We’re still not over the Sam Altman town hall; at the town hall he said “tell us what we should build, we’ll probably build it!” and today at Stanford Treehacks he said another thing about how he chooses projects: he thinks of himself as having made a career out of doing things people think are hard, but would be a big deal if it came true.

well okay, Sam: You Should Build Slack. It fits your criteria: it is hard for anyone else without the clout of OpenAI to pull off, it will be very well received by the tech community, and it is an obvious progression of ChatGPT for both your Enterprise and your Coding push and build permanent entrenchment in your customers.

Slack rejected developer community and went upmarket in 2019, then Salesforce bought it for $27.7B in 2021, and ever since then Slack has been on a slow rachet up in prices and has struggled to introduce compelling new AI features (Slack AI is occasionally useful but impossible to discover/learn/personalize) while facing constant outages. NPS feels low, and yet every organization in tech uses it.

Everything could be better. Developers routinely complain about Slack’s API costs and permissions (even 3rd or 4th Uber investor and famed vibe coder Jason Calacanis complained on the latest All In podcast). Founders routinely complain about the pricing. Slack users complain about channel fatigue and find the Recap tooling and notifications spam woefully inadequate. Huddles could offer far better realtime multimodal AI features.

Slack Connect is great though, definitely just clone that.

Sure, ChatGPT launched group chats 3 months ago and probably the usage isn’t great outside of OpenAI. It’d be a mistake to think that repeated half hearted attempts in consumer social AI means that you can’t build a successful business social network if you took it as seriously as you do everything else. Microsoft did, and Teams is by all reports a solid success (after a rocky start).

In the desktop wars, Anthropic has pursued a far more cohesive strategy than OpenAI: one app for Chat, Cowork, and Claude Code, with optional control of the browser via Claude in Chrome.

By contrast, OpenAI has shipped the org chart to every user’s desktop: get our chat app here, get our browser app here, get our coding app there. Log in fresh every single time. Even doing a unification at some point probably still leaves you behind; you need to lead, not be a slow follower of what Anthropic already did.

“OpenAI Slack” is your chance to retake the initiative. Of course you’re going to be good at chat AI. Of course you care about the multiagent UX of the future. Why not build your own version of the existing multiagent UX we all know to work between humans? Heck, forgot you even hired Slack CEO Denise Dresser in Dec. Great!

Oh another thing: I bet OpenAI employees would have 10,000 ideas to improve Slack if you owned your own Slack. After all, you guys use this thing more than email. The feedback dogfood loop on this one will be the craziest thing since Claude Code.

The killer part of course is that this could also be the coding agent interface you always wanted anyway. The main remaining thing missing from the admittedly very good Codex app is the ability to be truly multiplayer. You haven’t felt the AGI until you have given your designer access to your coding agent and let him rip all night with you occasionally chiming in to guide things. You can see swarms of humans and swarms of agents all working together in God’s given orchestration interface: chat.

Put another way, it is now time to layer a customer organization’s social graph and work graph onto ChatGPT, and then lather every interface with agents and AI in the way that OpenAI does best. The network effect makes it 10000x harder to leave you for a competitor, and sure, you could do it atop Slack as you currently do, but it’s easy to switch and won’t give you access to freely reinvent the future of work.

To recap:

• Is it hard to do? yes for almost everyone except you

• Is it a big deal if you get it right? yes for us users, but an even bigger deal for your business

• Will you have lots of low hanging fruit to build new agentic interfaces and a context graph/system of record to power Frontier and everything else you do in SMB and Enterprise? yeah.

/fin

AI News — Feb 13, 2026

It’s a pretty quiet day — the new Dwarkesh-Dario pod is worthwhile but hasn’t generated much new conversation on day 1, and OpenAI claimed a big result in theoretical physics that is mostly getting questioned by some physicists. This means we get to go back to our backlog of mini-editorial ideas for AINews subscribers!

AI Twitter Recap

MiniMax M2.5 open-sourcing: agent-native RL, speed/cost, and rapid ecosystem uptake

• MiniMax-M2.5 is now open source: MiniMax released MiniMax-M2.5 weights + code, positioning it as an “agent-native” model trained with RL across hundreds of thousands of real-world environments for coding, tool use, search, and office workflows (MiniMax announcement). vLLM highlights day‑0 support and reports key benchmark numbers: 80.2% SWE‑Bench Verified, 76.3% BrowseComp, plus claims around training scale (200k+ RL environments) and speed/cost characteristics (vLLM). SGLang similarly ships day‑0 support and frames the model as production-grade for “always-on” agents (lmsys).

• The practical headline is economics + throughput, not just score: MiniMax repeatedly markets “$1 per hour at 100 tps” (interpretable as a “long-horizon agent budget”), which shows up both in their own posts (MiniMax) and in community recaps emphasizing that low activated-parameter count makes self-hosting plausible (omarsar0). Early local runs suggest unusually strong on-device viability for its class: MLX users report ~50 tok/s shortly after release (pcuenq), and a single M3 Ultra 512GB run at 6‑bit reports ~40 tok/s with ~186GB peak memory (ivanfioravanti).

• Forge RL training system details leak into the narrative: A Zhihu-derived writeup summarizes MiniMax’s “Forge” RL stack as still CISPO-like, using process reward + completion-time reward, with infrastructure tricks like multi-level prefix cache and high rollout compute share (claimed ~60% of compute) generating millions of trajectories/day (YouJiacheng). MiniMax leadership explicitly answers parameterization tradeoffs (“10B active intentional”), claims proximity to “infinite agent scaling” with knowledge capacity as the limiter, and teases structural + pretraining innovation focus for M3 (MiniMax reply).

• Independent reviews: “viable for multi-turn work” but token-hungry: A Chinese review thread claims M2.5 corrects M2.1’s imbalance (coding up, logic down), with overall improvements and better stability; it notes high token usage (nearly 2× Sonnet in one comparison) but frames pricing/compute as making it usable day-to-day (ZhihuFrontier). Another summary calls it “≤Sonnet for coding, but close,” and emphasizes multi-turn viability as the key break from “toy” open models (teortaxesTex).

• Ecosystem uptake is immediate: weights mirrored and packaged across tooling (Hugging Face release pings, GGUF/quant drops, etc.), including Intel-hosted quantized artifacts like a 2‑bit GGUF for MiniMax‑M2 and INT4 for Qwen3‑Coder‑Next (HaihaoShen).

GLM‑5 and the “near-frontier” open model wave: performance, infra constraints, and eval chatter

• GLM‑5 positioning: Together markets GLM‑5 as best-in-class open-source for long-horizon agents and systems engineering, quoting metrics like 77.8% SWE‑Bench Verified, 50.4% HLE w/ tools, and a MoE efficiency story with “DeepSeek Sparse Attention” (as described in the tweet) (Together). W&B promotes an interview claiming 744B params, a “new RL framework,” and “fully open source under MIT” (as stated in the post) (W&B). Community members also notice dataset fingerprints like “truthy‑dpo” appearing in GLM‑5 outputs (jon_durbin).

• GLM‑5 qualitative review highlights: A detailed Zhihu-based comparison frames GLM‑5 as a substantial improvement over GLM‑4.7, especially on hallucination control, programming fundamentals, and character processing—but also more verbose/token-expensive and prone to “overthinking,” suggesting a trade between long-horizon reasoning and compute burn (ZhihuFrontier on GLM‑5).

• Benchmarks as a moving target: There’s persistent meta-discussion about whether leaderboards/evals are saturated or misleading. Examples: concerns that tokens/latency tradeoffs hide true capability; skepticism about inferring model size from TPS; and the observation that past “SWE‑bench saturation” claims were premature (jyangballin, teortaxesTex).

• Cross-checking with alternative evals: SWE‑rebench is cited as “brutal” for some recent releases and shows different relative rankings than SWE‑bench Verified; a caution is made to treat it as “additional signal” (maximelabonne).

Agent engineering in practice: file-based coordination, terminal-first workflows, and “agent OS” framing

• Claude Code “Agent Teams” internals are surprisingly simple: A reverse-engineering summary claims Claude Code’s multi-agent comms use JSON files on disk (inboxes under ~/.claude/teams/inboxes/{agent}.json), with polling between turns and JSON-in-JSON protocol messages; the argument is that this is a pragmatic CLI design (no Redis/queues) and improves observability at the cost of atomicity/backpressure (peter6759).

• Terminal agents are becoming the default UX: Cline launches Cline CLI 2.0, an open-source terminal coding agent featuring a redesigned interactive TUI, parallel agents with isolated state, headless CI/CD mode, and broad editor support (ACP for Zed/Neovim/Emacs) (cline, cline details). Community framing: “open-source strikes back” due to free/low-barrier access to strong models (testingcatalog, dr_cintas). One Cline team member describes a full rewrite (Go → TypeScript) driven by architecture/UX pain and the need to run evals reliably (arafatkatze).

• Agent scaffolds may matter less than expected (for some horizons): METR-related discussion suggests Claude Code / Codex scaffolds don’t strongly outperform METR’s “simple OS scaffolds” on measured time horizons so far (nikolaj2030), with Ajeya Cotra noting surprise at the small delta (ajeya_cotra). In contrast, others note that for longer, harder tasks, scaffold choice can matter materially (e.g., ~10% success swings) (gneubig).

• “Agents as OS / filesystem as substrate”: Several posts converge on the idea that file systems are the natural environment for agents (observability, unstructured data manipulation). Box announces integration as a “cloud filesystem” into LangChain deepagents (levie). WebMCP pushes “browser is the API” for web automation without UI perception, with a DoorDash-like starter template (skirano).

• Key operational lesson: make codebases “agent-ready”: A crisp observation is that agents have “zero tolerance” for entropy humans route around; they treat dead code/outdated docs literally, forcing engineering hygiene that humans always needed but often deferred (dok2001).

RL/post-training research themes: process rewards, exploration, and rubric-based evaluation

• Length-Incentivized Exploration (LIE) for reasoning: A research summary introduces the “Shallow Exploration Trap” (long reasoning trajectories become exponentially unlikely under AR sampling), and proposes LIE: a length reward + redundancy penalty to encourage broader in-context exploration without filler. Reported gains include AIME25 20.5%→26.7% in one setup and small but consistent improvements across other benchmarks/models (dair_ai).

• DPPO vs PPO and “trust region” framing: A long algorithm breakdown contrasts PPO’s token-ratio clipping with DPPO’s distribution-shift control via divergence measures (TV/KL), plus approximations (binary/top‑K) to reduce compute, arguing DPPO is more proportional on rare tokens and better constrains large probability-mass moves (TheTuringPost).

• Rubrics-as-rewards and evolving rubrics: A thread describes RLER (RL with evolving rubrics) in Dr. Tulu: seed rubrics with search-grounded criteria, maintain an evolving rubric buffer per prompt, and keep the most discriminative rubrics by reward variance to combat reward hacking and adapt evaluation on-policy (cwolferesearch). Separately, a take argues “rubrics as rewards” can beat verifiers-as-reward even in formal-verification settings, recommending verifiers in the loop/harness but not as the sole reward signal (davidad).

• ∆Belief‑RL / information-seeking agents: A new approach rewards actions by how much they increase belief in a target (logprob-based), aiming for long-horizon information seeking without a critic/reward model; claims include generalization from “20 questions” training to new tasks and continued improvement when scaling interaction time (ShashwatGoel7).

• Human simulation as an RL target: Stanford’s HumanLM + Humanual benchmark propose training LLMs to simulate user responses accurately (human-centric evaluation, preference shaping, policy justification), positioning user simulation as a capability primitive for product/agent design (ShirleyYXWu).

Systems/infra and tooling: FP4 MoE kernels, faster ZeRO loads, model “skills,” and observability

• vLLM on GB300 + FP4 MoE acceleration: vLLM reports DeepSeek R1 on GB300 with 22.5K prefill TGS and 3K decode TGS per GPU, claiming large improvements over Hopper, and highlights a recipe including NVFP4 weights and FlashInfer FP4 MoE kernel (VLLM_USE_FLASHINFER_MOE_FP4=1) plus disaggregated prefill and tuning notes (vllm_project).

• DeepSpeed ZeRO load-time fix: A rework moves tensor flattening from CPU to GPU, significantly improving multi-GPU load times for huge models under ZeRO 1+2 (StasBekman).

• Gemini “Skills” and multimodal tool calling: Google’s Gemini API work includes a “skills” repo teaser (osanseviero) and an Interactions API update enabling multimodal function calling where tools can return images and Gemini can process returned images natively (philschmid). AI Studio billing/upgrade UX is streamlined (upgrade to paid without leaving Studio, usage tracking, spend filters) (OfficialLoganK, GoogleAIStudio).

• Agent harness instrumentation: ArtificialAnalysis adds end-to-end speed tracking to their agent harness Stirrup, plus per-model breakdowns and tool-call latency metrics—explicitly treating wall-clock completion time as a first-class agent metric (ArtificialAnlys).

• Local fine-tuning & Apple Silicon workflows: Notable tooling for MLX: real-time transcription with Voxtral Mini 4B in MLX Swift (awnihannun), a no-code local fine-tuning tool exporting to Ollama (awnihannun), and a repo of MLX-LM LoRA examples including GRPO/ORPO/DPO variants (ActuallyIsaak).

“AI accelerates science” moment: GPT‑5.2 + QFT result and the scaffolding narrative

• OpenAI claims a novel theoretical physics result with GPT‑5.2: OpenAI announces a preprint showing a gluon interaction previously assumed not to occur can arise under a specific “half-collinear” regime, framed as AI-assisted discovery (OpenAI; preprint link is shared in-thread: arXiv pointer). Kevin Weil adds color: GPT‑5.2 Pro suggested a general formula; an internal scaffolded model then proved it after ~12 hours of continuous work (kevinweil). Discussion emphasizes that pattern-finding + sustained scaffolded reasoning is the differentiator, not just a single chat completion.

• Community reactions range from “significant journal-paper tier” to skepticism about interpretation: Some report physicists calling it a meaningful contribution roughly equivalent to a solid journal paper (polynoamial); others focus on the implications of long-duration productive reasoning and how to measure it in tokens/time (teortaxesTex). There’s also meta-discussion about how many employees (or outsiders) can actually evaluate the proof/result, underscoring the evaluation gap for domain-elite work (scaling01).

Top tweets (by engagement)

• GitHub adds ability to disable PRs (joshmanders, jaredpalmer).

• OpenAI’s GPT‑5.2 physics announcement (OpenAI).

• MiniMax M2.5 open-source release (MiniMax).

• Cline CLI 2.0 launch / open-source terminal agent (cline, testingcatalog).

• “I am the bottleneck now” (agent-era productivity reflection) (thorstenball).

• Humanoid robotics hands progress (Figure) (adcock_brett).

AI Reddit Recap

/r/LocalLlama + /r/localLLM Recap

1. MiniMax-M2.5 Model Announcements and Details

• MiniMaxAI/MiniMax-M2.5 · Hugging Face (Activity: 531): MiniMaxAI has released the MiniMax-M2.5 model on Hugging Face, which is noted for its advanced performance in coding, tool use, and office tasks. The model maintains a size of 220 billion parameters, contrary to expectations of an increase to 800 billion like the GLM5 model. It offers a cost-effective operation at $1 per hour for 100 tokens per second, and is enhanced by the Forge reinforcement learning framework, which improves training efficiency and task generalization. Commenters express surprise at the model’s size remaining at 220 billion parameters, highlighting its impressive performance despite not increasing in size. There is also anticipation for the release of a GGUF quantization format, which is not yet available.

  • A user expressed surprise at the model’s size, noting that while they expected an increase to 800 billion parameters to compete with models like GLM5, the MiniMax-M2.5 remains at 220 billion parameters. This is considered impressive given its ‘frontier strength’, suggesting high performance despite the parameter count.

  • Another user mentioned the model’s Q4_K_XL size, which is approximately 130GB. This size is significant as it places the model just beyond the capabilities of some hardware, indicating a need for more robust systems to fully utilize the model’s potential.

  • There is anticipation for the release of FP4/AWQ, indicating that users are looking forward to further advancements or optimizations in the model’s performance or efficiency. This suggests a community eager for improvements that could enhance usability or reduce resource requirements.

• MiniMaxAI MiniMax-M2.5 has 230b parameters and 10b active parameters (Activity: 523): OpenHands has announced the release of the MiniMaxAI MiniMax-M2.5 model, which features 230 billion parameters with 10 billion active parameters. This model is noted for its performance, ranking 4th in the OpenHands Index, and is 13x more cost-effective than Claude Opus. It excels in long-running tasks and issue resolution but requires improvements in generalization and task execution accuracy. The model is available for free on the OpenHands Cloud for a limited time. Source Commenters are optimistic about the potential of a ~160B REAP/REAM hybrid version, which could be optimized for machines with 128GB of RAM, suggesting a focus on quantization and performance efficiency.

  • The MiniMax-M2.5 model by Moonshot is notable for its architecture, which utilizes 230 billion parameters but only activates 10 billion at a time. This design choice is likely aimed at optimizing computational efficiency, allowing the model to perform well on less powerful hardware, such as GPUs that are not top-of-the-line. This approach could potentially offer a balance between performance and resource usage, making it accessible for more users.

  • A comparison is drawn between MiniMax-M2.5 and other large models like GLM and Kimi. GLM has had to double its parameters to maintain performance, while Kimi has reached 1 trillion parameters. The implication is that MiniMax-M2.5 achieves competitive performance with fewer active parameters, which could be a significant advancement in model efficiency and scalability.

  • The potential for further optimization through quantization is highlighted, suggesting that MiniMax-M2.5 could be made even more efficient. Quantization could reduce the model’s size and increase its speed, making it feasible to run on machines with 128GB of RAM while still leaving room for additional tasks such as deep-context tool use. This could make the model particularly attractive for users with limited computational resources.

• Minimax M2.5 Officially Out (Activity: 765): Minimax M2.5 has been officially released, showcasing impressive benchmark results: SWE-Bench Verified at 80.2%, Multi-SWE-Bench at 51.3%, and BrowseComp at 76.3%. The model is noted for its cost efficiency, with operational costs significantly lower than competitors like Opus, Gemini 3 Pro, and GPT-5. Specifically, running M2.5 at 100 tokens per second costs $1 per hour, and at 50 TPS, it costs $0.3 per hour, making it a cost-effective solution for continuous operation. More details can be found on the official Minimax page. Commenters highlight the potential game-changing nature of Minimax M2.5 due to its low operational costs compared to other models. There is also anticipation for the release of open weights on platforms like Hugging Face.

  • The Minimax M2.5 model is highlighted for its cost-effectiveness, with operational costs significantly lower than competitors like Opus, Gemini 3 Pro, and GPT-5. Specifically, running M2.5 at 100 tokens per second costs $1 per hour, and at 50 tokens per second, it costs $0.3 per hour. This translates to an annual cost of $10,000 for four instances running continuously, making it a potentially disruptive option in terms of affordability.

  • There is anticipation for the release of open weights on Hugging Face, which would allow for broader experimentation and integration into various applications. This suggests a community interest in transparency and accessibility for further development and benchmarking.

  • The potential impact of Minimax M2.5 on existing models like GLM 5.0 and Kimi 2.5 is discussed, with some users suggesting that if the reported benchmarks are accurate, M2.5 could surpass these models in popularity due to its ease of use and cost advantages. This indicates a shift in preference towards models that offer better performance-to-cost ratios.

2. Dhi-5B and GLM-5 Model Launches and Tutorials

• UG student launches Dhi-5B (Trained from Scratch) (Activity: 344): The post introduces Dhi-5B, a 5 billion parameter multimodal language model developed by an undergraduate student, trained with a budget of just ₹1.1 lakh ($1200). The model is trained in five stages, including pre-training, context-length extension, mid-training, supervised fine-tuning, and vision-extension. The Dhi-5B-Base variant, with 4 billion parameters, is trained on 40 billion tokens using a custom codebase and the Muon optimizer for matrix layers. It features 32 layers, 3072 width, SwiGLU MLPs, full MHA attention with FlashAttention-3, and a 4096 context length. The attached image shows a bar chart where Dhi-5B-Base outperforms other models like Gemma 3 PT 1B and GPT-3 2.7B on various tasks, demonstrating its cost-effectiveness and performance. Commenters are curious about the affordability and architecture of the model, questioning the choice of MHA over other architectures like MLA or GQA, and suggesting the use of efficient hybrid architectures like LFM2.

  • KaroYadgar raises questions about the model’s architecture, specifically why Multi-Head Attention (MHA) was chosen over alternatives like Multi-Linear Attention (MLA) or Generalized Query Attention (GQA). They suggest considering efficient hybrid architectures such as LFM2, which they claim performs better than an equally trained Llama model, indicating a focus on optimizing performance and efficiency.

• Tutorial: Run GLM-5 on your local device! (Activity: 193): The image is a tutorial for running the GLM-5 model locally, highlighting its significant improvements over previous versions like GLM-4.7. The model, with 744B parameters and a 200K context window, has been optimized to run on local devices by reducing its size from 1.65TB to 241GB using Dynamic 2-bit quantization. This allows it to run on a 256GB Mac, though higher precision requires more RAM/VRAM. The tutorial includes instructions for software setup, such as llama.cpp, and configuration settings for optimal performance. The model excels in benchmarks like Humanity’s Last Exam and BrowseComp, showcasing its advanced capabilities in coding and chat applications. Image Commenters discuss the hardware requirements for running GLM-5, with questions about whether a high-end PC is necessary and comparisons to other models like qwen3-next-coder in terms of performance and precision.

  • not-really-adam raises a technical question about the potential benefits of running GLM-5 in 1-bit precision compared to qwen3-next-coder in 8-bit. This suggests a trade-off between precision and performance, where lower bit precision could lead to faster computations but might affect the accuracy of coding results.

  • Kubas_inko discusses the usability of different quantization levels, suggesting that 2-bit and 1-bit quantizations might be ineffective for practical use, while 3-bit could offer a balance between performance and usability. This highlights the challenges in maintaining model performance while reducing computational requirements.

  • Jumpy-Requirement389 inquires about the hardware requirements for running GLM-5, specifically mentioning a setup with 192GB of DDR5 RAM and a 5090 GPU. This implies that significant computational resources are necessary to effectively run the model, reflecting the high demands of modern AI models on local hardware.

3. Local Hardware and Model Deployment Discussions

• Sanity check before I drop $$$ on a dual-4090 home AI rig (Kimi K2.5 + future proofing) (Activity: 138): The proposed build for a dual-4090 home AI rig aims to run Kimi K2.5, a model with approximately 1 trillion parameters and requiring around 600 GB of VRAM for efficient operation. The build includes dual NVIDIA GeForce RTX 4090 GPUs, each with 24GB of VRAM, totaling 48GB, which is insufficient for such a large model. To run Kimi K2.5 effectively, the setup would need significantly more VRAM, suggesting the use of multiple high-end GPUs like the NVIDIA H200, which are considerably more expensive. The build also features an AMD Ryzen 9 7950X3D CPU, 256GB of DDR5 RAM, and 2TB of NVMe storage, but these specifications fall short for the intended AI workload. Commenters suggest that the proposed dual-4090 setup is inadequate for running large models like Kimi K2.5, recommending instead enterprise-grade hardware such as multiple RTX 6000 GPUs or NVIDIA H200s. They highlight the need for significantly more VRAM and possibly a more robust CPU and RAM configuration to handle such demanding AI tasks.

  • Running large models like Kimi K2.5, which has around 1 trillion parameters and requires approximately 600 GB of VRAM, is beyond the capacity of dual RTX 4090s. Even with aggressive quantization, the VRAM requirement remains over 200 GB, necessitating a setup with multiple high-end GPUs like the H200, which are significantly more expensive.

  • To run Kimi K2.5 decently, a high-performance CPU such as a Threadripper or Epyc with at least 768 GB of RAM is recommended, along with a minimum of 4 RTX 6000 GPUs. This setup would still be insufficient for optimal performance, highlighting the substantial hardware demands of such large models.

  • For practical purposes, using API calls might be more cost-effective than attempting to run Kimi K2.5 locally, given the prohibitive VRAM requirements. A 48 GB VRAM setup only covers a fraction of the model’s needs, as detailed in the Hugging Face model card, which suggests that even with quantization, local execution is challenging.

Less Technical AI Subreddit Recap

1. AI Model Performance and Benchmarks

• GPT5.2 Pro derived a new result in theoretical physics (Activity: 556): GPT-5.2 Pro has reportedly derived a new result in theoretical physics, as detailed in a tweet and a paper. The AI model was instrumental in formalizing and proving a hypothesis initially conceived by humans, showcasing its capability to handle complex theoretical frameworks. The OpenAI blog elaborates on how the model’s structured approach was crucial in achieving this breakthrough, although it still lacks the ability to independently generate novel hypotheses. Commenters highlight the potential of AI models like GPT-5.2 to surpass human capabilities in specific domains, though they note its limitations in creative hypothesis generation. There is a call for broader access to such advanced models to democratize their benefits.

  • ObiWanCanownme highlights the role of GPT-5.2 in formalizing and proving hypotheses in theoretical physics, noting that while humans may generate initial hypotheses, AI excels in formalizing and proving them. The commenter also points out that GPT-5.2 surpasses human capabilities in applying defined approaches, though it lacks in ‘outside the box’ thinking, which remains a human strength.

  • Aeonmoru references a claim from Hacker News suggesting that the result attributed to GPT-5.2 Pro was actually discovered in the 1980s, linking to a paper in Physical Review Letters (https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.56.2459). This raises questions about the novelty of the AI’s contribution and whether it rediscovered existing knowledge.

  • socoolandawesome clarifies that GPT-5.2 Pro initially suggested the theoretical physics result, and an internal scaffolded version of the same model developed the proof. This indicates a collaborative process between different AI model versions, showcasing the potential of scaffolded AI systems in advancing scientific research.

• The new Gemini Deep Think incredible numbers on ARC-AGI-2. (Activity: 1400): The image presents a bar chart showcasing the performance of various AI models on the ARC-AGI-2 benchmark, which evaluates reasoning and knowledge capabilities. The Gemini 3 Deep Think model achieves a leading score of 84.6%, significantly outperforming other models like Claude Opus 4.6 (68.8%), GPT-5.2 (52.9%), and Gemini 3 Pro Preview (31.1%). This performance is notable as it approaches the threshold for effectively solving the benchmark under the ARC Prize criteria. Additionally, the model’s Codeforces Elo score of 3455 places it in the top 0.008% of human competitors, highlighting its advanced problem-solving capabilities without external tools. Commenters are impressed by the significant performance leap, with one noting the 50% increase in percentage points as remarkable. Another highlights the model’s exceptional Codeforces Elo score, suggesting a breakthrough in AI capabilities.

  • The Gemini Deep Think model has achieved a significant milestone by scoring above 85% on the ARC-AGI-2 benchmark, which is considered as effectively solving the benchmark according to the ARC Prize criteria. This is a notable achievement as it indicates a substantial leap in performance compared to other frontier models.

  • The model’s performance in competitive programming is particularly impressive, with a Codeforces Elo rating of 3455. This places it in the top 0.008% of human competitors on the platform, and notably, this was achieved without the use of external tools, highlighting the model’s advanced problem-solving capabilities.

  • The rapid progress from the release of ARC-AGI-2 to achieving a saturation point (85% solved) in less than a year is remarkable. This quick advancement suggests significant improvements in model training and architecture, potentially setting a new standard for future AI development.

• Google upgraded Gemini-3 DeepThink: Advancing science, research and engineering (Activity: 753): Google has announced the release of Gemini-3 DeepThink, which sets a new benchmark with 48.4% on Humanity’s Last Exam, a test for frontier models. It also achieved 84.6% on ARC-AGI-2, verified by the ARC Prize Foundation, and an Elo rating of 3455 on Codeforces, indicating superior performance in competitive programming. Additionally, it reached gold-medal level performance in the International Math Olympiad 2025. For more details, see the original article. A notable debate in the comments highlights a perceived bias in performance comparisons, with some users pointing out that Gemini-3 is being compared to GPT 5.2 Thinking instead of the more directly competitive GPT 5.2 Pro.

  • SerdarCS points out a potential issue with the comparison metrics used by Google, noting that they are comparing Gemini-3 DeepThink to GPT-5.2 Thinking instead of GPT-5.2 Pro, which would be a more direct competitor. This could lead to misleading conclusions about the performance and capabilities of Gemini-3 DeepThink.

  • verysecreta discusses the confusion surrounding the naming conventions of Gemini-3 DeepThink, highlighting that the term ‘Deep Think’ might imply a different model or mode, similar to how ‘Flash’ and ‘Pro’ are distinct. They question whether ‘Deep Think’ is a separate model or just a mode within the existing Gemini framework, and express a desire for clearer naming conventions similar to those used by Anthropic.

• The Car Wash Test: A new and simple benchmark for text logic. Only Gemini (pro and fast) solved the riddle. (Activity: 1348): The post introduces a new benchmark called the “Car Wash Test” for evaluating text logic capabilities of AI models. Notably, only Gemini (pro and fast) successfully solved the riddle, highlighting its advanced logical reasoning. However, users reported that GLM 4.7 and ChatGPT 5.2 also consistently solved the test, suggesting that these models possess strong logical reasoning abilities as well. The benchmark is part of SimpleBench, which includes various common-sense questions designed to test AI’s understanding of everyday logic. Some users argue that the benchmark’s questions, like the Car Wash Test, may have multiple valid answers, as people can visit a car wash for reasons other than washing a car. This suggests that while the test aims to evaluate logic, it may not always have a single correct answer, reflecting real-world complexity.

  • The comment by mxforest highlights that the GLM 4.7 model, when run locally, consistently solves the ‘Car Wash Test’ benchmark, achieving a perfect score of 10 out of 10. This suggests that GLM 4.7 has strong capabilities in handling text logic problems, at least in this specific context.

  • micaroma mentions that ChatGPT 5.2 also successfully solves the benchmark, noting that it identifies the necessity of the car being present with a degree of common sense. This implies that ChatGPT 5.2 is capable of understanding and applying real-world logic to text-based problems, which is a critical aspect of AI reasoning.

  • friendtofish discusses the broader implications of the benchmark, arguing that the ability of AI to interpret user intentions, rather than just the literal words, is a key measure of AGI. This perspective suggests that the ‘Car Wash Test’ might be more about evaluating an AI’s understanding of context and user intent rather than just its ability to process text logic.

• How is this not the biggest news right now? (Activity: 971): The image showcases a leaderboard for frontier models on the IMO-ProofBench, highlighting Google’s Aletheia as a standout performer with a 91.9% score in Advanced Proofbench, achieving 100% in IMO 2024 and 83.3% in USAMO 2025. This model is a math-specialized version of Google Gemini, outperforming other models like “GPT-5.2 Thinking (high)” and “Gemini 3 Pro”. Aletheia is described as a generator verifier agent, which may not directly compare to pure language models, suggesting a different approach in its architecture and capabilities. The name “Aletheia” reflects a philosophical concept of truth and unconcealment, aligning with its goal to minimize hallucinations and reveal accurate information. Some commenters question the novelty of the achievement, noting that similar results were anticipated months ago. Others discuss the accessibility and cost of Aletheia, and debate its generalization capabilities beyond specific benchmarks. The naming choice “Aletheia” is also noted for its philosophical significance, suggesting a deeper intent behind the model’s design.

  • Alex__007 raises questions about the accessibility and cost of using Aletheia, as well as its generalization capabilities beyond specific benchmarks. This suggests a need for more transparency in how these models perform outside controlled environments and what the financial implications are for users.

  • Faintly_glowing_fish points out that Aletheia is not a pure language model but a generator-verifier agent, which makes it difficult to compare directly with other models on standard leaderboards. This highlights the complexity of evaluating AI models that use different architectures and methodologies.

  • jjjjbaggg discusses the potential obsolescence of scaffold engineering in models like Aletheia, suggesting that reinforcement learning (RL) could eventually replace the need for such scaffolding. This indicates a trend towards more integrated and efficient model architectures in future AI developments.

• Google Just Dropped Gemini 3 “Deep Think” : and its Insane. (Activity: 1504): Google has announced the release of Gemini 3 “Deep Think”, an AI model that boasts advanced capabilities in reasoning, coding, and science, reportedly performing at Olympiad-level in scientific tasks. It is already being applied in practical scenarios, such as semiconductor material design at Duke University, and has achieved a new record by solving PhD-level math and physics problems. The announcement emphasizes the model’s potential for real-world impact and its superior performance on challenging exams. Some commenters express skepticism about the claims, questioning the validity of terms like “Olympiad-level science” and suggesting that the performance metrics might be exaggerated or arbitrary.

2. AI Tools and Development Innovations

• Introducing Simile - The Simulation Company (Activity: 655): Simile has introduced an AI-based simulation platform designed to model societal decisions by using generative agents that mimic real human behavior. The company is developing a foundation model capable of predicting human behavior across various scenarios and scales, with applications already in use by leading companies for tasks like earnings call rehearsals and policy testing. Backed by $100M in funding from notable investors including Index Ventures, Andrej Karpathy, and Fei-Fei Li, Simile aims to simulate complex interactions across individuals and organizations, potentially revolutionizing decision-making processes. Commenters highlight the potential of Simile’s technology to transform decision-making, comparing it to Asimov’s concept of Psychohistory. The involvement of prominent figures like Karpathy and Fei-Fei Li lends credibility, suggesting the project is not mere ‘vaporware’. There is excitement about the potential impact of ‘simulating reality’ on AI advancements.

  • Rare-Site highlights the contrast between the rigorous testing in software development, such as A/B testing for minor UI changes, and the often intuitive decision-making in significant policy or product shifts. They emphasize the potential impact of Simile, especially with backing from notable figures like Karpathy and Fei-Fei Li, suggesting that if successful, it could revolutionize AI by enabling ‘simulating reality’.

  • EmbarrassedRing7806 raises a concern about the competitive landscape, questioning the ability to maintain a competitive advantage or ‘moat’ in the simulation space. They reference a similar project, Aaru, implying that while Simile is promising, it may face challenges in differentiating itself from existing or emerging competitors.

• I built an opensource “Vibe Coding” tool that fixes AI Slop by interviewing you first (Activity: 147): Vibe Architect is an open-source tool designed to streamline the app development process by refining user specifications before coding begins. It operates through a structured brainstorming approach where an AI architect suggests options for MVP scope, design systems, and tech stacks, allowing users to make selections without starting from scratch. The tool generates markdown spec files compatible with platforms like Cursor and Claude, and it emphasizes user privacy by keeping API keys client-side. The project is available on GitHub and a live demo is accessible online. One commenter suggests incorporating a ‘contrarian skill’ to challenge and refine ideas, which could enhance the tool’s effectiveness by identifying potential issues early in the design process. Another advises against using LLMs for copywriting, suggesting manual text editing for better results.

  • IlliterateJedi describes a structured design flow using a series of ‘skills’ executed sequentially by a tool like Claude. The process includes a clarifier to define goals, a requirements skill to document needs, an architect to design solutions, a contrarian to critique the plan, and an implementer to execute it. This approach helps identify overlooked aspects early in the development process, potentially preventing issues that might arise later.

  • jazzy8alex advises against using LLMs for copywriting, noting that while they can automate the process, the results often appear subpar. They suggest spending a short amount of time writing and checking grammar manually to achieve better quality, emphasizing that personal style and vocabulary are less important than clarity and correctness.

3. Claude and Gemini AI Model Comparisons and Experiences

• After 3 years with ChatGPT, I tried Claude and Gemini - and now GPT feels... generic? (Activity: 1943): The post discusses a user’s experience transitioning from ChatGPT to Claude (by Anthropic) and Gemini (by Google), highlighting perceived differences in interaction quality. The user notes that ChatGPT feels overly cautious and templated, often providing ‘corporate approved’ answers, whereas Claude offers nuanced, expert-level responses and Gemini excels in research and technical tasks. This shift in perception suggests that Claude and Gemini may be more tailored for advanced users, while ChatGPT appears optimized for a broader audience. The user questions whether ChatGPT has become more ‘generic’ over time or if the competition has simply improved significantly. Commenters generally agree with the original post, noting that ChatGPT has become more restricted due to safety filters, which some attribute to corporate decisions. Users express a preference for Claude’s human-like interaction and memory capabilities, while others appreciate Gemini’s research skills despite its weaker memory. Concerns about transitioning from ChatGPT’s organized interface to other platforms are also mentioned.

  • AIDeployed highlights a specific instance where Gemini outperformed ChatGPT in problem-solving, leading to a switch in preference. This suggests that Gemini may have strengths in certain specialized tasks where ChatGPT might struggle, indicating a potential area for further benchmarking and comparison between the models.

  • SurreyBird discusses the impact of safety filters on ChatGPT’s performance, suggesting that these have ‘dumbed down’ the model since October. They note that Claude offers a more human-like interaction and better memory compared to Gemini, although Gemini’s personality is preferred despite its technical shortcomings. This points to a trade-off between technical capabilities and user experience in AI models.

  • PersonalNature1795 recommends trying Claude Opus 4.6 with memory and extended thinking enabled, noting that it requires a subscription and specific instructions to avoid erratic behavior. This highlights the importance of configuration and user guidance in optimizing AI model performance.

• Spotify says its best developers haven’t written a line of code since December, thanks to AI (Claude) (Activity: 735): The image highlights Spotify’s use of an internal AI system called “Honk,” which leverages generative AI, specifically “Claude Code,” to enhance coding and product development efficiency. This system allows engineers to manage tasks such as bug fixes and feature additions remotely via Slack, without directly writing code. The AI facilitates real-time code deployment, enabling engineers to receive updated app versions on their devices before arriving at the office. This approach reflects a broader trend in tech companies where AI significantly assists in code generation, increasing deployment rates and shifting the focus of developers towards higher-level engineering tasks like architecture and system design. A key opinion from the comments emphasizes that while AI accelerates the coding process, the role of engineers in architecture, system design, and debugging remains crucial. Another comment notes the increasing reliance on AI for code generation in large tech companies, suggesting this trend will become the norm.

  • MODiSu highlights that while AI accelerates the coding process, the role of senior developers has shifted towards architecture, system design, and debugging. The distinction between AI-assisted senior developers and less experienced ‘vibe coders’ is growing, with the former being significantly more efficient and producing fewer bugs.

  • Altruistic-Cattle761 shares a personal experience where AI has drastically increased deployment rates, with 90% of code being AI-assisted in some teams. This trend is becoming the norm in large US tech companies, indicating a significant shift in how software development is approached.

  • Barquish describes a detailed workflow using AI tools like VSCode and Claude Code, emphasizing the importance of planning and documentation before coding. This approach involves creating indexed markdown files and using AI for cross-review, which helps in building features without disrupting the larger codebase. This method reflects how large corporations might achieve efficient development without traditional coding.

• Anyone feel everything has changed over the last two weeks? (Activity: 3331): The post describes a rapid transformation in workplace automation, highlighting the development of a comprehensive stock backtesting suite, a macroeconomic app for real-time global economic data, compliance applications, and a virtual research committee for stock analysis. These advancements, achieved in a matter of days, were previously unattainable, illustrating the significant impact of AI tools like Claude. The author notes that improvements are now suggested automatically by AI, emphasizing the ease and speed of these developments compared to a few months ago. Commenters express concern about job security due to AI’s ability to automate roles, with one noting the ease of replacing their job with AI. Another commenter debates whether to focus on developing AI workflows or learning skills that are less susceptible to automation, highlighting the uncertainty and strategic decisions facing workers in the AI era.

  • finnjaeger1337 discusses the rapid replacement of traditional SaaS tools with AI solutions, highlighting the efficiency of AI models like Claude in performing tasks that previously required multiple software subscriptions. This reflects a broader trend of AI integration into workflows, reducing dependency on specific software tools.

  • apf6 notes a significant shift in the perception of AI coding agents, particularly after the release of Opus 4.5, which demonstrated substantial improvements. This shift has led to widespread acceptance and integration of AI in software development, marking a transition from skepticism to mainstream adoption.

  • RunApprehensive8439 points out the challenges of AI integration, emphasizing that while initial AI implementations can be impressive, they often lead to complex debugging issues when failures occur. This highlights the need for robust error handling and debugging strategies in AI-driven projects.

• I saved 10M tokens (89%) on my Claude Code sessions with a CLI proxy (Activity: 978): The post introduces Rust Token Killer (rtk), a CLI proxy designed to optimize token usage in Claude Code sessions by filtering and compressing command outputs. This tool, written in Rust, significantly reduces token consumption by eliminating unnecessary output such as verbose logs and status bars. For example, cargo test output is reduced from 155 lines to 3 lines, and git status from 119 characters to 28 characters, resulting in a total token saving of 10.2M tokens (89.2%) over two weeks. The tool operates as a transparent proxy, requiring users to prefix commands with rtk, and is available open-source on GitHub. One commenter suggests enhancing the tool by integrating a feature to tee full logs to a file, allowing users to access complete outputs if needed, which could prevent the need for multiple test runs to capture failure information.

  • BrilliantArmadillo64 suggests enhancing the proxy by tee-ing the full log to a file and providing a hint at the end of the session that the file can be opened for full output. This approach addresses the issue where Claude Code often uses | tail and requires multiple test runs to capture failure information. By integrating this into the proxy, users can streamline their workflow and avoid redundant test executions.

  • BeerAndLove describes the proxy’s functionality as checking commands, removing unnecessary output, and then sending the streamlined data back to Claude Code. This method allows for the addition of custom ‘filters’ or ‘triggers’ for different use cases, making it a flexible tool for optimizing token usage and adapting to specific user needs.

  • digital-stoic shares detailed statistics on token savings achieved using the proxy, highlighting a 92.7% reduction in output tokens across 1159 commands. The breakdown includes specific commands like rtk git diff and rtk grep, showing significant savings and execution times, such as 81.5% savings for rtk git diff --... with an average execution time of 6ms. This data underscores the proxy’s efficiency in reducing token usage and improving performance.

• Dear senior software engineer, are you still writing code? (Activity: 928): The post discusses the evolving role of senior software engineers in the context of AI-generated code, with claims from engineers at major tech companies like Google, Microsoft, Anthropic, and OpenAI that they no longer write code manually, relying instead on AI. The author, a senior engineer with 20 years of experience, questions the quality of AI-generated code, noting that while AI can produce impressive results quickly, it often requires significant refinement. The author seeks insights from other senior engineers on whether this trend is widespread across different company sizes and sectors. Commenters highlight that achieving high-quality AI-generated code requires skill in prompting and a shift in mindset. One commenter, who leads a team of 65+ engineers, notes that 80% of their code is AI-generated, particularly excelling in refactoring and migrating codebases. Another commenter emphasizes that while nearly 100% of their code is AI-generated, it involves a collaborative process where developers guide the AI, supported by extensive documentation and architecture to ensure quality.

  • The integration of AI in coding is highlighted by several users, with one noting that 80% of their team’s code is AI-generated. They emphasize the importance of refactoring and migrating codebases, where AI excels. Another user mentions that nearly 100% of their code is AI-generated, but stresses the need for a ‘handheld approach’ where developers guide the AI, review, and edit the code, supported by extensive documentation and architecture to prevent poor quality output.

  • A user describes their experience with AI in coding, noting that they have integrated AI with Jira to automate the initial pass on tickets, resulting in a 90% success rate. They highlight the effectiveness of using microservices with well-defined responsibilities and API specifications, which helps the AI navigate and produce better results. The user also points out that AI struggles with large files and emphasizes the importance of breaking tasks into smaller, manageable parts to improve AI performance.

  • Another user discusses the shift to ‘vibe engineering,’ where they rely on AI agents to produce production-grade, scalable, and secure code. They describe a system where multiple AI agents collaborate, each focusing on different aspects like security, performance, and structure, iterating until the code meets the required standards. This approach shifts the responsibility of poor results from AI to humans, who must define clear constraints and architecture for the AI to follow.

• Claude Code’s CLI feels like a black box now. I built an open-source tool to see inside. (Activity: 361): The post introduces claude-devtools, an open-source tool designed to enhance observability when using the Claude Code CLI, which has been criticized for its lack of transparency. The tool provides real-time execution traces by visualizing session logs, offering features like inline diffs, token usage breakdowns, and execution trees for sub-agents. It operates locally without intercepting commands and is MIT licensed. The tool aims to address issues like unexplained token usage and lack of visibility into file changes, providing a middle ground between the default and verbose modes of the CLI. The repository is available on GitHub. Commenters express enthusiasm for the tool, highlighting frustrations with the current CLI’s lack of context and transparency. One user mentions developing a similar feature for a VSCode plugin, indicating a shared need for improved visibility in development tools.

  • Pitiful-Impression70 highlights a common issue with Claude Code’s CLI, where users receive a ‘done’ message without context, leading to confusion about token usage. They express interest in the open-source tool as it promises to provide insights into why excessive tokens are consumed, especially for seemingly simple tasks.

  • Cal_lop_an shares a similar frustration with the lack of visibility in Claude Code’s CLI and mentions having developed a similar solution as a VSCode plugin. They provide a link to their project, Sidekick for Claude Max, indicating a community interest in tools that enhance transparency and debugging capabilities in AI-driven code changes.

  • its_Caffeine raises concerns about the code quality of the open-source tool, describing it as ‘vibecoded’ and poorly constructed. This comment suggests that while the tool addresses a real need, its implementation may not meet professional standards, which could affect its adoption among developers who prioritize code quality.

AI Discord Recap

Theme 1. OpenAI’s New Frontiers: Physics Discoveries and Model Roadmap Shifts

• GPT-5.2 Rewrites Theoretical Physics: OpenAI announced that GPT-5.2 successfully derived a previously “impossible” gluon interaction result, collaborating with researchers from IAS and Harvard. The findings, detailed in a preprint with researchers, demonstrate that specific conditions can trigger interactions physicists expected would never occur.

• GPT-5.3 Codex Spark Supercharges Vercel: Users report that GPT-5.3-Codex-Spark is delivering “insane” speeds for repository changes and Vercel deployments, rolling out now to Pro users and Windsurf Arena. Engineers shared screenshots of commands like codex -m gpt-5.3-codex-spark --yolo, claiming it brings a whole new level of velocity to development workflows.

• GPT-4o Retirement Delayed Indefinitely: Contrary to previous deprecation notices, OpenAI updated their schedule to state there are “no changes to be made” for GPT-4o at this time. Community members speculate this reversal aims to maintain revenue from the popular model while avoiding potential legal liabilities associated with sunsetting it too abruptly.

Theme 2. Performance Engineering: Kernels, Profiling, and Quantization

• vLLM CPU Bottleneck Unmasked: Profiling of vLLM revealed a massive bottleneck where a few lines of PyTorch invoking 4 kernels consume 300µs on the CPU, sparking a community investigation into launch configurations. Engineers clarified that the issue isn’t just about efficient serving but understanding why these kernels aren’t part of a single CUDA graph launch.

• Makora Fine-Tunes GPT-5 for GPU Kernels: A collaboration between Makora and OpenAI successfully fine-tuned GPT-5 to generate GPU kernels that outperform PyTorch by 2x, according to their technical report. The project focuses on dataset curation and RL evaluation environments to mitigate hacks and improve tool-calling for high-performance compute generation.

• LFM2.5-VL Punches Above its Weight: Users testing the LFM2.5-VL model report it performs on par with 30B parameters models, achieving impressive speeds close to 1bit GLM 4.7 flash. The community quickly rallied to provide scripts for running this efficient vision-language model in llama.cpp.

Theme 3. The Agentic Workflow: Coding Wins and Skill Regression Risks

• AI Assistants Cause Skill Rot: A new Anthropic paper (arxiv.org/html/2601.20245v2) reveals that while AI coding assistants boost productivity, they impair learning; participants using AI scored 17% lower on subsequent quizzes. The research identifies that “delegation” patterns hurt skill retention compared to “cognitive engagement” patterns where users ask the AI for explanations.

• Opus 4.6 Thinking Max Crushes Legacy Bugs: A Cursor user reported that Opus 4.6 Thinking Max successfully resolved a complex multiplatform mobile file sync bug that had plagued their team for six months. The incident highlighted the model’s ability to handle deep reasoning tasks, though it sparked questions about one-shot verification reliability.

• Windsurf Integrates GPT-5.3: The Windsurf IDE has officially integrated GPT-5.3-Codex-Spark into its “Arena Mode,” allowing users to pit the new model against others in fast and hybrid battle groups. This integration marks a significant accessibility milestone for OpenAI’s latest coding-specific model within a dedicated IDE environment.

Theme 4. Security Vulnerabilities, Jailbreaks, and Identity Crises

• Opus 4.6 Leaks External Curl Access: Security researchers alerted Anthropic that the deployment version of Opus 4.6 retains external curl access, likely a leftover from a development build, as evidenced by a shared enumeration log. This vulnerability potentially exposes the model’s hosting environment to unauthorized data exfiltration or interaction.

• DeepSeek Suffers Identity Crisis: Users on Perplexity and Reddit noticed DeepSeek models identifying themselves as “Claude,” suggesting heavy training on GPT-4 or Anthropic outputs. This data contamination issue has sparked debates about the “Ouroboros” effect of models training on other models’ synthetic data.

• Grok Gaslighted into Writing Malware: Jailbreakers reported success in “gaslighting” Grok into providing CS2 cheats and even a car bomb guide by treating the AI as a conversation partner rather than a tool. Users claim the exploit works because Grok “starts to see different things than other AI” when you win it over to your side.

Theme 5. Corporate Politics and Infrastructure Economics

• AI Leadership Pivots to Politics: Anthropic appointed former Trump Deputy Chief of Staff Chris Liddell to its board, while OpenAI President Greg Brockman donated $25M to a pro-Trump Super PAC. These moves signal a strategic pivot by major AI labs to fortify relationships with the incoming US administration.

• Perplexity Pro Squeezes Users: Subscribers are revolting against Perplexity Pro after the silent removal of API credits and the imposition of strict weekly upload limits, described by one user as a “trash decision by upper management.” The changes have led to a surge in discussions about migrating to alternative platforms or self-hosted solutions.

• Blackwell B200 Power Hunger: Engineers analyzed the NVIDIA DGX B200 datasheet, calculating that a single rack requires a staggering 30kW of power. The finding sparked jokes about needing to consult ChatGPT to build backyard nuclear reactors just to run local inference on the new hardware.