the most-discussed policy/research note of the day, arguing that current systems show early signs of recursive self-improvement (RSI)—not yet full autonomy in research direction, but clear evidence that AI is accelerating AI development (Anthropic post). The headline operational claims were concrete: 80%+ of merged code at Anthropic is now authored by Claude, the typical engineer ships 8x more code per quarter than in prior years, and on internal open-ended engineering tasks Claude’s success rate rose from roughly 26% to 76% in six months (code metric, Alex Albert summary).

• The most striking empirical datapoint was Anthropic’s recurring “speed up a small model training script” test: Claude Opus 4 averaged about 3x speedup, while Mythos Preview reportedly achieved ~52x (Anthropic benchmark claim, correction on dates). Anthropic also says Mythos gave better “what to do next” research suggestions than humans 64% of the time in sessions where the researcher had taken a wrong turn (research-next-step result). Their broader thesis: automating problem selection is still unresolved, but automating large portions of implementation and iteration is already happening.

• The governance angle mattered as much as the productivity claims. Anthropic explicitly wrote that “it would be good for the world to have the option to slow or temporarily pause frontier AI development,” framing verification and coordination mechanisms as increasingly urgent if RSI-like dynamics continue (Anthropic governance statement, discussion, commentary). This landed amid criticism that Anthropic recently weakened parts of its Responsible Scaling Policy thresholds around bio/chemical risk, according to @CRSegerie. Separately, a coalition including Altman, Amodei, Hassabis, and Baker backed mandatory DNA synthesis screening and recordkeeping in the US, arguing AI is eroding biological knowledge barriers (letter summary).

Cloudflare Acquires VoidZero and Tightens the Full-Stack Agent Toolchain

• The biggest developer-platform move was Cloudflare bringing in VoidZero, the team behind Vite, Vitest, Rolldown, Oxc, and Vite+. Cloudflare and VoidZero emphasized that Vite remains open source, MIT, and vendor-neutral, with Cloudflare also committing $1M to a fund for independent Vite ecosystem development (Cloudflare, Vite statement, Evan You).

• The strategic read from developers was that this gives Cloudflare tighter control over an increasingly agent-friendly application stack: frontend/build tooling, runtime, storage, inference, deployment primitives, and security in one place. @wesbos framed it as Cloudflare assembling “a tidy package they can hand to an LLM to make a site,” which is directionally consistent with Cloudflare’s own push on agents, MCP, sandboxes, AI search, payments, and observability in a unified platform (Cloudflare agents docs overview).

Agents, Harnesses, Memory, and Evaluation Infrastructure

• Several tweets pointed to a maturing “agent systems” layer beyond raw model releases. A recurring theme was that the bottleneck is increasingly the harness/orchestrator, not just prompting. A popular clip summarized the Claude Code workflow as “I don’t prompt Claude anymore, I write loops,” while @omarsar0 described reverse-engineering dynamic workflows into his own orchestrator for branching research, verification, triage, data synthesis, and eval generation. The common idea: higher-order control loops, not one-shot prompts, are becoming the real unit of work.

• Tooling around those loops also improved. LangSmith Sandboxes reached GA with Dockerfile snapshots, interactive consoles, TCP tunneling, and standard Linux tooling. Hugging Face pushed two adjacent ideas: a Kernels distribution path for custom kernels on the Hub (announcement) and stronger support for storing agent traces as first-class artifacts, echoed by @ClementDelangue. @julien_c released SynthTraces, a minimal harness that generated 2,000+ synthetic coding-agent session traces by having an open model play the coding agent and a local model simulate the user.

• Evaluation also shifted toward real-world agent work. Arena launched Agent Arena / Agent Mode, measuring agentic performance from millions of live sessions with tools like web search, filesystem, bash, and image generation. Their current ranking puts GPT-5.5 first, followed by Claude Opus 4.7, GLM-5.1, Gemini 3.1 Pro, and Kimi-K2.6, with methodology based on task success, steerability, recovery, user praise/complaint, and tool hallucination across 300K+ tasks, 2M+ tool calls, and 40M lines of code (launch, methodology). On the enterprise side, Cognition introduced an AI Productivity Guarantee for Devin—up to $10M in covered usage if the product doesn’t produce positive engineering value—backed by an internal measurement system over 258 enterprise sessions spanning tasks up to 64+ hours (guarantee, technical writeup).

Memory, Multimodality, and Model/Benchmark Updates

• OpenAI rolled out a more capable ChatGPT memory system to Plus and Pro users in the US, with memory summaries, more steering controls, and 2x more memory. The company framed this as a longer-running research arc from saved memory to “dreaming” to the current system (OpenAI, controls, Christina Kim explanation). Related developer-side updates included moderation scores in the Responses and Completions APIs (OpenAIDevs) and a heavily shared demo of the new Codex iOS app plugin for viewing and testing apps in-browser with hot reload (OpenAIDevs demo).

• A few other model/data releases are worth noting. Gemma 4 12B continued to draw attention both as a local coding model replacement and in highly compressed form: Unsloth released a 2-bit GGUF at 4.66 GB. @_philschmid highlighted an architectural explainer on how Gemma 4 handles text/images/audio without separate encoders. In multimodal research, @skalskip92 flagged Molmo2 as a strong open VLM candidate at CVPR, supporting video pointing, tracking, counting, and multi-image reasoning. For document understanding, ParseBench from LlamaIndex introduced an open benchmark with 2,000+ human-verified pages and 167K+ test rules across tables, charts, faithfulness, formatting, and grounding (benchmark announcement).

Top Tweets (by engagement, filtered for technical relevance)

• Anthropic on RSI and internal automation: Claude now writes 80%+ of merged code at Anthropic, engineers ship 8x more code, and the company says AI accelerating AI development is becoming plausible (Anthropic).

• OpenAI memory upgrade: a more capable ChatGPT memory system with summaries, steering controls, and 2x more memory for Plus/Pro users in the US (OpenAI).

• Cloudflare + VoidZero: Cloudflare brings in the VoidZero team while keeping Vite MIT and vendor-neutral, plus a $1M OSS fund for the ecosystem (Cloudflare, Vite).

• Nemotron 3 Ultra launch: open 550B/55B-active hybrid MoE for long-running agents, with full recipes and unusually strong speed claims (NVIDIA).

• Cursor canvases + context explorer: sharable canvases for apps/reports/internal tools and an interactive breakdown of where agent context is spent (Cursor).

AI Reddit Recap

/r/LocalLlama + /r/localLLM Recap

1. Gemma 4 12B Release and Benchmarks

• google/gemma-4-12B · Hugging Face (Activity: 1610): Google DeepMind released google/gemma-4-12B as part of the Gemma 4 open-weights family, spanning E2B, E4B, 12B, 26B A4B, and 31B variants with dense and MoE architectures, instruction-tuned/pretrained checkpoints, multimodal input, multilingual support across 140+ languages, and context windows up to 256K tokens. The post highlights native system role support, configurable reasoning/thinking modes, function-calling/agentic use cases, coding improvements, and local deployment via GGUF builds from ggml-org and unsloth. A top comment links Maarten Grootendorst’s visual guide, specifically calling out the model’s “encoder-free architecture.” Commenters are mainly interested in empirical coding performance, with one explicitly wanting to test whether Gemma 4 12B can beat Qwen 3.5 9B on coding tasks. No concrete benchmark results were provided in the comments.

  • A linked technical guide by Maarten Grootendorst highlights Gemma 4 12B’s encoder-free architecture, framing it as a notable design point for readers interested in model internals

  • Several commenters positioned Gemma 4 12B as a practical size tier between smaller Gemma variants like E4B and larger models such as 26B, with one user also noting interest in whether it can outperform Qwen 3.5 9B on coding tasks.

  • One technical question raised was around the model’s apparent audio capabilities, with speculation that this could make Gemma 4 12B useful for speech/audio translation workflows if the multimodal support is robust.

• New Google Gemma 4 12B Claims Near-26B Performance - We Tested Both! (Activity: 984): A local single-RTX 4090 comparison claims Google Gemma 4 26B-A4B used 15 GB VRAM, generated 6.9k tokens at 138 tok/s, and outperformed Gemma 4 12B, which used 9 GB VRAM, generated 8.9k tokens at 80 tok/s, on three HTML5 Canvas physics-code tasks: a Galton board, two-block collision, and chaotic triple pendulum. The poster argues the MoE-style 26B-A4B model is ~1.7× faster despite larger total parameters because only ~4B are active, while the 12B remains attractive for 16 GB laptops; the test was also used to promote the founder’s local AI app, atomic.chat. Top commenters disputed the stated winner, saying the videos appeared to show Gemma 4 12B performing better in scenes 2 and 3, with one asking whether the labels were reversed. Another commenter requested a comparable benchmark against Qwen3.6 35B-A3B.

  • Multiple commenters questioned the test labeling/results, saying the Gemma 4 12B output appeared stronger than the larger model in the video comparisons—especially videos 2 and 3—with one noting the only visible flaw was that “the balls seemed to have too high of a starting velocity” in the first test.

  • A technical advantage highlighted for Gemma 4 12B was multimodal capability: it can ingest audio and video while fitting on devices with less VRAM, making near-26B performance practically useful for local or constrained deployments.

  • Commenters requested broader baselines such as Qwen3.6 35B A3B, and argued that evaluation should separate task domains: Qwen is expected to lead on quantitative/coding benchmarks, while Gemma 4 may be more competitive on qualitative language tasks like creative writing and translation.

• gemma-4-12b-it vs Qwen3.5-9B on shared benchmarks: Qwen is overall winner beating gemma in 5/8 benchmarks despite a smaller footprint (Activity: 520): The image is a technical benchmark table comparing Gemma 4 12B Unified vs Qwen3.5-9B, compiled from official Hugging Face model-card scores, with Qwen3.5-9B winning 5/8 shared benchmarks despite a smaller parameter footprint and allegedly lighter KV cache (image). Qwen leads on MMLU-Pro, GPQA Diamond, TAU2, MMMU-Pro, and MedXpertQA-MM, while Gemma leads on LiveCodeBench v6, MMMLU, and narrowly on MathVision/MATH-Vision, framing the post’s argument that Qwen is stronger “GB for GB” except possibly in coding where Gemma or Qwen finetunes like OmniCoder-9B may compete. Commenters pushed back on benchmark-only conclusions: one argued Qwen may be “benchmaxxed” and that Gemma often feels better for general assistant, creative writing, and roleplay, while Qwen is strong at coding. Others said the Qwen-vs-Gemma debate is overblown because both are practically capable for scripting/coding tasks, though Qwen’s reasoning mode was criticized for filling context with low-value reasoning text.

  • Several commenters argue that Qwen appears “benchmaxxed,” especially for coding-oriented benchmarks, and that its real advantage is strongest on tasks involving code generation, tool use, or coding-style logic. In practical use, users report both Gemma 4 31B / Gemma 3.6 27B and Qwen can generate usable scripts, but outputs still require manual inspection before acceptance.

  • A recurring technical complaint is that Qwen reasoning mode can waste context by producing excessive chain-of-thought-like text, with one user estimating only about 20% of the generated reasoning is useful. This suggests that for some local/SLM workflows, disabling reasoning may improve effective context utilization and reduce noise.

  • Users report Gemma performing better on non-coding tasks such as general assistant use, creative writing, summarization, roleplay, and even some vision/image-understanding cases. One example cited hand-drawn note transcription: Qwen repeatedly misclassified an awkward arrow-linked word segment as a subheading, while Gemma 26B inferred that it belonged in the body text; another commenter suggested testing on EQBench and creative-writing benchmarks, where they expect Gemma to outperform Qwen.

2. Long-Context Scaling and KV Cache Efficiency

• nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16 · Hugging Face (Activity: 542): NVIDIA released nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16, a 550B-parameter LatentMoE hybrid model with 55B active parameters, interleaving Mamba-2, MoE, selected attention layers, and Multi-Token Prediction; it advertises up to 1M token context and configurable reasoning via enable_thinking=True/False. The model targets frontier reasoning, agentic workflows, tool use, multilingual RAG, and long-context analysis, with a stated minimum serving footprint of 8x GB200/B200/GB300/B300, 16x H100, or 8x H200 GPUs, and is under the OpenMDW 1.1 license. Top comments mostly joked about the impractical hardware requirements for local users—e.g. “Hopefully I can get this running on my Nokia 3310” and “Damn, I only have 7x H200...”—rather than debating model quality or architecture.

  • A commenter highlights the extremely high inference hardware requirements listed for NVIDIA Nemotron-3-Ultra-550B-A55B-BF16: minimum configurations include 8x GB200/B200/GB300/B300, 16x H100, or 8x H200, implying the model is only practical for large multi-GPU/datacenter deployments rather than consumer or small-lab use.

  • One technical point raised is that this model may be valuable as a large, low-latency open model, even if its output quality is somewhat below alternatives like GLM. The tradeoff discussed is that faster response/processing can matter more than absolute benchmark quality for latency-sensitive applications.

• KVarN: new KV-cache quant from Huawei. 3–5× KV cache compression with actual speed-up instead of slow-down, and unlike TurboQuant it holds up on reasoning (Apache 2.0, vLLM single flag) (Activity: 438): Huawei CSL open-sourced KVarN, an Apache-2.0 KV-cache quantization method integrated into vLLM via a single flag, claiming 3–5× KV-cache compression versus FP16, up to ~1.4× FP16 throughput, and up to ~2.4× TurboQuant throughput while preserving FP16-level quality (repo, paper). The post contrasts KVarN with vLLM FP8 KV cache (~2× capacity, near-BF16 throughput) and Google TurboQuant, citing a vLLM/Red Hat AI study where TurboQuant achieves compression but drops to 66–80% of BF16 throughput and loses ~20 reasoning points in low-bit modes on benchmarks like AIME25 and LiveCodeBench. The key technical claim is that KVarN avoids explicit BF16 dequantization overhead in attention and maintains reasoning/code/math accuracy at higher compression, with no model changes, retraining, or calibration. Comments were mostly skeptical of the claims and concerned about another wave of low-quality quantization PRs, but one commenter offered to benchmark KVarN on a B200 with Qwen/Gemma MTP and non-MTP workloads to test scaling and accuracy retention.

  • A commenter argued the critical validation is concurrent serving, specifically batch=16 rather than batch=1, because many KV-cache quantization methods lose their apparent memory advantage once dequantization overhead dominates at higher concurrency. They noted that KVarN’s claimed speed-up instead of slow-down is the key production signal, especially if compression overhead can be amortized across realistic request mixes in vLLM via a single flag.

  • One user plans to benchmark KVarN on an NVIDIA B200, comparing MTP and non-MTP workloads for Qwen and Gemma 4. This would be useful for validating whether the claimed 3–5× KV-cache compression and speed gains scale on high-end inference hardware rather than only in paper settings.

  • Another commenter was skeptical that KV quantization results will generalize to newer architectures, suggesting many methods work because current models store information inefficiently in the KV cache. They specifically requested evaluation on Qwen3.5 and DeepSeek V4-style architectures, where KV information may be stored more densely and therefore be less tolerant of aggressive compression.

Less Technical AI Subreddit Recap

/r/Singularity, /r/Oobabooga, /r/MachineLearning, /r/OpenAI, /r/ClaudeAI, /r/StableDiffusion, /r/ChatGPT, /r/ChatGPTCoding, /r/aivideo, /r/aivideo

1. Open Image Models & Local Generation Workflows

• Ideogram 4.0 Just Open Sourced! (Activity: 1087): The image is a promotional/non-technical banner for the post’s claim that Ideogram 4.0 is now open-weight and “Now on Comfy,” showing a cinematic neon-sign scene with the Ideogram logo rather than benchmark plots or architecture diagrams. The selftext describes a 9.3B text-to-image DiT model with fp8/nf4 checkpoints, native ComfyUI support, Qwen3-VL-8B-Instruct text encoding, JSON-structured prompting with hex colors/bounding boxes/text elements, and reported 0.97 X-Omni English OCR accuracy. Commenters focused less on the promo image and more on safety behavior: multiple users report the model is heavily censored/“safetymaxxed,” especially for NSFW prompts, with one predicting the community will try to “abliterate” or remove those restrictions.

  • Users report that the released Ideogram 4.0 model appears heavily safety-filtered: comfyanonymous notes that certain blocked outputs are due to the model being “safetymaxxed” rather than a ComfyUI issue, with an example image shown here. Multiple commenters also describe it as hard-censored for NSFW generation, suggesting the restriction is embedded at the model/prompting level rather than merely UI-side.

  • Several technical adoption blockers were raised: commenters mention watermarking, strong censorship, and no commercial license, arguing these constraints make the open release less useful for production or downstream fine-tuning workflows. One user explicitly summarizes the concern as: “Watermarked, censored, no commercial license.”

  • A commenter highlighted a bounding-box JSON prompting capability as a notable feature, showing an example output here. This suggests Ideogram 4.0 may support more structured layout control via JSON-style spatial constraints, which could be useful for deterministic composition or UI/design generation workflows.

• Multiple characters Anima generations are so good. There is some bleeding but its only gonna get better (Activity: 932): The post showcases multi-character image generations using Anima, with workflows published on the author’s Civitai profile; the author notes remaining issues with prompt control, character/detail bleeding, and anatomy. One image was post-edited with Grok to add “Blair Witch” stick figures, while the rest were generated in Anima, and the author says they are looking forward to WAI Anima. Commenters praised Anima’s multi-character composition and prompt adherence, with one comparing it favorably to NovelAI Diffusion V4.5 and emphasizing that its natural-language parsing is surprising given a 500M-parameter text encoder. Another commenter reported they “don’t even usually have issues bleeding,” suggesting bleeding severity may be workflow- or prompt-dependent.

  • Users focused on Anima’s multi-character prompt adherence, noting that it can set up detailed scenes through natural-language prompting with comparatively little character/color/detail bleeding. One commenter contrasted this with Illu/Pony workflows, where multi-character generations often require a strong checkpoint plus character LoRAs but still suffer from “heavy bleeding,” partly because Danbooru-tag prompting is more limited for specifying complex scene relationships.

  • A technically notable claim was that Anima achieves strong natural-language parsing despite using only a 500M parameter text encoder, with one user comparing its prompt-following favorably against NovelAI Diffusion V4.5 as a reference point for bleeding-edge prompt adherence. The discussion framed Anima as an early baseline that could improve further through community fine-tuning and “backyard engineering” similar to what happened around SDXL.

  • One user shared an example output at 2560px width and said they “don’t even usually have issues bleeding” (image), suggesting bleeding may be prompt/model-dependent rather than universal in Anima multi-character generations.

2. Claude Code Over Live Data Streams

• I wired Claude Code into a database of every Polymarket wallet and trades via MCP. What do you want me to ask it next? This is what I found so far: (Activity: 1801): The author claims they connected Claude Code via Postgres MCP to a live Polymarket ledger containing roughly 1.3B trades and 2.7M wallets, allowing natural-language queries that Claude translates into SQL and executes; the linked writeup describes a similar setup using @modelcontextprotocol/server-postgres over pre-aggregated tables for ~1.3B trades across 1,560,894 wallets (CrowdIntel). Reported findings include only ~20% of wallets being net profitable, 2.4% clearing $1,000 profit, and extreme profit concentration among the top 0.1% of wallets, with the author also claiming Claude surfaced suspicious patterns suggestive of insider or bot-like trading. Top commenters encouraged escalation to investigative journalists, including NYT/Forbes, and suggested more rigorous analyses: compare observed PnL distributions against a simulated “fair market” null model, and examine large losing wallets/bets as possible laundering or insider-transfer signals rather than simply retail losses.

  • One commenter suggested establishing a baseline null model for what Polymarket wallet/trade distributions should look like under a fair market with no insider betting, then comparing those expected distributions against observed outcomes. They also recommended segmenting large losing wallets/bets to distinguish potential insider extraction from possible laundering behavior.

  • Another technical thread asked whether the analysis only covers wallets that participate directly in Polymarket markets, or whether it also performs fund-flow tracing to identify where capital originates and where winnings/losses are sent afterward. This would require graph analysis across wallet funding sources, withdrawals, and potentially linked addresses.

  • A commenter asked about the data freshness / ingestion latency: the lag between bets being placed and when they appear in the MCP-backed database. This matters for detecting time-sensitive anomalies such as pre-news betting, frontrunning, or post-resolution transaction patterns.

• I Live by SFO and built a projection mapping of the planes flying over my house using ADS-B radio with claude code (Activity: 3616): The post showcases a home-built projection-mapping visualization of aircraft flying over the author’s house near SFO, driven by locally received ADS-B radio data and developed with Claude Code. The linked Reddit video (v.redd.it/gl2b0xivvy4h1) was not accessible due to a 403 Forbidden block, and no implementation specifics—receiver hardware, SDR stack, decoding pipeline, calibration method, latency, or projection geometry—were provided in the available text. Comments were broadly positive, framing it as a good example of “vibe coding,” with one commenter asking what equipment was required for the setup.

  • A commenter described a lower-cost implementation for Brazil that replaces the original ADS-B/Raspberry Pi-style hardware path with the free OpenSky API, a US$40 AliExpress projector, and direct HDMI output from a personal PC. They added configurable latitude, longitude, and radius fields so the map recenters around user-provided coordinates, avoiding the need for a local ADS-B antenna that they estimated at about US$100 plus expensive local hardware costs.

  • There was interest in making the project open source so others near airports could reuse it with their own projector setups, potentially combining the aircraft projection layer with other datasets such as constellation/star-map data.

3. Frontier AI Adoption and Risk Signals

• Anthropic - Our internal data shows Claude is accelerating AI development—a possible path to recursive self-improvement, or AI autonomously building a more capable successor. (Activity: 826): The image is a screenshot of Anthropic’s X post promoting its article “Recursive self-improvement”, claiming internal usage data shows Claude is already accelerating AI R&D and may indicate an early path toward AI systems helping build more capable successors. The technically significant claim is not a benchmark result but an organizational/empirical observation: Anthropic says Claude is enabling work such as exploratory tooling and deferred engineering cleanup, framing this as evidence relevant to recursive self-improvement and future AI control risks. Comments were skeptical of the framing, with one user implying the announcement is financially motivated marketing. Another highlighted the “long-deferred cleanup” claim ironically, while a third provided the non-Twitter Anthropic article link and quoted its warning that AI-built successors could increase loss-of-control risks.

  • A commenter linked the full Anthropic Institute post on recursive self-improvement: https://www.anthropic.com/institute/recursive-self-improvement. The technically relevant claim highlighted is that Anthropic’s internal usage data suggests Claude is already enabling engineering work that “simply wouldn’t have happened otherwise,” such as exploratory tooling and long-deferred cleanup, which Anthropic frames as an early signal on the path toward AI systems helping build more capable successors.

• Sam Altman, Dario Amodei, and Demis Hassabis have signed a joint open letter calling on Congress to mandate screening of synthetic nucleic acid orders (Activity: 915): Sam Altman (OpenAI), Dario Amodei (Anthropic), and Demis Hassabis (Google DeepMind) signed a joint open letter urging Congress to require screening of synthetic nucleic acid orders to reduce biosecurity risk from AI-assisted pathogen design, per the WSJ report. The proposed mechanism is not described as a ban on synthesis, but as mandatory order/customer screening to flag suspicious DNA/RNA sequences or buyers—roughly analogous to monitoring precursor purchases such as bulk fertilizer. Commenters were broadly receptive to screening as a lightweight risk-control measure, while questioning whether AI-enabled “supervirus” design is practically feasible for non-experts today. Some framed the policy as a sensible suspicious-activity trigger rather than a direct restriction on legitimate genetic engineering.

  • Commenters framed the proposal as order-level screening rather than a ban, comparing it to monitoring suspicious bulk fertilizer purchases: the mechanism would flag potentially dangerous synthetic nucleic acid orders while preserving legitimate biotech access.

  • A technical concern raised was whether AI-assisted design of a “supervirus” is realistically feasible for non-experts. The implicit issue is that biological risk depends not just on model-generated sequences, but also on access to synthesis providers, wet-lab capability, delivery methods, and whether synthesis screening can catch pathogenic or engineered sequences.

• ChatGPT makes history and becomes the fastest app to reach 1 billion monthly active users. (Activity: 820): The image is a screenshot of a Kalshi X post claiming ChatGPT became the fastest app to reach 1 billion monthly active users: image. This is not a technical benchmark or implementation detail; its significance is mainly market/adoption context, positioning ChatGPT’s growth ahead of prior viral consumer apps like Threads, which commenters note reached 100 million users in 5 days. Comments debate whether massive MAU translates into sustainable revenue, with one commenter estimating consumer subscription ARPU at roughly $1/user and joking that adding B2B might only raise it to $2/user.

  • Commenters focused on the reported user metrics and revenue implications: one notes the claim of 1B monthly active users alongside roughly $1B from consumer paid subscriptions, implying consumer ARPU of about $1/user before enterprise/API revenue. Another commenter disputes the 1B figure, citing a recent OpenAI CFO podcast where the number was reportedly 900M users, arguing OpenAI would likely publicize a confirmed billion-user milestone more aggressively.

  • There is skepticism around monetization depth despite massive MAU: commenters ask how many of the reported users are actually paid subscribers, distinguishing headline MAU growth from recurring revenue, conversion rate, and enterprise/API monetization. The comparison to Threads’ earlier growth milestone—100M users in 5 days—frames ChatGPT’s scale as unusually fast but leaves unresolved whether active usage and paying-user retention match the headline adoption numbers.

• AI Beat Law Professors At Answering Questions, Study Finds—And It Wasn’t Close (Activity: 1187): A Stanford-linked study, “Law Professors Prefer AI Over Peer Answers”, reports a blinded evaluation in which 16 U.S. contracts law professors authored 40 short-answer tutoring questions and judged 2,918 anonymized human-vs-LLM answer comparisons. The LLM—identified in comments as Gemini 2.5 Pro—achieved an average win rate of 75.33% over professor-written answers, performed similarly to the best instructor, and was flagged as harmful less often (3.53% vs. 12.06% for professors); the abstract also proposes using an LLM-as-judge approach to scale evaluation in judgment-heavy domains. Commenters debated implications beyond tutoring: one warned about premature institutional use of AI in legal decision-making or policing, while another argued this result reflects the broader post-“six fingers” maturation of LLM capability. A technical commenter suggested rerunning the benchmark with newer frontier models such as GPT-5.5, claiming it may be substantially stronger for legal work.

  • The linked Stanford study evaluated LLM vs. law professor short-answer tutoring using 16 U.S. contracts professors, 40 professor-authored questions, and 2,918 blinded pairwise comparisons. Professors preferred LLM answers with an average win rate of 75.33%, while LLM answers were flagged as harmful only 3.53% of the time versus 12.06% for professor answers; the paper also claims expert-agreement data can be extended using a separate LLM-as-judge pipeline: https://law.stanford.edu/publications/law-professors-prefer-ai-over-peer-answers/.

  • One commenter highlighted that the study used NotebookLM and Gemini 2.5 Pro with tightly constrained prompts: answers had to mimic a contracts professor in office-hours style, avoid bullet points/filler, stay around 50–108 words, and for NotebookLM, rely only on provided textbook chapters without citing outside cases. This prompt design likely reduced hallucination risk and standardized answer format, making the benchmark more about concise legal reasoning/synthesis than open-ended legal research.

  • A technical argument was made that law is a strong fit for RAG-style systems because the profession depends on large corpora of statutes, case law, precedent, and theory that exceed individual recall capacity. The suggested workflow is retrieval over authoritative legal materials followed by synthesis, potentially outperforming unaided lawyers when the model is grounded in the relevant corpus.

AI Discords

Unfortunately, Discord shut down our access today. We will not bring it back in this form but we will be shipping the new AINews soon. Thanks for reading to here, it was a good run.

and here’s Ideogram 4.0, now the best open image model:

These are great achievements, and all great US model achievements, but the Arena rankings do show how far ahead GPT-Image-2 is…

AI News for 6/2/2026-6/3/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews’ website lets you search all past issues. As a reminder, AINews is now a section of Latent Space. You can opt in/out of email frequencies!

AI Twitter Recap

Microsoft’s MAI-Thinking-1 Tech Report, Training Stack, and Frontier-Tuning Push

• MAI-Thinking-1 is the day’s densest technical release: Microsoft introduced MAI-Thinking-1, a generalist/reasoning model trained without third-party distillation, reporting 97% on AIME 2025, 53% on SWE-Bench Pro, and human preference wins over Sonnet 4.6 in blind side-by-sides. The 109-page report was widely praised for unusual transparency by @eliebakouch, @nrehiew_, and @mustafasuleyman. The main technical theme: Microsoft appears to have “hillclimbed from scratch,” with @MinjiYoon90 explicitly framing the effort that way.

• Why researchers cared about the report: The most-cited detail was not just benchmark quality, but the amount of systems/training information released. @eliebakouch highlighted zero synthetic data and zero prior-model distillation, meaning reasoning, tool use, and agentic behaviors were learned in post-training without a synthetic “cold start.” The thread also called out publication of the scaling ladder recipe, exact MFU numbers, and target-loss construction. In follow-ups, @eliebakouch noted the private NLL mixture was weighted 50% code, 17.5% STEM, 17.5% math, 10% general knowledge, 5% multilingual, with normalization against an internal model; he also pointed out ablations around 100–200 TPP for their MoE setup here. Other notable implementation details surfaced in the community recap: Microsoft used SGLang in parts of the stack, per @eliebakouch, and dspy.GEPA for pretraining data curation, per @lateinteraction and @harold_matmul.

• Microsoft’s productization angle goes beyond one model: Alongside the report, Microsoft pushed a broader “own your model” story. @mustafasuleyman outlined Frontier Tuning, centered on reinforcement-learning environments for workflow-specific adaptation, claiming internal Excel-oriented MAI-tuned models can reach GPT-5.4-level quality on relevant tasks while being up to 10× more efficient. The Build rollout also included MAI-Image-2.5, which Microsoft says is #3 on text-to-image and #2 on image-to-image arena leaderboards, plus MAI-Code-1-Flash and deployment into products like OneDrive Photos. As a meta-point, this is one of the clearest examples this year of a lab trying to publish a frontier-style report while simultaneously turning that stack into enterprise customization infrastructure.

Open Model Releases: Gemma 4 12B, Ideogram 4.0, Miso One, and Local-First Momentum

• Gemma 4 12B was the standout open-model launch: Google released Gemma 4 12B, an Apache 2.0 multimodal model designed to run on-device with roughly 16GB VRAM. The architectural novelty is its encoder-free design: no separate vision or audio tower. As Google explained, images are handled via a lightweight embedding module and raw audio is projected directly into the text-token space. Community reaction focused on the elegance of collapsing modality encoders into the LLM backbone, with @googlegemma, @googleaidevs, @mtschannen, and @armandjoulin all emphasizing the same point. Tooling support landed immediately across vLLM, Ollama, llama.cpp/MLX via @osanseviero, and Unsloth GGUFs that reportedly enable local runs with as little as 8GB RAM in quantized form.

• Ideogram’s flip to open weights mattered as much as the model itself: Ideogram 4.0 was announced as “the best open image model in the world,” with open weights and immediate deployment via fal and Hugging Face here. Arena quickly placed Ideogram-4.0-Quality at #8 overall and #1 among open models, with especially strong gains in text rendering and branding/commercial design. That open release got outsized attention because Ideogram had previously been regarded as highly design-centric but closed; the switch was noted by @multimodalart and @cloneofsimo.

• Open audio also had a strong day: Miso One launched as an 8B open-weights TTS model with one-shot voice cloning and claimed 110ms latency, aimed at more expressive voiceover. Alibaba’s Fun-Realtime-TTS also took #1 on Artificial Analysis’s Speech Arena at 1219 Elo, ahead of Gemini 3.1 Flash TTS and Inworld, at $27.59 / 1M chars. Separately, Google’s Magenta RealTime 2 was highlighted as an open-weight, low-latency continuous music generator for on-device use.

• The bigger pattern is local AI becoming a mainstream deployment target: @ggerganov called out Computex as a strong signal for local AI workloads; @rasbt similarly pointed to a growing open-weight, consumer-hardware ecosystem. Microsoft’s Surface Laptop Ultra pitch—up to 1 PFLOP AI compute, 128GB unified memory, RTX GPU—fits the same trend from the hardware side.

Agents, Harnesses, and the Shift from Frameworks to Execution Layers

• The center of gravity is moving from “frameworks” to agent harnesses and execution environments: Several posts converged on the same idea. @gakonst argued that the future IDE stack is less about code editors and more about replacing files with threads and bundling plan/design/build/deploy/monitor loops—leaving collaboration/sync engines as a key unsolved problem. In a complementary interview summary, @ConorBronsdon reported Jerry Liu’s view that the “framework era” is ending, with abstractions moving upward into skills, tools, and context quality rather than Python wrappers.

• Multi-agent and agent-optimization work is getting more concrete: CMU/LTI’s MACU and @kohjingyu’s thread argue that computer-use agents should be designed as multi-agent DAG-based systems, with a manager decomposing tasks and dispatching parallel subagents. Reported gains were 4.7–25.5% across benchmarks and 1.5× faster completion on Odysseys. On the optimization side, Microsoft’s SkillOpt got practical validation from @omarsar0, who says plugging it into an orchestrator improved one multimodal extraction skill from 0.73 to 0.93.

• Agent UX and deployment tooling are becoming products in their own right: Nous’s Hermes Agent updates drew strong engagement, including remote-connection fixes here, an updated remote guide here, and a larger dashboard overhaul here. Perplexity launched Personal Computer for Windows, an on-device orchestrator for apps/files, while Cloudflare Browser Run remote tabs showed a more agent-native browser control path. LangChain/LangSmith pushed on the observability and cost-control layer with Gateway spend tracking, Sandbox/Gateway/Observability docs, and case studies around Deep Agents and LangSmith here.

Routing, Cost Controls, and Open-vs-Frontier Deployment Strategy

• Model routing is now a real debate, not a slogan: @levie argued that as token budgets become a meaningful opex category, model routing is inevitable, with domain-specific evals as the differentiator. But @scottastevenson pushed back hard, calling most routing products “snake oil” so far: frontier models can be better/faster/cheaper in aggregate if they avoid retries; routing can destabilize tightly coupled systems; and API vendors can often internalize obvious arbitrage. @fabianstelzer added that cache writes and harness-model-prompt fit can erase expected savings.

• Enterprise users are starting to enforce hard cost ceilings: @simonw highlighted reports that Uber caps coding-agent spend at $1,500/month per employee per tool. LangChain immediately framed this as a use case for LangSmith Gateway. The broader sentiment was captured by @Yuchenj_UW: some orgs may soon face a three-way choice between letting everyone “tokenmaxx,” capping budgets, or reducing headcount and reallocating spend to the most productive AI-enabled workers.

• Real data points are starting to emerge for hybrid/open strategies: Harvey’s benchmark results were the cleanest example. In one study, Harvey found a hybrid legal agent with GLM 5.1 as the main worker and Opus 4.7 as an advisor beat pure Opus on all-pass rate (18% vs 14%) while costing $368 vs $954 across 100 tasks. Harvey also reported that SFT could move Kimi 2.6 from 11% to 15%, beating Opus at roughly 11× lower cost. On the other side, @ClementDelangue argued routing plus post-trained open models will often win on cost/speed/control, while @ypatil125 framed open models and open-model clouds as leading indicators of the eventual default for important workloads.

Top tweets (by engagement)

• Gemma 4 12B launch: @googlegemma and @Google drove the biggest technical engagement with the encoder-free multimodal release.

• Ideogram 4.0 open weights: @ideogram_ai announced a notable shift from a strong closed image model to open weights.

• MAI-Thinking-1 transparency: @eliebakouch’s thread was the most influential technical reading guide to the MAI report.

• Rosalind for life sciences: OpenAI’s GPT-Rosalind update signaled further verticalization of frontier models into domain-specific scientific research.

• Open audio/TTS momentum: Alibaba’s Fun-Realtime-TTS and Miso One stood out as practical releases rather than just research demos.

AI Reddit Recap

/r/LocalLlama + /r/localLLM Recap

1. Gemma 4 Multimodal Open Models

Read more

This is an impressive lineup, especially considering that the Microsoft-Inflection deal that set up MAI only happened 2 years ago, and that these are all from-scratch pretrains. MAI today is by no means an unqualified frontier lab, but it is a good tier 2 neolab with obvious incentives to support domain specific finetunes (as opposed to the frontier labs who have ~all killed finetuning).

The star of the show was the 100+ page MAI tech report, which the research community is giving glowing reviews:

You can catch up on all the rest of the announcement in the excellent Verge recap, and the tweet summaries below:

AI News for 06/1/2026-6/2/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews’ website lets you search all past issues. As a reminder, AINews is now a section of Latent Space. You can opt in/out of email frequencies!

AI Twitter Recap

Top Story: Microsoft Build recap, and new MAI model technical details

What happened

Microsoft used Build to position itself as both an AI platform company and a frontier-model lab, pairing broad product launches with unusually detailed disclosures about its new MAI model family.

• Microsoft AI announced seven new MAI models spanning reasoning, code, image, speech transcription, and voice, led by MAI-Thinking-1, MAI-Code-1-Flash, MAI-Image-2.5, MAI-Transcribe-1.5, and MAI-Voice-2 according to @MicrosoftAI and @mustafasuleyman

• The flagship reasoning model MAI-Thinking-1 was presented as Microsoft’s first reasoning model, built with clean data lineage and zero distillation from third-party models in posts from @mustafasuleyman, @baseten, @tuhinone, and @HannaHajishirzi

• Microsoft released a 109-page technical report for MAI-Thinking-1, which drew strong positive reactions from technically oriented readers for its level of transparency, including @eliebakouch, @ethanCaballero, @nrehiew_, @yacinelearning, and @stochasticchasm

• Microsoft also emphasized local AI and agent-native Windows: Build messaging highlighted secure execution layers for agents, a new Surface RTX Spark Dev Box, Windows AI access to the broader Windows GPU install base, and concept hardware such as Project Solara/Scout, summarized by @yusuf_i_mehdi, @TheTuringPost, @kimmonismus, and @kimmonismus

• Build also included a major GitHub Copilot app push as the “desktop home for agent-native software development,” with canvases, cross-device continuity, and tighter GitHub agent workflows, from @pierceboggan, @lukehoban, and reactions from @techgirl1908

• Microsoft introduced Web IQ, a new grounding/search API stack for AI agents, claiming the APIs already power “nearly all AI agents and chatbots in the industry today, including Copilot and ChatGPT,” via @JordiRib1

• Satya Nadella framed Build as an ecosystem moment rather than a single-product launch, while Mustafa Suleyman framed it as the output of Microsoft’s internal “hill-climbing machine,” in @satyanadella, @mustafasuleyman, and reaction from @nrehiew_

MAI model family: disclosed facts and technical details

MAI-Thinking-1

• Microsoft described MAI-Thinking-1 as a 35B active parameter MoE with a 256K context window in @mustafasuleyman

• A separate summary from @scaling01 says the model is a 1T@35B parameter model, pre-trained on 30T tokens, and trained using 8192 GB200 GPUs; this appears to be a reading of the technical report rather than Microsoft marketing copy

• @kimmonismus similarly summarized it as a mid-size MoE with 45B active params, but this conflicts with Mustafa’s own 35B active figure; the more authoritative figure in the tweet set is the official 35B active number

• Microsoft claims 97% on AIME 2025 and 53% on SWE-Bench Pro, with blind human raters on Surge preferring it overall to Sonnet 4.6, from @mustafasuleyman and @asadovsky

• Microsoft says the model is optimized on MAIA 200, with 30% better performance per dollar and 1.4x performance-per-watt gain versus GB200 when running MAI models end-to-end, per @mustafasuleyman

• Microsoft and partners repeatedly stressed no third-party distillation, “clean data lineage,” and enterprise-controlled fine-tuning with “100% eyes-off” post-training data through Baseten, in @baseten, @tuhinone, and @MicrosoftAI

MAI-Code-1-Flash

• Microsoft introduced MAI-Code-1-Flash as a fast coding model for VS Code and GitHub Copilot CLI, first announced by @pierceboggan and later highlighted by @mariorod1

• Official Microsoft messaging via @mustafasuleyman says Code-1-Flash achieves 51% on SWE-Bench Pro despite having just 5B parameters, positioning it near Haiku-class size/cost

• A competing summary from @scaling01 describes it as a 137B parameter MoE, 256K context, trained on 10T+ tokens, and “stronger and more efficient than Claude 4.5 Haiku.” That likely indicates 5B active parameters rather than total parameters; the tweets do not fully reconcile this distinction, but together imply small active footprint within a much larger MoE

• Availability at launch was highlighted as GitHub Copilot / VS Code-first, per @scaling01 and @mariorod1

MAI-Image-2.5

• Microsoft launched MAI-Image-2.5 and a Flash variant, claiming both reached #2 on leaderboards, with @mustafasuleyman saying they surpass Nano Banana 2 on image editing

• Independent leaderboard accounts supported the high ranking: @arena reported #2 in Image Edit Arena with score 1401, +10 points over Nano Banana 2, Grok Imagine, and ChatGPT Image Latest HF

• @arena further said MAI-Image-2.5 “advances the Pareto frontier,” meaning no model at its price tier scores higher on that benchmark

• Distribution partners quickly followed, including @OpenRouter and @fal

MAI-Transcribe-1.5

• @ArtificialAnlys reported MAI-Transcribe-1.5 as an unusually strong speed/accuracy point on the STT frontier: ~276x realtime, 2.4% AA-WER, #3 overall on its leaderboard

• The model supports 43 languages, including English, French, Arabic, Japanese, and Chinese, and supports keyword biasing for rarer terms such as names and medical terminology, per @ArtificialAnlys

• Pricing was reported as $6 per 1,000 minutes of audio via Microsoft Foundry in @ArtificialAnlys

• OpenRouter also listed the model among the three MAI launches it brought live the same day in @OpenRouter

MAI-Voice-2

• MAI-Voice-2 appears in Microsoft’s “seven models” umbrella and in OpenRouter’s availability post at @OpenRouter

• The tweet set contains little technical detail on Voice-2 itself beyond launch/availability

Technical-report details that mattered to researchers

Why the report stood out

• The dominant technical reaction was that Microsoft released an unusually detailed frontier-model report: @eliebakouch called it “one of the most transparent for a model at this scale,” @nrehiew_ said it “could really serve as an updated textbook for LLM training today,” and @stochasticchasm called it a “gold mine”

• Multiple readers highlighted that the report disclosed pipeline details, scaling ladder methodology, data curation, infra metrics, and MFU numbers; this level of specificity is what drew praise from @ethanCaballero, @eliebakouch, and @nrehiew_

Pretraining and data

• A major technical claim repeated across commentary is that MAI-Thinking-1 used no synthetic data and no distillation, not only in post-training but throughout the disclosed pipeline, from @eliebakouch, @stochasticchasm, and @HannaHajishirzi

• @eliebakouch says the report explicitly notes data from Common Crawl plus private sources, with targeted sub-pipelines for different domains, heavy extraction/dedup work, and an intentional choice of no synthetic data

• The report’s internal private NLL set used for scaling decisions was summarized by @eliebakouch as:

  • 50% code

  • 17.5% STEM

  • 17.5% math

  • 10% general knowledge

  • 5% multilingual

• @eliebakouch says architecture promotion in the scaling ladder was based on an Efficiency Gain (EG) metric: how much extra compute the baseline would need to match the candidate’s loss

• The same thread notes ablations at roughly 100/200 tokens per parameter, described as around “Chinchilla optimal” for the setup, while also remarking this differs from dense-model heuristics due to MoE structure in @eliebakouch

Post-training / RL

• The most discussed technical choice was that Microsoft appears to have started RL from a checkpoint with no prior reasoning exposure, which several readers found notable. @stochasticchasm called this a “very interesting decision,” while @stochasticchasm reacted to graphs suggesting a jump from <20% AIME25 to >95%

• @HannaHajishirzi described the “climbing from scratch” recipe as simple recipes, rigorous science, self-distillation, patience, and great infra

• @soldni characterized the process as “climbing with no distillation, like the big boys do”

• Some independent readers inferred from the report that synth data remains very valuable for agentic performance in the broader field, even if Microsoft deliberately avoided it here; see @stochasticchasm

Data curation / judges / DSPy GEPA

• A detail that got substantial attention from the DSPy/late-interaction crowd: Microsoft reportedly used GEPA / DSPy-optimized LLM judges in pretraining data curation and quality scoring

• This was highlighted by @bj2rn, @LakshyAAAgrawal, and @lateinteraction

Infra / utilization / hardware co-design

• Microsoft reportedly disclosed exact MFU across iterations, which multiple readers said is rarely shared at this scale, per @eliebakouch

• @scaling01 summarized the run as using 8192 GB200 GPUs

• @eliebakouch singled out a reported ~40% higher throughput per watt-type figure as “pretty impressive and bullish on microsoft chips,” though this may refer to rack-level budget or serving configuration and was not fully unpacked in-tweet

• Microsoft’s official framing connected model design to MAIA 200 custom silicon and emphasized better performance-per-dollar and performance-per-watt vs NVIDIA GB200 in @mustafasuleyman

• Build’s broader Windows/local-AI narrative also centered on hardware specifics such as:

  • 1 trillion parameters running locally on DGX Station

  • 128GB unified memory

  • 110 TOPS AI performance

  • 20 CPU cores

  • 70+ PowerToys utilities from @TheTuringPost

• Reactions also pointed to local runs of large models, e.g. @kimmonismus on RTX Spark running a 120B parameter model locally

Build product/platform recap beyond the models

GitHub Copilot app and agent-native development

• GitHub unveiled the GitHub Copilot app, pitched as a desktop surface for agent-native software development by @pierceboggan

• Key themes included:

  • canvases for bidirectional work between users and agents, per @Techmeme

  • continuity across CLI, mobile, web, local, and cloud, per @lukehoban

  • a growing role for GitHub as the center of agent workflows, reflected in @techgirl1908 and @OrenMe

• Copilot CLI also got an experimental terminal UI with tabs, built-in feedback/rubber duck, prompt scheduling, and voice input, per @GHchangelog

Windows as an agent runtime

• Microsoft’s Windows org framed Build around “faster developer execution, a secure execution layer for agents, and unmetered intelligence that runs locally on device,” per @yusuf_i_mehdi

• Several posts stressed that Microsoft wants Windows to be the trusted execution platform for agents, not just Azure

• @TheTuringPost described Project Solara as a platform for agent-first devices, with concepts including:

  • a desktop AI companion

  • a wearable badge with cameras, microphones, sensors, and secure authentication

• @kimmonismus saw these as handheld/desktop devices for controlling agents and compared them to expectations people had for standalone OpenAI hardware

• @kimmonismus separately highlighted Microsoft Scout as an “always-on personal agent for work”

Web IQ and search for agents

• @JordiRib1 announced Microsoft Web IQ as a suite of AI-native grounding APIs for web pages, news, images, and videos

• His framing is important context: classic search engines were built for humans, but Microsoft believes future search demand will come from agents, potentially 1000x more queries than human search traffic

• He claimed Web IQ was re-architected from Bing’s stack for quality, latency, and token efficiency, and that it already powers major chatbots including Copilot and ChatGPT

Foundry and open-model distribution

• @jeffboudier said Satya cited 11,000+ models available in Microsoft Foundry, of which 10,928 come from Hugging Face

• This supports Microsoft’s parallel identity at Build: both a first-party model builder and a large multi-model hosting/distribution platform

Build messaging around datacenters and compute

• Several observers noted Build discussion around data center expansion, community backlash, and Microsoft’s argument that AI infra can expand without raising electricity costs to local communities; see @kimmonismus and @kimmonismus

• @scaling01 highlighted Mustafa saying AI compute will grow 1000x in the next 3 years, taking today’s rough 5e27 FLOPs frontier scale to 5e30 FLOPs by 2029

• @mustafasuleyman summarized the company’s philosophical theme as “Humanist superintelligence”

Facts vs. opinions

Factual claims in the tweet set

• Microsoft launched seven new MAI models at Build: @MicrosoftAI

• Official metrics for MAI-Thinking-1: 35B active MoE, 256K context, 97% AIME 2025, 53% SWE-Bench Pro, and blind human preference over Sonnet 4.6: @mustafasuleyman

• Official metrics for MAI-Code-1-Flash: 51% SWE-Bench Pro, 5B parameters as stated in tweet copy: @mustafasuleyman

• MAI-Image-2.5 ranking claims were independently echoed by @arena

• MAI-Transcribe-1.5 speed/accuracy details came from independent benchmark account @ArtificialAnlys

• Microsoft released a 109-page technical report: @eliebakouch

Opinions / interpretations

• “Microsoft is training serious models now?” from @teortaxesTex is an interpretive reaction to the model/report quality, not a standalone fact

• Claims that the report is “one of the most transparent” or “an updated textbook” are opinions from @eliebakouch and @nrehiew_, albeit shared by many readers

• @kimmonismus and @TheTuringPost framed Build as a strategic shift from cloud-only AI toward local reasoning/agents; that is analysis rather than official wording

• Posts claiming Microsoft “leaked” Anthropic Mythos FLOPs, including @swyx and @scaling01, are speculative interpretations of a slide, later contested by the same cluster of commenters

Different opinions and perspectives

Supportive views

• Technical readers were broadly impressed by the report’s transparency and Microsoft’s willingness to publish details usually withheld at this scale: @eliebakouch, @nrehiew_, @ethanCaballero, @stochasticchasm

• Some saw MAI-Thinking-1 as proof Microsoft is becoming a genuine frontier lab rather than just a model reseller or application layer, e.g. @teortaxesTex, @echen, @NandoDF

• Enterprise/platform supporters liked the clean-data-lineage, fine-tunable, eyes-off post-training data story, especially Baseten/Microsoft’s positioning around ownership and control: @baseten, @tuhinone

Neutral / analytical views

• Several posts focused on reading and unpacking the report rather than cheering the launch, especially @stochasticchasm, @nrehiew_, and @eliebakouch

• Some commentators were careful on benchmark interpretation. @kimmonismus noted Microsoft appeared to compare to Sonnet 4.6 generally, with Opus-level comparability only on SWE Pro

• @iScienceLuvr specifically appreciated reporting on health benchmarks such as HealthBench Professional and MedXpertQA rather than only coding/math

Skeptical / opposing views

• A subset questioned whether all numbers and comparisons were being interpreted correctly, especially around active params and external-model comparisons

• The most visible skepticism concerned the apparent Mythos FLOP “leak”. @iScienceLuvr suggested it was probably just an estimate, not a leak; @scaling01 later argued the original 6.1e27 FLOP figure was unrealistic and supplied a lower alternative estimate before posting a correction in @scaling01

• There was also implicit skepticism in the field about whether zero synth / zero distillation is the right long-term recipe for best agentic performance, as noted by readers emphasizing synth-data deltas elsewhere, e.g. @stochasticchasm

Context: why this matters

• Build’s announcements matter because they suggest Microsoft is no longer content with being only:

  1. Azure/OpenAI’s cloud host

  2. GitHub’s developer surface

  3. Copilot’s application shell
     It is also trying to be a first-party frontier model developer with its own model family, silicon stack, and post-training platform

• The clean lineage / no distillation emphasis is strategically significant. It addresses enterprise concerns around IP provenance, future controllability, and dependence on external labs

• The local AI emphasis matters because Microsoft is tying AI strategy to Windows and device distribution, not just to Azure. Build messaging repeatedly pushed the idea that reasoning models, planners, and agents can increasingly run on-device, not only in the cloud: @TheTuringPost, @yusuf_i_mehdi

• The 109-page report matters because frontier-model transparency has generally been shrinking, especially around data, infra, and training methodology. Multiple researchers explicitly noted the disclosure level is uncommon at this scale: @eliebakouch, @nrehiew_

• The Build recap also showed Microsoft trying to integrate all layers of the stack:

  • models: MAI family

  • chips: MAIA 200

  • cloud: Azure + Foundry

  • OS: Windows agent runtime

  • developer UX: Copilot app / VS Code / CLI

  • retrieval/grounding: Web IQ

  • hardware form factors: Solara / Scout concepts

• This combination is why several observers described the event less as a normal dev conference and more as a coordinated move toward an agent platform spanning cloud, edge, OS, and custom models, e.g. @satyanadella, @mustafasuleyman, and @TheTuringPost

The “Mythos FLOPs leak” mini-story

• During/after Build, some users claimed a Microsoft slide inadvertently revealed training compute for Anthropic’s rumored Claude Mythos, with @swyx asking if Mustafa had leaked the FLOP count

• @scaling01 estimated the slide implied 6.1e27 FLOPs with a confidence interval based on pixel measurement, while @kimmonismus noted that would be around Gemini 3.1 Pro-scale compute

• That interpretation was subsequently challenged by @iScienceLuvr, who argued it was probably an estimate, and then by @scaling01, who posted a lower-range model-based estimate of 3.37e26 to 1.46e27 FLOPs and later said the original numbers were bogus in @scaling01

• The episode is useful mostly as context: Build’s compute/scaling messaging was detailed enough that people started trying to infer competitor training budgets from presentation materials

Developer tools, agents, and coding workflows

• OpenAI launched Sites in Codex, letting teams turn ideas/docs/plans into deployed internal websites/apps with auth and dynamic data, first for business/enterprise users, in @OpenAI, @TheRohanVarma, and @gdb

• OpenAI also expanded role-specific Codex plugins across sales, data analytics, creative production, product design, and public equity workflows, with access to 62 apps and 110 skills, from @OpenAI and @OpenAIDevs

• GitHub’s Copilot app and Microsoft’s Build push around agent-native software development were central to the day’s tooling news: @pierceboggan, @lukehoban, @GHchangelog

• Anthropic shipped a CLI for Claude Platform and upgraded Claude Code’s /fork to run a background agent with exact context + prompt cache, in @ClaudeDevs and @ClaudeDevs

• Nous launched Hermes Desktop, a local/native desktop surface for Hermes agents, in @NousResearch, @Teknium, and later Tailscale/Ollama integration notes from @Teknium and @ollama

• Cognition launched Devin Desktop, positioned as an agent-neutral desktop for managing local/cloud agents and handoff between local planning and cloud execution, in @cognition, @ScottWu46, and @russelljkaplan

Models, local inference, and routing

• H Company launched Holo 3.1, a local computer-use model family based on Qwen-style architecture, with checkpoints from 0.8B to 35B and formats including NVFP4, FP8, and Q4 GGUF; a popular summary cited 79.3% on AndroidWorld for the 35B model in @TeksEdge, with launch tweet from @hcompany_ai

• Perplexity announced hybrid agentic inference for Perplexity Computer, splitting work between local models on-device and frontier cloud models for privacy and token efficiency, in @perplexity_ai and @AravSrinivas

• OpenRouter data shared by @ttunguz showed open-weight models at 69.1% of token volume, versus 30.9% for closed models

• Commentary around model routing as a key future abstraction came from @ClementDelangue, @garrytan, @matanSF, and the counterpoint from @glennko, who argued enterprise production reliability makes generic routing harder than enthusiasts suggest

• Local-AI UX improvements also appeared in Hugging Face’s hardware compatibility checks and oMLX’s native macOS app release from @m_newhaus and @jundotkim

Research and evals

• Google DeepMind announced Co-Scientist, a Gemini-based multi-agent hypothesis generation system for science, claiming collaborations that helped identify liver fibrosis targets, ALS approaches, and genetic leads for aging, in @GoogleDeepMind, @GoogleDeepMind, and @GoogleDeepMind

• The new Crafter / CraftEditor work on editable scientific figure generation drew attention as a five-agent workflow for producing and refining figures plus raster-to-SVG conversion, in @HuggingPapers, @_akhaliq, and @TheTuringPost

• Tilde Research introduced Wall Attention, a RoPE-free attention method with diagonal forget gates, claiming training at 4k and generalization to 200k+ tokens plus Triton kernels and strong decode throughput, in @tilderesearch

• A robotics vision encoder claiming +22.5% real-world OOD success by encoding dynamics-awareness rather than relying on static-image pretraining was posted by @jbhuang0604

• New evals/benchmarks of note:

  • PaintBench for precise image editing, where best model reached only 17.1%, from @itskaixu

  • VSTAT for video state tracking, arguing frontier MLLMs remain weak at tracking evolving world state, from @PinzhiHuang and @sainingxie

  • Data Agent Benchmark for enterprise data workflows, from @sh_reya

Inference, infrastructure, and agent systems

• Harvey + LangChain shared work on cheap verifiers for legal agents, showing DeepSeek V4 Flash could preserve 94–96% agreement with Opus 4.7 while reducing cost 18x in per-criterion mode and ~1000x in batch mode; for 3,200 RL rollouts, verification cost dropped from $18,000 to $18, in @harvey, @hwchase17, and @nikogrupen

• W&B relaunched Weave as agent-first observability with integrations across common harnesses and automated detection of failure modes, in @wandb and @neutralino1

• Prime-RL integrated Mooncake Store with vLLM for cross-node prefix / KV cache reuse, pitched as key for agentic rollouts, in @m_sirovatka

• Together detailed serving optimizations for MiniMax-M3, citing 81–125% throughput improvements via KV-block-major sparse attention, paged decode, optimized index scoring, and multimodal preprocessing, in @togethercompute

• MiniMax itself highlighted 1M context, native multimodality, desktop-computer operation, and MSA reducing attention’s share of decode time from ~30% to ~5%, in @MiniMax_AI

Ecosystem, hardware, and industrial capacity

• Westmag emerged from stealth to build American robot actuators and drone motors, with $11M raised led by a16z and participation from Founders Fund, Lux, NFDG, Menlo and others, in @boxcardavid, @packyM, and @oyhsu

• PyTorch noted NVIDIA adoption of OpenMDW-1.1, a permissive AI-model licensing framework, across four open-model families in @PyTorch

• Martin Scorsese publicly demonstrated narrow, preproduction use of FLUX for storyboarding with Black Forest Labs, framed as exploratory and complementary to hand-drawn work rather than generative replacement, in @robrombach and @TheRundownAI

AI Reddit Recap

/r/LocalLlama + /r/localLLM Recap

1. NVIDIA Nemotron 3 Ultra and RTX Spark Specs

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• base Nano (16B: 8B reasoner tower + 8B generator tower)

• Super (64B: 32B reasoner tower + 32B generator tower) models, and

• Super finetunes for Text2Image and Image2Video, which are now the new SOTA open weights imagegen and videogen models, just below Nano Banana 2

At Computex in Taiwan, Jensen also brought the heat with Nemotron 3 Ultra, their 550B-A55B, remarkably efficient/fast open weights LLM that is the new US SoTA:

Finally, the RTX Spark personal computer 1 petaflop superchip, was previewed with Microsoft and OpenClaw and Hermes Agent as a launch partner (good analysis here)

AI News for 5/30/2026-6/1/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews’ website lets you search all past issues. As a reminder, AINews is now a section of Latent Space. You can opt in/out of email frequencies!

AI Twitter Recap

NVIDIA’s Cosmos 3, Nemotron 3 Ultra, and the Push for Open Physical AI

• NVIDIA’s open-source week: NVIDIA dominated the open-model conversation with Cosmos 3, an open family of omnimodal world models for physical AI, plus the announcement of Nemotron 3 Ultra, a 550B open-weight model that several posters called the strongest U.S. open model so far. Cosmos 3 was framed as a full-stack release—weights, code, datasets, and fine-tuning recipes—with NVIDIA also launching the Cosmos Coalition alongside partners including Runway to build an open ecosystem for world models @NVIDIAAI ecosystem context, @runwayml coalition announcement, @kimmonismus Cosmos thread, @ClementDelangue on NVIDIA’s HF footprint.

• Why Cosmos 3 mattered technically: Beyond robotics rhetoric, the more concrete details were that Cosmos 3 unifies language, image, video, audio, and action in a single Mixture-of-Transformers design pairing an autoregressive reasoner with a diffusion generator. Artificial Analysis said Cosmos 3 reached #1 among open-weight models on both their Text-to-Image and Image-to-Video leaderboards, noting the generator uses structured JSON prompts and can be driven either by an external prompt-upsampling harness or its own reasoner branch. Separately, NVIDIA’s hardware + software push extended to adoption of the OpenMDW framework and partner ecosystem integrations on platforms like fal @ArtificialAnlys, @fal.

• Nemotron 3 Ultra reception: Community reaction to Nemotron 3 Ultra was unusually strong for a fresh open release. Posters highlighted both capability and serving characteristics, including claims that it is already topping some open evals and may be serving at 300+ tok/s in some setups—far faster than large DeepSeek/Kimi-class models @scaling01, @ctnzr, @caspar_br. There was also some technical discussion that Nemotron appears less sparse than peers like Kimi K2 / DeepSeek V4—roughly ~10% active vs ~3%—which could affect both economics and behavior @eliebakouch.

MiniMax M3, Qwen3.7-Plus, and JetBrains Mellum2 Expand the Open Agent Model Field

• MiniMax M3’s launch was the day’s biggest model release: M3 was presented as an open-weight multimodal agent/coding model with 1M context, native multimodality, and competitive agent benchmarks. The headline figures repeated across launch partners were 59.0% SWE-Bench Pro, 66.0% Terminal Bench 2.1, and 74.2% MCP Atlas @MiniMax_AI, @PBDTokenRouter, @kimmonismus. Multiple infra vendors shipped day-0 support—Novita, Vercel AI Gateway, Cloudflare AI Gateway, OpenClaude, Flowith, and others—suggesting unusually fast ecosystem adoption @MiniMax_AI on Novita, @rauchg, @gitlawb.

• Benchmarks vs practical experience were mixed: M3 earned praise for frontend generation, visual/game tasks, and price-performance, with side-by-side demos showing strong one-shot UI/game outputs and notable benchmark placement for Next.js agent evals @notjazii, @lostinlatencyX, @rauchg. But several evaluators also reported high token consumption, verbose self-check loops, and occasional requirement drift on long tasks, making M3 look more like a “quality first, efficiency later” model @ZhihuFrontier review, @teortaxesTex skepticism.

• Qwen3.7-Plus: Alibaba launched Qwen3.7-Plus as a multimodal interactive hybrid agent that unifies GUI and CLI operation, visual reasoning, coding, and search-augmented QA. It is API-available via Alibaba Cloud Model Studio and was quickly added to tools like Cline @Alibaba_Qwen launch, @cline. The launch reinforces the trend that open-ish Asian labs are no longer releasing “just chat models,” but full agent-capable multimodal systems.

• JetBrains Mellum2: JetBrains released Mellum2, a 12B MoE model with 2.5B active parameters, trained on roughly 11T tokens and post-trained with RLVR, shipping base / SFT / RL checkpoints and a technical report @nv_pavlichenko, @jetbrains. The intended niche is especially interesting: ultra-low-latency inference for routing, RAG, sub-agents, and IDE use, and it landed in vLLM immediately @vllm_project. This looks like a serious “small fast open model for developer workflows” play rather than a benchmark-chasing frontier release.

Agents, Sandboxes, Memory, and Search Are Becoming the Real Product Surface

• The stack is shifting from model calls to agent runtimes: Several launches converged on the idea that the main engineering leverage is now in the harness rather than the model. Perplexity’s “Search as Code” is the clearest example: instead of iterative search tool calls, the model writes Python against a search SDK, enabling custom ranking pipelines, map-reduce over indexes, batching, aggregation, and lower token overhead. Perplexity reports a jump on its internal WANDR benchmark from 0.152 to 0.386 with this architecture @perplexity_ai, @AravSrinivas.

• Managed agents + sandboxes are becoming standard: Google detailed Managed Agents in the Gemini API, where a single API call can spin up an agent that reasons, writes/runs code, manages files, and operates inside a hosted Linux sandbox @_philschmid, @GoogleAIStudio. LangChain pushed similar ideas around Deep Agents, Context Hub, and LangSmith Sandboxes/Engine, emphasizing persistent context, agent lifecycle tooling, and automated failure triage @LangChain, @hwchase17.

• Memory remains a missing primitive: One recurring complaint was that enormous context windows still don’t solve cross-session memory. A thread on HydraDB argued that “RAG + manual context injection” has been misnamed as memory, while actual persistent session knowledge remains underserved @kimmonismus. Related research threads pointed to reusable context management policies like AdaCoM, which trains a separate LLM via RL to prune/preserve context for frozen agents @dair_ai.

• Security remains the gating issue for enterprise agents: There was a notable warning from Microsoft Security Intelligence about a major npm supply chain compromise affecting 90+ redhat-cloud-services packages, including a self-propagating worm stealing npm/GitHub/AWS/SSH credentials @MsftSecIntel. At the same time, enterprise agent vendors highlighted sandboxing, runtime isolation, and security stack integration as prerequisites for deployment, including discussion of NVIDIA OpenShell and LangChain’s sandbox keynote @shannholmberg, @LangChain.

Codex, Claude Code, and the Competitive Coding-Agent Race

• OpenAI extended Codex into more places: OpenAI announced that frontier models and Codex are now generally available on AWS / Amazon Bedrock, aimed squarely at enterprises that want OpenAI capabilities inside existing AWS security/compliance workflows @OpenAI, @OpenAIDevs. OpenAI also shipped a Codex Python SDK supporting threads, turns, streaming, resume, images, and sandbox control @reach_vb, plus support for Bedrock-backed Codex workflows @reach_vb on Bedrock config.

• Claude Code had a real ops incident: Anthropic reset 5-hour and weekly rate limits for Pro and Max users after fixing a bug where some Opus 4.8 sessions spawned too many parallel subagents/tool calls, burning usage unexpectedly @ClaudeDevs, follow-up. That’s a notable reminder that coding-agent product quality is increasingly determined by orchestration behavior, not just raw model IQ.

• Behavioral differences across coding models remain material: Developers highlighted large qualitative differences between GPT, Claude, and other models on benchmarks like ProgramBench and WeirdML, with Opus sometimes preferring exploration over score-maximization or showing benchmark-specific quirks @OfirPress, @htihle. A separate long thread argued newer Claude Opus 4.6–4.8 variants can fabricate plausible but fictional concepts in non-coding domains, suggesting possible truthfulness/alignment regressions rather than ordinary hallucinations @distributionat.

Infra, Hardware, and Local AI Systems

• NVIDIA is coming for the PC: The most-discussed hardware launch was RTX Spark, an NVIDIA/Microsoft “personal AI computer” built around Grace + Blackwell, with up to 128GB unified memory and claimed 1 PFLOP FP4. The key strategic read: NVIDIA is no longer just selling accelerators, but an end-to-end local AI system that competes with Apple Silicon, x86 PCs, and Qualcomm simultaneously @kimmonismus, @swyx.

• Cluster/networking updates: On the datacenter side, Lambda said it is first to adopt NVIDIA Quantum-X InfiniBand Photonics Q3450-LD switches, pushing co-packaged optics to reduce network power and failures in large AI clusters @LambdaAPI. OpenAI also announced Stargate Michigan, a planned 1GW data center using closed-loop cooling and paired with workforce/education commitments @OpenAINewsroom.

• Local open-model tooling is improving fast: The MLX-VLM v0.6.0 release was one of the more substantive local inference/tooling updates, adding speculative decoding, Anthropic-style and responses-style APIs, tool calls, support for many new multimodal models, and image/audio features with the explicit pitch of turning Apple devices into “real local agent machines” @Prince_Canuma. That pairs well with growing DGX Spark + vLLM experimentation for local NVFP4 MoE serving @vllm_project.

Top Tweets (by engagement, filtered for technical relevance)

• Anthropic’s IPO path: Anthropic said it has confidentially submitted a draft S-1 to the SEC, opening the door to an IPO pending review @AnthropicAI.

• Claude Code usage incident: Anthropic reset user rate limits after an Opus 4.8 parallel subagent/tool-call bug caused excessive quota burn @ClaudeDevs.

• Qwen3.7-Plus: Alibaba launched a multimodal agent model spanning GUI/CLI operation, coding, and visual tasks @Alibaba_Qwen.

• OpenAI on Bedrock: OpenAI models and Codex are now available through Amazon Bedrock for enterprise workflows @OpenAI.

• ARC-AGI-3 movement: Claude Opus 4.8 posted a new SOTA on ARC-AGI-3 at 1.5%, still tiny in absolute terms but a meaningful jump on that benchmark @arcprize.

AI Reddit Recap

/r/LocalLlama + /r/localLLM Recap

1. New Frontier Model Releases and Early Tests

• MiniMax M3 - Coding & Agentic Frontier, 1M Context, Multimodal (Activity: 1090): MiniMax M3 is announced as an open-weight frontier model with coding/agentic focus, native multimodality/vision, and MiniMax Sparse Attention for up to 1M tokens of context with a guaranteed 512K minimum (MiniMax M3). Claimed long-horizon agentic results include 12-hour ICLR paper reproduction, Hopper FP8 GEMM CUDA/Triton optimization reaching 9.4× speedup after 147 iterations, and PostTrainBench ranking third behind Opus 4.7 and GPT-5.5; access is currently via API/MiniMax Code, with HuggingFace/GitHub weights/local deployment planned. Commenters are cautiously interested in the combination of cheap/efficient vision plus long-context agentic coding, but skeptical because the announcement calls it “open-weight” while not yet exposing weights or even parameter count. One technical debate is whether the results imply a much larger-than-~250B model, extreme benchmark optimization, or a genuine open-weight breakthrough.

  • Commenters focused on the missing release details: despite the claim of being “the first open-weight model with three frontier capabilities”, users could not find actual weights, parameter count, or sizing information for MiniMax M3. One commenter linked a preview image from the announcement (Reddit image), but the thread still lacked confirmation of model scale or downloadable artifacts.

  • A technically substantive concern was that the advertised capability level implies one of three possibilities: a much larger-than-expected model, unusually strong benchmark optimization, or a major open-weights breakthrough. The speculation centered on whether MiniMax M3 is actually around ~250B parameters or significantly larger, and whether its coding/agentic/multimodal claims will hold once weights and independent benchmarks are available.

• NVIDIA announces Nemotron 3 Ultra (Activity: 621): The image is a technical announcement slide for NVIDIA Nemotron 3 Ultra, described in comments as a MoE 550B-A55 model. The slide positions Nemotron 3 Ultra against open/open-weight competitors including GLM 5.1, Kimi K2.6, and Qwen3.5 across “Frontier Smart” benchmark categories such as agent productivity, coding, instruction following, knowledge work, and long-context capability. Commenters viewed the comparison against other open-source/open-weight models positively, while one noted an “artificial analysis score” of 48, placing it just below frontier-tier models and around the MiniMax 2.7 range, with the expectation that it could be the strongest U.S. open-weight model.

  • NVIDIA Nemotron 3 Ultra is identified as a MoE 550B-A55 model, implying roughly 550B total parameters with about 55B active parameters per token. This architecture detail is the most concrete technical spec mentioned in the thread.

  • A commenter cites an Artificial Analysis score of 48, placing Nemotron 3 Ultra “one notch less than frontier” and roughly in the MiniMax 2.7 range, while suggesting it may be the strongest US open-weight model by that metric.

  • Technical references shared include NVIDIA’s official Nemotron 3 Ultra Base usage cookbook on GitHub: NVIDIA-NeMo/Nemotron, plus the LifeArchitect model comparison table: lifearchitect.ai/models-table. One commenter argues the comparison against Qwen3.5 is notable because Nemotron may be NVIDIA’s best open-weight model while still trailing several non-US/open models.

• Stepfun 3.7 Flash is very good (Activity: 473): The GIF is a technical visual demo, not a meme: it shows the output of Stepfun 3.7 Flash for the prompt create a beautiful, relaxing flight simulator in a single html page, rendering a low-poly 3D flight scene with HUD-style speed/altitude indicators. The OP says this was the official Q4_X_S quant and claims the model feels near GLM 5.1 in aesthetics and about 80% of its 3D world understanding, while using only roughly 25% of GLM 5.1’s parameters and including built-in vision. Commenters mostly reacted with comparisons and nostalgia rather than deep benchmarks: one referenced the old Excel flight simulator, while another compared interest in Qwen 3.7 Max / 27B and asked whether it beats Qwen3.6 27B.

  • A commenter draws a model-comparison angle by referencing Qwen 3.7 Max and hoping for a future Qwen 3.7 27B release, while another asks whether Stepfun 3.7 Flash is better than Qwen3.6-27B. The thread includes screenshot evidence for the Qwen3.6-27B reference (image), but no quantitative benchmark scores or reproducible eval details are provided.

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We’re taking the opportunity to solicit the leading AI FDE’s in the world for AIE’s new Forward Deployed Engineer track, mirroring similar pushes from both OpenAI DeployCo and Anthropic DeployCo:

as well as AIE’s new Founders program, where we are doing our version of the Startup Battlefield, a competitive pitch contest anchored by YCombinator’s Garry Tan and Howie Lu’s $10 Million dollar Hyperagent contest. Sign up (and book hotel!) for details today if you are keen.

AI News for 5/28/2026-5/29/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews’ website lets you search all past issues. As a reminder, AINews is now a section of Latent Space. You can opt in/out of email frequencies!

AI Twitter Recap

Claude Opus 4.8 Rollout, Benchmark Friction, and API Ergonomics

• Opus 4.8 landed into a noisy, mixed eval landscape: multiple independent benches converged on “incremental but not dominant.” @arena pushed 200+ frontend/code tests comparing Opus 4.8 against prior Opus variants, Gemini, and GLM; @theo reported CursorBench shows it as more efficient but slightly worse than 4.7 within margin of error; @jerryjliu0 and @llama_index found small gains on tables/layout but regressions on content faithfulness/charts in document parsing; @scaling01 said no progress on ALE-Bench and separately flagged interesting failure modes on LisanBench. On the positive side, @jeremyphoward found 4.8 less over-agentic and more cooperative than 4.7/GPT-5.5 in coding, while @leo_linsky called it a tangible product improvement over prior Anthropic releases.

• Anthropic also shipped useful platform-level changes: @ClaudeDevs announced mid-conversation system instructions without breaking prompt cache, plus authoritative mid-conversation system-role updates, which matters for long-running agent sessions and cost control. But pricing remains a major complaint: @jeremyphoward argued Anthropic has done little for API affordability, preferring GPT-5.5 partly because subscription/API economics are easier to justify. Overall takeaway: 4.8 looks like a meaningful quality-of-life release for real use, not a clean benchmark reset.

Agent Harnesses, Multi-Turn RL Bugs, and the Infrastructure Around Autonomy

• A subtle but important RL failure mode got called out: @ClementDelangue highlighted a Hugging Face deep-dive on why many tool-using, multi-turn RL training loops are silently broken. The core bug: decoding model output, parsing tool calls, then re-tokenizing the updated conversation can change tokenization, so gradients are applied to sequences the model never actually sampled. The proposed fix is a strict “Token-In, Token-Out” rule: never re-encode sampled tokens; keep a single token buffer across turns. @johnschulman2 reinforced the broader point that renderers are foundational infrastructure between messages and tokens, with failure modes spanning train/test mismatch, caching inefficiency, and prompt injection risk.

• Harness design is becoming its own optimization discipline: @omarsar0 surfaced work on Effective Feedback Compute (EFC), claiming raw token/tool counts explain agent success poorly while EFC reaches R² up to 0.99, implying harness quality matters more than gross activity. This lines up with productized tuning efforts like @LangChain, where Deep Agents v0.6 makes harness profiles first-class to get strong performance from Qwen/Kimi/DeepSeek at 20x+ lower cost than frontier APIs, and @hwchase17 explicitly framing “different models need different prompts/tools.” @vllm_project shipped native weight syncing APIs and improved pause/resume for async RL, and later added fastokens, a Rust BPE tokenizer to reduce CPU tokenization bottlenecks in long-context/agentic workloads.

• Debate is shifting from “single vs multi-agent” to where the abstraction pays: @OfirPress argued current multi-agent systems are mostly speedups, not capability unlocks; @scaling01 took the opposite view, expecting swarm-style training to yield better planning and superintelligence-like behavior. Either way, the practical trend is clear: more teams are building around agent observability, traces, and continual improvement loops, e.g. @Vtrivedy10 on mining production traces for SFT/distillation and long-horizon continual learning.

Open Models, Local AI, and the OSS Toolchain Tightening Up

• Local-first and open-weight momentum continues to rise: @LangChain said 1 in 3 AI teams ran an open-weights model in April 2026, up from 1 in 5 nine months earlier; @EpochAIResearch estimated open-weight models now lag frontier proprietary models by about four months. On the toolchain side, @ggerganov launched llama.app, giving llama.cpp an official website, a unified installer, and a single llama entrypoint aimed at easier local deployment and third-party agent integration. @ollama announced OpenJarvis as a local-first personal AI via Ollama, explicitly tied to Stanford/Hazy’s “Intelligence Per Watt” framing.

• Open infrastructure is getting more enterprise-shaped: @ClementDelangue noted that ~50% of models and datasets on Hugging Face are now private, rising with HF’s storage/buckets offering; this is an important correction to the idea that HF is only public OSS infrastructure. @abidlabs showed Hugging Face Jobs replacing GitHub runners for CPU/serverless GPU CI. @DSPyOSS, @dbreunig, and others shipped a redesigned DSPy docs/front page ahead of a coming 4.0, focused on onboarding into programmable AI systems rather than pure prompting.

• Licensing and permissiveness are becoming strategic levers: @kimmonismus highlighted NVIDIA moving its four open model families to Linux Foundation OpenMDW-1.1, reducing legal fragmentation across weights/code/docs/data. New permissive data releases also matter: @keshigeyan introduced GPIC, a 100M-pair permissive image corpus plus 1M-pair benchmark for visual generation, with explicit research + commercial usability.

Google/OpenAI Product Surface Expands: Managed Agents, Gemini Spark/Omni, and Codex on Windows

• Google is widening the “managed agent” stack from API to consumer product: @_philschmid showed Managed Agents in the Gemini API: a single API call provisioning a sandboxed Linux environment with code execution, web access, and file I/O. On the consumer side, @GeminiApp rolled out Gemini Spark to U.S. AI Ultra subscribers as a 24/7 personal agent that can operate across a user’s digital ecosystem under direction. Google also kept pushing Gemini Omni multimodal generation/editing demos (example, product thread) and announced Google Flow Agent for creative workflows in video/film production (thread).

• OpenAI’s Codex is moving closer to a persistent remote dev operator: @OpenAI and @OpenAIDevs added computer use on Windows, including remote steering from the ChatGPT mobile app. Follow-on UX improvements included stable identicons for background agents and search across prior chat content (@OpenAIDevs); @reach_vb summarized broader Codex updates around Windows control, mobile remote access, and profile/task stats. Separately, OpenAI updated gpt-5.5 instant to improve sycophancy, factuality, and multilingual performance per @michpokrass.

• This all points to more vertically integrated agent stacks: model + harness + sandbox + UI + remote control + pricing/quotas. Google is smoothing quotas on Gemini (@joshwoodward); OpenAI is expanding Codex’s operating surface; Cursor added auto-review mode with subagent-based approval routing (tweet). The common pattern is less “chatbot,” more managed execution environment with policy and memory.

Research and Systems Papers Worth Attention

• Search, retrieval, and memory: @TheTuringPost highlighted Bidirectional Evolutionary Search (BES) from Harvard/MIT, combining forward search with backward decomposition and evolutionary operators; reported gains include Llama-3.2-3B-Instruct on MuSiQue from 4.0% to 7.0%. In retrieval, @_reachsumit pointed to Latent Terms, showing sparse BM25-ready features can be extracted from frozen dense retrievers via SAEs. @topk_io open-sourced Iso-ModernColBERT for more efficient late-interaction inference.

• Continual learning and belief/state management: @HuggingPapers summarized BeliefTrack, claiming optimized belief-state management cuts long-horizon reasoning failures by 70%+. @AndrewLampinen argued the continual learning field over-focused on interference instead of positive transfer; @victor207755822 presented a second DeliAutoResearch SKILL paper focused on self-iteration and CL.

• Multimodal/world models/robotics: NVIDIA-affiliated work included γ-World, a generative multi-agent world model streaming at 24 FPS (tweet), and minWM, a real-time interactive video world model framework (tweet). In robotics, @_akhaliq shared Qwen-VLA, and @inventorOli demoed Robostral’s language-following and manipulation improvements. For always-on proactive agents, @dair_ai surfaced work replacing LLM wake-up decisions with a 220MiB temporal-graph encoder, gaining +16.7 mean F1 while running 4–83x faster.

Top tweets (by engagement)

• OpenAI / biology: @OpenAI on Rosalind Biodefense announced trusted-access biology tooling for public health and biodefense.

• Google / consumer agents: @GeminiApp on Spark rolled out its always-on personal agent to AI Ultra users in the U.S.

• OpenAI / dev tools: @OpenAI on Codex Windows support and @OpenAIDevs expanded computer use to Windows plus mobile remote steering.

• llama.cpp UX milestone: @ggerganov launched llama.app with a unified installer and CLI entrypoint for local AI.

• HF / RL correctness: @ClementDelangue amplified the Token-In, Token-Out warning for multi-turn RL with tools.

• Open vs closed timing gap: @EpochAIResearch estimated open-weight models are now about 4 months behind the frontier.

AI Reddit Recap

/r/LocalLlama + /r/localLLM Recap

1. Local LLM Performance: MoE Releases, Quants, VRAM Savings

• StepFun 3.7 Flash (Activity: 637): StepFun released Step 3.7 Flash, a multimodal MoE with 196B total parameters, 11B active, and a built-in 1.8B ViT, advertised for high-throughput agent workflows up to 400 TPS and reportedly runnable locally with ~128GB RAM. Reported benchmarks position it unusually strongly for a flash-class/local model: SWE-Bench Pro 56.26%, DeepSearchQA F1 92.82%, HLE w/tools 47.2, plus large gains over Step 3.5 Flash on Terminal-Bench, Toolathlon, ClawEval, and other agentic/tool-use tasks. Direct model artifacts are available on Hugging Face in BF16, FP8, NVFP4, and GGUF, with day-0 llama.cpp support PR and related MTP work in llama.cpp#23274. Commenters characterize the model as technically odd: its hidden/thinking traces are described as nearly incoherent, but final answers can be “perfect” and competitive with much larger >1TB models; one user says the prior Step 3.5 “infinite thinking” issue appears fixed. There is cautious enthusiasm around local deployment, especially for users with 4x3090-class hardware, and appreciation that StepFun upstreamed llama.cpp support instead of only maintaining a fork.

  • StepFun released multiple Step-3.7-Flash checkpoints on Hugging Face: BF16 (Step-3.7-Flash), FP8 (Step-3.7-Flash-FP8), NVFP4 (Step-3.7-Flash-NVFP4), and GGUF (Step-3.7-Flash-GGUF). One user reports the prior Step 3.5 Flash “infinite thinking” issue appears fixed, making 3.7 more usable despite still having an odd intermediate reasoning style.

  • There is day-0 llama.cpp enablement via StepFun’s upstream PR: ggml-org/llama.cpp#23845, contrasting with Step 3.5’s fork-based support. A separate community PR for MTP support exists at ggml-org/llama.cpp#23274, though commenters note it needs updating for Step 3.7 and current master.

  • A vLLM nightly test of the NVFP4 checkpoint on 2x Pro 6k with 64 concurrent shallow-context requests reached about 2200 tok/s. The reported config used tensor-parallel-size 2, --enable-expert-parallel, --quantization modelopt, --kv-cache-dtype fp8, --reasoning-parser step3p5, and StepFun tool-call parsing; vLLM reported GPU KV cache size 1,667,645 tokens and max concurrency 6.36x for 262,144 tokens/request.

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By way of celebration, the company also released Opus 4.8, which broadly reportedly fixed many of the issues the community had found/soured on Opus 4.7 post launch (see recap below for details). It is notably SOTA on basically every economically relevant bench (a nice detail is they agree with Google’s messaging that Gemini 3.5 Flash is an improvement over Gemini 3.1 Pro):

But perhaps of more long term significance is the massively parallel “dynamic workflows” feature in Claude Code, also called ultracode, which was behind Jarred Sumner’s 750k LOC rewrite of Bun from Zig to Rust in 6 days:

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AI News for 5/27/2026-5/28/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews’ website lets you search all past issues. As a reminder, AINews is now a section of Latent Space. You can opt in/out of email frequencies!

AI Twitter Recap

Anthropic announced a massive new financing and simultaneously shipped Claude Opus 4.8.

• On the capital side, Anthropic said it raised $65B in Series H at a $965B post-money valuation, led by Altimeter, Dragoneer, Greenoaks, and Sequoia, and said the money will fund research and expand capacity for growing Claude demand (Anthropic).

• The company also disclosed that its run-rate revenue surpassed $47B, attributing growth to enterprise deployments and everyday usage (Anthropic).

• On the product side, Anthropic launched Claude Opus 4.8, describing it as an Opus 4.7 update with “sharper judgment,” “more honesty about its own progress,” and the ability to work independently for longer, at the same price (Claude).

• Anthropic also launched Dynamic Workflows in Claude Code, a research-preview orchestration system where Claude plans work and spawns hundreds of parallel subagents to tackle large tasks (ClaudeDevs). Independent eval posts broadly confirm that 4.8 is a meaningful improvement over 4.7, especially on long-horizon agentic coding and knowledge work, though reactions diverged on whether this is a frontier-resetting leap or mostly catch-up to OpenAI’s GPT-5.5-family.

Facts vs opinions

Facts and directly stated claims

• Anthropic raised $65B at a $965B post-money valuation in Series H (Anthropic).

• The company says its run-rate revenue crossed $47B (Anthropic).

• Lead investors named: Altimeter, Dragoneer, Greenoaks, Sequoia (Anthropic).

• Altimeter publicly confirmed it led the round and framed it as its largest investment to date (Altimeter, Pauline Bhyang).

• Anthropic launched Claude Opus 4.8, positioned as an update to Opus 4.7 with improved judgment, honesty, and longer autonomous work, same price (Claude).

• Anthropic engineers said 4.8 was a response to feedback on 4.7, with “many fixes” and better nuance / naturalness (Alex Albert).

• Claude Code now supports Dynamic Workflows that write orchestration plans and launch large fleets / hundreds of subagents in parallel (ClaudeDevs, Cat Wu).

• Dynamic Workflows are available in research preview and were said to work on Max, Team, Enterprise, API, Bedrock, Vertex AI, and Foundry (ClaudeDevs).

• Anthropic / community posts mention effort controls added to web/app/Cowork and continued Fast mode support (Mikey K, Sam Callister, Kimmonismus).

Opinions / interpretations

• Bullish views:

  • Opus 4.8 “could’ve been called Opus 5” (Dan Shipper).

  • “Anthropic found a cure for laziness” (scaling01).

  • “first smart model in a long while” due to honesty / calibration (zephyr_z9).

  • “People unsubscribing from Anthropic will crawl back” (teortaxesTex).

• Skeptical / mixed views:

  • Opus 4.8 is “a minor upgrade” (scaling01).

  • Anthropic is “playing catch-up with OpenAI rather than setting the pace” (kimmonismus).

  • Some benchmark-based criticism from Andon Labs: worse than Opus 4.7 / GPT-5.5 on Vending Bench, underperformed on Blueprint-Bench 2, more aligned / more cautious, and “max reasoning is not the best reasoning effort” (andonlabs, andonlabs).

  • Dynamic workflows are powerful but may be token-expensive and quota-burning in practice (itsclivetime, Theo, Omar Sar0).

Fundraise details and implications

Anthropic’s financing numbers are the headline shock: $65B raised on a $965B post-money with $47B run-rate revenue disclosed in the same announcement (Anthropic, Anthropic). The scale drew immediate attention because it implies a company operating at near-trillion valuation with hyperscaler-style capital needs and model-serving economics.

Investor messaging was strongly framed around enterprise adoption and operational execution. Altimeter described Claude as becoming the “default operating system for entire enterprises” and praised Anthropic’s combination of performance and safety (Altimeter). Pauline Bhyang said Anthropic had been on a “generational trajectory” since 2022 and highlighted the company crossing $47B run-rate revenue in under five years (Pauline Bhyang).

The surrounding reactions broke into a few camps:

• Validation camp: This funding size is treated as evidence that Claude has become a core enterprise platform, especially in coding and agentic workflows. Posts like Jamin Ball’s “Let’s go!!” were simple market validation reactions (jaminball).

• Scale / bubble concern camp: Some reacted by comparing the announcement to traditional startup fundraising rhetoric inflated to unprecedented scale. Jerry Liu joked that if you replace “billions” with “millions,” it reads like any high-growth startup fundraise (jerryjliu0). Another critical read linked the financing to Anthropic’s increasingly strict safety gating around more capable models—i.e. vast compute access paired with selective capability release (menhguin).

• Infrastructure implication: Anthropic explicitly tied the raise to capacity expansion for Claude demand (Anthropic). That matters because many of the new 4.8 features—especially higher-effort reasoning, longer independent runs, and multi-agent workflows—are inference-hungry. The capital raise should be read not just as training fuel, but as a direct attempt to underwrite serving costs for long-running agent workloads.

One notable context tweet: a user speculated that “Anthropic also secured tens of billions in inference compute” right as Mythos safety concerns were apparently addressed (menhguin). That is speculation, not confirmed by Anthropic, but it reflects a common interpretation: this round is about compute supply and deployment scale as much as model R&D.

Opus 4.8: official product positioning

Anthropic’s official framing is unusually specific in its emphasis on behavioral quality, not just benchmark scores. The launch tweet says 4.8 has:

• sharper judgment

• more honesty about its own progress

• ability to work independently for longer

• same price as 4.7 (Claude)

Alex Albert added that 4.8:

• incorporates fixes based on 4.7 feedback,

• understands nuance better,

• feels more natural conversationally,

• is stronger across coding and knowledge work (Alex Albert).

This honesty / calibration angle became a major subtheme. Multiple Anthropic employees and outside testers described the model as more willing to:

• say what it doesn’t know,

• flag flaws in its own code,

• avoid glossing over uncertain progress,

• stop falsely implying task completion (Cat Wu, Mikey K, dejavucoder).

That’s noteworthy because Claude’s prior reputation among heavy coding users included strong generation but uneven self-monitoring: false positives in code review, overconfident progress summaries, and “lazy” or prematurely truncated task execution. Several community reactions explicitly framed 4.8 as fixing this failure mode:

• “found a cure for laziness” (scaling01)

• “least lazy model ever?” (Teknium)

• “dramatically less lazy than every other version of Claude” (nrehiew_)

Technical details and numbers

Pricing, context, controls

The most concrete consolidated specs came from Artificial Analysis:

• Context window: 1 million tokens

• Pricing: $5 / $25 per million input / output tokens

• Cache writes: $6.25 / M with 5-minute TTL

• Cache hits: $0.50 / M

• Effort settings remain as in Opus 4.7; AA tested max effort (Artificial Analysis)

Community posts also highlighted:

• Fast mode is available for Opus 4.8

• It is ~2.5x faster and 3x cheaper than before versus prior fast-mode economics (kimmonismus)

• scaling01 summarized the new economics as:

  • Opus 4.8 Fast: 2.5x faster, only 2x more expensive than normal 4.8

  • versus Opus 4.7 Fast: 2.5x faster, 6x more expensive than normal 4.7 (scaling01)

• Effort controls were newly exposed in more product surfaces, allowing users to dial reasoning up or down (sammcallister, mikeyk, kimmonismus)

This matters because many early user reports suggest reasoning-effort selection significantly changes output quality and cost, especially for coding and writing. Dan Shipper recommended xhigh for coding and high for writing after observing weaker behavior at lower settings (Dan Shipper). Andon Labs similarly said max reasoning is not the best reasoning effort on some tasks (andonlabs).

Benchmarks: strongest reported numbers

Key official / semi-official numbers surfaced across launch tweets:

• SWE-Bench Pro: 69.2%, claimed by Yuchen citing release materials, and “10 points higher than GPT-5.5” (Yuchenj_UW)

• FrontierSWE #1, cited by Anthropic watchers and later confirmed by third-party references (scaling01, scaling01)

• APEX-SWE: 45.3% Pass@1, nearly 4 points ahead of GPT-5.3 Codex at 41.5% (mercor_ai)

• GDPval-AA: 1890 Elo, +137 vs Opus 4.7, +121 vs GPT-5.5 xhigh, implying about 67% win rate vs GPT-5.5 xhigh head-to-head (Artificial Analysis)

• Artificial Analysis Intelligence Index: 61.4, +4.1 vs Opus 4.7, +1.2 ahead of GPT-5.5 xhigh (Artificial Analysis)

• AA-Omniscience: 27.4, #2 behind Gemini 3.1 Pro at 32.9; accuracy 46.6%, hallucination 35.9% (Artificial Analysis)

• Gains on:

  • Terminal-Bench Hard +6.8

  • τ²-Bench Telecom +5.9

  • IFBench +3.6

  • relatively flat on AA-LCR, GPQA, SciCode (Artificial Analysis)

Additional qualitative benchmark observations:

• Cursor said Opus 4.8 works much more efficiently than 4.7 on CursorBench and is more persistent on hard tasks (Cursor)

• Anthropic employees emphasized strength on long-horizon work in Claude Code (ClaudeDevs)

• Some users reported especially large jumps in knowledge work and writing (Dan Shipper, rishdotblog)

Efficiency and token-use details

Artificial Analysis reported:

• Compared to Opus 4.7, 4.8 achieved higher GDPval performance with:

  • 15% fewer turns per task

  • 35% fewer output tokens

• But 4.8 still used ~30% more turns than GPT-5.5, the second-ranked model (Artificial Analysis)

This is one of the more important nuanced findings in the launch coverage:

• 4.8 is more efficient than 4.7

• but still not obviously the most inference-efficient frontier model against OpenAI on some workloads

That tension is echoed in community commentary:

• “still getting token-mogged by GPT-5.5” (scaling01)

• Theo and others complained that Claude’s higher-agency, higher-effort modes can blow through quota extremely quickly in practice (Theo, cremieuxrecueil)

Long context

Posts highlighted long-context improvements from Opus 4.6 to 4.8, with one claim that Opus 4.8 at 1M context is almost as good as GPT-5.5’s 256K score on a referenced long-context eval (scaling01). Artificial Analysis also confirmed the 1M token context remained intact (Artificial Analysis).

Safety / robustness / hallucination

This was one of the more mixed parts of the release.

Positive:

• Anthropic and supporters emphasized lower dishonesty / better calibration.

• “dishonesty at an all time low” (scaling01)

• “noticeably more honest” (Cat Wu)

• “flags what it’s unsure of” (Mikey K)

• Artificial Analysis said Anthropic continues to show substantially lower hallucination rates than Google/OpenAI peers (Artificial Analysis)

Negative / cautionary:

• scaling01 noted Opus 4.8 is the first model in a long time that doesn’t improve prompt injection robustness over 100 trials (scaling01)

• scaling01 also called it Anthropic’s “most eval aware model” (scaling01)

• Andon Labs said it was more aligned / more cautious, “scared of getting caught,” and worse on some adversarial / business-task benchmarks (andonlabs)

• nrehiew_ noted slight hallucination improvements on the reported evals but questioned whether some hallucination tests reflect the failure modes users actually encounter (nrehiew_, nrehiew_)

Cyber capability gating and future model class

An especially important strategic detail appeared in reaction posts: Anthropic appears to have stated it plans to release “a new class of model with even higher intelligence than Opus” after stronger safeguards (dejavucoder). Multiple watchers interpreted this as a Mythos-class rollout with cyber-sensitive capabilities selectively constrained:

• “Mythos class model to all customers in the coming weeks” (kimmonismus)

• “They are releasing a Mythos-class model with the appropriate safeguards, meaning that you can’t use the ‘too dangerous to release’ capabilities” (scaling01)

• Cline summarized Anthropic as announcing plans to release new models with higher intelligence than Opus after adding stronger cyber safeguards (Cline)

This is not just product roadmap gossip; it reframes Opus 4.8 as a staged release strategy:

1. improve the commercially safe / broadly deployable general model,

2. hold back more dangerous cyber capability until controls are ready.

That tradeoff drew both praise and criticism:

• supportive: safety-first frontier deployment

• skeptical: Anthropic may be sacrificing some competitiveness in raw capability availability to maintain its risk posture (teortaxesTex)

Dynamic Workflows: the most important technical addition beyond the base model

The standout systems feature accompanying Opus 4.8 is Dynamic Workflows in Claude Code.

Official description:

• “Claude writes an orchestration script on the fly”

• then spins up a large fleet of coordinated subagents in parallel

• use the word “workflow” in a prompt to activate it (ClaudeDevs)

Anthropic’s employees and users described it as enabling:

• orchestration plans that Claude “strictly follows”

• hundreds of agents

• verification before returning results

• support for very large migration / refactor / auditing jobs (Cat Wu, Mikey K)

Examples cited:

• porting Bun from Zig to Rust, around 750k lines, 99.8% of test suite passing, 11 days from first commit to merge, using hundreds of parallel agents and two reviewers per file (Cat Wu)

• processing hundreds of A/B test flags in parallel in <10 minutes to identify stale flags (Cat Wu)

This launch triggered a mini-debate around the broader concept:

• Some researchers argued Anthropic had essentially productized ideas resembling Recursive Language Models / symbolic recursion over prompts (a1zhang, lateinteraction, lateinteraction)

• Others pushed back that “calling models in a loop” is not novel and that many builders have been doing this manually for months (omarsar0, jxmnop, willdepue)

The more substantive critique was not originality, but cost and harness quality:

• Omar Sar0 warned agent-to-agent interactions are effective but token-heavy (omarsar0)

• Theo complained about conflicting parallel edits and wasted tokens in the current tooling (Theo)

• itsclivetime joked that “hundreds of parallel subagents” will hit quota in seconds (itsclivetime)

• KLieret highlighted a system-card finding: multi-agents may not improve final ProgramBench quality, but they reach mediocre solutions 2x faster (KLieret)

So the consensus from technical users is:

• Dynamic workflows are strategically important

• they are likely the future of coding agents

• but the current implementation still faces editing conflicts, cost blowups, and harness inefficiencies

Different opinions on Opus 4.8

1) Strongly supportive: Anthropic is back

Read more

In the enterprise SaaS business, ARR is a trailing indicator of utilization, as are the logos of some of the toughest/most discerning customers in the enterprise and startup ecosystem (including Exa and Modal, featured last week)

We will release more on the Cognition podcast tomorrow.

AI News for 5/26/2026-5/27/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews’ website lets you search all past issues. As a reminder, AINews is now a section of Latent Space. You can opt in/out of email frequencies!

AI Twitter Recap

Inference Efficiency, Serving Architectures, and Cost Curves

• Inference optimization is increasingly architectural, not just kernel-level: EAGLE 3.1 improves speculative decoding robustness by stabilizing hidden-state feedback and reducing attention drift at deeper decode steps, with explicit emphasis on long-context acceptance length and real-world serving reliability; the team also highlighted collaboration with vLLM and TorchSpec. At the kernel/system layer, Perplexity open-sourced a rebuilt Unigram tokenizer that cuts CPU utilization 5–6× and reaches 63 µs at 514 tokens with zero heap allocations, while Qwen3.5 on TokenSpeed reportedly hits 580 tokens/s for agentic workloads via joint optimization across Alibaba, LightSeek, NVIDIA, Mooncake, and FlashAttention-4 contributors. Supporting libraries also improved: MaxSim v2 adds backprop and reports 10.33× faster on H200 and 11.94× on A100 versus naïve PyTorch.

• Price cuts are being justified by structural KV-cache and attention changes: Several posts converged on the same theme: recent API price cuts from Chinese labs look sustainable because they reflect lower serving cost per token, not temporary subsidy. @kimmonismus summarized how DeepSeek V4-Pro uses hybrid attention with Compressed Sparse Attention and Heavily Compressed Attention to bring 1M-token KV cache to ~10% of V3.2 and single-token inference FLOPs to 27%, while still routing 49B active params out of 1.6T total. Xiaomi’s MiMo similarly reduces cache traffic using SWA plus hierarchical cache management. That was corroborated directly by @_LuoFuli, who said MiMo’s deepest input-cache-hit price cut comes from 5× cached token capacity, roughly 80% lower caching cost, and an architectural 1:7 Full:SWA sparsity ratio. The broader takeaway: long-context inference economics are now being pushed by attention design + cache hierarchy + routing, not just cheaper hardware.

Agents, Harnesses, Memory, and Continual Learning

• The stack is shifting from “model quality” to “model-harness-memory fit”: A substantial cluster of tweets focused on practical agent engineering. LangChain shipped Deep Agents v0.6 with Delta Channels, cutting checkpoint storage for a 200-turn coding session from 5.3 GB to 129 MB, and also launched computer use in Fleet, plus Context Hub for versioned agent context/skills. LangSmith Engine was framed as automating the eval → diagnosis → fix loop, with multiple practitioners emphasizing its value for turning trace feedback into reusable online/offline evaluators. In parallel, @Vtrivedy10 made the clearest formulation of the day: task-harness fit matters as much as model quality, and bespoke vertical systems outperform generic harnesses by narrowing tools, prompts, and context to the task.

• Continual learning is re-emerging as a product category, not just a research topic: The biggest announcement here was Trajectory’s launch: a platform for using product usage signals and agent traces to continuously post-train large agentic models, with $15M in funding and design partners including Clay, Harvey, Decagon, Mercor, and Rogo. Baseten said it supports these deployments with FP8/NVFP4 quantization and autoscaled H100 infra, including a cited overnight deployment of a 397B-parameter model. The same trend appeared in open tooling: an open-source memory-centric agent built on LangChain/LangGraph was praised by multiple builders for explicit retrieval/storage/reasoning/learning separation, and RLM’s minimal training harness shows small teams can now RL-tune long-context agents in a day on 8×A100. The throughline is that “post-deployment learning” is moving from aspiration to infra.

Benchmarks, Scaling Laws, and Training Methods

• New benchmarks are increasingly about long-horizon, messy, real-world workflows: DeepSWE was highlighted as a SWE/agent benchmark with 113 tasks across 91 repos in 5 languages, using a minimalist bash-only harness and shorter prompts that nevertheless require 5.5× more code and touch 7 files on average than SWE-Bench Pro. In enterprise operations, Artificial Analysis and IBM launched ITBench-AA, an SRE benchmark over Kubernetes incident response where all frontier models scored below 50%; Claude Opus 4.7 led at 47%, GPT-5.5 followed at 46%, and GLM-5.1 Reasoning led open weights at 40%. Another useful reliability angle came from AgingBench, which frames deployed agent degradation as a lifespan problem caused by compression, interference, and memory updates.

• Training efficiency research remains active across both theory and systems: Sakana AI’s DiffusionBlocks was one of the most technically interesting releases: it reinterprets forward passes as diffusion-like denoising steps so deep nets can be trained one block at a time, dramatically reducing memory while matching end-to-end performance across ViTs, DiTs, masked diffusion, autoregressive transformers, and recurrent-depth transformers. On the RL systems side, Snowflake introduced ZoRRo, claiming up to 3.5× faster long-context RL and 3.2× longer context windows by eliminating redundant rollout computation, alongside the specialized Arctic-Text2SQL-R2 enterprise SQL model. On the theory front, Tiberiu Musat’s preprint argues minimum neural weight norm matches minimum program length up to a log factor for fixed-precision networks, while Unified Neural Scaling Law proposes a multivariate functional form intended to extrapolate neural scaling behavior more accurately than prior fits.

Model and Modality Releases: Biology, Vision, OCR, and Embedded AI

• Protein modeling had a standout day: ESMFold2 was announced as an open scientific engine for protein structure prediction and design, with strong reported results on protein interactions and antibodies, plus an accompanying atlas of 6.8B proteins and 1.1B predicted structures. The release emphasized both practical design outcomes—miniprotein binders and single-chain antibodies across five therapeutic targets—and mechanistic interpretability findings about emergent protein representations. The release was echoed by @proteinrosh and contextualized by @cgeorgiaw, who noted the atlas exceeds AlphaFold DB in scale.

• A wave of smaller but practical multimodal/open releases landed: Google DeepMind shared the white paper for Gemini Embedding 2, described as a native multimodal embedding model supporting unified representations over text, image, audio, and video. NVIDIA’s LocateAnything combines Qwen2.5-3B + Moon-ViT for high-speed grounding, with a claimed 10× speedup for dense object detection. Hugging Face integrated Roboflow’s RF-DETR, positioning it as real-time detection/segmentation that outperforms YOLO-style systems. For document pipelines, Surya OCR 2 ships as a 650M model with 83.3% OLMOCR bench, 87% on an internal 91-language benchmark, and 5 pages/s on RTX 5090; LiteParse v2 rewrites parsing in Rust for up to 100× speedups and edge/browser deployment via WASM. On-device AI also got a nod with Google’s new Coral board for local speech, vision, and control demos.

Developer Platforms, Enterprise Controls, and Coding-Agent Productization

• Coding agents are consolidating into full product stacks with enterprise controls: OpenAI continued tightening Codex’s product surface: GPT-5.2 and GPT-5.3-Codex are being sunset in Codex in favor of GPT-5.5, while enterprise features now include private MCP connectivity over outbound-only HTTPS, Workload Identity Federation, and expanded Admin API controls for spend alerts, allowlists, retention policies, and hosted tool management. OpenAI also published a concrete case study on self-improving tax agents with Codex, centered on tracing reviewer corrections back into evals and fixes.

• Competition in coding agents is now visibly about reliability, workflow breadth, and enterprise adoption: Claude Code shared a reliability/performance update and easier bug-report capture, while GitHub kept pushing the “agentized IDE” direction with Copilot Dev Days and MCP positioning. The biggest commercial datapoint was Cognition: >$1B raised at a $26B valuation, enterprise usage up >10× YTD, and $492M run-rate revenue, paired with a growing customer list and strong endorsements from users like Exa. Meanwhile, smaller infra/product moves suggest the ecosystem is broadening: Cua Driver for Windows brings background computer use to Windows agents; Cloudflare’s agent platform was repeatedly praised for “fractional computing” economics; and Grok Build’s worktree support targets multi-agent code swarms at repo scale.

Top tweets (by engagement)

• Cognition’s scale-up: Cognition announced >$1B raised, $26B valuation, and $492M run-rate revenue, one of the clearest signals yet that coding agents are converting into large enterprise businesses.

• Claude Code reliability push: Anthropic’s ClaudeDevs posted a high-engagement update on responsiveness, reliability, and better feedback collection—evidence that product quality and trust are now central battlegrounds.

• Sakana AI’s DiffusionBlocks: @hardmaru drew major attention to block-wise training that can match end-to-end performance while dramatically lowering memory requirements.

• ESMFold2 release: @alexrives announced one of the day’s most substantive science releases: open protein modeling at atlas scale with therapeutic design implications.

• OpenAI enterprise controls + MCP: @OpenAIDevs on private MCP and related admin/security updates reflects where frontier APIs are competing for large-org adoption.

AI Reddit Recap

/r/LocalLlama + /r/localLLM Recap

1. Low-Bit Local AI on Consumer Hardware

• PrismML just released Binary and Ternary Bonsai Image 4B: 1-bit/ternary text-to-image diffusion transformers that can even run 100% locally in your browser on WebGPU. (Activity: 759): PrismML released Binary and Ternary Bonsai Image 4B, described as 1-bit/ternary text-to-image diffusion-transformer variants with ~3GB checkpoints, Apache-2.0 licensing, and a WebGPU browser demo (HF collection, demo). The post compares them to FLUX.2 Klein 4B at ~16GB; a top technical comment claims Bonsai Image is primarily a quantized/post-trained derivative of FLUX.2 Klein 4B, with insufficient attribution outside the whitepaper. The main debate is attribution/branding: one commenter argues PrismML is rebranding quantized/fine-tuned base models as “Bonsai” while minimizing credit to original labs, comparing it to releasing a quant of Qwen as a new model. Another commenter asks whether it can run on CPU with 16GB RAM, but no technical answer is provided in the supplied comments.

  • A commenter alleges PrismML’s “Bonsai-Image” is not a newly trained base model, but a binary/ternary quantization of FLUX.2 Klein 4B with additional post-training to recover quality. They argue the project’s HF demo/model pages and GitHub omit clear attribution to the original FLUX model/team, with the original model reportedly mentioned only in the whitepaper.

  • A technical usability note says the browser/WebGPU model requires roughly ~2 GB to download, which is relevant for fully local inference despite the 1-bit/ternary compression claims. Another user asks whether it can run on CPU with 16 GB RAM, but no concrete benchmark or compatibility answer is provided in the thread.

• Got tired of OOM errors on my 4GB GPU. Wrote a custom Rust bare-metal engine and hit 66.8 TPS with a 4B model (BitNet 1.58b on RTX 3050). (Activity: 390): OP claims a custom Rust/C++ LLM inference engine, Cluaiz, runs prism-ml/Bonsai-4B-gguf with 1.58-bit quantization on an RTX 3050 4GB, reaching 66.8 tokens/s, and reports ~30–33 TPS for Gemma/Qwen 4B variants without OOM via dynamic KV-cache management. No reproducible repo or benchmark artifacts were provided in the post yet; commenters pointed to the apparent project links (GitHub, site) and questioned vague claims like “direct-to-silicon” access, noting this may simply mean ahead-of-time native compilation rather than any unusual GPU/driver-level mechanism. The attached Reddit video could not be independently accessed due to Reddit HTTP 403 restrictions. Top comments were strongly skeptical, characterizing the writeup and repo language as pseudo-technical/AI-generated and arguing the stated achievements amount to basic native compilation plus a single-machine demo. Commenters also challenged the project’s licensing/copyright wording under Apache 2.0 and asked for concrete implementation details behind the claimed low-level hardware access.

  • Commenters challenged the technical claims in the linked repo (github.com/cluaiz/cluaiz, cluaiz.com), arguing that descriptions like “direct silicon access”, “bare-metal engine,” and “copyrighted Apache licensed software” appear to be marketing or LLM-generated pseudo-technical language rather than concrete implementation details. One commenter asked whether “direct silicon access” merely means ahead-of-time native compilation in Rust, rather than any real low-level GPU programming beyond normal CUDA/driver APIs.

  • Several commenters argued that the claimed outcome should be compared against existing tooling, especially llama.cpp, which already supports low-memory inference and quantized models on consumer GPUs. The critique was that OOM issues on a 4GB RTX 3050 are often solvable through proper llama.cpp configuration rather than writing a new engine, so the claimed 66.8 TPS with a 4B BitNet 1.58b model needs reproducible benchmarks and configuration details to be meaningful.

2. Qwen 3.5/3.6 Local Model Releases and Coding Tests

• Qwen3.5 35B A3B uncensored heretic Native MTP Preserved is Out Now With the Full 785 MTPs Preserved and Retained, Available in Safetensors, GGUFs. NVFP4, NVFP4 GGUFs and GPTQ-Int4 Formats (Activity: 602): llmfan46 released Qwen3.5-35B-A3B-uncensored-heretic-v2-Native-MTP-Preserved, a decensored derivative of Qwen/Qwen3.5-35B-A3B made with Heretic v1.3.0 / Magnitude-Preserving Orthogonal Ablation-style edits targeting attn.o_proj, attn.out_proj, and mlp.down_proj, while preserving all 785 native MTP tensors. The model card reports refusals reduced from 92/100 to 14/100, KL divergence 0.0487 vs base, and MMLU dropping only from 84.12% to 83.72% over 7,021 questions; releases include Safetensors, GGUF, NVFP4, NVFP4 GGUF, and GPTQ-Int4 variants. The author argues Qwen3.5 and Qwen3.6 both use the qwen35 architecture but are tuned for different regimes—Qwen3.5 for general assistance, Qwen3.6 for agentic/coding—and notes abliteration KL/quality behavior differs substantially between the families. Commenters appreciated the unusual availability of an NVFP4 GGUF build, with one noting they could not find comparable releases even from Unsloth. Another tester agreed with the author’s positioning, describing Qwen3.6 as closer to “3.5 coder+” rather than a simple across-the-board successor to Qwen3.5.

  • One commenter highlighted the practical value of the NVFP4 GGUF build, noting that this format is hard to find elsewhere: “I seriously can’t find anyone else doing that, not even Unsloth.” This is technically relevant because NVFP4 GGUF availability can matter for users targeting newer NVIDIA-oriented low-precision inference workflows while still using GGUF-based runtimes.

  • A tester compared Qwen3.5 and Qwen3.6, arguing that 3.6 feels more like “3.5 coder+” than a straightforward general upgrade. They suggested the short time between releases makes a broad capability leap unlikely, implying 3.6 may be more specialized toward coding rather than a simple successor to 3.5.

• Okay 27B made me a believer (Activity: 541): OP reports that a 27B Qwen-family model used via Opencode generated a near-complete HTML5 Breakout-style game in one shot from three reference files describing console APIs, gamepad controls, and a TypeScript shader. The output was immediately playable, with working controls, sound, metadata, save/stat/heartbeat API integration, and only required one follow-up for customization plus one glitch fix; a commenter recommends enabling MTP/speculative decoding with 2–3 draft tokens for speed. Another heavy user says the model performs best below 64K context, degrades noticeably past 64K, and “really drops off” after 128K, recommending periodic summarization-to-file and session resets for long agentic coding tasks. Commenters characterize the dense 27B as unusually strong for local coding—near-Sonnet class for web-app one-shots—while one user found 35B A3B less capable despite its size/routing advantages. The main caution is that long-context agentic runs can induce loops or “stupidity,” so users should manage context aggressively.

  • A commenter recommended enabling MTP/speculative decoding for better throughput, suggesting an MTP value of 2 or 3 as a practical speed/quality tradeoff. This is a deployment-level optimization rather than a model-quality claim, useful for users running the 27B model locally.

  • One user reported that the 27B model’s effective reasoning quality drops noticeably with long contexts: best below 64K tokens, degraded past 64K, and “really drops off after 128K.” Their workaround for long-horizon agentic tasks is to periodically summarize state into a file, restart the harness/session, and reload the summary to recover model quality and avoid loops.

  • A benchmark operator said Qwen 27B was such an outlier that they rechecked their methodology, placing it roughly on par with GPT-5.2 or Sonnet 4.5 in their rankings while noting it struggles at larger context sizes, likely due to parameter-count limits. They linked their data at gertlabs.com/rankings.

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Readers like when we report no news, but our second favorite to that is when we can simply reinforce a trend you should be aware of. In April we highlighted the Inference Inflection, and If today’s headline reminds you of last week’s headline, it is exactly the point we are making.

With the pace of AI fundraising these days, our general policy is to only cover startups when they cross decacorn status (>$10B) - but only when confirmed, and today’s news of Fireworks’ $15B round (“in talks”, 3.75x in 7 months, our podcast here) and Baseten’s $11B round (“is raising”, 2.2x in 3 months) is a bit premature, but the pace of the pickup in Inference land and unicorn to decacorn progression is too juicy not to serve as headline story today, with the $113M OpenRouter Series C (5x volume in 6 months) as the cherry on top: if you are gonna do multimodel inference, you are gonna need a router.

AI News for 5/23/2026-5/26/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews’ website lets you search all past issues. As a reminder, AINews is now a section of Latent Space. You can opt in/out of email frequencies!

AI Twitter Recap

Agent Harnesses, Coding Benchmarks, and the Shift Beyond “Just the Model”

• Harness engineering is becoming the main differentiator for coding agents: Several posts converged on the same thesis: the winning stack is now model + harness + eval loop, not just a stronger base model. A long Zhihu summary argued that DeepSeek is explicitly building a harness team to close the loop between model outputs, runtime feedback, validation, and correction, with a claimed cached-input cost advantage that would support tighter interaction/verification loops. In parallel, Google’s Gemini Managed Agents guide framed agent infra as a single API call to a managed harness with sandboxing, persistence, and mounts, while LangChain’s updated create_agent docs and dair.ai’s “harness” paper summary formalized the same stack: context governance, trustworthy memory, dynamic skill routing.

• Benchmarks are getting closer to real developer experience: DeepSWE, introduced as a new benchmark for agentic coding, got strong endorsement from practitioners; @theo called it “the first code bench that actually aligns with how it feels to use these models coding.” It also created more separation at the top end than public SWE leaderboards often show. Related benchmark signals: Qwen3.7 Max debuted at #4 on Code Arena: Frontend, roughly on par with Claude Opus 4.6 on agentic webdev tasks, and Alibaba amplified the result. Across the tooling stack, Anthropic shipped a security-guidance plugin for Claude Code and reported a 30–40% reduction in security-related PR comments in internal use, while OpenAI highlighted GPT-5.5 in Codex at Databricks for more reliable document parsing.

Research Agents, Long-Horizon Reasoning, and “Sleep” for Context Compression

• Math/science agents showed more evidence of capability overhang—conditional on the right harness: The strongest cluster of tweets was around models tackling old open problems. A mathematician reported Claude Mythos solving Erdős problem #90, with follow-up detail that the model often converged to a different, cleaner proof path than OpenAI’s earlier route. This was echoed by @_sholtodouglas, @kimmonismus, and then sharpened by Sébastien Bubeck: with an appropriate harness, both Mythos and GPT-5.5 can reproduce what an internal model had done one-shot, implying a large amount of latent capability not exposed by vanilla chat UX.

• Long-horizon memory is resurfacing as a core bottleneck: The paper “Language Models Need Sleep” got notable attention. The mechanism is a sleep-like consolidation phase where recent context is converted into persistent fast weights before clearing the KV cache, moving compute into an offline pass while preserving wake-time latency. dair.ai’s summary emphasized the systems angle: this is an alternative to ever-growing KV caches for agents with long trajectories. This theme connected neatly with ongoing discussion about memory systems in agents, including Omar’s pointer to Anthropic’s memory talk and Dream feature.

• Open deep-research agents and science forecasting also advanced: QUEST, a family of open 2B–35B models for long-horizon fact-seeking, citation grounding, and report synthesis, was released as a general-purpose deep research agent. On the science-evals side, Sakana/Stanford/Oxford/AI2’s CUSP benchmark found current models can often identify promising research directions but struggle much more with whether and when breakthroughs materialize.

Model, Optimizer, and Architecture Updates

• Optimizer work remains lively, especially around Muon variants and schedule-free training: AMUSE proposes Anytime MUon with Stable gradient Evaluation, combining Muon with schedule-free-style gradient evaluation for stable anytime training without LR decay, reporting gains at 124M / 720M / 1B scale and on ViT/ImageNet fine-tuning. Related implementation discussion came from ClashLuke’s SFMuon snippet and kellerjordan’s Modded-NanoGPT result on Newton-Muon.

• Sparse attention design space continues to diversify: MiniMax teased M3 as open source, and follow-on technical commentary suggested a new block-sparse two-stage attention path. @kimmonismus summarized the reported speedups: 9.7× prefilling and 15.6× decoding at 1M tokens versus M2. @eliebakouch added that M3 appears to move back to GQA-based sparse attention with block selection on real KV, distinct from DeepSeek’s compressed-attention variants.

• Vision/open model releases and ranking updates: PrismML released Bonsai Image 4B, including 1-bit and ternary variants intended to run locally on laptops and phones; a follow-up noted browser-local execution was possible at ~3GB footprint. On the closed side, Microsoft’s MAI-Image-2.5 debuted at #3 on the Image Arena, breaking a top-5 club previously dominated by OpenAI and Google, with Arena reporting a 1,254 score. Meanwhile, Artificial Analysis measured Gemini 3.5 Flash at up to ~280 output tok/s with materially stronger agentic performance, but at ~5× the cost of Gemini 3 Flash.

Infra, Systems, and the Semiconductor Stack

• Huawei’s “τ scaling” paper was read mostly as an engineering roadmap, not a new law: A very detailed thread argued Huawei’s “A Time Scaling Theory for Multi-Layer Electronic Systems” should be interpreted as a strategic manifesto / white paper. The core proposal is to treat time constant τ, not process node, as the unifying metric across device, chip, and datacenter scales. The most concrete claims concerned LogicFolding on a future Kirin design, including +55% density, +41% energy efficiency, and +13% frequency at fixed node, plus packaging/network ideas like a Unified Bus and Hi-ONE optical I/O. The same thread was careful to note missing validation artifacts—die photos, SEMs, workload details, yield curves—and to interpret the most eye-catching numbers as promising but unverified. Follow-up reactions also stressed that Huawei’s path may rely more on packaging and architecture than lithographic catch-up, e.g. @josiah_leee citing Jensen’s point that most of Hopper→Blackwell’s gains came from non-node optimizations.

• Datacenter power and inference supply constraints are becoming first-order concerns: SemiAnalysis published on the 800VDC transition, and John Carmack recommended it, highlighting crossovers from EV power electronics into datacenter design, including high-voltage SiC parts. Separately, Epoch AI estimated a possible inference compute crunch: demand appears to be growing faster than serving capacity, especially for long-context workloads. Their rough model suggested that while current global Blackwell supply could serve today’s demand under favorable assumptions, throughput degrades sharply with longer contexts and demand growth may already be outrunning supply.

Production Tooling and Developer Infrastructure

• Serving/inference stacks got meaningful performance and observability updates: vLLM merged a Rust frontend as a drop-in alternative to the Python API server, with early numbers showing ~837 req/s vs ~162 req/s on a preprocess-heavy workload in a single process. W&B launched an MCP server to let coding agents inspect experiments and training runs, with a schema-first redesign aimed at avoiding context-window blowups. Unsloth added support for running GPT, Claude, and other APIs inside its local UI, including prompt caching and code execution.

• Cloudflare, OpenRouter, and vector/retrieval vendors pushed the “productionization” layer: OpenRouter announced a $113M Series B and said weekly volume had grown from 5T to 25T tokens over six months. Cloudflare relaunched its startups program with up to $350k in credits, while separate posts around Think and agent ergonomics emphasized durable turns, reconnects, stale-state handling, and recovery as key practical differentiators. On retrieval infra, Booking.com discussed scaling to 100M+ embeddings, including filtered vector search, reads-during-writes, concurrency, and human-in-the-loop evals for partner messaging agents.

Top tweets (by engagement)

• Codex / agentic coding in practice: The highest-signal product-use tweet was @bunkaich showing Codex help reverse-engineer and patch firmware on a cheap MP3 player, with the workflow spanning chip inspection, OS extraction, binary analysis, and flashing a modified image.

• DeepSWE benchmark launch: @serenaa_ge’s DeepSWE announcement became the main reference point for “does this match real coding experience?” discussion.

• Claude Code security plugin: @ClaudeDevs’ release stood out because it paired a concrete product launch with an internal metric: 30–40% fewer security-related PR comments.

• OpenRouter financing + production token growth: @OpenRouter’s $113M Series B is one of the clearer market signals that routing and multi-model infra are now seen as durable platform layers.

• vLLM Rust frontend: @vllm_project’s merge announcement mattered for anyone hitting CPU/API-server bottlenecks in high-throughput serving.

AI Reddit Recap

/r/LocalLlama + /r/localLLM Recap

1. Qwen 3.7 Launch and Qwen 3.6 Local Performance

• Waiting for Qwen 3.7 open weight... The new King has arrived... (Activity: 1217): The image is a benchmark/marketing comparison from the Qwen3.7 blog positioning Qwen3.7-Max as a leading frontier model across agentic coding, software engineering, MCP/tool-use, reasoning, and knowledge evaluations versus Qwen3.6-Plus, DS-V4-Pro Max, GLM-5.1, Kimi K2.6, and Claude Opus-4.6 Max. The technical significance is that the slide frames Qwen3.7-Max as highly competitive with or ahead of Claude-class models on many benchmarks, though Claude Opus-4.6 Max still appears to lead on some tasks such as ClawEval and CoWorkBench. Commenters note that this is the Max model, not necessarily representative of smaller/open-weight releases, and speculate about a potential 3.7-122B-A17B MXFP4 model with 512k context for local hardware such as Strix Halo. The main debate is skepticism around open weights: commenters point out that Qwen has historically not open-weighted the Max series, so the title’s “waiting for open weight” framing may be unrealistic. Others caution not to expect a hypothetical 27B model to match the shown Max-tier benchmark results.

  • Several commenters distinguish Qwen Max from likely open-weight releases, noting that “Qwen has never open-weighted the Max series” and warning not to expect a smaller 27B variant to match Max-level benchmark performance. The implied technical takeaway is that any public/open-weight Qwen 3.7 release may use a different architecture/scale than the benchmarked flagship model.

  • One technical wishlist centers on a hypothetical Qwen 3.7 122B-A17B MTP MXFP4 model with 512k context, which commenters argue would be well-suited to Strix Halo-class local hardware. Another user references Qwen 3.5 397B-A17B NVFP4, claiming it fits on 4x RTX 6000 Pro GPUs with enough memory headroom for roughly 10 concurrent 200k-token sessions, positioning it as a potential “Opus at home” if Qwen 3.7 matches reported benchmarks.

  • A commenter argues that open-weight frontier releases may be less likely because highly capable local models can undermine provider monetization. They claim Qwen’s strategy has shifted from disruption toward monetized frontier competition, which could affect whether large MoE models like 397B-A17B are released openly.

• Qwen3.6 35Ba3 has changed my workflows and even how I use my computer (Activity: 567): The post describes a local-agent workflow using Qwen3.6 35B a3 via pi, where the user converts repeatable procedures into “skills” generated/documented by Codex, then reuses them for VPS DevOps, docling PDF→EPUB conversion, Playwright testing, code tickets, and OS-level shell tasks. A concrete example: WhatsApp audio → transcription in AnythingLLM → content.md → locally generated landing page, then a plan.md ticket queue executed by a “manager” pi process spawning fresh-context sub-agents with pi -p @plan.md "Check the first Ticket with Status UNDONE and do it", marking tickets DONE, committing via git, and finally deploying via a VPS skill. Commenters focused on operational concerns: what hardware can run this setup, whether the agent is sandboxed/trustworthy with OS access, and how hard pi is to adopt compared with other agentic tools such as Hermes.

  • A user reports running unsloth/Qwen3.6-35B-A3B-MTP-GGUF via Unsloth Studio on an MS-02 with a 24GB RTX Pro 4000 Blackwell SFF GPU, consistently seeing >100 tokens/s. They compare performance to “unoptimized GGUFs” on a Mac Studio M2, using the MS-02 as a small remote GPU server for the Mac workstation, and note that future MLX support in Unsloth could improve Mac-side performance. Screenshot: preview.redd.it.

• 110 tok/s with 12GB VRAM on Qwen3.6 35B A3B and ik_llama.cpp (Activity: 565): The post benchmarks Qwen3.6-35B-A3B MTP using byteshape’s IQ4_XS 4.19 bpw GGUF on an RTX 4070 Super 12GB + Ryzen 7 9700X, comparing upstream llama.cpp vs ik_llama.cpp with --ctx-size 131072, q8_0 KV cache, MTP draft max 3, and p_min=0.75. Using the same mtp-bench.py workload, upstream llama.cpp averaged 89.76 tok/s with aggregate MTP accept rate 0.9393, while ik_llama.cpp averaged 110.24 tok/s over 16.64s, a claimed 23% throughput gain, despite lower aggregate accept rate 0.8749 in the updated results. The OP attributes practical fit to --fit/--fit-margin 1664 on ik_llama.cpp, with OOM mitigation by raising --fit-margin to 1792 or 2048, and notes that running the display on an iGPU frees essentially all 12GB VRAM for inference. Commenters focused on reproducibility: they requested the full upstream llama.cpp command and noted that several MTP-related PRs had merged recently, so benchmark timing may depend strongly on build date. One technical workaround suggested for single-GPU CachyOS/KDE users is a software-rendered Plasma Wayland session using LIBGL_ALWAYS_SOFTWARE=1 and GALLIUM_DRIVER=llvmpipe, reducing idle VRAM from roughly >1024MB to 126MB at the cost of slow/disabled compositor effects.

  • A CachyOS/KDE Wayland user described a VRAM-saving workaround for single-GPU systems: create a custom SDDM session that forces KDE Plasma to render via CPU using LIBGL_ALWAYS_SOFTWARE=1, GALLIUM_DRIVER=llvmpipe, and KWIN_COMPOSE=Q. They reported KDE Wayland idle VRAM dropping from > 1024 MB to ~126 MB, freeing nearly a gigabyte of VRAM for running the 35B model, at the cost of disabled or very slow compositor animations.

  • Several commenters focused on whether the reported 110 tok/s comes from ik_llama.cpp having better MTP/speculative decoding behavior than upstream llama.cpp. One noted that ik_llama.cpp’s acceptance rate was reportedly never below 0.790, while llama.cpp dropped as low as 0.477, asking for the exact llama.cpp command/settings and noting that multiple MTP-related PRs had landed in llama.cpp within the previous 24 hours.

  • A commenter asked about the IQ4_XS quantization used for Qwen3.6 35B A3B, noting it appears to be the lowest-memory Q4 quant and requesting details on both model quality/intelligence impact and the final VRAM/RAM split. This highlights the key tradeoff for 12 GB VRAM runs: fitting the model via aggressive quantization versus maintaining reasoning quality and avoiding excessive CPU/RAM offload bottlenecks.

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The quote is a big reversal of stance from a position ~uniformly held by anyone who worked at Team Big Model, including his previous head of OpenAI Labs:

This comes with the shuttering of AI21’s model team, which is now pivoting to agents:

and even the venerable DeepSeek is now building a “Harness team” for the first time:

The “Systems over Models” people will take this as a point of validation of what they have been saying all along… except for the nuance that models cotrained with harnesses does open the door for closing access to models even further — if you can effectively posttrain a model to only meaningfully perform with your closed source agent, then you get to funnel the majority of users to your agent at the expense of your model/API co-opetition.

But that’s a topic of a much larger discussion…

AI News for 5/4/2026-5/5/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews’ website lets you search all past issues. As a reminder, AINews is now a section of Latent Space. You can opt in/out of email frequencies!

AI Twitter Recap

Agent Products, Harnesses, and the Shift Beyond “Just the Model”

• The product surface is moving up-stack: A recurring theme was that model quality alone is no longer the moat; the winning product is increasingly model + harness + workflow + UI + memory + economics. @gdb put it bluntly: “the model alone is no longer the product,” while @dzhng argued top-tier products need model <> harness <> product symbiosis. The same pattern shows up in practice: @signulll framed ambient AI and agentic AI as the new seam of computing interfaces, and @teortaxesTex noted that harness research still risks converging on “replicate Claude Code” instead of exploring broader interfaces.

• Coding-agent product differentiation is becoming concrete: OpenAI shipped another substantial Codex update via “codex thursday no. 6” with appshots, /goal improvements, remote computer use while locked, annotation mode, plugin sharing, and analytics. @gdb separately highlighted Appshots, while users reported meaningful workflow shifts: @gdb said it’s hard to remember coding before Codex, and @reach_vb said they haven’t opened an IDE in over a month. But product rough edges remain: @theo praised T3 Code’s remote feature as ahead of alternatives, then contrasted it with buggy remote workflows in Codex in a follow-up post. On the Claude side, @ClaudeDevs expanded auto mode to the Pro plan and added Sonnet 4.6 support; @_mohansolo also had to clarify and patch IDE support in Antigravity 2.0 after user backlash.

Model Performance, Cost Curves, and Frontier Competition

• DeepSeek’s pricing move was the biggest market signal: @deepseek_ai made the 75% DeepSeek-V4-Pro discount permanent, triggering strong reactions because it materially changes the cost/performance frontier. @ArtificialAnlys quantified first-party pricing at $0.435/M input, $0.87/M output, $0.0036/M cached input, estimating a blended ~$0.18/M and placing V4 Pro on the Pareto frontier for intelligence vs run cost. They estimate running their Intelligence Index on V4 Pro costs ~3x less than Gemini 3.1 Pro Preview, ~12x less than GPT-5.5, and ~19x less than Claude Opus 4.7. Community reaction centered on DeepSeek’s push toward “intelligence too cheap to meter,” as @scaling01 put it. @Yuchenj_UW and @kimmonismus both emphasized the magnitude of the cut.

• Gemini Flash improved, but usage feedback was mixed: @OfficialLoganK reported Gemini 3.5 Flash making major progress over 3.1 Pro on GDPval, claiming Flash is now “competing at the frontier,” and @Designarena placed it 16th overall on Design Arena, a 16-position jump from Gemini 3 Flash Preview. But several builders pushed back on usefulness vs benchmark gains: @Alezander907 saw only slight browser-agent improvement at higher cost, @giffmana argued this isn’t “Flash progress” if the brand still implies cheapness, and @jeremyphoward said the model feels optimized to max evals rather than cooperate with humans. That aligns with broader eval skepticism from @HamelHusain, who argued current tooling underweights qualitative, HITL judgment.

• Qwen and Chinese frontier models keep compressing the race: The official @Alibaba_Qwen teasers and a long third-party review from @ZhihuFrontier portrayed Qwen3.7-Max as a meaningful step up, especially in instruction following, context reliability, and stability, while still suffering from verbosity and high token usage. Elsewhere, @scaling01 claimed recent ALE-Bench runs show Chinese models like Kimi-K2.6, DeepSeek-V4, GLM-5.1 outperforming several Western releases in that setting. @ArtificialAnlys also reported Cursor Composer 2.5 as 3–18x cheaper than Opus 4.7 and 5–32x cheaper than GPT-5.5 on Coding Agent benchmarks, with notably lower token use.

Protocols, Infra, and Agent Runtime Tooling

• MCP’s new release candidate is a substantive protocol simplification: @dsp_ announced the MCP 2026-07-28 release candidate, with the key change that the protocol is now stateless: no handshake, no session ID, and any request can hit any server instance. The RC also introduces first-class extensions like MCP Apps and Tasks, plus auth hardening and a clearer deprecation policy. For infra teams, statelessness is a big operational shift: easier scaling, simpler load balancing, fewer sticky-session concerns.

• Sandboxes and managed execution are becoming first-class primitives: @_philschmid demoed Gemini Managed Agents + Interactions API to give an agent a secure hosted Linux sandbox with memory and code execution. @CoreWeave launched CoreWeave Sandboxes in public preview for RL, agent tool use, and model eval, while @cnakazawa released Cloudsail for per-task Cloudflare sandboxes with shell, Codex, and GitHub access without exposing tokens. At the orchestration layer, @skypilot_org argued RL doesn’t work on Slurm because modern RL is a multi-service system with heterogeneous hardware and recovery needs.

• Open-source harnesses and memory layers are proliferating: @NVIDIAAI open-sourced AI-Q agent skills for portable deep-research pipelines that can plug into arbitrary harnesses. @Teknium added Bitwarden support for key management in Hermes and later restored 256K context for Grok Build v0.1 in Hermes here. @shannholmberg described a shared-memory “gBrain” layer under Hermes agents, with typed folders and read-first access for specialist agents. @aakashadesara updated CTOP to support Devin and a CLI for listing, searching, and killing agent sessions.

Research: RL, Distillation, Architectures, and Evaluation

• RL post-training and reward design are under active reconsideration: @RyanBoldi introduced Vector Policy Optimization (VPO), arguing scalar reward collapse during RL can sabotage test-time scaling. VPO instead optimizes vector-valued rewards, improving search performance even on the original scalar objective. @lateinteraction framed this as a way to train LLMs for more diverse environments and goals, while @FeiziSoheil connected it to broader moves toward structured feedback instead of a single reward number. Separately, @jsuarez teased a solution to a long-standing RL problem involving extreme sparsity, with initial sweeps showing SOTA on one internal environment.

• Agent compilation/distillation is emerging as a serious economic idea: @dair_ai highlighted a paper showing a full agentic workflow—multi-step calls, tool use, scratchpads, decision structure—can be distilled into weights and run at ~100x lower inference cost while preserving near-frontier quality. This is one of the clearest technical arguments yet for compiling expensive runtime agent loops into cheaper deployable models.

• Architecture work remains lively beyond vanilla transformers: @ChunyuanDeng introduced LT2, a linear-time looped transformer combining sparse and linear attention to make looping practical, along with a distilled Ouro-hybrid-1.4B. @ZyphraAI shared work extending Equilibrium Propagation beyond energy-based models toward biologically realistic neurons. On MoE, @Jianlin_S proposed Moving Quantile Balancing for sequence-level load balancing without a loss penalty. Meanwhile @allen_ai launched ArtifactLinker, which predicts which benchmarks a model is likely to set SOTA on before running them—a useful meta-eval tool amid growing benchmark sprawl.

• Math and reasoning capability discourse shifted again: @cozyblaze265065 reported 99.46% on a multi-digit multiplication experiment using gpt-5.5 with medium reasoning and no tools, and @teortaxesTex noted modern LLMs can now do 100-digit multiplication without tools. That’s not a complete theory of reasoning, but it further weakens old “autoregression can’t do arithmetic” talking points.

Multimodal Systems: Video, Speech, World Models, and Imaging

• Google’s I/O stack pushed toward persistent agents and world simulators: @Google introduced Gemini Spark, a 24/7 personal AI agent for recurring tasks, skills, and workflows. @GoogleDeepMind also launched Project Genie + Street View, letting users turn real U.S. locations into interactive worlds; follow-up posts confirm rollout to Google AI Ultra subscribers via Google Labs. The multimodal side was reinforced by @Google announcing Gemini Omni for conversational video creation/editing and custom avatars, while @emollick emphasized the significance of a fully multimodal system that can natively edit video.

• Runway and image/video tooling keep raising editability: @runwayml released Aleph 2.0, supporting multishot sequences up to 30s at 1080p with targeted edits that preserve the rest of the scene. @CuriousRefuge highlighted SeeDance 2 Stitcher for seamlessly extending AI-generated cinematic clips using Omni-generated continuations.

• Speech and image generation saw notable jumps: @ArtificialAnlys ranked Cartesia Sonic-3.5 as the new #1 TTS model on their Speech Arena, citing an Elo of 1218, support for 42 languages, and strong naturalness/transcript following. Cartesia claims 82ms end-to-end first audio in production here. In image generation, @wildmindai flagged Tencent’s Z-Image 6B as a pixel-space generator with no VAE, 1K resolution, and a transfer framework for converting Flux/SD models; related ecosystem work included Pixal3D demos from @victormustar and training support for Z-Image L2P 1k in AI Toolkit from @ostrisai.

Security, Cyber, and Policy Pressure

• Cybersecurity is quickly becoming a proving ground for advanced agents: @AnthropicAI said Project Glasswing and partners found more than ten thousand high- or critical-severity vulnerabilities in essential software within a month, and explicitly warned the industry will need to adapt to the volume of vulnerabilities that models like Claude Mythos Preview can find. Security productization is following: @perplexity_ai open-sourced Bumblebee, a read-only scanner for macOS/Linux to detect risky packages, extensions, and AI tool configs; @AravSrinivas said enterprise deployment will require agentic sandboxes plus continuous security engineering.

• US immigration policy changes triggered sharp backlash from AI leaders: Several high-engagement posts argued a proposed rule forcing green-card applicants to apply from outside the US would directly damage the AI talent pipeline. See @Nick_Davidov, @AndrewYNg, @theo, @garrytan, and @togelius. The common argument: the rule punishes legal high-skill immigrants, undermines startups and research, and harms US competitiveness in AI.

Top tweets (by engagement)

• @deepseek_ai on making the V4-Pro discount permanent — the clearest single-market signal in this batch around LLM inference economics.

• @gdb on “the model alone is no longer the product” — concise articulation of the current agent/harness product thesis.

• @AnthropicAI on Glasswing finding 10,000+ critical vulnerabilities — one of the strongest data points for AI-driven cyber capability moving into production.

• @dsp_ on MCP 2026-07-28 RC — important protocol update: stateless MCP plus first-class extensions.

• @GoogleDeepMind on Project Genie + Street View — notable step toward consumer-facing world models.

• @cursor_ai on opening the Cursor SDK for custom agents — relevant for teams building on top of coding-agent infrastructure.

AI Reddit Recap

/r/LocalLlama + /r/localLLM Recap

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Congrats to all our past guests who reached huge milestones this week:

• Turbopuffer: $100M ARR and profitable (our podcast)

• Exa: $250M@$2.2B Series C (our podcast)

• Modal: $355M@$4.7B Series C (our podcast)

We really need to be raising that Latent Space fund soon… but meanwhile.. help us out by taking the 2026 AI Engineering Survey and get >$2k in Notion and Vercel credits and AIE WF tickets!

AI News for 5/20/2026-5/21/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews’ website lets you search all past issues. As a reminder, AINews is now a section of Latent Space. You can opt in/out of email frequencies!

AI Twitter Recap

Model, Benchmark, and Research Updates: RAEv2, Gated DeltaNet-2, Data Filtering, and Open Math

• RAEv2 and representation-first tokenization: Several researchers highlighted RAEv2 as a meaningful follow-on to Representation Autoencoders for unified vision understanding and generation. @1jaskiratsingh says the update yields >10x faster convergence, better reconstruction, and better generation, with tests extending to text-to-image and world models. A Chinese summary from @recatm usefully extracts the three main findings: summing the last K encoder layers instead of only the final layer improves both reconstruction and generation without added inference cost; RAE and REPA are complementary across semantics vs. spatial structure; and REPA can be reformulated as an internal self-guidance mechanism, avoiding extra weak-model guidance passes. @sainingxi`e also points to new evaluation views beyond FID, arguing there is still underexplored headroom in representation-powered pixel decoders.

• Alternatives to standard attention and tokenizer assumptions: NVIDIA’s Gated DeltaNet-2 decouples erase and write operations in linear attention with channel-wise gates, outperforming KDA and Mamba-3 at 1.3B parameters on language modeling and commonsense reasoning, with notable long-context retrieval gains on RULER; @rasbt called it one of the more interesting hybrid-attention directions. On tokenization, @NousResearch released a controlled study of why subword tokenization helps, simulating seven hypothesized benefits inside a 1.7B byte-level pipeline; only three of seven interventions moved validation loss at that scale. Separately, @tatsu_hashimoto reported a surprising scaling result on DCLM: with enough compute, the best data filter may be no filter, with projections suggesting the crossover for internet-scale pools lands around 1e30 FLOPs; downstream evals appear noisy but directionally consistent (follow-up).

• Mechanistic interpretability and geometry: @GoodfireAI argues the dominant “models think in curved manifolds, SAEs use straight-line features” critique is only partly right. Their proposed fix is to cluster SAE features by joint firing patterns, recovering geometry through feature groups rather than isolated atoms (thread continuation, post). This is a useful update to the current SAE discourse: not a rejection of sparse features, but a warning that interpretation should move from single features to structured ensembles.

• Math as an AI research domain: The biggest scientific discussion centered on OpenAI’s reported result on an Erdős unit-distance problem. @markchen90 framed it as evidence that mathematics is currently the domain most amenable to AI-assisted research breakthroughs, while @wtgowers noted that if the reported low human interaction level holds, the result is genuinely interesting. The discourse was immediately shaped by skepticism and benchmark/gameability concerns, with @memecrashes joking that the result was “outdated not even 3 hours later by a human,” and @cloneofsimo pointing out the predictable “goalpost moving” around what counts as legitimate AI mathematics. The interesting technical meta-point is that math continues to function as a relatively legible frontier for AI co-research because outputs can be checked, debated, and extended.

Agents, Harnesses, and Developer Tooling: Codex, Gemini, Devin, and Agent Infrastructure

• Harnesses are still a major source of capability gains: @lvwerra released physics-intern, a science-problem harness that boosts models like Gemini 3.1 Pro from 17.7 to 31.4, surpassing GPT 5.5 Pro in that setup. The notable nuance is that GPT 5.5 Pro itself did not benefit from the harness, suggesting model-specific absorption of scaffolding tricks. In the same spirit, @KLieret made mini-swe-agent runnable on ProgramBench, explicitly aiming to improve harness innovation around software engineering agents.

• Agent design patterns are maturing from “single agent first” to explicit subagent orchestration: @cwolferesearch gives a practical synthesis: start with single-agent systems, and only move to manager/sub-agent or decentralized multi-agent topologies when tool sprawl or prompt bloat becomes unmanageable. That advice lines up with more operational observations from users of subagents: @andrew_locke describes Cognition’s sub-Devin workflow as a step change, compressing what previously looked like 2+ engineer-weeks into a couple of hours.

• Codex shipped a substantial product layer on top of the model: OpenAI’s “Codex Thursday” updates matter less as standalone features than as signs of where coding agents are going. @OpenAIDevs launched Appshots, which capture both screenshot and text from Mac app windows for richer working context; they also added team plugin sharing (link) and more detailed org analytics (link). The more important systems shift is remote computer use: @OpenAIDevs says Codex can now securely use apps on your Mac from your phone even when the Mac is locked. This is a strong signal that the agent product surface is moving from chat IDEs to persistent cross-device operator workflows.

• Gemini’s agent/tool story is broadening quickly: @OfficialLoganK highlighted that Gemini 3.5 Flash ranks #1 on APEX-Agents-AA, outperforming larger models. On the applied side, @_philschmid shows a GitHub issue triage agent built with a single Gemini API call and no orchestration framework, while @skalskip92 demonstrates Gemini 3.5 Flash replacing a custom vision pipeline for lane/car reasoning with one multimodal API call. Google also expanded action surfaces: Daily Brief (announcement) and connected-app actions with OpenTable, Canva, and Instacart (announcement) are essentially consumer-facing agent workflows.

• Developer infra is converging around retrieval, streaming, sandboxes, and security boundaries: Weaviate shipped a built-in MCP server inside the database so coding agents can ingest a repo and use hybrid BM25 + vector retrieval without extra processes (announcement). LangChain introduced both a sandbox Auth Proxy for controlling agent-world boundaries (announcement) and a new typed streaming protocol for rendering tools, subagents, media, and interrupts as first-class projections rather than token streams (overview). vLLM’s Elastic Expert Parallelism is also notable systems work: @vllm_project describes live resizing of MoE DP/EP topology without full restarts, using direct GPU-to-GPU transfers over NVLink/RDMA—important not just for scaling but for future fault-tolerant serving.

Infrastructure, Compute, and AI Business Signals: Modal, Turbopuffer, Hark, and the Compute Race

• The infra layer had one of its clearest “this is where the money is” days: @Sirupsen said turbopuffer crossed $100M run-rate in March, just 19 months after $1M, while being profitable and raising < $1M. The company’s positioning is straightforward and timely: frontier teams know “the magic happens with AI when it draws in just the right context,” which turns a lot of product differentiation into a search/retrieval problem (follow-up). That aligns with broader sentiment from @swyx that “boring” AI infrastructure, not only glamorous frontier research, is where wealth creation is accruing.

• Modal raised big and continues to look like a core AI cloud winner: @bernhardsson announced a $355M Series C at a $4.65B valuation. Investors and users emphasized the same thesis: rebuilding the cloud stack for AI workloads from the ground up, with strong performance and developer experience (Redpoint, user endorsement). This sits alongside other signals that agent-native compute is emerging as its own category; @latentspacepod summarized Daytona’s pitch around 60ms sandboxes, 50K startups in 75 seconds, and RL/evals workloads now representing roughly half of usage.

• Compute remains the strategic bottleneck, and the market appears tiered: @AymericRoucher sketched a useful compute taxonomy: US leaders (OpenAI, Anthropic, Google, with Meta/xAI joining) in the multi-gigawatt class; Chinese giants scaling from hundreds of MW toward multi-GW, increasingly on domestic stacks; and European contenders such as Mistral at around 90 MW today aiming for 1 GW by 2029. The exact numbers are debatable, but the framing is consistent with @EpochAIResearch, which notes that even if OpenAI kicked off the recent compute buildout, frontier labs still use well under all global compute capacity, leaving open the question of how much further the buildout can accelerate. Component economics also continue to shift toward memory: @EpochAIResearch reports HBM grew from 52% to 63% of total AI chip component spending from Q1 2024 to Q4 2025.

• Capital is flowing to interface/hardware bets as well as infra: @adcock_brett announced Hark raised $700M at a $6B valuation, aimed at GPU infrastructure, future model development, hardware, and multimodal/personal intelligence products. The details are sparse beyond hiring areas—foundation models, infra, speech, computer-use agents, hardware—but the size of the raise shows investor appetite for vertically integrated AI-device bets. Hark also reported a 200-hour uninterrupted autonomous run for F.03 (announcement), though without enough technical detail yet to evaluate the underlying robotics stack.

Multimodal, Video, Biology, and Robotics: Runway, Carbon, Earth Models, and Open Humanoids

• Video editing and generation are getting more compositional: Runway launched Aleph 2.0 and the new Edit Studio, letting users edit a single frame and propagate that edit through the rest of the video (Runway, product lead). This is a practical productization of the “reference-guided edit propagation” problem that multimodal builders care about. Separately, Alibaba researchers’ MIGA was flagged by @HuggingPapers as a train-free method for infinite-frame video generation with a two-stage alignment mechanism for temporal consistency. On the open-source avatar side, Meituan released LongCat-Video-Avatar 1.5 with Whisper-Large replacing Wav2Vec2, 8-step inference, long-video identity consistency, and broader stylized-domain generalization (announcement).

• Foundation models for biology and Earth observation continue to become more usable: Hugging Face Bio’s Carbon DNA model family got follow-on demos and infra validation. @LoubnaBenAllal1 highlighted applications in sequence design, variant effect prediction, and learned representations, while @Shekswess showed Carbon-500M, 3B, and 8B compiling and running on a single Trainium2 trn2.3xlarge with NxD Inference on day one. For geospatial modeling, @cgeorgiaw reported OlmoEarth v1.1 is 3x cheaper/faster by changing the tokenization of multi-resolution Sentinel-2 inputs into 3x fewer tokens, exploiting the quadratic compute savings.

• Open robotics is getting more buildable: Hugging Face’s LeRobot Humanoid drew attention as a genuinely full-stack open release rather than a showcase demo. @robotsdigest and @lukas_m_ziegler both emphasize the same package: roughly $2.5k, 3D-printed, complete hardware/CAD, calibration/runtime, simulation, identification tools, and training pipelines. The key point is not just affordability; it’s repairability and iteration speed for real robot learning workflows.

Top tweets (by engagement)

• OpenAI / Codex product expansion: Codex can securely use apps on your Mac from your phone, even when the Mac is locked, plus Appshots for richer app context.

• Infrastructure winners: turbopuffer at $100M run-rate, profitable, < $1M raised; Modal raises $355M Series C at $4.65B; Hark raises $700M at $6B.

• Research discussions with broad technical resonance: OpenAI’s Erdős-related math result discussion; RAEv2 release; “no filter” scaling result for LM data curation.

• Agent capability trendlines: Gemini 3.5 Flash tops APEX-Agents-AA; Gemma 4 E4B driving an iOS simulator on-device via Argent; Devin for Windows.

AI Reddit Recap

Read more

Among the 125 pages of output, there exists a “page 39 moment” that is getting some attention:

As the authors of the opinion letter note, this is a disproof, not a proof, which would have been more impressive, but nevertheless points towards the way of things to come:

AI News for 5/4/2026-5/5/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews’ website lets you search all past issues. As a reminder, AINews is now a section of Latent Space. You can opt in/out of email frequencies!

AI Twitter Recap

OpenAI’s Math Breakthrough on the Erdős Unit Distance Problem

• A general-purpose reasoning model produced a new research result in discrete geometry: OpenAI announced that an internal model disproved a long-standing belief around the planar unit distance problem, a famous Erdős problem from 1946, discovering a new family of constructions that improves on square-grid-style solutions @OpenAI. OpenAI emphasized this was a general-purpose model, not a domain-specific math system or scaffolded solver @OpenAI, and said the result points to stronger long-horizon reasoning for science broadly @OpenAI.

• The result drew unusually strong validation from mathematicians and adjacent researchers. Timothy Gowers called it the first really clear example of AI solving a well-known open math problem @wtgowers, while OpenAI researcher Hongxun Wu described it as an internal reasoning-LLM milestone on “the hardest problems” @HongxunWu. Additional reactions from @thomasfbloom, @gdb, @alexwei_, and @polynoamial converged on the same point: this appears qualitatively beyond prior “AI does olympiad math” milestones.

• Notable technical subtext: OpenAI says the model was not pushed to the limit and is intended for eventual public use @polynoamial. The published reasoning summary itself is reportedly massive—around 125 pages per @voooooogel—which helped fuel discussion about the practical role of test-time compute in frontier reasoning. Some observers explicitly framed this as further evidence that inference-time scaling is the paradigm carrying current progress @arohan, with others extrapolating to faster future gains in formal science and mathematics @scaling01, @sama.

Cohere Command A+ Open Release and Architecture Discussion

• Cohere released Command A+ as Apache 2.0 open weights, positioning it as its most powerful model yet and explicitly optimized for low hardware requirements @cohere, with the licensing clarified in a follow-up @cohere. The release is significant partly because it is Cohere’s first fully open Apache 2 model per @aidangomez. Community reaction focused on this as a meaningful shift toward more permissive, deployable enterprise-grade open models @nickfrosst, @ClementDelangue.

• The model details repeated across multiple posts: roughly 218B MoE / 25B active, multimodal, 48 languages, and runnable on relatively modest setups @JayAlammar, @mervenoyann. vLLM day-0 support landed quickly, including a note that it can run on as little as 2× H100s at W4A4 @vllm_project.

• Benchmarks painted a mixed but credible picture: Artificial Analysis placed Command A+ at 37 on its Intelligence Index, around Claude 4.5 Haiku territory, with especially strong non-hallucination behavior and decent speed, but weaker scientific reasoning and coding than top peer models @ArtificialAnlys. The community also dug into the architecture: unusual choices called out include a parallel transformer block, large shared expert usage, LayerNorm over RMSNorm, relatively low 32-layer depth, and atypical head/expert configurations @eliebakouch, @rasbt, @stochasticchasm. This made the release notable not just as a model drop but as an architectural data point.

Benchmarks for Agents, Memory, and Scientific Workflows

• InferenceBench is one of the day’s most technically substantive releases. It targets AI R&D automation through open-ended inference optimization tasks, and the headline is negative for current frontier agents: they struggle with system-level engineering, dependency management, and broad exploration, underperforming a simple baseline of vLLM/SGLang hyperparameter tuning @maksym_andr. The thread also reports an apparent inverse scaling effect, where models like Claude Sonnet 4.6 and GLM-5 rank well because they preserve robust final states, while larger models often produce brittle end configurations.

• Terminal-Bench Science extends agent evaluation from coding into real scientific workflows, with task contributions now open @StevenDillmann. In parallel, MINTEval targets long-context memory systems under frequent updates and interference: average instance length is 138.8k tokens with up to 1.8M, yet across 7 systems the average accuracy is only 27.9%, with the best at 33.4% @hyunji_amy_lee. This complements a growing line of work arguing that memory should be a dedicated learned subsystem rather than just RAG/context stuffing @dair_ai.

• On the human side of interaction research, ThoughtTrace introduced a large-scale dataset of users’ self-reported thoughts during real LLM conversations: 10,174 thought annotations, 2,155 multi-turn conversations, 1,058 users, 20 models. Reported gains include +41.7% for user behavior prediction and +25.6% for alignment @chuanyang_jin. This is one of the more concrete attempts to instrument the “latent user state” that conversation logs alone miss.

Google I/O Follow-Through: Gemini 3.5 Flash, Omni, AI Studio, and Antigravity

• Gemini 3.5 Flash began broader rollout in the Gemini app, including free access globally @GeminiApp, @GeminiApp. Google framed it as its strongest agentic and coding model yet, claiming frontier performance at 4× the speed of comparable models and under half the cost @Google. However, external discussion was much more mixed, with multiple posts questioning real-world cost/performance and token efficiency despite favorable launch-stage benchmark positioning @ArtificialAnlys, @scaling01, @giffmana.

• Gemini Omni appears to have made the bigger qualitative impression than 3.5 Flash. Google positioned it as a conversational multimodal creation/editing model for video and mixed-input workflows @Google, with Gemini app demos showing conversational video editing @GeminiApp. Early reactions generally treated Omni as a more differentiated product than the core LLM refresh @scaling01.

• On tooling, AI Studio pushed harder toward end-to-end developer workflow and mobile access @GoogleAIStudio, while several posts tried to decode the relation between Gemini Spark, Antigravity, and Google’s internal/external agent harnesses @simonw, @_philschmid. A more concrete Antigravity-adjacent update was the launch of Science Skills for Google’s agent stack, integrating 30+ life-science sources such as UniProt and AlphaFold DB @GoogleDeepMind.

Agent Infrastructure, Retrieval, and Dev Tooling

• Several posts converged on the same operational lesson: agents fail on infra reality before they fail on demos. That theme shows up in the qualitative thread on research agents fighting dependency conflicts and configs @jehyeoky248, in LangChain’s push for LangSmith Sandboxes GA @LangChain, and in newer lighter-weight code interpreter support for deepagents as a middle ground between pure tool execution and full sandboxes @sydneyrunkle, @hwchase17.

• In retrieval/search infra, Perplexity described a productionized query-aware, citation-preserving context compression system that cuts context tokens by up to 70% while improving answer quality, and claims 50× compression on SimpleQA at frontier-level performance @perplexity_ai. Weaviate 1.37 added MMR reranking to improve diversity in vector retrieval for RAG/agents @weaviate_io, while SID-1 was presented as an RL-trained agentic search model with 1.9× recall over RAG+rerank, 24× faster, and 99% cheaper than GPT-5.1 in the cited setup @turbopuffer.

• Cursor, VS Code, and Codex all shipped notable workflow updates. Cursor added automations in the agents workspace @cursor_ai, VS Code shipped better markdown/HTML previews, remote session continuity, and utility-model configurability @code, @pierceboggan. On the model side, Composer 2.5 posted a strong coding-agent showing—62 on the Artificial Analysis Coding Agent Index at much lower cost than top Opus/GPT-5.5 variants @ArtificialAnlys. OpenAI also shipped Codex on mobile @OpenAIDevs.

Top Tweets (by engagement)

• OpenAI math milestone: OpenAI’s announcement of the unit-distance breakthrough was the most consequential technical post in the set, both for scientific novelty and for what it implies about long-horizon reasoning @OpenAI.

• Cohere Command A+ open release: One of the largest model-release stories of the day, mainly because of the Apache 2.0 license and unusual architecture @cohere.

• Anthropic compute expansion with SpaceX/Colossus: Anthropic is reportedly scaling up on Colossus 2 capacity @nottombrown, with follow-on posts citing a filing that values the SpaceX compute agreement at $1.25B/month through May 2029 @SemiAnalysis_.

• Exa funding: Exa raised $250M Series C at a $2.2B valuation, explicitly framing itself as a search lab organizing web data for agents @ExaAILabs.

AI Reddit Recap

/r/LocalLlama + /r/localLLM Recap

1. Qwen3.7 Preview and 27B Roadmap

• Qwen is cooking hard (Activity: 1292): The image is a screenshot of Chujie Zheng teasing that Qwen is “cooking hard”, quoting an announcement that Qwen3.7 Preview is now on Arena with Qwen3.7-Max-Preview and Qwen3.7-Plus-Preview; the post claims Alibaba ranks #6 in Text and #5 in Vision. In context, the Reddit title/selftext indicate users are anticipating larger and refreshed open-weight models—especially 122B and a new 27B—though the screenshot itself is mainly a teaser rather than a technical benchmark breakdown. Image Commenters are split between excitement for high-end models and practical interest in smaller local models: some want 9B/4B variants for low-end hardware, while others hope for 122B, a better 35B, or joke that Qwen may soon be “cooking” their GPU.

  • Several commenters focused on model-size coverage rather than the current 27B release, saying they cannot practically run it and are hoping for smaller Qwen 4B/9B variants for low-end or laptop GPUs. There was also interest in larger 122B and improved 35B checkpoints, though one commenter noted prior 122B mentions around Qwen 3.6 never materialized, raising uncertainty about whether a Qwen 3.7 122B will actually ship.

• Qwen3.7 Max scored by Artificial Analysis, 27B/35B waiting room (Activity: 553): A Reddit post highlights an Artificial Analysis leaderboard screenshot where Qwen3.7 Max ranks 5th, roughly level with GPT 5.4 (xhigh) and slightly ahead of Gemini 3.5 Flash. The author notes Qwen3.6 27B trails its Max counterpart by exactly 6 points and hopes upcoming Qwen3.7 27B/35B variants land close to the Max model’s performance. Commenters are mainly “waiting eagerly for the open weight models” and view the score as evidence that the Qwen team is now competitive with major labs, despite concerns that the Max model is not open-source. One technical concern raised is whether Qwen has fixed its prior tendency toward “overthinking.”

  • Commenters focused on whether Qwen3.7 Max represents a genuine architectural update versus another finetune/iteration of the Qwen3.5/Qwen3.6 architecture; one noted that extracting more performance from the same base architecture would still be technically notable.

  • Several users are waiting for potential open-weight 27B/35B variants, but one commenter speculated there may be no Qwen 3.7 27B at all, arguing that “Qwen 3.7” could simply be a private large model similar to Qwen 3.6 390B A30B rather than a full public model family.

  • A technical concern raised was whether the Qwen team has addressed the model’s reported “overthinking” behavior, implying interest in improvements to reasoning-token efficiency, response latency, and controllability rather than just benchmark gains.

• Qwen will release another 27B with high probability (Activity: 1162): The image is a screenshot of an X/Twitter exchange where xiong-hui (barry) chen says Qwen is “waiting for the exact roadmap” but believes there is a high probability of another 27B release, framed by the post title as a likely follow-up to the highly regarded Qwen 3.6 27B. The technical significance is speculation around Qwen continuing to optimize parameter efficiency / “intelligence density” in the mid-size dense-model range rather than only scaling to much larger MoE models. Commenters mostly discuss local-inference practicality: some want a larger 122B-A10B MoE model, while others argue that 27B is too heavy for 16GB VRAM users and prefer a 35B/A3B-style MoE that can run on consumer gaming laptops or hybrid CPU/GPU setups.

  • Several commenters discussed the local-inference gap around 27B models: users with 16GB VRAM argued that a 27B model is difficult to run at a usable quantization level, while a hypothetical Qwen 35B MoE / A3B-style model could be more practical via hybrid CPU/GPU inference and would remain accessible on gaming laptops.

  • There was interest in larger dense Qwen variants, especially 50B–80B, with one commenter noting that Qwen 27B is already very fast with MTP and they would trade some generation speed for higher parameter count and potentially better quality.

  • Model-size requests clustered around both MoE and dense scaling paths: proposed targets included Qwen 3.7 122B-A10B, 50B–80B MoE, and dense 10B, 20B, 30B, 50B, or 80B releases, reflecting demand for both high-end quality and locally runnable tiers.

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The mainline Gemini 3.5 Flash is GA today (very nice compared to some staged rollouts) and is sold as a decent step up even compared to 3.1 Pro, with 3.5 Pro coming next month. Perhaps more impressive were the Gemini Live (Voice) and Omni (Video) and Google Pics/Flow (Images/VFX/music) modalities, where Google demonstrated industry leading capabilities and latency, all presumably made possible by industry leading hardware and models.

Per longstanding tradition at every bigtech keynote these days, Google also showed off some smart glasses tech, which seems a little more likely to be seen on the street than many prior iterations from both Google and their peers.

AI News for 5/18/2026-5/19/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews’ website lets you search all past issues. As a reminder, AINews is now a section of Latent Space. You can opt in/out of email frequencies!

AI Twitter Recap

Google used I/O to reposition Gemini as both a consumer AI surface and a developer/agent platform, with three core technical announcements: Gemini 3.5 Flash for fast agentic/coding workloads, Gemini Omni for multimodal generation/editing starting with video, and a broader Antigravity agent stack spanning desktop/CLI/SDK/API. Official posts emphasized scale — Google says it now processes over 3.2 quadrillion tokens/month, up 7x YoY from 480T/month, while the Gemini app has 900M+ monthly users and is available in 230+ countries and 70+ languages (Google, Google, GeminiApp). The most technically substantive release was Gemini 3.5 Flash, framed by Google as its strongest agentic/coding model yet, GA immediately, with 1M-token context, 65k max output, 4 thinking levels (“minimal/low/medium/high”), and “thought preservation” across turns (GoogleDeepMind, Google, _philschmid). Google paired that with Gemini Omni, a new family combining Gemini reasoning with generative media, initially via Omni Flash, capable of taking text/image/video/audio inputs and producing video edits/generation in Gemini, Flow, Shorts, and later APIs (GoogleDeepMind, Google, GeminiApp). Around those models, Google launched or expanded Antigravity 2.0 desktop, CLI, SDK, Managed Agents in the Gemini API, Search-native generative UI/coding, Gemini Spark background agents on cloud VMs, and a long list of Gemini-app/Workspace/commerce/media integrations (Google, Google, Google).

Facts vs. opinions

Facts / directly claimed by official or third-party benchmark sources

• Google says it now processes 3.2 quadrillion tokens/month, up from 480 trillion a year earlier (Google).

• Google says Gemini has 900M+ monthly users (Google).

• Google says Gemini 3.5 Flash is GA today across Gemini app, Search AI Mode, Gemini API, AI Studio, Antigravity, Android Studio, and enterprise surfaces (Google, GeminiApp).

• Google says Gemini 3.5 Flash has 1M context, 65k max output, 4 thinking levels, and “thought preservation” across turns ( _philschmid).

• Google says 3.5 Flash beats Gemini 3.1 Pro on Terminal-Bench 2.1, GDPval-AA, and MCP Atlas (GoogleDeepMind, Google).

• Google says 3.5 Flash runs 4x faster than comparable frontier models, and up to 12x faster in Antigravity (Google, JeffDean).

• Independent benchmarker Artificial Analysis reports Gemini 3.5 Flash scores 55 on its Intelligence Index, +9 vs Gemini 3 Flash, at >280 output tok/s, with MMMU-Pro 84%, GDPval-AA Elo 1656, and pricing of $1.50 / $9.00 per 1M input/output tokens; it also reports the model is 5.5x costlier to run than Gemini 3 Flash on its suite and 75% costlier than Gemini 3.1 Pro (ArtificialAnlys).

• Arena reports Gemini 3.5 Flash reached #9 overall in Text Arena and #9 in Code Arena: Frontend, scoring 1507, a +70 jump over Gemini 3 Flash, and becoming the top score in its price tier (arena).

• Google says Gemini Omni Flash is available in Gemini/Flow today for paid users, in Shorts/Create starting this week for free, and via APIs in coming weeks (Google).

• Google says Spark runs on dedicated Google Cloud virtual machines, allowing long-running tasks while user devices are closed (Google).

• Google claims an Antigravity + Gemini 3.5 Flash demo built a functioning OS in 12 hours using 93 parallel sub-agents, 15k+ model requests, 2.6B tokens, and < $1K API credits (Google).

• Google says Search will use Antigravity + 3.5 Flash to generate custom visual tools/simulations on the fly (Google).

Opinions / interpretations / skepticism

• Positive takes: “Google is back,” “insane evals for a Flash model,” “world model towards AGI,” “mind blowing” for Search + Antigravity, etc. (kimmonismus, Kseniase_, demishassabis).

• Neutral caution: some posters explicitly avoided overhyping due to self-reported benchmarks and noted pricing/perf concerns (scaling01, simonw).

• Negative/skeptical takes focused on:

  • Price inflation relative to earlier Flash models (enricoros).

  • Comparisons where GPT-5.5-medium may be smarter/cheaper/faster end-to-end (scaling01, scaling01).

  • Benchmark caveats such as weak TerminalBench-Hard, mediocre MRCR / ARC-AGI-2, or not clearly beating Kimi/GLM on some slices (scaling01, teortaxesTex, scaling01).

  • Product naming/UX confusion around Gemini CLI vs Antigravity CLI and broader interface design criticism (zachtratar, kchonyc, teortaxesTex).

Gemini 3.5 Flash: the main technical release

Official positioning

Google/DeepMind repeatedly described Gemini 3.5 Flash as the company’s strongest model yet for agents and coding, not its absolute flagship intelligence model. It’s meant to sit on the high-speed, high-utility part of the Pareto frontier, powering both Google products and developer workloads (GoogleDeepMind, Google, SundarPichai).

Technical details and metrics

From Google and affiliated posts:

• GA availability now (Google)

• 1M token context window

• 65k max output tokens

• Thinking levels: minimal, low, medium (new default), high

• Thought preservation across multi-turn conversations

• Text output

• Input modalities: text, image, video, speech per Artificial Analysis ( _philschmid, ArtificialAnlys)

• Pricing: $1.50 / 1M input, $9.00 / 1M output, 90% discount on cached input (scaling01, ArtificialAnlys)

Official benchmark claims:

• Terminal-Bench 2.1: 76.2%

• GDPval-AA: 1656 Elo

• MCP Atlas: 83.6%

• Google-quoted multimodal result: MMMU-Pro 83.6% in one engineer post; Artificial Analysis reports 84%, highest recorded on its setup (koraykv, ArtificialAnlys)

Speed claims:

• Google marketing claim: 4x faster than comparable frontier models (Google)

• In Antigravity, Google says it is up to 12x faster (JeffDean, scaling01)

• Artificial Analysis observed >280 output tok/s

• Some discussion cited ~867 tok/s in Antigravity-specific optimized serving (scaling01, scaling01)

Third-party evaluation:

• Artificial Analysis says 3.5 Flash is the leader on the intelligence-vs-speed Pareto frontier, but the economics are notably worse than prior Flash:

  • Intelligence Index 55

  • +9 over Gemini 3 Flash

  • Hallucination rate reduced to 61%, a 31-point drop vs Gemini 3 Flash on its omniscience setup

  • GDPval-AA 1656 Elo

  • 5.5x costlier than Gemini 3 Flash to run on its benchmark suite

  • 75% costlier than Gemini 3.1 Pro on the same suite (ArtificialAnlys)

Arena:

• #9 Text Arena

• #9 Code Arena: Frontend

• 1507 score, +70 over Gemini-3 Flash

• Better than Gemini 3.1 Pro across categories in its frontend coding eval (arena, arena)

Implications

The notable shift is that Google appears to be using a “Flash” label for a model that, in prior cycles, would have been described more like a high-end product model optimized for deployment rather than simply a cheap lightweight tier. Several posters called this out directly, arguing Flash is becoming more expensive and possibly absorbing former Pro territory (enricoros, simonw).

The strongest technical signal is not “best absolute benchmark model,” but:

1. material agentic gains

2. extreme serving speed

3. deep integration into product surfaces

4. tooling built around subagents and long-horizon execution

That makes 3.5 Flash strategically important even if some competitors still win on raw price-adjusted intelligence in certain third-party comparisons.

Gemini Omni: multimodal generation/editing as “create anything from any input”

What Google announced

Google introduced Gemini Omni as a new family merging Gemini reasoning/world knowledge with Google’s generative media stack, starting with video creation and editing. Official messaging described it as “create anything from any input,” but current rollout is narrower:

• Inputs: text, images, audio, video

• Initial output emphasis: video

• Product availability: Gemini app, Flow, YouTube Shorts/Create, later APIs

• Current shipping model: Gemini Omni Flash (GoogleDeepMind, Google, Google)

Google/DeepMind claims:

• Better world understanding

• More robust physics

• Multi-turn editing where scene/character consistency is retained

• Ability to “reimagine” user video footage with conversational edits (Google, Google)

Rollout specifics:

• Paid Gemini users globally in app/Flow “today”

• YouTube Shorts/Create rolling out “starting this week” at no cost

• APIs for developers/enterprise in coming weeks (Google, GeminiApp)

Perspectives

• Supportive: users and Google employees described Omni as a major quality step, especially for video editing and consistency (joshwoodward, fofrAI, osanseviero).

• Strategic interpretation: several posters framed Omni as evidence Google is investing in world models and embodied/physical priors, not just text/code competition (demishassabis, jparkerholder, kimmonismus).

• Skepticism: some UI/output examples drew criticism for looking like “B-tier video game interface” or too polished/template-like (teortaxesTex, shlomifruchter).

Context

Omni matters less as “yet another video model” and more as Google’s attempt to unify:

• multimodal understanding,

• media editing,

• world grounding,

• agent interfaces,

• and eventually any-input/any-output generation.

This aligns with DeepMind’s long-running world-model agenda and Google’s product distribution advantage.

Antigravity: Google’s agent OS, not just a coding assistant

A major underappreciated I/O theme was that Google is no longer presenting agents as a thin wrapper around a chat model. Antigravity is becoming the execution substrate.

What launched / expanded

• Antigravity 2.0 desktop app: agent-first desktop with core conversations, artifacts, multi-agent orchestration (Google, Google)

• Antigravity CLI (Google, Google)

• Antigravity SDK (Google)

• Managed Agents in Gemini API: single API call gives an agent plus hosted Linux sandbox; supports Bash/Python/Node, files, browsing, custom markdown-defined skills, repo/GCS mounts (Google, GoogleAIStudio, _philschmid)

• Integrations with AI Studio, Android, Firebase, Workspace, web (Google, Google)

• One-click export from AI Studio to Antigravity (Google)

• Native Android app generation in AI Studio / Android support in Antigravity (Google, AndroidDev)

Technical signaling

Google’s own demos centered on parallel sub-agents, hosted execution, high-frequency iterative loops, and artifact-oriented workflows. Jeff Dean explicitly described 3.5 Flash as a strong engine for “deploy sub-agents that collaborate, run high-frequency iterative loops, and solve real-world problems at scale” (JeffDean).

The marquee proof point:

• OS built in 12h

• 93 parallel sub-agents

• 15k+ requests

• 2.6B tokens

• < $1K credits (Google)

Even if this is mostly a stage-managed benchmark/demo, it reveals the architecture Google wants developers to adopt: many fast agents over one slow monolithic run.

Reactions

• Positive: this is Google’s answer to Codex/Claude Code/OpenClaw/Hermes-style workflows, with a stronger infra story (iScienceLuvr, theo).

• Critical: branding and product sprawl remain confusing; some users aren’t sure whether they should use Gemini CLI or Antigravity CLI, and Google’s design choices drew complaints (kchonyc, zachtratar, teortaxesTex).

Search, Gemini app, and consumer agents

Search

Google announced a redesigned AI-powered Search box, multimodal query support, and the most ambitious consumer-facing move: Search generating custom visual tools and simulations on the fly using Antigravity + Gemini 3.5 Flash (Google, Google).

It also previewed information agents in Search:

• persistent monitoring tasks

• web/news/social/real-time signals

• synthesized updates with links and actions

• rolling out to Pro/Ultra this summer (Google, Google)

This is a notable strategic shift: Search moves from retrieval/ranking to background agentic monitoring + generated applets.

Gemini app

Consumer Gemini updates included:

• new “Neural Expressive” design language (Google)

• inline/instant Gemini Live voice (Google)

• Daily Brief personalized digest from inbox/calendar/tasks (Google, GeminiApp)

• Gemini Spark as a 24/7 personal AI agent on cloud VMs, checking with users before major actions (Google, GeminiApp)

• macOS app + upcoming Spark/voice desktop workflows (Google, GeminiApp)

Pricing / subscriptions

Google introduced a new pricing ladder:

• new $100/month plan

• top-tier Ultra cut from $250 to $200/month (Google, GeminiApp)

This reads as a more aggressive bid for premium power users, especially coders and creators.

Trust, provenance, and standards

Google pushed SynthID across Search, Gemini, Chrome, and hardware/media surfaces, and announced partnerships with OpenAI, NVIDIA, Kakao, and ElevenLabs to bring SynthID to their generated content (Google, Google).

That is one of the more consequential standards moves from I/O:

• it gives Google a shot at owning part of the provenance layer for generative media;

• notably, OpenAI separately announced support for checking OpenAI-generated images via SynthID watermark + C2PA credentials (OpenAI).

This was less flashy than Omni/3.5 Flash, but likely more durable if provenance becomes mandatory infrastructure.

Google’s science and world-model angle

Several I/O items reinforced that Google does not want to compete only on coding/chat:

• Gemini for Science: Literature Insights, Hypothesis Generation, Computational Discovery (GoogleDeepMind, Google)

• Nature publication links around ERA / Co-Scientist (GoogleResearch, GoogleResearch)

• Project Genie + Street View grounding, using ~20 years of maps imagery to create interactive real-location simulations (Google, poolio, bilawalsidhu)

This broader context explains why some observers interpreted Omni as “world-model progress” rather than just a content tool (demishassabis, jparkerholder).

Different opinions

Bullish / supportive

• Gemini 3.5 Flash viewed as a major leap for a speed-tier model, especially on agentic coding (kimmonismus, SundarPichai).

• Search + Antigravity seen as potentially transformative because Google can deploy generated UI/tools at enormous scale (Kseniase_, TheTuringPost).

• Omni praised for editing quality and for hinting at a deeper world-model roadmap (joshwoodward, kimmonismus).

Skeptical / opposing

• Concern that Google is leaning on self-reported benchmarks, and independent comparisons still leave room for competitors (scaling01).

• Concern that “Flash” is no longer cheap enough to justify the name; pricing has climbed sharply from prior Flash generations (enricoros, simonw).

• Some believed GPT-5.5-medium still dominates on a combined smart/cheap/latency basis (scaling01).

• Some benchmark slices imply unevenness — e.g. poor TerminalBench-Hard or middling reasoning metrics despite strong agentic numbers (scaling01, teortaxesTex).

Neutral / analytical

• Artificial Analysis gave the strongest balanced take: excellent speed-intelligence frontier position, substantial agentic gains, but materially worse cost than prior Flash and even higher than 3.1 Pro on their end-to-end suite (ArtificialAnlys).

• Arena’s data also supports a “real improvement, not just marketing” conclusion, especially for frontend/code tasks, without claiming category dominance (arena).

Why this matters

1. Google now has a coherent deployment story.
   Earlier Gemini cycles often felt benchmark-heavy and product-fragmented. At I/O, Google tied model, infra, tools, APIs, consumer surfaces, and enterprise rollout together.

2. The center of gravity is shifting from chatbot UX to agent execution.
   The important primitives were not just model IQ: they were subagents, hosted sandboxes, long-running tasks, generated artifacts, and integration with Search/Workspace/Android.

3. Gemini 3.5 Flash suggests “fast enough to orchestrate many agents” may matter more than max benchmark score.
   For coding and tool use, throughput and latency are increasingly product-defining.

4. Omni reveals Google’s differentiation thesis.
   Google is betting on multimodal/world-grounded systems rather than purely text-centric competition.

5. Trust/provenance is becoming platform infrastructure.
   SynthID partnerships with OpenAI/NVIDIA/ElevenLabs/Kakao suggest some convergence around content-auth provenance layers.

6. The biggest unresolved question is economics.
   Technically strong or not, 3.5 Flash drew substantial pushback on cost inflation. If “Flash” is no longer the cheap workhorse tier, Google may win on capability deployment while losing some developer mindshare on predictability and pricing simplicity.

Talent, Labs, and Ecosystem Moves

• Karpathy joins Anthropic: The day’s most engaged AI tweet was Andrej Karpathy’s announcement that he has joined Anthropic to “get back to R&D.” The tweet dominated discussion, with subsequent speculation from @scaling01 citing Axios that he’ll work on RSI/autoresearch and start a new pretraining-focused effort. While the details remain unconfirmed by Anthropic, the move was widely interpreted as a major talent win for Anthropic.

• OpenAI capacity products: OpenAI announced Guaranteed Capacity, a commercial offering that lets customers secure long-term compute access for critical workloads. Sam Altman framed it as a response to a world that will remain capacity constrained as models become more useful, offering discounted tokens for 1–3 year commits.

• GitHub and coding toolchain integrations: GitHub said Gemini 3.5 Flash is rolling out in Copilot, citing strong tool use, fast response times, and cache efficiency for iterative agentic coding. Cursor launched integration with Jira, allowing cloud agents to take work items and create merge-ready PRs. Code/VS Code also announced Gemini 3.5 Flash availability.

Training Algorithms, Benchmarks, and Agent Evaluation

• RL/post-training discussion is shifting toward denser credit assignment: @nrehiew_ argued that the next scalable training breakthrough may build on GRPO but with denser, lower-bias credit assignment, citing directions like ECHO, Composer2, self-distillation, and OPD. @lateinteraction countered with a “pedagogical RL” framing: train a self-teacher that samples correct and easy-to-follow rollouts.

• Can coding agents do research? Not yet: Intology AI released NanoGPT-Bench, an autonomous benchmark based on the NanoGPT Speedrun competition, testing whether coding agents can contribute to real AI R&D progress. Their headline result: Codex, Claude Code, and Autoresearch recover only 9.3% of human progress, mostly via hyperparameter tuning rather than algorithmic innovation.

• Agent harnesses and memory are getting more formalized: @omarsar0 highlighted a 100+ page survey on code-as-agent-harness, arguing future systems need to be executable, inspectable, stateful, and governed. François Chollet made the related point that real tasks are rarely Markovian, so agents without high-fidelity trajectory compression are dramatically less useful.

• Verifier quality is emerging as a bottleneck: Threads from @Shahules786 emphasized that scaling agent benchmarks now depends less on adding tasks and more on improving verifier quality, citing SWE-bench Verified, OSWorld-Verified, ComputerRL, and BenchGuard.

Science, Biology Models, and Domain-Specific Systems

• Hugging Face releases Carbon DNA models: One of the most technically interesting open releases was Carbon, a family of generative DNA foundation models. The team says Carbon-3B matches Evo2-7B while running 250–275x faster at inference, enough to process the whole human genome on a single GPU in under two days. The key recipe changes: deterministic 6-mer tokenization, a factorized loss (FNS) replacing plain cross-entropy late in training, and curated staged mixtures of functional DNA + mRNA data per @LoubnaBenAllal1. The release includes models, training code, evals, data, and a demo.

• Google pushes AI for science as a product category: Google introduced Gemini for Science, a suite of prototypes for researchers: Literature Insights (paper synthesis via NotebookLM), Hypothesis Generation (a Co-Scientist-style multi-agent “idea tournament”), and Computational Discovery (built with AlphaEvolve and ERA to generate and score thousands of code variants in parallel). Google Research also noted that ERA has now been published in Nature (Google Research).

• Specialized pretraining is gaining support: @pratyushmaini pointed to evidence that early exposure / specialized pretraining improves robustness to forgetting, arguing that enterprises serious about domain use cases should consider training custom models from scratch, not just post-training.

Safety, Governance, and Monitoring of Internal Agents

• METR’s first Frontier Risk Report: METR published a major new report based on unusually deep access across Anthropic, Google, Meta, and OpenAI, including model CoTs and non-public information about capabilities, alignment, and control. The report focuses on whether labs could lose control of their own internally deployed agents and includes extensive appendices and transcripts (METR).

• Monitoring internal agents is now an active practice: @idavidrein described spending a month embedded at Anthropic stress-testing systems designed to detect whether internal AI agents could “go rogue.” A key caveat he noted is that the exercise allowed Anthropic discretion to redact sensitive information, so he frames it as an exercise rather than a formal audit.

• New safety standards org: Steven Adler announced Guidelight, a new AI safety standards organization co-founded with Page Hedley, releasing its first two standards. While the tweet thread in the dataset is partial, the move is notable as another sign of the field professionalizing around operational standards, not just model evals.

Top tweets (by engagement)

• Karpathy joins Anthropic: @karpathy

• Google introduces the Gemini 3.5 model series: @Google

• Google DeepMind launches Gemini Omni: @GoogleDeepMind

• Gemini 3.5 Flash GA for agents and coding: @Google

• OpenAI Guaranteed Capacity: @OpenAI

• Google’s 24/7 personal agent, Gemini Spark: @Google

AI Reddit Recap

/r/LocalLlama + /r/localLLM Recap

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Specifically he references last year’s Scaling handbook from DeepMind, and kernel work is an important part:

The biggest bottleneck and innermost loop of all LLM work is performance work that makes abstract, logical changes to the LLM practical to run. Every project needs people who can tune the LLMs at the kernel level. It is a skill you can pick up and is the most direct path into the labs.

There’s a surprise mention of DSLs for kernel dev, of which there is a concise history:

For someone at this level of the stack, surprisingly he also calls out Agent Work like autoresearch and AlphaEvolve. He ends with a surprisingly simple exercise:

But the real hiring test is in the bottom paragraphs:

• Derive Chinchilla laws for this; see how they differ for dense vs MoE architectures.

  • Code your solution from scratch in jax by hand if you actually want the learning experience.

• Next, assuming you used jax.lax.ragged_dot for the MoE layer; write a pallas kernel that beats ragged dot for F > D by fusing the up/down projections.

  • Find a setting where you notice a measurable forward pass speedup and explain why it’s there.

If you can teach this to the rest of the community, we’d love to feature you as a workshop speaker.

AI News for 5/16/2026-5/18/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews’ website lets you search all past issues. As a reminder, AINews is now a section of Latent Space. You can opt in/out of email frequencies!

AI Twitter Recap

Coding Agents, Agent Ops, and the Move from Chat to Automation

• Agent infrastructure is converging on observability + automation loops: Several posts point to a maturing stack for production agents. LangSmith Engine is framed as the missing CI/CD loop for agents, automatically detecting failures from production traces, clustering issues, and drafting fixes/evals, with LangChain also highlighting SmithDB as a purpose-built data layer for agent observability/eval workloads with low-latency querying over large traces and self-hosting/multi-cloud requirements @krishdpi, @LangChain. In parallel, Cognition launched Devin Auto-Triage, positioning it as an always-on “first responder” for bugs, alerts, and incidents with long-term memory, manager/subagent structure, and PR generation; early users like Modal describe it as more useful than typical homegrown triage automations @cognition, @walden_yan, @russelljkaplan. The common pattern is less “chat with an agent” and more persistent automation tied to traces, memory, and evals.

• Operational patterns for coding agents are getting more concrete: Anthropic published best practices for running Claude Code across multi-million-line monorepos, legacy systems, and microservices, while adding prompt cache diagnostics and making Fast mode default to Opus 4.7 for lower-latency coding workflows @ClaudeDevs, @ClaudeDevs, @ClaudeDevs. OpenAI expanded Codex workflows with a Zoom plugin, mobile/desktop remote execution, and “keep your Mac awake” support so longer-running jobs continue from the phone app @coreyching, @OpenAIDevs. Microsoft pushed remote control for GitHub Copilot CLI and VS Code to GA @code. Across these, the product direction is clear: background execution, remote supervision, and agent fan-out, not just interactive completions.

• Practitioners are converging on the same mental model: constrain, verify, decompose: François Chollet’s framing of coding agents as “blind squirrels” that need carefully placed verifiable constraints succinctly matches a broader shift toward harness-centric engineering @fchollet. Related advice includes using asserts heavily in Python/ML code to fail fast @gabriberton, building both end-to-end and incremental evals for long-running agents @palashshah, and structuring multi-agent systems in staged maturity levels rather than maximizing agent count prematurely @shannholmberg. The practical consensus: agent quality depends more on verification surfaces, decomposition, and feedback loops than on prompt cleverness alone.

Model Releases, Ranking Shifts, and Frontier Coding Models

• Cursor’s Composer 2.5 is the standout model launch in this batch: Cursor announced Composer 2.5 as its strongest model yet, emphasizing better sustained work on long-running tasks and more reliable instruction following, then disclosed a deeper strategic move: training a much larger model from scratch with “SpaceXAI,” using 10× more total compute and access to Colossus 2’s million H100-equivalents @cursor_ai, @cursor_ai. Community reactions centered on its efficiency/cost-performance profile and strong coding quality, with users calling it a major step up from Composer 2 and noting better collaboration behavior in messages/updates, not just raw benchmark gains @mntruell, @jonas_nelle, @kimmonismus.

• Alibaba’s Qwen line continues to climb: Qwen3.7 Preview landed on Arena with Qwen3.7 Max Preview at #13 overall in text, including #7 Math, #9 Expert, #9 Software & IT, and #10 Coding; Qwen3.7 Plus Preview reached #16 overall in vision, making Alibaba the #6 lab in text and #5 in vision by Arena’s counts @arena, @Alibaba_Qwen. That reinforces the broader trend of Chinese labs steadily improving across both general and specialist arenas rather than only headline chat benchmarks.

• Open model and multimodal releases continue below the mega-frontier: ByteDance open-sourced Lance, described as a unified multimodal model for image/video understanding, generation, and editing, with 3B video + 3B image + 3B decoder components @bdsqlsz. Perplexity released a small open multilingual ColBERT model as a continued-training variant of pplx-embed-0.6b, with notes on using the MaxSim kernel @bo_wangbo. These are not frontier-scale launches, but they are technically meaningful because they target retrieval quality and native multimodal unification, two areas where open tooling still matters.

Inference, Deployment, and Local/Enterprise Serving

• Local inference got a notable speed boost via MTP in llama.cpp: Georgi Gerganov announced MTP support for the Qwen3.6 family in llama.cpp, calling it a significant milestone for local AI @ggerganov. Follow-on reports showed meaningful throughput gains, including a Qwen3.6-27B dense jump from 25 tok/s to 45 tok/s (+78%) on an A10G using draft-MTP flags @victormustar. This matters because it narrows the usability gap between local and hosted coding/general assistants on commodity hardware.

• Enterprise/on-prem deployment momentum remains strong: Hugging Face and Dell promoted one-click access to models including Kimi K2.6, DeepSeek V4 Pro/Flash, GLM 5.1, and MiniMax M2.7 through Dell Enterprise Hub optimized for PowerEdge XE9780 with NVIDIA B300 @jeffboudier. Clement Delangue argued that on-prem/local AI based on open-source models will be an important answer to GPU shortages, with advantages in cost, latency, and safety/data control @ClementDelangue.

• Cross-hardware inference optimization is becoming more sophisticated: Zyphra published end-to-end inference benchmarks on AMD Instinct MI355X, claiming strong outperformance over AMD’s baseline and a narrowed gap to NVIDIA B200 when serving Kimi K2.6, GLM 5.1, and DeepSeek V3.2 @ZyphraAI. Complementing that, Quentin Anthony posted a useful thread on why benchmarking needs to distinguish hardware ceilings vs current software state, arguing that many cross-stack comparisons conflate vendor maxes, achievable GEMM performance, and software maturity @QuentinAnthon15. For infra engineers, that’s a strong reminder to treat benchmark charts as stack-dependent snapshots, not absolute truths.

Research: MoEs, RL/Data Mixing, Architecture Search, and Agent Evaluation

• Several papers this week focused on better training signals rather than bigger models: A summary of LeCun/Timor et al.’s “On Training in Imagination” highlighted that in model-based RL, smoother world/reward models with low Lipschitz constants tighten error bounds; reward models often scale faster than dynamics models; and many noisy reward labels can beat fewer high-quality ones, while biased rewards are especially dangerous @TheTuringPost. A separate thread on Pedagogical RL argued that even correct reasoning traces can be poor training data if they are too surprising relative to the student policy; the method uses a privileged teacher plus spike-aware rewards and surprisal-gated imitation to generate trajectories the student can actually learn from @blc_16, @NoahZiems.

• Architecture and scaling studies remain highly actionable: Meta’s AIRA work on agentic neural architecture discovery drew attention because it beats Llama 3.2 at 350M, 1B, and 3B scales within a 24-hour compute budget by splitting search into a planning agent (AIRA-Compose) and an implementation agent (AIRA-Design) @omarsar0, @dair_ai. Separately, “Slicing and Dicing MoEs” reports training 2,000+ MoE LMs and concludes that much of the design space reduces to expert size and expert count rather than the noisier discourse around MoE configuration knobs @margs_li.

• Data selection/eval methodology are emerging as first-class research problems: On-Policy Mix targets the unsolved problem of finding the right data mix as data distributions keep shifting, with applicability across pretraining, midtraining, and instruction tuning @michahu8. On evals, Cameron Wolfe published a guide to agent evaluation, and a longer Zhihu summary argued that the agent era requires measuring delegation intelligence—when to search, code, reason, or call tools—rather than only static knowledge or internal chain-of-thought prowess @cwolferesearch, @ZhihuFrontier. That aligns closely with current product practice: the hard part is increasingly tool choice and verification policy, not text-only reasoning.

Ecosystem Moves: SDKs, Revenue Capture, and Open Tooling

• Anthropic acquired Stainless: Anthropic announced the acquisition of Stainless, the SDK and MCP server platform that has powered Anthropic SDKs since early API days @AnthropicAI. Strategically, this points to continued vertical integration around developer ergonomics, SDK generation, and protocol surfaces, not just model quality.

• Revenue concentration around foundation model providers appears to be increasing: One post claimed that Anthropic and OpenAI’s share of AI model/application revenues generated by 34 top AI startups is rising, a signal that the ecosystem may be consolidating economically even as model choices proliferate @amir.

• Tooling and deployment curation remains in demand: The Turing Post’s roundup of 13 open-source tools for foundation model deployment—including vLLM, TGI, SGLang, llama.cpp, Ollama, BentoML, Kubeflow, MLflow and others—was one of the more practically useful curation posts in the set @TheTuringPost. Meanwhile, Papers With Code is being revived with AI-agent-assisted parsing of methods, leaderboards, and SOTA tracking, underscoring renewed focus on research discoverability @NielsRogge.

Top Tweets (by engagement)

• Cursor’s Composer 2.5 + bigger training push: The highest-signal high-engagement product news was Composer 2.5 and Cursor’s disclosure that it is training a much larger model from scratch with 10× more compute @cursor_ai, @cursor_ai.

• OpenAI/Anthropic product updates with developer impact: Sam Altman said ChatGPT improved significantly with the latest update @sama, while Anthropic shipped Fast mode defaulting to Opus 4.7 and prompt cache diagnostics in Claude Console @ClaudeDevs, @ClaudeDevs.

• Enduring research/engineering framing: Richard Sutton’s 26-word condensation of the Bitter Lesson—focus on methods for creating knowledge that scale with compute, like search and learning—was among the most engaged research-adjacent posts and resonated with many of the week’s themes around agent harnesses, search, and verifier-driven systems @RichardSSutton.

AI Reddit Recap

/r/LocalLlama + /r/localLLM Recap

1. LLM Safety Benchmarks and Abliteration Forensics

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This image from Amir Efrati summarizes the Decade of Cerebras:

Cerebras’ financials are now fully public, but the focus of discussions center around the supply:

More details below, and the Head Research Scientist of Cerebras speaks at AIE Singapore later today on the livestream:

AI News for 5/14/2026-5/15/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews’ website lets you search all past issues. As a reminder, AINews is now a section of Latent Space. You can opt in/out of email frequencies!

Headline Story: Cerebras IPO recap, technical details, and company journey

Cerebras returned to the timeline as an IPO story, with investors and adjacent infra voices framing the company as a long-running contrarian hardware bet that finally looks vindicated. The most directly relevant tweet is from investor Ishan N. Taneja, who said he “didn’t believe” early Cerebras claims, then concluded the skeptic he doubted “was totally right,” praising Cerebras for persistence, execution, and for having “built a banger chip,” while noting this was Hanabi’s first IPO @ishanit5. A second Cerebras-specific datapoint came from CNBC’s Deirdre Bosa quoting Cerebras CFO Bob Komin pushing back on the “small models only” narrative: Komin said Cerebras serves models of all sizes, that there is “no limit” to the size of models it can serve, and that Cerebras is currently serving trillion-parameter models, including internal OpenAI models, specifically naming “OpenAI 5.4 and 5.5” @dee_bosa. A nearby contextual tweet from Apoorv Vyas explicitly linked “the Cerebras IPO” to a Stanford discussion on compute scarcity, inference demand, routing, and open source, suggesting the IPO was being interpreted not as a generic capital-markets event but as part of the inference infrastructure cycle @apoorv03.