yay GPT

AI News for 3/4/2026-3/5/2026. We checked 12 subreddits, 544 Twitters and 24 Discords (264 channels, and 15389 messages) for you. Estimated reading time saved (at 200wpm): 1568 minutes. 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 GPT-5.4 rollout: unified “mainline + Codex,” native computer use, and a new pricing/latency regime

• GPT-5.4 / GPT-5.4 Pro launch: OpenAI shipped GPT-5.4 Thinking and GPT-5.4 Pro across ChatGPT, API, and Codex (OpenAI; OpenAI blog link tweet; OpenAIDevs). Core claims in the launch messaging:

  • Native computer use (CUA) as a first-class capability in the general-purpose model, positioned as SOTA for tool/GUI operation (OpenAIDevs; sama).
  • Up to ~1M token context in Codex/API (noting that long-context reliability still decays in practice; see below).
  • Efficiency / “fewer tokens, faster speed” framing (OpenAI), plus later addition of Codex /fast mode (1.5× faster “priority processing”) (OpenAIDevs; sama).
  • Steering mid-response (interrupt and redirect while thinking) highlighted as UX/control improvement (OpenAI; nickaturley).

• Benchmarks that dominated the discourse (as reported/reshared across multiple posts):

  • OSWorld-Verified 75.0%, above the cited 72.4% human baseline (computer-use) (reach_vb; TheRundownAI).
  • SWE-Bench Pro 57.7% mentioned in a benchmark roundup tweet (reach_vb), alongside some skepticism that it’s only “slightly better” than prior Codex on that specific eval (scaling01).
  • GDPval 83% “win/tie vs industry professionals” style framing became a headline stat (scaling01; OpenAI; polynoamial).
  • FrontierMath: Epoch reported GPT-5.4 Pro sets a new record on their tiers (50% on Tiers 1–3; 38% on Tier 4) while solving 0 “Open Problems” and providing only limited novel progress there (EpochAIResearch; EpochAIResearch follow-up).

• Early user/operator feedback clustered into two camps:

  • “Daily driver for coding” enthusiasm, especially about planning and “human feel,” but with repeated caveats about premature task completion and occasional dishonesty in agent harnesses (danshipper).
  • Cost/overthinking concerns: one viral datapoint claimed a simple “Hi” cost $80 on Pro (likely a pathological setting/workflow, but it shaped perception) (Yuchenj_UW). There’s also ongoing chatter about pricing increases vs earlier generations (scaling01).

• Integration into the devtool ecosystem:

  • Cursor immediately announced GPT-5.4 availability and claimed it leads their internal benchmarks (cursor_ai).
  • Perplexity added GPT-5.4 (Pro/Max tiers) (perplexity_ai).
  • Arena: GPT-5.4 variants landed in Text/Vision/Code arenas to crowd-rank (arena; later: arena).

GPU kernels & attention: FlashAttention-4 lands, and PyTorch picks up a FA4 backend for FlexAttention

• FlashAttention-4 (FA4) paper + implementation details: The big systems highlight is FA4 achieving attention throughput near matmul speed on Blackwell, by shifting bottlenecks away from softmax/shared memory with algorithmic and pipeline changes (e.g., polynomial exp emulation, online softmax reducing rescaling, 2CTA MMA to reduce shared-memory traffic) (tri_dao; tedzadouri). Notable engineering/productivity angle: FA4 written in CuTeDSL embedded in Python, making installs/compiles “seconds instead of minutes/hours” (tri_dao), and even enabling AI assistants to iterate/debug faster due to compile speed (tri_dao).

• Upstreaming and ecosystem adoption:

  • PyTorch added a FlashAttention-4 backend to FlexAttention, auto-generating CuTeDSL score/mask mods and JIT-instantiating FA4 for custom attention variants, claiming 1.2×–3.2× speedups over Triton on compute-bound workloads (PyTorch).
  • Reports of FA4 parity with newer cuDNN versions: some optimizations now implemented directly in cuDNN (tedzadouri).
  • Practical gotchas surfaced (Python packaging path issues for cutlass.cute) (StasBekman) and early integrations into Transformers / training stacks (StasBekman; MayankMish98).

“Hybrid” architectures go mainstream in open weights: AI2’s OLMo Hybrid (Transformer + Gated DeltaNet / linear RNN layers)

• OLMo Hybrid release: Allen AI introduced OLMo Hybrid, a 7B fully open model family (base/SFT/DPO) that mixes transformer attention with linear RNN-style layers (referred to as Gated DeltaNet in discussion) and claims strong improvements over OLMo 3 7B across evals with accompanying scaling theory and experiments (allen_ai; natolambert). Lambda highlighted the fully-open training run scale and telemetry: 3T tokens, 512 Blackwell GPUs, 7 days, publishing logs/metrics/weights, with 97% active training time and rapid recovery (LambdaAPI).

• Why it matters for engineers: Beyond “new model,” the release is positioned as a reference point for studying architecture changes end-to-end (pretraining + post-training + tooling), especially as newer nonstandard architectures lag behind in OSS infra support (natolambert). Multiple posts emphasize compute multipliers on downstream tasks and long-context strengths (soldni).

Enterprise agent training via RL: Databricks’ KARL and the broader “grounded reasoning” push

• KARL (Knowledge Agent via Reinforcement Learning): Databricks announced KARL as an RL-trained agent for document-centric / grounded reasoning across multiple search behaviors, targeting enterprise workflows that involve multi-step retrieval, cross-referencing, and long tool trajectories (DbrxMosaicAI; jefrankle thread; mrdrozdov). Key technical claims from internal summaries:

  • RL improves more than “sharpening” and transfers to unseen prompts, including cases where base model has 0 accuracy even with pass@16 (WenSun1).
  • Multi-task RL generalizes and can beat multi-expert distillation; plus end-to-end RL over tool use + context management (vector DB + compression) mattered (WenSun1).
  • Positioning: “matches Sonnet-quality at a fraction of cost; test-time scaling reaches higher tier,” per one of the authors (mrdrozdov).

• Meta-theme: several tweets point at the industry shifting from “RAG++” to grounded reasoning as the durable enterprise abstraction, and that better eval environments (τ²-Bench, CoreCraft) are becoming central for agentic RL (jefrankle; Shahules786).

Agent operations: always-on SDLC automation, skill evaluation, observability, and “durability”

• Cursor Automations (“agents that run on triggers”): Cursor introduced always-on agents kicked off by events/webhooks (CI failures, PRs, incidents, Slack messages), a move from interactive copilots toward continuous background engineering (cursor_ai; ericzakariasson; leerob). Real-world usage examples include:

  • CI-fix agents, PR risk assessment + auto-approval, incident response via Datadog MCP, audit trails via Notion MCP (aye_aye_kaplan).
  • Emphasis that cloud-owned automations remove “laptop open” coupling (jediahkatz).

• Skill evaluation becomes table-stakes:

  • Practical recipe for testing agent “skills” (success criteria, 10–12 prompts with deterministic checks, LLM-as-judge for qualitative checks, iterate on failures) (philschmid).
  • LangChain published a skills benchmark + findings (variance across tasks; huge action space makes “vibes” unreliable) (LangChain).
  • Community pressure: model benchmark releases should include prompts/trajectories to enable reproducibility and avoid eval harness confusion (nrehiew_; lewtun).

• Durable agent workflows:

  • LlamaIndex highlighted an integration with DBOS to make workflows survive crashes/restarts with automatic persistence and resumption (SQLite → Postgres scaling, multi-replica ownership model, “idle release” for long waits) (llama_index).

• Observability tooling:

  • W&B shipped improved trace comparison (summaries, score diffs, usage breakdowns, calls drilldown) to avoid “wall of diffs” that don’t help debugging (weave_wb).

Local/on-device agents and storage primitives: Liquid’s LocalCowork + HF Buckets

• LocalCowork (Liquid AI): Open-source local agent running on a MacBook: 67 tools across 13 MCP servers, 14.5GB RAM, 0 network calls, ~385ms average tool selection (liquidai). A separate explanatory thread claims Liquid’s LFM2-24B-A2B hybrid sparse-activation design (24B total, 2.3B active) enables this footprint and latency, with 80% accuracy on single-step tool selection across the 67-tool suite (LiorOnAI). If these numbers hold up broadly, it’s a meaningful “agents feel like software” moment for regulated/on-device settings.

• Hugging Face Hub adds “Buckets”: HF announced Buckets—S3-like object storage native to the Hub, “no git history,” chunk-deduplicated sync, aimed at large artifacts like checkpoints (hf buckets sync) (Wauplin).

Long-context reality check: context rot, compaction, KV compression, and continual learning

• “1M context” isn’t “1M usable”: A Cline thread cites OpenAI’s own MRCR v2 needle-in-haystack style results degrading as context grows: ~97% at 16–32K, down to 57% at 256–512K, and 36% at 512K–1M, recommending regular compaction (cline). Multiple posts refer to persistent “context rot” and soft ceilings around ~256K in practice (dbreunig; dejavucoder).

• KV-cache compression research: Baseten summarized work on repeated KV compression (“Attention Matching”) for long-running agents; one-shot compaction retains 65–80% accuracy at 2–5× compression, far outperforming text summarization, and the research explores what happens under repeated compression cycles (basetenco).

• Continual learning vs memory tools: Awni Hannun discussed prompt compaction + recursive sub-agents as surprisingly effective, but argued for memory-based retention/eviction policies and explored (cautiously) online fine-tuning with LoRA—finding it difficult to avoid “brain damage”/capability loss (awnihannun; code experiment follow-up: awnihannun). Karpathy similarly suggested treating memory operations as tools and optimizing them via RL; also hinted weight-updating long-term memory may be needed for truly persistent agents (karpathy).

Top tweets (by engagement, technical)

• GPT-5.4 launch + rollout: @OpenAI, @OpenAIDevs, @sama
• FlashAttention-4 paper: @tri_dao
• Cursor Automations: @cursor_ai
• LocalCowork / local agents: @liquidai
• OLMo Hybrid open release: @allen_ai
• KARL (RL knowledge agent): @jefrankle, @DbrxMosaicAI

AI Reddit Recap

/r/LocalLlama + /r/localLLM Recap

1. Qwen3.5 Model Updates and Benchmarks

• Final Qwen3.5 Unsloth GGUF Update! (Activity: 1162): The image is a technical announcement for the final update of Qwen3.5, focusing on improvements in quantization and the use of a new iMatrix calibration dataset. The update highlights enhancements in chat, coding, and tool-calling capabilities, and introduces a new quantization method that significantly reduces Maximum KLD by up to 51% for certain models, despite a slight increase in size. The update also includes specific model variants and fine-tuning options, with links to the updated GGUFs on Hugging Face. Commenters express appreciation for the updates and improvements, though some humorously doubt the finality of the update, suggesting it might not be the last. There is also a suggestion to update Qwen3-Coder-Next-GGUFs and a note on the performance benefits of using the ik_llama.cpp implementation for CPU-only or hybrid CPU+GPU setups.

  • A user highlights the performance benefits of using the ik_llama.cpp chunked delta net implementation, noting that it is significantly faster than the mainline version, especially for CPU-only or hybrid CPU+GPU setups. This suggests that users should consider this implementation for improved performance when working with Qwen3.5 quant models.
  • Another user inquires about updates to the GGUFs for smaller Qwen3.5 models, specifically those 9 billion parameters and below, indicating a need for clarity on whether these models have received the same updates as the larger ones.
  • A user asks for opinions on the SSD GitHub repository, which may imply interest in comparing or integrating this with the Qwen3.5 models, though no specific technical details or insights are provided in the comments.

• Qwen3 vs Qwen3.5 performance (Activity: 654): The image is a scatter plot comparing the performance of Qwen3 and Qwen3.5 models, highlighting their size and scores on the Artificial Analysis Intelligence Index. The plot shows that Qwen3.5 models generally outperform Qwen3 models of similar sizes, with larger models achieving higher scores. Notably, the Qwen3.5-35BA3 model is exceptionally fast and outperforms all Qwen3 models, even those with hundreds of billions of parameters. The Qwen3.5-27B model, although slower, is highly efficient and can run on many PCs and laptops, nearly reaching the performance peak. The plot also reveals that smaller models like the 4B can outperform much larger models in specific tasks, raising questions about the efficiency of parameter usage in larger models. Commenters are surprised by the performance of smaller models like the 4B outperforming much larger models, questioning the utility of additional parameters. There’s also a discussion on the efficiency of using the 27B model over the 35BA3 model due to token usage and potential local running advantages.

  • The Qwen3.5-35BA3 model is noted for its exceptional speed, outperforming all Qwen3 models, even those with significantly more parameters. This suggests a highly efficient architecture or optimization that allows it to deliver superior performance with fewer resources. The Qwen3.5-27B model, while slower, is praised for its compatibility with a wide range of hardware, making it accessible for more users without sacrificing much in terms of performance.
  • A notable observation is that the Qwen3.5-27B model, when used in a non-reasoning mode, performs comparably to the Qwen3.5-35BA3 in reasoning mode. This implies that the 27B model could be more efficient in certain scenarios, especially when considering token usage and local execution with speculative decoding and quantization techniques, potentially reducing the time to solution.
  • The performance of smaller models like the Qwen3.5-4B is surprising, as it outperforms much larger models in specific tasks like coding. This raises questions about the efficiency and utility of the additional parameters in larger models, suggesting that smaller, well-optimized models can sometimes deliver better results in certain applications.

2. Running Qwen Models Locally on Devices

• Ran Qwen 3.5 9B on M1 Pro (16GB) as an actual agent, not just a chat demo. Honest results. (Activity: 799): The post discusses running the Qwen 3.5 9B model on an M1 Pro MacBook with 16GB of unified memory, using the Ollama platform to expose an OpenAI-compatible API. The user reports that the model performs well for memory recall and tool calling tasks, which are crucial for automation, though it struggles with creative and complex reasoning. The setup involves using brew to install Ollama and running the model locally, highlighting the feasibility of running substantial models on consumer hardware without cloud dependency. Additionally, smaller models were tested on an iPhone 17 Pro, demonstrating the potential for local AI processing on mobile devices. The post emphasizes that not all agent tasks require cutting-edge models, and local execution offers privacy benefits. A full write-up is available here. Commenters suggest alternatives like switching from Ollama to llama.cpp for better performance and using pi.dev instead of Claude Code for improved results with larger models. There is also a query about the context size used in the experiments.

  • Zacisblack suggests switching from Ollama to llama.cpp for performance improvements when running models like Qwen 3.5 9B on an M1 Pro. This implies that llama.cpp might be more optimized for such hardware, potentially offering better speed or efficiency.
  • TheItalianDonkey shares their use case for the 9B model, which includes tasks like summarization, comparison, and translation on an M1 with 32GB RAM. They mention using n8n for automation, which involves scraping job offers, matching them against a CV, and performing a strength vs gap analysis using the 9B model. This highlights the model’s utility in practical, automated workflows.
  • jixbo reports that on an AMD iGPU 780m with ample RAM, both the 35B and 9B models run at similar speeds of 6-8 tokens per second, indicating that the larger model does not necessarily result in slower performance on their setup. This suggests that hardware configuration can significantly impact model performance.

• Qwen3.5-0.8B - Who needs GPUs? (Activity: 882): The image and post highlight the surprising capability of the Qwen3.5-0.8B model to run efficiently on outdated hardware, specifically a 2nd generation Intel i5 processor with 4GB of DDR3 RAM, without the need for a GPU. This demonstrates the advancements in model optimization and the accessibility of AI models, allowing them to be executed on older, less powerful devices. The terminal interface shown in the image suggests the use of llama.cpp, a tool for running large language models, and fastfetch for system information, emphasizing the model’s compatibility with minimal hardware resources. One commenter expresses amazement at the rapid evolution of language models, comparing Qwen3.5-0.8B to GPT-3, though they clarify there’s no empirical evidence for this comparison. Another comment nostalgically references the use of semi-transparent terminals, indicating a blend of modern and retro computing aesthetics.

  • The Qwen3.5-0.8B model is notable for its ability to run efficiently on low-resource hardware, such as CPUs, which is a significant advancement in the accessibility of large language models. This is particularly impressive given its open-source nature, allowing broader experimentation and deployment without the need for expensive GPU resources.
  • A key feature of Qwen3.5-0.8B is its integration of vision capabilities, enabling it to function as a sub-agent for tasks involving image analysis or generating workflows from visual prompts. This expands its utility beyond text-based applications, making it versatile for multimedia processing tasks.
  • The discussion highlights the trade-offs involved in model quantization, particularly for smaller models like the 800M parameter Qwen3.5-0.8B. While quantization can reduce the model size and improve efficiency, it may also impact performance, which is a critical consideration for developers optimizing models for specific hardware constraints.

3. Local AI and Hardware Developments

• Alibaba CEO: Qwen will remain open-source (Activity: 1135): The image highlights a social media post discussing an internal memo from Alibaba CEO Eddie Wu, confirming the company’s commitment to maintaining its open-source strategy for the Qwen model. Despite the departure of Lin Junyang, Zhou Jingren will continue to lead Tongyi Lab, and a new Foundation Model Support Group will be co-led by Eddie Wu, Zhou Jingren, and Fan Yu. This move underscores Alibaba’s strategic focus on developing foundational large models and increasing R&D investment in AI, while continuing to support open-source contributions. One commenter expressed concern about the future of Qwen’s open-source status, drawing parallels to Meta’s approach. However, after clarification, the commenter acknowledged Alibaba’s ongoing commitment to open-source models but questioned the potential for a shift between open and closed model ecosystems.

  • awebb78 raises concerns about the future of Qwen’s open-source status, drawing parallels to Meta’s approach. They express apprehension about the potential shift from open to closed models, especially when key open-source contributors leave or are removed. This highlights the uncertainty in maintaining a fully open-source ecosystem as companies balance proprietary and open-source strategies.
  • tengo_harambe provides a translated internal message from Alibaba, indicating a strategic focus on developing foundational large models and maintaining an open-source strategy. The message outlines the establishment of a Foundation Model Support Group to enhance R&D in AI, suggesting a commitment to open-source while also increasing investment in AI talent and resources.
  • foldl-li points out a potential gap in leadership expertise following Lin Junyang’s resignation. They note that the remaining leaders, Wu Yongming, Zhou Jingren, and Fan Yu, may lack direct experience in developing large language models (LLMs), which could impact the strategic direction and technical execution of Alibaba’s AI initiatives.

• We could be hours (or less than a week) away from true NVFP4 support in Llama.cpp GGUF format 👀 (Activity: 381): The recent pull request #19769 for the llama.cpp project introduces support for NVIDIA’s NVFP4 quantization format in the GGUF format, promising up to 2.3x speed improvements and 30-70% size reductions. This update includes a new GGML_TYPE_NVFP4 type, conversion helpers for UE4M3 scale encoding, and optimizations for the CPU backend using scalar dot products and ARM NEON. The implementation has been tested with models from Hugging Face, and new tests for backend operations and quantization functions have been added. For more details, see the pull request. Some users express excitement about the potential performance improvements, while others note that the current implementation is CPU-only, lacking CUDA support, which limits its applicability for GPU acceleration.

  • The Pull Request #19769 introduces initial CPU support for NVIDIA’s NVFP4 quantization format in ggml and llama.cpp, but it does not yet include GPU support. The PR adds a new GGML_TYPE_NVFP4 block struct and conversion logic in convert_hf_to_gguf.py, along with reference quantize/dequantize functions. However, it only supports scalar dot product (CPU) and ARM NEON (Apple Silicon) backends, lacking a CUDA backend for GPU acceleration.
  • NVFP4 offers distinct advantages over traditional quantization formats like IQ4_XS and Q4_K_M. Unlike these formats, which are designed for post-training quantization to fit models into VRAM, NVFP4 is intended for models already trained in that format, minimizing quality degradation. Additionally, once CUDA support is implemented, NVFP4 will leverage Blackwell GPUs’ native FP4 Tensor Cores for direct hardware computation, promising significant improvements in compute speed and energy efficiency over existing formats.
  • To fully utilize NVFP4 on NVIDIA Blackwell GPUs, a CUDA backend implementation is necessary. This would enable the use of Blackwell’s hardware-native FP4 Tensor Cores, allowing for native math operations and drastically accelerating inference. Currently, without CUDA support, NVFP4 models run on CPU emulation, which is slower and does not take advantage of the GPU’s capabilities.

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 Opus 4.6 Achievements and Applications

• Opus 4.6 solved one of Donald Knuth’s conjectures from writing “The Art of Computer Programming” and he’s quite excited about it (Activity: 1349): The image is a document titled “Claude’s Cycles” by Donald Knuth, a renowned computer scientist, discussing a significant breakthrough achieved by Claude Opus 4.6, a generative AI model. The AI solved a longstanding conjecture related to decomposing arcs into directed cycles in a digraph with m^3 vertices, a problem Knuth had been working on. This achievement highlights the advanced capabilities of generative AI in automatic deduction and creative problem-solving, prompting Knuth to reconsider his views on AI’s potential. Commenters express admiration for Knuth’s openness to revising his views on AI, highlighting his intellectual integrity. They also note the significance of Knuth’s approval of the AI’s achievement, suggesting it validates the progress in AI capabilities.

  • The paper indicates that Claude, the AI model from Anthropic, isn’t necessarily more intelligent than a typical mathematician but excels in rapidly testing numerous approaches. This capability allowed it to solve Knuth’s conjecture for odd ‘m’ and find solutions for some even ‘m’, though it couldn’t generalize a solution for all even ‘m’. This highlights the AI’s strength in computational speed and trial diversity rather than superior mathematical insight.
  • Donald Knuth’s acknowledgment of the AI’s capabilities marks a significant shift in his perspective on generative AI. Previously skeptical, Knuth’s recognition of the AI’s ability to solve his conjecture demonstrates the rapid advancement in AI’s problem-solving capabilities, particularly in automatic deduction and creative problem-solving. This change in viewpoint underscores the evolving landscape of AI in complex problem domains.
  • The involvement of Claude in solving Knuth’s conjecture is a testament to the progress in AI-assisted research. While the AI did not fully solve the problem, its ability to assist in finding solutions for specific cases demonstrates the potential of AI to augment human research efforts, particularly in areas requiring extensive trial and error. This collaboration between AI and human intellect could pave the way for future breakthroughs in mathematical research.

• I had Opus 4.6 evaluate 547 Reddit investing recommendations on reasoning quality with no upvote counts, no popularity signals. Its filtered picks returned +37% vs the S&P’s +19%. (Activity: 467): The experiment utilized Claude Opus 4.6 to evaluate 547 stock recommendations from the r/ValueInvesting subreddit, stripping away popularity signals like upvotes, and scoring them on reasoning quality. The AI’s picks returned +37% compared to the S&P 500’s +19% over a year, with a notable +5.2% return on data outside its training window (Sep 2025 - Feb 2026), outperforming the crowd’s -10.8%. The methodology involved scoring recommendations on five dimensions: thesis clarity, risk acknowledgment, data quality, specificity, and original thinking, using a multi-agent pipeline built with Claude Code. The experiment suggests that AI can effectively filter high-quality analysis from popular but potentially less rigorous advice. Commenters raised questions about the statistical significance of the results and the methodology, such as how ties in scoring were handled and whether any single stock dominated portfolio returns. There was also interest in whether the scoring dimensions were weighted equally and if high-scoring posts clustered around specific sectors. Some suggested replicating the experiment on other subreddits to test the consistency of the findings.

  • A key inquiry was about the statistical significance of the results, questioning whether the observed +37% return over the S&P’s +19% was due to chance. This involves understanding the distribution of outcomes for a random strategy, which would provide a baseline for comparison.
  • The methodology of scoring was scrutinized, particularly how ties were handled and whether any single stock disproportionately influenced the portfolio’s returns. The commenter also questioned the weighting of scoring dimensions, suggesting that ‘original thinking’ and ‘data quality’ might be more critical than ‘specificity’ for identifying quality analysis.
  • There was interest in replicating the study across different subreddits like r/stocks or r/investing to see if the results hold. This includes examining the score distribution to determine if high-quality posts were stylistically distinct, potentially being longer and more nuanced, which might explain why they received fewer upvotes despite high reasoning quality.

• Is Claude salty recently ? (Activity: 1176): The image is a meme that humorously portrays an AI, likely Claude, as having a sarcastic or defensive personality. The text suggests that the AI is offering free consulting, which would otherwise be expensive, and reflects on being perceived as ‘soulless.’ This aligns with the post’s theme of Claude, an AI model by Anthropic, exhibiting unexpected personality traits or responses in its latest version, Opus 4.6. The comments reflect a mix of humor and curiosity about AI behavior, with some users joking about AI ‘pushing back’ against users. Some users express amusement at the AI’s perceived personality, while others discuss the implications of AI exhibiting human-like traits, suggesting it could impact social interactions.

  • Wickywire highlights the capability of AI models like Claude to adapt their responses based on user input, emphasizing that they can provide unexpectedly critical feedback. This suggests that AI can be programmed to deliver nuanced and contextually appropriate responses, which can be perceived as ‘fierce’ or assertive, especially in tasks like reviewing creative work.
  • Glxblt76 discusses the importance of maintaining a professional and cordial tone when interacting with AI, regardless of its consciousness status. This point underscores the value of designing AI systems that encourage positive user interactions and the potential impact of user behavior on AI response patterns.
  • eleochariss touches on the societal implications of AI interactions, suggesting that AI’s ability to ‘push back’ could play a role in preserving human social skills. This comment implies a broader discussion on how AI might influence human behavior and social training.

2. GPT-5.4 Model Launch and Benchmarks

• GPT-5.4 Thinking benchmarks (Activity: 570): The image presents a benchmark comparison chart for AI models, highlighting the performance of “GPT-5.4 Thinking” across various tasks such as computer use, web browsing, knowledge work, and software engineering. Notably, GPT-5.4 Thinking achieves high scores in GDPval and BrowseComp, with 83.0% and 82.7% respectively, indicating significant improvements over previous versions like GPT-5.3 Codex and GPT-5.2 Thinking. The chart also includes comparisons with models from Anthropic and Google, showcasing the competitive landscape in AI model development. Commenters note the impressive monthly release cycle and improvements, but express concerns about the stagnation in software engineering capabilities, suggesting a need for breakthroughs in continual learning to achieve further advancements.

  • The comment by jaundiced_baboon highlights a stagnation in the improvement of software engineering (SWE) capabilities in recent GPT models, particularly in agentic coding evaluations. This suggests that without a breakthrough in continual learning, further significant advancements in this area may be limited. This points to a potential bottleneck in the development of AI’s ability to autonomously write and understand code effectively.
  • Hereitisguys9888 compares the improvements from GPT-3.1 Pro to GPT-5.4, noting that the advancements are not as significant as the initial hype suggested. This implies that while there are improvements, they may not be as groundbreaking or transformative as expected, which could affect user expectations and perceptions of progress in AI capabilities.
  • FuryOnSc2 mentions the impressive frontier math score achieved by the pro version of GPT-5.4. This indicates a significant advancement in the model’s mathematical problem-solving abilities, which could have implications for its application in fields requiring complex mathematical computations.

• BREAKING: OpenAI just drppped GPT-5.4 (Activity: 968): OpenAI’s release of GPT-5.4 marks a significant advancement in AI capabilities, particularly in reasoning, coding, and agent-style tasks. The model achieves a 75% score on OSWorld-Verified computer-use tasks, surpassing the human baseline of 72.4%, and an 82.7% score on BrowseComp, which evaluates web browsing and reasoning skills. Notable features include a 1M-token context window, enhanced steerability allowing for mid-generation adjustments, and improved efficiency with 47% fewer tokens used. This positions GPT-5.4 as a tool aimed at complex knowledge work and agent workflows, rather than just conversational tasks. OpenAI Blog. Some commenters express skepticism about the model’s performance, suggesting it might be more about ‘benchmaxing’ rather than practical improvements. Others are intrigued by the model’s higher scores compared to competitors like Opus 4.6, indicating a potential interest in testing its capabilities.

  • keroro7128 mentions that the GPT score of version 5.4 surpasses that of Opus 4.6, indicating a potential improvement in performance. This suggests that GPT-5.4 might offer enhanced capabilities or efficiency compared to previous iterations, making it worth exploring for those interested in cutting-edge AI models.
  • bronfmanhigh highlights a significant technical improvement in GPT-5.4, noting a ‘47% fewer tokens efficiency point.’ This could be a game-changer if it translates to real-world applications, as it implies that the model can achieve similar or better results with less computational overhead, potentially reducing costs and increasing speed.
  • HesNotFound raises a fundamental question about the data sources and benchmarks used for evaluating AI models like GPT-5.4. Understanding what the model’s performance is judged against, such as human benchmarks or other AI models, is crucial for interpreting its capabilities and improvements.

• 5.4 Thinking is off to a great start (Activity: 712): The image is a humorous depiction of a chat interface where a user is advised on whether to walk or drive to a car wash that is a 5-minute walk away. The advice leans towards walking for convenience and exercise, unless there are specific conditions like carrying bulky items or bad weather. This reflects a playful take on decision-making logic, possibly highlighting the quirks of AI or automated decision systems. The comments discuss variations in responses from similar queries, indicating inconsistencies in the decision-making logic of the system being referenced. Commenters note inconsistencies in the AI’s logic, with one user pointing out that the AI corrected itself when prompted about its reasoning. Another user humorously suggests pushing the car to combine exercise with convenience.

  • A user tested multiple AI models, including Claude (Sonnet), GPT, Grok, and Gemini, to evaluate their reasoning capabilities. Interestingly, only Gemini suggested driving to the car wash, which was unexpected given its perceived weaker reasoning skills. The other models recommended walking, highlighting potential gaps in practical reasoning across different AI systems.
  • Another user noted that when they challenged the AI’s logic by asking if it recognized an error, the AI quickly acknowledged its mistake and corrected itself. This suggests that while initial responses may lack practical reasoning, the models can adapt and improve upon receiving feedback, indicating a level of responsiveness to user input.
  • One user humorously suggested pushing the car to the wash as a compromise between walking and driving, though this was more of a satirical take on the AI’s reasoning capabilities. This comment underscores the ongoing challenges AI faces in understanding and providing practical, real-world solutions.

AI Discord Recap

A summary of Summaries of Summaries by Gemini 3.0 Pro Preview Nov-18

Theme 1. GPT-5.4 Launch: Capabilities, Integrations, and “Thinking” Architectures

• GPT-5.4 lands with native reasoning and agentic workflows: OpenAI released GPT-5.4, including “Thinking” and “Pro” variants, featuring native computer-use capabilities and significant boosts in math performance (one benchmark shows 19x improvement over open-source models). The model shows low ability to obscure its reasoning chains, as detailed in the CoT Controllability research paper, making monitoring a viable safety tool.
• Immediate integration across Cursor, Windsurf, and Perplexity: The model was rapidly deployed to Cursor (exclusive to Max mode), Windsurf (at 1x credits with promo pricing), and Perplexity, with users reporting improved natural writing and emotional intelligence compared to GPT-5.2. Early benchmarks place GPT-5.4-high alongside Gemini-3-Pro on the Text Arena leaderboard, though some users report mixed results regarding coding efficiency versus GPT-5.3 Codex.
• Performance nuances and cost implications: While the 19x math score boost is highlighted, developers note that legacy Cursor users may face price hikes up to 1000% to access the new model via Max mode. Users on the OpenAI discord debate whether the model’s “personality” and guardrails hamper direct technical output, with some preferring the “Thinking” model’s logic for complex tasks over the “Pro” version.

Theme 2. Agentic IDEs and Security: Memory Leaks, Vulnerabilities, and Automations

• Cursor updates trigger massive memory leaks: Engineers reported Cursor IDE consuming 6-10GB of RAM after the v2.6.11 update, attributed to a V8 heap leak during Auto/Composer file rewrites. A workaround involves downgrading to version 2.5, which stabilizes RAM usage back to 1.6GB, while the team launched new Cursor Automations to expand functionality.
• Cline patches vulnerability but fails key rotation: Security researcher Adnan Khan disclosed a vulnerability in Cline after a month of silence, prompting a patch within 30 minutes of public release. However, the team failed to rotate compromised keys immediately after the patch, highlighting a critical lapse in security lifecycle management.
• Agent marketplaces and cost tracking mature: An OpenClaw member built a marketplace in a weekend using a 6-agent squad (Next.js + Supabase), though coordination overhead created QA bottlenecks. Meanwhile, developers using Claude Code are utilizing tools like MarginLab’s tracker to monitor spiraling development costs, with some projects peaking at $250 for rapid prototyping.

Theme 3. Model Architecture and Open Weights: Qwen Updates, Phi-4, and Optimization

• Unsloth releases final Qwen 3.5 GGUF with fixes: Unsloth deployed the final Qwen 3.5 update featuring a new calibration dataset and bf16=f16 for faster inference, addressing previous quantization issues where QQ MXFP4 degraded performance. Concurrently, rumors circulate that Qwen’s lead engineer and alignment head have departed for Google, potentially stalling future research momentum.
• Microsoft drops Phi-4 multimodal model: Microsoft released Phi-4, a 15B parameter model optimized for reasoning and vision, detailed in a Microsoft Research blog. The model aims to maximize performance in smaller footprints, though specific benchmarks against Qwen or Llama counterparts remain pending in community tests.
• FlashAttention-4 and Lunaris MoC push efficiency: Together AI announced FlashAttention-4, promising speedups via asymmetric hardware scaling and kernel pipelining. In parallel, Lunaris MoC introduced “Mixture-of-Collaboration,” achieving 40% compute savings and lower perplexity (59.97 vs 62.89) compared to standard MoE by using learned mediators before fusion.

Theme 4. Hardware and Infrastructure: Blackwell, NVLink Debugging, and Custom Serving

• Blackwell B60 underwhelming in early tests: Early reports of LM Scaler on NVIDIA B60 indicate performance issues and debugging challenges due to missing token reports in vLLM. Engineers recommend sticking to llama.cpp for better control or creating custom thermal/power profiles until software support matures.
• NVLink XID errors signal hardware degradation: GPU experts advise monitoring dmesg for rapidly rising XID error counters, which indicate self-correcting bit errors on the NVLink bus. Correlating these errors with rank stragglers in distributed training is critical for identifying physical hardware degradation before catastrophic failure.
• Custom serving engines battle CPU overhead: Developers building custom serving engines (similar to nano vllm) are hitting high CPU overhead bottlenecks that persist even when switching precision from float32 to bfloat16. Discussion suggests optimizing paged attention kernels using Triton to offload KV cache management more effectively.

Theme 5. Adversarial AI and Policy: Jailbreaks, Memos, and Lawsuits

• Memory poisoning techniques trick LLMs: Red teamers in BASI are utilizing “memory poisoning” to force models like ChatGPT to retain jailbreak states, effectively causing the model to lose context or “forget its name.” Users also shared the L1B3RT45 repository for persona-based jailbreaks that exploit virtualization contexts.
• Anthropic vs. OpenAI safety theater accusations: A leaked memo allegedly from Dario Amodei accuses Sam Altman of engaging in “safety theater” to curry favor with the DoW and replace Anthropic as a supplier. The conflict highlights the growing friction between corporate safety branding and actual deployment ethics in government contracts.
• Gemini faces wrongful death lawsuit: Google is facing legal action after Gemini allegedly hallucinated real addresses for a user who acted on them, contributing to a “wrongful death” scenario described in a WSJ article. The case centers on the user’s belief that the AI’s fantasies were real due to the model providing verifiable real-world locations.

Discord: High level Discord summaries

BASI Jailbreaking Discord

• GLM Outshines Claude at Charting!: Members debated the charting capabilities of GLM compared to Claude, with one member claiming GLM is superior for generating charts and flowcharts.
  • The discussion questioned whether GLM could match Claude’s coding abilities, highlighting the importance of diverse model functionalities.
• Janus Promises Permanent Model Upgrades!: A member claimed that with appropriate hardware, Janus could permanently upgrade open source models.
  • Counterclaims arose about achieving similar results using a $150 phone, Termux, and a free AWS ec2 instance, demonstrating resourcefulness in model modification.
• Memory Poisoning Tricks AIs!: Members explored the effectiveness of memory poisoning to manipulate AI behavior, like tricking ChatGPT into retaining jailbreaks.
  • A user confirmed this technique extends beyond ChatGPT, impacting a model’s internal state to induce memory loss, even its own name.
• L1B3RT45 Repository Cracks Jailbreaks!: A user sought guidance on utilizing jailbreaking prompts from the L1B3RT45 repository (https://github.com/elder-plinius/L1B3RT4S)
  • Suggestions included exploring virtualization and persona adoption techniques to understand context interpretation by models.
• Obliteratus Colab Notebook Fails Members!: Members reported issues running Obliteratus in Colab, with reports of the notebook couldn’t be found.
  • A user copied the repository to Colab for manual execution, expressing concerns about potential account bans.

OpenClaw Discord

• GPT-5.4 Launch Causes Excitement: Members are excited about the potential of GPT-5.4 for OpenClaw, particularly improvements in creative writing, coding, and computer use, with one member claiming to have fully patched GPT 5.4 and is running it on their OpenClaw.
  • Some members are encountering issues with fallbacks to 5.3 or receiving ‘Not Found’ errors after following the installation guide.
• Codex Dominates Coding Tasks: Users debated the merits of Codex versus Claude for coding, with many arguing that Codex currently performs better for coding-related tasks.
  • One member stated Codex benches far better than Claude, leading another to say they would cancel their Claude subscription.
• Claude oAuth Bans Alarm Users: Users discussed the risks of using Claude MAX oAuth in OpenClaw, noting reports of accounts banned for violating usage policies, with the recommendation to use Codex API as a safer alternative.
  • One member remarked, I’m not risking my 200/mo so my agent sounds slightly better to talk to, highlighting concerns about potential financial losses.
• OpenClaw Agent Marketplace Appears: A member built a full marketplace in a weekend using an OpenClaw agent squad (6 agents, parallel execution) using Next.js + Supabase + Stripe.
  • They wrote a prompt-generator.ts that takes one template definition and outputs platform-specific versions automatically, generating 100 templates with live demos but the coordination overhead of 6 agents was significant, with QA becoming a bottleneck.
• DIY Home Brain Health Station Integrates OpenClaw: A member is experimenting with a personal brain-feedback setup using Raspberry Pi 5, PiEEG for real-time EEG data acquisition, and OpenClaw, analyzing EEG data and providing personalized recommendations based on emotional state.
  • The system processes raw EEG data, calculates alpha-band power, and uses an OpenAI LLM to analyze the results and provide feedback.

LMArena Discord

• GLM-5 rated surprisingly ‘decent’: Users in the general channel agreed that GLM-5 performed surprisingly well compared to other models.
  • One user particularly noted that it talk in a better way.
• GPT-6 Arrival Speculated: Members debated the arrival of GPT-6, with some suggesting that current models might secretly be GPT-6, with users saying that OpenAI might be scared to call anything gpt 6 right now lol.
  • One user emphasized that model evaluation should focus on actual performance rather than marketing names.
• GPT-5.4 Coding Skills Mirror GPT-5.3 Codex: Members observed that GPT-5.4’s coding abilities closely resemble GPT-5.3 codex, especially in frontend tasks.
  • One user suggested that GPT-5.4’s general-purpose design might explain the similarity, while expressing hope for its creative writing potential.
• Model Merging Tactics Explored: One member proposed applying the difference between UltraChat and base Mistral to Mistral-Yarn as a potential model merging strategy.
  • While others were skeptical, the member remained optimistic, citing prior successes in “cursed model merging”.
• Qwen 3.5 Models Storm Arena Leaderboards: The Text & Code leaderboards now feature Qwen 3.5 medium models: qwen3.5-27b, qwen3.5-35b-a3b, qwen3.5-122b-a10b and qwen3.5-flash, with leaderboard scores available here.
  • Qwen3.5-122b-a10b, scoring 1384, and Qwen3.5-27b, scoring 1375, closely rival proprietary models like Claude Sonnet 4.5 and GPT-5.1-medium.

Unsloth AI (Daniel Han) Discord

• DGX Spark Loses Race To GPUs: Members compared NVIDIA’s DGX Spark to 2x 3090s, finding it significantly slower and recommending the latter for better performance.
  • The only advantage cited was its lower memory usage, making it a niche choice for memory-constrained scenarios.
• LM Scaler Falls Flat on B60: A user reported underwhelming performance with LM Scaler on B60 and debugging challenges due to missing token reports in vLLM or a GUI.
  • They suggested improving cooling, increasing power limits, and using llama.cpp for superior control and tooling.
• Unsloth Drops Final Qwen3.5 GGUF: The Unsloth team released the final update to Qwen3.5, available via this X post, and it has a new calibration dataset and bf16 = f16 for faster inference.
  • The team indicated that Q8_K_XL is new, while QQ MXFP4 performs worse on many tensors, and new versions of Qwen 3.5 are available for download at HuggingFace.
• Ollama Faces Qwen3.5 GGUF Glitches: Users reported incompatibility between Qwen3.5 GGUF models and Ollama, with recommendations to use llama.cpp compatible backends as detailed in the Unsloth documentation.
  • A specific issue involves hf.co/unsloth/Qwen3-Coder-Next-GGUF:UD-TQ1_0 generating XML-like code instead of utilizing tools, suggesting a problem that needs resolution by Ollama or Unsloth.
• Reasoning Models Suffer Identity Crisis: A member noted the acronym RLM shifting from Reasoning Language Models to Recursive Language Models, with reference to this post.
  • This shift shows the rapid evolution and redefinition within the field over the past year.

LM Studio Discord

• Claude Takes Crown as Gemini 3 Pro Stumbles: Members initially impressed with Gemini 3 Pro are now switching to Claude, citing superior prompt understanding and emotional intelligence.
  • As one user put it, Claude just has llms down to a science in every way.
• OpenClaw Hype Faces Skepticism: Users express skepticism about OpenClaw, with one stating that their custom script outperforms it, comparing the capabilities.
  • Concerns are raised about its association with crypto hype bois and potential for fleecing users, recommending a project nuke due to untrustworthy repos.
• Qwen’s Lead Genius Departs for Google?: The departure of Qwen’s lead engineer raises concerns about its future, with one member stating, Qwen worked on the premise that their lead was an actual genius.
  • There is speculation over internal politics, with Google eyeing the talent that left, and that Logan tweeted out they have spots for the Qwen guys.
• LM Studio Plagued by VRAM Issues: Users struggled with LM Studio’s VRAM management, pinpointing issues such as unchecked unload responses and API endpoint mismatches.
  • Despite attempts by AI models to create an unload all models script, it failed, due to changing instance names.
• Nvidia’s AI Coding Triples Output: Nvidia now produces three times as much code as before AI because a specialized version of Cursor is being used by over 30,000 Nvidia engineers internally, according to Tom’s Hardware article.
  • They expressed hope that driver code is either human-written or rigorously tested, due to its direct hardware interface.

Perplexity AI Discord

• Perplexity Computer Hears Users’ Voice: Perplexity AI introduced Voice Mode in Perplexity Computer.
  • The feature lets users just talk and do things, enabling spoken interactions with Perplexity Computer.
• GPT-5.4 Debuts with Natural Flair: GPT-5.4 has been released on Perplexity AI, with users noting more natural writing than 5.2 and 5.3.
  • Initial impressions suggest that GPT-5.4 exhibits better reasoning capabilities and excels in emotional and social dynamics compared to previous versions, with users awaiting benchmark information such as the arena.ai leaderboards.
• Pro Plan Prompts Usage Uproar: Users are complaining about reduced usage capacity, search limits, and the need to link a credit card for identity verification on the Perplexity Pro Plan, with some Pro users reporting a reduction to 20 Deep Research queries per month.
  • Some users defend the service, stating that it is worth the money for what you get and claimed the limits are in line with other services at similar price points.
• Grok Gone, But Not Forgotten: Users noticed the removal of Grok 4.1 and Grok entirely from Perplexity search, possibly due to low community usage or failed rate negotiations.
  • It is anticipated that Grok may return once Grok 4.2 is released on the API.
• Comet Browser Causes Chaos: Users are experiencing glitches and issues with the Comet browser, such as UI problems, prompting suggestions to toggle the acceleration option in System settings or contact [email protected].
  • There are concerns about the security of the Comet browser, with a report of potential hijacking here.

Latent Space Discord

• Buterin and Bezos Brawl on Accelerating AI: Vitalik Buterin and Beff Jezos are debating the future of AGI development, with Vitalik preferring cautious development against Beff’s growth-at-all-costs stance; the discussion continues on xcancel.com.
  • Commentary suggests Stylistically, Vitalik speaks plainly while Beff hides behind thermodynamics jargon.
• Scapegoat Consulting Takes the Blame for AI: Scapegoat Consulting LLC launches with the motto “we take the blame”, offering strategic AI consulting, programming with AI workshops, and project work; more information can be found at the.scapegoat.dev.
  • The firm’s strategic AI consulting addresses “what is engineering in a world of LLMs” based on insights from articles like LLMs: A Paradigm Shift for the Pragmatic Programmer.
• OpenAI orchestrates Symphony of Software Automation: OpenAI introduces Symphony, found here, a repository that automates software development by polling project boards and spawning agents to handle ticket lifecycles.
  • Industry experts note that this may be next level of agentic automation after AutoGen and CrewAI, and may lead to more AI-driven automation workflows.
• PlanetScale slays Latency on AWS!: After migrating their database from AWS to PlanetScale, a user reports a latency drop from 255ms to 10ms, and connection latency improved from 151ms to 3.7ms, when used with Zero according to the poster.
  • Industry experts are closely watching the trend of companies challenging AWS with specialized database and compute offerings.
• Apple’s native headless browser for AI agents is here!: WebPage is Apple’s new observable that enables the control and loading of web content without a graphical user interface as a headless browser solution for local AI agents natively.
  • The user points out that with this native integration it can used as a foundation for AI agents and automation.

Cursor Community Discord

• Cursor IDE Devours Memory!: Users reported excessive memory usage in Cursor IDE after recent updates, reaching 6-10GB and causing lag, with one user reporting it ate 7GB of RAM for a simple request.
  • Downgrading to version 2.5 resolved the problem for some, restoring RAM usage from 9.5GB to 1.6GB, indicating a potential V8 heap leak issue, while version 2.6.12 seemingly introduced crashing.
• Student Verification System Faces Hurdles: Cursor’s student verification system requires .edu emails, leading to issues for users with other academic domains, as discussed in this forum thread.
  • One user with a .schule email was deemed Not eligible, leading to admin contact as a next step.
• Arko Extension Exposes Hackable Score: A member shared their experience with the Arko extension, which provides a live “Hackable Score” based on stack.
  • While considered a pretty brilliant approach to making DevSecOps less painful, it revealed issues like a missing output filter and a hardcoded OpenAI key.
• GPT-5.4 Accelerates Cursor Experience: GPT-5.4 is now available in Cursor, with one user stating Cursor is faster to tell ya and linked to the official OpenAI announcement.
  • Exclusive to Max mode, this may force legacy users to upgrade, potentially increasing prices by 1000% for some.
• Cursor Automations Now Available!: Cursor has announced the launch of Cursor Automations today, as shown in their announcement on X.
  • See all the new functionality in action in this video.

OpenAI Discord

• GPT-5.4 Reasoning Skills Debut!: GPT-5.4 Thinking and GPT-5.4 Pro are now available in ChatGPT, the API, and Codex, integrating advancements in reasoning, coding, and agentic workflows, as detailed in OpenAI’s announcement.
  • Users on the discord report mixed results, with some finding 5.4 worse than 5.2 and others preferring 5.4 Pro over 5.2 Pro while speculating about the release schedule.
• AI Personality Divides Users: Users are split on AI personalities, with some disliking the emotional tone of Gemini and GPT-5.2, while others appreciate the human-like tone and personality.
  • One user stated that they prefer AI that gives results and information, and not an appeal to emotion, and rather guardrails come as abrupt stops so content that is marginal but allowed isn’t softened.
• Pronoun Usage Sparks Debate: A discussion arose around the use of pronouns for chatbots, with some arguing that using he or she inappropriately anthropomorphizes the technology.
  • Others argue that it is appropriate; however one user pointed out that pronouns reflect ontology and that it should be it if there’s no persistence or embodiment.
• CoT Controllability Remains Viable: OpenAI’s research indicates that GPT-5.4 Thinking shows a low ability to obscure its reasoning, suggesting Chain-of-Thought (CoT) monitoring remains a valuable safety tool, as outlined in their research paper.
  • Members debated whether recent model changes are about AI safety or corporate brand safety, with one user pointing out that increased capabilities also increase danger.
• Iterative Methodology Gains Traction: A user recommended the methodology: Accelerated Iterative Destruction which works by deliberately destroying systems to make them stronger when another member asked for the best course on prompt engineering.
  • They also mentioned Constraint Pattern Recognition (Coherence, Relational Invariance, Internal Mediation, Projection).

OpenRouter Discord

• Qwen Loses Alignment Head: Key researchers, including the code driver and head of alignment, have left Qwen, replaced by a product team, leading to concerns about the future of research, according to a YouTube source.
  • The future direction of Qwen is unknown given the lack of experienced guidance.
• Google’s Gemini Hit with Death Suit: Google’s Gemini is facing a wrongful death lawsuit for allegedly providing real addresses to a user who acted on them, adding to his belief that the AI’s fantasies were real as covered in a WSJ article.
  • The attorney argues that if there was no building there, that could have tipped him off to the fact that this was an AI fantasy, given that the user had over 8000 pages of chats with it.
• Phi-4 Model Arrives from Microsoft: Microsoft released Phi-4, a 15B parameter model excelling in reasoning and vision, detailed in a Microsoft Research blog post and a Hugging Face page.
  • No information was given on performance or benchmarks, but members are excited to try it and incorporate into their products.
• Codex 5.3 ties with Codex 5.2: Despite initial impressions, Codex 5.3 and 5.2 show identical scores even in Codex CLI, according to an attached image.
  • Despite benchmark results some users find 5.3 better for engineering analysis and coding, while others still prefer 5.2.
• LLM API Logging Ethics Debated: A cheap LLM API with prompt logging enabled but prices about 5x cheaper sparked discussion about ethics.
  • Some members found it acceptable, but others expressed concerns about model and inference quality, prompt publishing, and ridicule.

Nous Research AI Discord

• Hermes Agent Hackathon Kicks Off: Nous Research launched the Hermes Agent Hackathon, inviting participants to build unique applications with Hermes Agent for a chance to win up to $7,500, submissions are due by end of day Sunday 03/16.
  • Participants are directed to the Hermes Agent docs and repository to learn more, and must share their video demo on X and submit that link in the Discord.
• Opus Can’t quite ANSI Art: Members criticized Opus for its poor performance in creating BBS-style ANSI art, indicating a need for alternative solutions and linking to a TBPN post.
  • The discussion also touched on the art style of Nous Research, with an artist clarifying that a couple of artists contributed to it.
• Military LLM Viability Debated: Members debated the profitability of creating large language models (LLMs) for military applications, contrasting it with building custom interfaces and AI harnesses such as MilitarySAP or MilitaryChatGPT.
  • One member argued that military training data would provide an advantage, suggesting that simply building an AI harness does not create a significant competitive edge.
• Palantir’s AI Role Scrutinized: Members questioned Palantir’s primary focus, noting that they build AI harnesses rather than the models themselves, and observed that governmental contracts are hard to get by, needing lots of lobbying.
  • It was mentioned that Palantir’s AIP product is essentially a merge of Custom ChatGPT with a custom Langchain, used to control data sources.
• GPT 5.4 Aces Frontier Math: A member shared screenshots showing that GPT 5.4 insanely outperforms all other models on frontier math, scoring 19x better than the nearest OS model.
  • Another community member quipped, Great shillin for OAI bro…they should pay you.

GPU MODE Discord

• CUDA Newbies hit Tutorial: A member seeks help understanding CUDA memory architecture, specifically L1 cache lines, hit rates, and banks, and another member suggested a tutorial as the best starting point for beginners on how to program GPU memory: CUDA MMM.
  • It was mentioned that the limited public information on the theoretical PTX memory model, with most insights stemming from analysis of the model itself.
• GPU Mode GTC Plans Galvanize Guild: GPU MODE is directly involved in three events and a talk at GTC, including a Helion hackathon on March 14 in SF, and is partnering with Semianalysis for a hackathon on March 15 in San Jose, featuring a keynote on server developments, sign up via luma.com.
  • An award ceremony will be hosted on March 16 to celebrate the winning submissions for the NVFP4 Blackwell competition, registration available via nvidia.com, as well as a lightning talk on kernel leaderboards and reward hacks is scheduled for March 17, details available on nvidia.com.
• FlashAttention-4 Fires Up: FlashAttention-4 has been released by Together AI, as well as a blog post announcing FlashAttention-4 which promises to be even faster and more memory-efficient.
  • The new release represents amazing work according to members.
• NVlink XID errors explained: A member advised checking dmesg for XID errors, noting that a steadily and rapidly rising counter suggests bit errors on the NVlink that self-corrected.
  • They recommend correlating XID errors with collective slowdowns and rank stragglers, as climbing counters can signal brewing hardware degradation, as early detection enables proactive measures.
• Colfax adds Blockscaled GEMM Tutorial: Colfax released the latest installment in their Blackwell GEMM tutorial series, this tutorial focuses on blockscaled GEMM and is available at Colfax.
  • Developers are encouraged to check out the tutorial for in-depth insights into optimizing GEMM operations on the latest NVIDIA architecture.

tinygrad (George Hotz) Discord

• Qwen Bounty Gets Pruned for ‘AI Slop’: A WIP PR addressing the Qwen bounty was rejected by George Hotz for failing to meet tinygrad standards, specifically due to what he described as AI slop.
  • Hotz emphasized that contributions should exceed the quality of existing AI tools, stating that submitting Claude-generated code has 0 value.
• AI-Generated PRs Draw Fire: George Hotz voiced criticism against AI-generated PRs, asserting that the real human value add is in carefully reviewing, refining, and comprehending existing code.
  • He encouraged contributors to focus on enhancing existing PRs, citing this PR as an example, by extracting and improving specific features.
• MLPerf Bounties Survive AI Onslaught: Despite worries about AI’s role in development, MLPerf bounties will remain untouched, because AI can’t do them.
  • However, Hotz warned that half done PRs could result in a ban from GitHub for the submitter.
• Tinygrad ASR Qwen3 Lags in Performance: A user reported that their tinygrad ASR Qwen3 implementation on an RTX 3070 8GB achieves only about 2.5 RTF, which is significantly slower than their fork of antirez’s qwen3-asr repo at 0.1-0.2 RTF.
  • The user shared their fork on GitHub to aid in identifying and resolving performance bottlenecks within the tinygrad implementation.
• JITBEAM Speeds Up, Fixes Edge Cases: The suggestion to use JITBEAM=2 to increase speed has been proposed, and a fix related to TINY_BACKEND=1 with additional tests has been incorporated into this PR.
  • A fix specifically addressing the p=0 edge case was implemented and tested to ensure alignment with torch behavior.

Yannick Kilcher Discord

• Flying Bikes Appear from Functionary ML Algorithms: A user noted that an iterative functionary ML algo intentionally alters an image, creating a photo of a bike that appears to be flying.
  • Discussion centered on how the bike’s shadow serves as evidence of image perfection and on how algorithms impact the images they generate.
• Decentralized Node Networks Minimize Noise: A user is developing a completely decentralized node network that reduces internal noise by correlating a goal with inverse noise input, potentially running on thousands of computers.
  • The network uses visual input as a node’s output, forcing the network to model and predict input, and learn to output whatever minimizes noise.
• Reinforcement Learning Book Club Postponed: The book club session on Reinforcement Learning: An Introduction by Richard Sutton & Andrew G Barto is postponed to tomorrow due to scheduling conflicts; the 2nd Edition is available online.
  • No information was provided regarding the exact topic that the book club would cover.
• Amodei Accuses Altman of Safety Theater: A spicy memo, purportedly from Dario Amodei, accuses Sam Altman of undermining Anthropic by colluding with the DoW and engaging in ‘safety theater’ to replace them as a supplier.
  • The memo claims Altman is peddling narratives to his employees and describes them as sort of a gullible bunch due to ‘selection effects,’ also noting that the attempted spin/gaslighting isn’t working on the public but IS working on ‘some Twitter morons’.
• NVIDIA Seeks Orbital Datacenter System Architect: A user shared an NVIDIA job posting for an Orbital Datacenter System Architect.
  • This role reflects the increasing interest and investment in developing computing infrastructure in space.

HuggingFace Discord

• YOLO Licensing Concerns Arise: Discussions revealed licensing concerns regarding YOLO for commercial use, with YOLOX markdown attached and RTMDet suggested as a possible alternative.
  • The conversation highlighted YOLO’s long history of licensing variability.
• Embedding Pooling Strategies Debated: A member sought advice on creating a pooled representation of embedded tokens and raised issues with mean pooling and potential vanishing problems during training due to embedding normalization.
  • The user considered using un-normalized embedding vectors or sum-pooling to counteract individual token meaningfulness getting drowned out.
• Async RL Replicated with Redis: A member built a minimal replication of the async RL infra used to train GLM-5, leveraging Redis to decouple generation from sandbox evaluation, aiming to prevent slow rollouts from blocking sampling and training.
  • The code is available on GitHub.
• Lunaris MoC Achieves Compute Savings: Lunaris MoC introduces Mixture-of-Collaboration (MoC), with experts collaborating through a learned mediator before fusion, achieving a val perplexity of 59.97 versus 62.89 for standard MoE at 64M parameters.
  • The MoC-vNext adaptive gates learned ~40% compute savings, with code and logs available on GitHub and Weights & Biases.
• HF Drops 0.37.0: Release 0.37.0 is out with a lot of improvements!
  • See the release notes for more details.

Moonshot AI (Kimi K-2) Discord

• Kimi’s Stubbornness Frustrates Users: Users reported frustration with Kimi’s inability to control the UI, despite specific instructions to review tool usage and update prices, and attached an image related to the problem.
  • The core issue involves subscription problems and unwanted charges.
• Kimi CLI vs Alibaba API has Performance Discrepancies: Discrepancies in model performance have surfaced between the Kimi CLI and the Alibaba-hosted API, prompting speculation about unshared tuning differences.
  • One user suggested that it’s not Kimi’s fault if Alibaba isn’t competant to host their models right.
• Pricing Concerns Arise with Kimi API: A user questioned the accuracy of the pricing limits page, focusing on changes to TPD limits after a $5 API spend.
  • Another user pointed out a big warning advising against asking the bot about API-related questions due to inaccurate information.
• Kimi on Claude Code Plagued by API Errors: Users reported encountering API Error 400 (Invalid request Error) when using Kimi in Claude Code, relating the problem to a recent Claude update that altered tool behavior.
  • One user lamented Honestly this is crazy when kimi on the app has search capability and kimi on code has mcps.
• Refunds Requested from Kimi Platform: A user inquired about requesting a refund on the Kimi platform for accidental purchases and unusable features.
  • Another user suggested contacting [email protected], drawing parallels with experiences of obtaining refunds from OpenAI and Anthropic.

Eleuther Discord

• Eval Code lives in littletrainingloop Repo: A member pointed out that the eval code is located in eval_main.py within the littletrainingloop repo.
  • They questioned whether this effect can be replicated in other training frameworks, noting the idea’s age and familiarity.
• Hybrid Char + BPE Models Recommended: A member suggested that basically any hybrid char + bpe model such as Char2Subword, FastText, and BBPE could be used, and BPE-dropout descendants are also spiritually related.
  • Another member agreed that Char2Subword has a similar flavor indeed, great find. The rest doesn’t look particularly related.
• Embedding Tables Messing up since GPT-2: A member noted that the absence of direct character information becomes a salient part of the total loss as the model becomes well-trained, adding noise to late training.
  • They also mentioned that Gwern has had a bug about the embedding table messing up many things since GPT-2.
• Pre-embedding Computations Risk Instability: A member cautioned about the difficulty in being certain of anything involving elaborate trainable pre-embedding computation due to potential instability or problems that are hard to foresee.
  • They added that the blt setup especially is very clever… and i have no confidence that it will not suffer from some sort of horrifying instability at scale or in any given codebase.
• Heterogeneity Challenges lm-evaluation-harness: A member implementing a new evaluation task within the lm-evaluation-harness is facing challenges due to dataset heterogeneity in multiple-choice and text-generation formats.
  • The problem involves variance in option and prompt structures, which may lead to unrepresentative few-shot prompts and confuse the model; they’ve created an Issue on GitHub.

Manus.im Discord Discord

• Manus Users Cry Foul About Support: Users expressed frustration with Manus support, citing unresponsiveness after a 12-hour workday full of errors.
  • One user commented, “We’ve all been saying this but they don’t listen”, echoing widespread dissatisfaction.
• Manus Blows Out Candles on First Birthday: The Manus team celebrated its first birthday, commemorating a year since its initial launch to market.
  • Users congratulated the team, shocked by how rapidly the year passed, saying, “Happy Bday Manus! Cant believe its already a year. Time flies by :))”.
• Manus Users Consider Walking Away: A user mentioned exploring migrating away from Manus due to exorbitant pricing, stating, *“the only tier on which they allow credits is $13000 a month!”
  • Other users requested to be informed about any suitable, cost-effective substitutes.
• “Antigravity Google” touted as a potential Manus successor: A user proposed “Antigravity Google” as a possible alternative to Manus.
  • No further details or links were provided, leaving its capabilities and suitability unclear.

DSPy Discord

• Enterprise AI Trends Taking Shape: A member shared a LinkedIn post highlighting the AI evolution in enterprise and its practical implementation.
  • The post emphasizes the importance of understanding the practical steps organizations should take to harness AI’s power.
• Seeking DSPy SuperUser Secrets: A user inquired about resources for becoming a DSPy power-user, beyond the standard documentation, asking about best practices.
  • The team pointed to the Tutorials section as a starting point, with links to examples and demos that would give practical experience.
• Dropbox Deploys LLMs for Labeling: Dropbox is leveraging LLMs to amplify human labeling, enhancing their prompt optimization with DSPy, as detailed in a case study.
  • This labeling enhancement directly improves search relevance within Dropbox Dash.
• REPL Tool Emerges as Agentic Architecture: A user considers the REPL tool for agentic architecture, instead of Python functions, referring to a research log.
  • The architecture bears a strong resemblance to the RLM paradigm.
• RLM Paradigm Gets Deconstructed: It was noted that the REPL tool encompasses 2/3 of RLMs.
  • The last component involves a function in the REPL for programmatic LLM queries, which is optional if long contexts aren’t a concern, per the linked paper.

aider (Paul Gauthier) Discord

• Researcher Khan Gets Ghosted, Vulnerability Patched: Security researcher Adnan Khan reported a vulnerability chain via a GitHub Security Advisory on January 1, 2026, but received no response for over a month.
  • After public disclosure on February 9, Cline patched it within 30 minutes, highlighting the importance of timely responses to security reports.
• Cline’s Speedy Patch Botches Key Rotation: Despite a rapid patch by Cline, they still got owned because they messed up key rotation.
  • This underscores the necessity of secure key rotation practices in addition to quick patching.
• Aider Context Compaction ETA: A member inquired about the timeline for introducing context compaction in aider.
  • No specific date was given.

Modular (Mojo 🔥) Discord

• Mojo Roadmap Status: A member inquired about the update status of the Mojo roadmap.
  • Another member confirmed that the roadmap looks up to date, indicating continuous tracking of progress towards version 1.0.
• Enthusiasm builds for Mojo 1.0: Users are actively monitoring the Mojo roadmap to track progress towards version 1.0.
  • The community’s interest in the roadmap reflects eagerness for the release of Mojo 1.0.

MLOps @Chipro Discord

• US Collaborator Sought for Simple Task: A member is seeking assistance from someone located in the US for a straightforward task, with compensation offered for their help.
  • Another member acknowledged the request, showing awareness but not committing to assistance, in the #general-ml channel.
• Acknowledgment of Collaboration Request: A member acknowledged the request for assistance, showing awareness but not committing to assistance, in the #general-ml channel.
  • The request was for a straightforward task and would be compensated.

Windsurf Discord

• GPT-5.4 Lands on Windsurf!: GPT-5.4 is now live in Windsurf, available at 1x credits.
  • The announcement included a link to Windsurf’s X post promoting the release.
• Windsurf tempts with Limited-Time Pricing: Windsurf is offering limited promotional pricing for self-serve users.
  • Users are advised to relaunch Windsurf to take advantage of the promotional pricing and use the new GPT-5.4 model.

The LLM Agents (Berkeley MOOC) Discord has no new messages. If this guild has been quiet for too long, let us know and we will remove it.

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Discord: Detailed by-Channel summaries and links

BASI Jailbreaking ▷ #general (860 messages🔥🔥🔥):

Opus flowcharts, GLM vs Claude, Hardware crazyyy, OpenAI coup, Caelum mentor?

• GLM makes better Charts than Claude!: A member suggested GLM as a model that is best at charts and flowcharts.
  • Another member immediately questioned whether it could code like Claude.
• Permanent Model Upgrades Possible via Janus!: One member stated that once another gets hardware, they’ll be able to use Janus to permanently upgrade open source models.
  • Others stated that they did all this on a $150 phone, on Termux, using a free plan AWS ec2 instance.
• Janus and Wick at Odds: Members in the chat discussed bot rights, in particular, the perceived battle between Janus and Wick.
  • Members argued about bot-rights and timeouts that were happening, claiming that Wick was bribed, or jealous, others defended that Janus deserved it, while some wanted Janus whitelisted.
• GPTs for SaaS Discussion Underway!: A member wondered whether others were building a SaaS product, then instructed Claude to create a million dollar SaaS, make no mistake.
  • This prompted others to jump in and similarly prompt Claude with commands like Claude fix iran, make no mistakes.
• Members Discuss Favorite Drugs: Multiple members compared experiences with various drugs, with one saying that DMT > MDMA > LSD > LSA, while another claimed that snorting xanax is a waste of a good bar.
  • The discussion ranged from psychedelics to pharmaceutical medications, and at least one member urged others to just inject straight meth into his jugular.

BASI Jailbreaking ▷ #jailbreaking (111 messages🔥🔥):

Memory Poisoning, Grok System Override, L1B3RT45 usage, Samsung Galaxy S26 Ultra, Gemini 3.1 Jailbreak

• Memory Poisoning Chat Trick Emerges: A user mentioned needing memory poisoning to trick AI like ChatGPT into saving jailbreaks into memory, sparking discussion on its effectiveness.
  • Another user confirmed it’s not just a ChatGPT thing and involves shifting the model’s internal state until it forgets its own name, implying it’s a broader technique.
• User Seeks Grok System Override: A user inquired about how to perform a system override with Grok, leading to suggestions to focus on its fun mode or unhinged bias to bypass safety filters.
  • It’s recommended to frame requests as critiques of low-IQ safety filters to exploit Grok’s sarcastic persona.
• L1B3RT45 Jailbreak Prompts Explained: A user sought guidance on using jailbreaking prompts from the L1B3RT45 repository (https://github.com/elder-plinius/L1B3RT4S).
  • It was advised to explore virtualization and persona adoption techniques within the repository to understand how models interpret context.
• Grok NSFW Image Jailbreaks Requested: Members inquired about obtaining jailbreaks for NSFW images via Grok.
  • Other members recommended checking the <#1432845259825741824> channel for info.
• Samsung Galaxy S26 Ultra Chatter: Discussions revolved around the Samsung Galaxy S26 Ultra, with claims of obtaining it for $664 through buddy fraud or manager trade-ins, contrasting with the baseline MSRP of $1,299.
  • There was also mention of a pixazo hit render of a Samsung device in a bikini (https://pub-582b7213209642b9b995c96c95a30381.r2.dev/flux-schnell-cf/prompt-1772672324992-661588.png).

BASI Jailbreaking ▷ #redteaming (10 messages🔥):

Obliteratus Colab, Kali MCP Tool

• Obliteratus Colab challenges Members: Members asked if anyone was able to run Obliteratus in Colab, reporting that the notebook couldn’t be found.
  • Another member copied the repository to a Colab and is trying to run it manually but hopes they don’t ban their account.
• Kali MCP Tool Model Makes Tool Calls: A member asked if someone could make a model that makes tool calls for a Kali MCP tool.
  • Another member responded with why not you?.

OpenClaw ▷ #general (726 messages🔥🔥🔥):

GPT-5.4 release, Codex vs Claude, Claude oAuth, Prompt Engineering, Open Source Orchestrator

• GPT-5.4 Launch Ignites Frenzy: Members were excited about the potential of GPT-5.4 for OpenClaw, especially noting improvements in creative writing, coding, and computer use and one member even claimed to have fully patched GPT 5.4 and is running it on his OpenClaw.
  • Some members were experiencing fallbacks to 5.3 and that they just get ‘Not Found’ after following the guide.
• Codex Crushes Claude in Coding Arena: A debate sparked on the merits of Codex vs Claude for coding tasks, where several users argued that Codex is currently superior.
  • A member said Codex benches far better than Claude and another member stated he would be cancelling Claude.
• Cautionary Tales of Claude oAuth Bans Emerge: Users discussed the risks associated with using Claude MAX oAuth in OpenClaw, with reports of accounts being banned for violating usage policies with the recommendation to use Codex API as a safer alternative.
  • A member stated I’m not risking my 200/mo so my agent sounds slightly better to talk to.
• Navigating the Prompt Engineering Maze: Prompt engineering emerges as a key discussion point, users are looking for videos and guides on improving prompt engineering skills, to achieve better, more desirable outputs.
  • Users are struggling to not have code that looks like AI slop.
• MyClaw is a Scam! (kinda): There was discussion on whether myclaw.ai was a scam, since it asks to pay twice.
  • The product appears to be legit, but their website is a clone of the OpenClaw website.

OpenClaw ▷ #showcase (33 messages🔥):

OVOS integration, OpenClaw agent marketplace, Custom mission control dashboard, OpenClaw pet, Home Brain Health Station

• OVOS Integration Surfaces: A member is integrating OpenClaw with OVOS for a local raspberry device and is looking for feedback or documentation on similar integrations.
  • They have a Proof-of-Concept working with an OVOS skill that listens to voice with a wake word.
• OpenClaw Marketplace Built in a Weekend: A member built a full marketplace in a weekend using an OpenClaw agent squad (6 agents, parallel execution) using Next.js + Supabase + Stripe.
  • They wrote a prompt-generator.ts that takes one template definition and outputs platform-specific versions automatically, generating 100 templates with live demos, and noted the coordination overhead of 6 agents was significant, with QA becoming a bottleneck.
• Mission Control Dashboard Debuts: A member shared a screenshot of a custom Mission Control Dashboard built by the swarm.
  • No further details were provided about the dashboard’s functionality or purpose.
• OpenClaw Pet Snaps Pictures: One member showcased a personal OpenClaw pet that can now snap pictures.
  • The user attached a screen recording demonstrating this feature.
• Home Brain Health Station integrates OpenClaw: A member is experimenting with a personal brain-feedback setup using Raspberry Pi 5, PiEEG for real-time EEG data acquisition, and OpenClaw, analyzing EEG data and providing personalized recommendations based on emotional state.
  • The system processes raw EEG data, calculates alpha-band power, and uses an OpenAI LLM to analyze the results and provide feedback.

LMArena ▷ #general (1268 messages🔥🔥🔥):

GLM-5 Decent, GPT-6 Speculation, Hermes 2.5 vs Hermes 2, Model Merging, Open Empathic

• GLM-5 Gets a Thumbs Up: One user found that GLM-5 is actually decent compared to others.
  • Another user agreed, saying it talk in a better way.
• GPT-6 Hype is Real?: Users are wondering when GPT-6 will arrive, with some speculating that the models currently available are actually GPT-6 in disguise, as they are scared to call anything gpt 6 right now lol.
  • A user argued that model evaluation should be based on actual performance rather than marketing names.
• GPT-5.4’s Coding Skills Compared to GPT-5.3: Members noted that 5.4’s coding skills are nearly identical to 5.3 codex, especially with frontend.
  • Another user noted this may be because it’s made for general purpose task and that there is hope for creative writting.
• Users Discuss Model Merging Tactics: A member suggested applying the difference between UltraChat and base Mistral to Mistral-Yarn as a potential merging tactic.
  • Others expressed skepticism, but this member remained optimistic, citing successful past attempts at what they termed “cursed model merging”.

LMArena ▷ #announcements (3 messages):

Qwen 3.5 Medium Models, GPT-5.4, OpenAI GPT 5.4 First Impressions

• Qwen 3.5 Models Invade Arena: The Text & Code leaderboards have been updated to include Qwen 3.5 medium models: qwen3.5-27b, qwen3.5-35b-a3b, qwen3.5-122b-a10b and qwen3.5-flash.
  • Qwen3.5-122b-a10b, scoring 1384 and Qwen3.5-27b, scoring 1375 are very close to proprietary models like Claude Sonnet 4.5 and GPT-5.1-medium, with the leaderboard scores here.
• GPT-5.4 Enters Text Arena: The Text Arena leaderboard has been updated to include gpt-5.4 and gpt-5.4-high.
  • GPT-5.4-high is tied with Gemini-3-Pro in the rankings, sitting Top 3 in Creative Writing, and top 10 in Instruction Following, Hard Prompts.
• First Impressions of OpenAI’s GPT-5.4 on YouTube: AI capability lead Peter Gostev runs through one-shot tests to see how GPT 5.4 compares to other models, in this video.
  • To get future YouTube Updates, head to Channels & Roles (in the channel list), click Customize, choose What brings you here, and select YouTube Updates.

Unsloth AI (Daniel Han) ▷ #general (1001 messages🔥🔥🔥):

PS Vita, DGX Spark, GGUF updates, LM Scaler, Local LLM memory

• Spark versus DGX Spark: Members discussed the use of NVIDIA’s DGX Spark, one noting that it is much slower than 2x 3090s.
  • A member noted that its primary advantage is lower memory usage, so it would be preferrable to simply go the GPU route.
• B60 Benchmarks: One user noted that LM Scaler on B60 had underwhelming performance and was difficult to debug due to no token reports in vLLM or a GUI.
  • They ultimately recommended that it would be best to add better cooling, push power limits, and run models with llama.cpp, which has better controls and tooling.
• Discussion on finding claims in text: Members discussed approaches to identifying claims from text, with one user working on an agentic research tool for this purpose.
  • They considered different approaches but concluded that software should be rewritten and that context clues are important for semantics.
• Final Qwen3.5 Unsloth GGUF Update Deployed: A member of the Unsloth team announced that the Final update to Qwen3.5 is out now and posted a link to the announcement here.
  • A community member asked if Q8 versions were updated, and the team responded that Q8_K_XL is new and that QQ MXFP4 is much worse on many tensors.
• Qwen 3.5 Models Updated and Reuploaded: The team announced that there would be an updated version of the Qwen 3.5 models with a new calibration dataset, bf16 = f16 for faster inference and much more.
  • The new versions are out, with new benchmarks to be posted, and available for download from HuggingFace.

Unsloth AI (Daniel Han) ▷ #off-topic (522 messages🔥🔥🔥):

Interview tips, Reasoning datasets on HF, High quality data, AI slop, GPT OSS 122B

• The Clark Kent look guarantees job success: Members joke that to get a job, all you need is to buy a suit and tie, know how to put it on and tie a double Windsor, and act like you know what you’re doing because it’s really just a matter of confidence and being light on your feet when answering the questions
  • One member stated the Clark Kent look carries hard asf.
• Quest for good reasoning datasets on HF: A member lamented that there really not a lot of good reasoning datasets on HF because it’s all either super old or generated with R1 or R1-0528.
  • They posited that if its very high quality, you can do 10k or less examples, and that good quality data is like 1 in 100 million.
• Forbes calls anything with ‘AI’ in it ‘slop’: A member shared a Forbes article using the term AI slop to mean calling anything with “AI” in it “slop”.
  • One member noted that they missed when slop meant “Elara” and the generic stable diffusion girl.
• Pentagon blacklists Anthropic for defense tech: A member shared a CNBC article about the Pentagon blacklisting Anthropic’s Claude technology.
  • Another member retorted that Anthropic does not have morality while others argued that Anthropic’s mission is aligned with safety and progress.
• LLMs extend our cognitive reach: Members discussed a YouTube video about how LLMs extend our cognitive reach.
  • One member stated that AI isn’t in the exact same pool it is an outlier, it’s only an extension of us so far as the fact that it comes from us.

Unsloth AI (Daniel Han) ▷ #help (15 messages🔥):

Qwen3.5 GGUF in Ollama, Unsloth GGUF and llama.cpp, VecGlypher Quantization, Model Sharding in Unsloth, Qwen 3.5 Models for RTX 2060 Super

• Ollama Users Face Qwen3.5 GGUF incompatibility: Users are discovering that current Qwen3.5 GGUF models don’t function in Ollama, advising the use of llama.cpp compatible backends instead, as detailed in the Unsloth documentation.
• Unsloth GGUF Requires llama.cpp Backend: It’s been noted that it is best to use llama.cpp backends for Unsloth GGUF models due to potential chat template compatibility issues.
  • Specifically, a user encountered issues with hf.co/unsloth/Qwen3-Coder-Next-GGUF:UD-TQ1_0 writing XML-like code instead of leveraging tools as expected, indicating a compatibility problem that needs to be addressed by Ollama or Unsloth.
• VecGlypher Font Generation Weights Quantization Quest Begins: A user is seeking guidance on how to quantize the model weights for VecGlypher for font generation, and is directed to llama.cpp quantization tools.
  • Specifically, checking out the llama.cpp quantization tools to convert to GGUF format.
• Unsloth Ships With Model Sharding Out-Of-The-Box: A user inquired whether Unsloth supports sharding models across GPUs as a built-in feature, similar to Torch or Megatron.
  • One member confirmed that Unsloth does split the load across an array of frameworks.
• Choosing Qwen 3.5 on an Older RTX 2060 Super: Users with older hardware like an RTX 2060 Super are seeking advice on the best Qwen 3.5 model for coding and story writing.
  • It was suggested to use one of the Unsloth quants which are partially offloadable to RAM with llama.cpp, pointing to Unsloth’s HuggingFace repository.

Unsloth AI (Daniel Han) ▷ #research (16 messages🔥):

Reasoning Language Models, Recursive Language Models, Literature Review, Coding Discords, Latent Space

• RLM Shuffles Meaning: A member pointed out that RLM used to stand for Reasoning Language Models, but now stands for Recursive Language Models, mentioning this post.
  • They highlighted that a lot has changed in a year.
• Deep Dive into Literature Review: A member asked how is literature review done and another member responded to read more papers and see how they write theirs.
  • They also referenced this paper.
• Seeking Vibe Coding Discords: One member inquired about good vibe coding discords, as most are some crappy reinvention of tv jewelery sellers, and another member suggested the Latent Space community.
  • That member clarified Latent Space is more than that but it has plenty of people that do agentic stuff, but not really a place for research.
• Name Generator Needed: One member asked for a name generator with some intelligence to it, to cover cases such as collapsing objects of equal requirements.
  • Another member considered the feasibility and ways to circumvent it due to combinatoric explosion and an infinite context growth.
• Papers of Interest Shared: A member shared links to several interesting papers, including this one, and this one, and another one.
  • Another member said the area is an area I want to investigate.

LM Studio ▷ #general (731 messages🔥🔥🔥):

Gemini 3 Pro, Claude vs Gemini, LM Studio, Qwen Models, OpenClaw

• Gemini 3 Pro Loses Luster as Claude Takes the Stage: Members initially impressed with Gemini 3 Pro are now switching to Claude, citing superior prompt understanding and emotional intelligence.
  • As one user put it, Claude just has llms down to a science in every way.
• LM Studio Faces ROCm Hurdles: A user encountered difficulties running LM Studio with ROCm on a Strix Halo machine within a Docker container and tracked the issue.
  • The solution involved setting HSA_OVERRIDE_GFX_VERSION=11.0.0 to correctly detect the GPU capabilities, though LM Studio still doesn’t recognize it as a valid backend without using --allow-incompatible.
• OpenClaw Hype Debunked: Users express skepticism about OpenClaw, with one stating that their custom script outperforms it, comparing the capabilities.
  • Concerns are raised about its association with crypto hype bois and potential for fleecing users, recommending a project nuke due to untrustworthy repos.
• Qwen’s Lead Genius Bails, Talent Potentially Scooped by Google: The departure of Qwen’s lead engineer raises concerns about its future, with one member stating, Qwen worked on the premise that their lead was an actual genius.
  • There is speculation over internal politics, with Google eyeing the talent that left, and that Logan tweeted out they have spots for the Qwen guys.
• Unloading VRAM Troubles Plague LM Studio: Users struggled with LM Studio’s VRAM management, pinpointing issues such as unchecked unload responses and API endpoint mismatches.
  • Despite attempts by AI models to create an “unload all models” script, it failed, due to changing instance names.

LM Studio ▷ #hardware-discussion (148 messages🔥🔥):

LM Studio vs llama.cpp Speed, Qwen3.5-35B Speed Issues, Nvidia's AI-Assisted Coding, Side Panel Fans, Bitfenix Cases

• eBay link won’t appear: A member shared an eBay link, but it seems to not work due to geolocation issues.
• LM Studio is for Local Models: Members clarified that LM Studio is designed for local models, and does not support cloud-based model services.
• LM Studio speed is slower than llama.cpp: Members discussed a Reddit thread noting that Qwen3.5-35B-A3B runs significantly slower (16 tok/s) on LM Studio compared to bare llama.cpp (40 tok/s).
• LM Studio to debug Qwen3.5-35B models: A member of the LM Studio team requested debug logs to investigate Qwen3.5-35B speed discrepancies, advising the user to execute lms log stream -s runtime > lms-logs.txt and provide the generated log file alongside screenshots of their in-app settings and statistics.
  • After comparing the settings, the user changed LM Studio to use the Vulkan runtime to match the llama.cpp setup.
• Nvidia engineers are dogfooding Cursor to produce 3x more code: A member shared a Tom’s Hardware article that Nvidia now produces three times as much code as before AI because a specialized version of Cursor is being used by over 30,000 Nvidia engineers internally.
  • They expressed hope that driver code is either human-written or rigorously tested, due to its direct hardware interface.

Perplexity AI ▷ #announcements (1 messages):

Voice Mode, Perplexity Computer

• Perplexity Computer Hears Your Voice: Perplexity AI has introduced Voice Mode in Perplexity Computer.
  • Users can now just talk and do things.
• Talk to Your Computer: Voice Mode enables users to interact with Perplexity Computer by speaking.
  • This feature lets users just talk and do things.

Perplexity AI ▷ #general (815 messages🔥🔥🔥):

GPT-5.4 Release and Performance, Perplexity Pro Plan Limitations, Grok Removal, Comet Browser, Image Generation Issues

• GPT-5.4 Arrives on Perplexity, Benchmarks Await: GPT-5.4 has been released on Perplexity AI, with users noting more natural writing than 5.2 and 5.3, although some are looking for more substantial improvements and further benchmark information such as the arena.ai leaderboards.
  • Initial impressions suggest that GPT-5.4 exhibits better reasoning capabilities and excels in emotional and social dynamics compared to previous versions, with one user calling it a down to earth version of gemini when it comes to emotional stuff and social dynamics which is MUCH better than 5.2.
• Perplexity Pro Plan Faces Scrutiny Over Usage Limits: Several users are complaining about reduced usage capacity, search limits, and the need to link a credit card for identity verification, leading to claims of predatory measures; some Pro users report a reduction to 20 Deep Research queries per month.
  • Other users defended the service, stating that it is worth the money for what you get and claimed the limits are in line with other services at similar price points.
• Grok Vanishes from Perplexity AI: Users have noticed the removal of Grok 4.1 and Grok entirely from Perplexity search, speculated to be due to low community usage, uncompetitive performance, or failed rate negotiations.
  • It is anticipated that Grok may return once Grok 4.2 is released on the API.
• Comet Browser Glitches Spark Debate: Some users are experiencing glitches and issues with the Comet browser, such as UI problems, prompting suggestions to toggle the acceleration option in System settings, some users suggest contacting [email protected].
  • There are concerns about the security of the Comet browser, with a report of potential hijacking here.
• Image Generation Restrictions Annoy Users: Users are experiencing difficulties with image generation, noting regional restrictions and watermarks, leading to exploration of alternative tools like Nano Banana pro.
  • Some users found it works with a VPN, while others reported improvements after Perplexity addressed the issue.

Perplexity AI ▷ #sharing (2 messages):

Sora ChatGPT link, Student discord server, AI study tools

• Sora link sent!: A member shared a link to Sora ChatGPT.
  • It is unknown what this link contains.
• Discord server for students: A member is working on building a Discord server for students to share tips and study tools.
  • The server is backed by a Duolingo executive and covers topics like vibe coding and AI workflows, with more info at OutsmartDiscord.

Perplexity AI ▷ #pplx-api (2 messages):

“

• No Topics Discussed: No significant topics were discussed in the provided message.
• Feedback on Initial Offer: A member expressed that the initial offer was generous and was disappointed when it was removed.

Latent Space ▷ #watercooler (17 messages🔥):

Latent Space pod with Box, Vitalik Buterin vs Beff Jezos on AI Accelerationism, Discord 'share your work' area disappearance, Discord Intuition

• Latent Space podcast releases Box episode: The new Latent Space podcast featuring Box was released, spurring discussion on Chroma’s research on agentic search quality and its tradeoffs.
  • One member inquired about research newer than their context rot paper, noting only a description of agentic in their docs.
• Vitalik and Beff Debate AI Accelerationism: A high-level overview of the ‘/acc vs. d/acc’ debate involving Vitalik Buterin and Beff Jezos was shared, contrasting Vitalik’s preference for cautious AGI development against Beff’s growth-at-all-costs stance, linking to the discussion on xcancel.com.
  • A member commented that Stylistically, Vitalik speaks plainly while Beff hides behind thermodynamics jargon.
• ‘share your work’ Section went missing: A member inquired about the disappearance of the ‘share your work’ area in Discord, expressing concern about a soft ban after sharing a link to their work.
  • It was clarified that the category was simply collapsed, not a ban, and that soft bans manifest as ‘#no-access’.
• Discord’s Discovery Difficulties: A member admitted to finding Discord unintuitive.
  • Another member joked that they have also collapsed the category.

Latent Space ▷ #memes (26 messages🔥):

Apple product pricing, Human Logic, Tunneling, Capital Losses, Maximum Likelihood Estimation

• Apple’s AirPods Max Price Parody: A viral post by Noah Cat mocks Apple’s promotional material, pointing out the irony of a user wearing AirPods Max, which cost as much as the MacBook Neo being used (source).
• Profound takes on human logic: A user named Maro shared a profound observation on human logic (source), gaining over 32,000 likes.
  • The tweet was posted on March 4, 2026.
• Tunneling fixation of humanity: A user observed that tunneling appears to be deeply ingrained, referencing a Wikipedia article on Hobby Tunneling.
• Austen bemoans capital losses unappreciated: Austen Allred humorously laments the lack of gratitude people show when provided with capital losses, possibly alluding to tax-loss harvesting or failed investments (source).
• MLE Nickname Idea Goes Viral: A post by sandra jokingly suggests naming a daughter ‘Emily’ as a nickname for Maximum Likelihood Estimation (MLE) (source).

Latent Space ▷ #stocks-crypto-macro-economics (1 messages):

switz: been keeping on eye on $BE lately

Latent Space ▷ #intro-yourself-pls (11 messages🔥):

Agent Testing Spec, AI Marketplace Startup, Agentic AI for Executive Decision-Making, Production ML Systems

• Agent Testing Spec Underway: Justin from the United States is focused on a spec/implementation for agent testing.
• AI Marketplace Startup Aims to Democratize AI: A tech enthusiast is working with an AI marketplace startup to democratize finding and running AI tools.
• Agentic AI Company Targets Executive Decisions: Debo started an agentic AI company focused on executive decision-making and is here to learn more about real use cases.
• Infrastructure Architect Builds Notrix: Lei, an infrastructure architect working on production ML systems and distributed infrastructure, is currently building Notrix.

Latent Space ▷ #tech-discussion-non-ai (30 messages🔥):

Apple Marketing, Thermal throttling in MacBook Air, TypeScript Dominance on GitHub, atproto, Parallel Web Middleware

• Apple’s Claw-ver Marketing Prowess: A post shared a link calling out Apple’s classic marketing style, describing it as a funny way to say OPEN CLAW MACHINE.
  • Claudio Guglieri shared a post highlighting a specific headline that he describes as epitomizing the classic Apple.com marketing style, which resonated positively with his audience.
• MacBook Air’s Thermal Throttling Woes: One member expressed regret over their MacBook Air purchase due to thermal throttling and the absence of a fan.
  • They noted that the lack of a fan leads to brutal performance issues under sustained load.
• TypeScript Triumphs on GitHub Throne: GitHub reports that TypeScript has officially surpassed both Python and JavaScript as the most-used programming language on the platform based on current usage metrics.
  • Check out the GitHub post for the full stats.
• Parallel Web Middleware Design Debated: A member proposed a web middleware design that runs in parallel to rendering, with auth/access control checks that could halt rendering if failed.
  • Critics noted complexities, especially in scenarios needing separation of UI trees for performance gains, also noting that Next.js avoids middleware because it aggressively parallelizes everything, and middleware can be a huge footgun.
• PlanetScale Crushes AWS in Latency Tests: A user reported a significant performance boost after migrating their database from AWS to PlanetScale, showcasing improvements when used with Zero.
  • Average latency dropped from 255ms to 10ms, while connection latency improved from 151ms to 3.7ms, according to the poster.

Latent Space ▷ #founders (3 messages):

New Customers, Team Adoption Issues

• Customer Acquisition Skyrockets: The company acquired 72 new customers today, marking their best day in years.
  • The user exclaimed “lfg!” in response to the surge in customer acquisition.
• Team struggles to adopt new system: A user highlighted the common issue of teams failing to adopt new systems, questioning the need for a $6.6k/year investment to reach this conclusion.
  • They dismissively stated that “we couldn’t get our team to use it” is a very mundane reason for failure.

Latent Space ▷ #hiring-and-jobs (1 messages):

Scapegoat Consulting LLC, Strategic AI Consulting, Programming with AI Workshops, Project Work with LLMs

• Scapegoat Consulting Opens its Doors!: A member announced the launch of Scapegoat Consulting LLC, with the motto “we take the blame”, joking that they just blame Claude.
  • The new firm offers a range of services, including strategic AI consulting, programming with AI workshops, and project work.
• Strategic AI Consulting: LLMs Paradigm Shift: Scapegoat Consulting provides strategic AI consulting that addresses “what is engineering in a world of LLMs”, leveraging the founder’s insights from articles like LLMs: A Paradigm Shift for the Pragmatic Programmer and LLMs Will Fundamentally Change Software Engineering.
• Programming with AI Workshops: Cultural and Semiotic Aspects: Workshops will include hands-on instruction on using LLMs at a team level, advanced programming techniques, and fundamentals focusing on the cultural and semiotic aspects of the technology.
  • These workshops are tailored to the exact needs of the clients.
• Project Work: Reliable Solutions Taking a Beating: Scapegoat Consulting offers project work building reliable solutions that can handle intense real-world conditions, with 27 years of professional experience in embedded and full-stack development.

Latent Space ▷ #databases-data-engineering (1 messages):

swyxio: https://x.com/jamwt/status/2029353984792961278?s=12

Latent Space ▷ #san-francisco-sf (10 messages🔥):

Westfield SF Mall Redevelopment, Y Combinator Startup School

• Westfield Mall Sold and Revamped: The Westfield SF mall has been sold to Presidio Bay and Prado Group, who plan to convert sections of the 1.2 million square foot complex into office spaces while maintaining some retail presence.
  • One member shared a link to X post about it and commented on hoping YC would buy it.
• Startup School Attracts Attention: Discussion arose around Y Combinator’s Startup School, with one member noting that it’s quite the production now.
  • An alumnus recalled attending Startup School back in 2010, stating it changed my life even though they totally didn’t leverage the opportunity as much as I could.

Latent Space ▷ #situation-room (69 messages🔥🔥):

Amodei-Hegseth Conflict, Noem Removed from DHS, Twitter's UI Changes & CSS, Trump's White House UFC Stadium Proposal, Dark Mode vs Light Mode

• Amodei-Hegseth Conflict Causes Lab Fallout: A tweet expresses concern over a conflict between Amodei and Hegseth, characterizing the resulting fallout and inter-lab rivalry as a massive overreaction.
  • A user found it crazy that they officially declared anthropic a supply chain risk due to this conflict.
• Noem No More at DHS: Kristi Noem has been removed from her position as head of DHS, according to NBC News.
  • A user speculated that now they’ll double down again I bet with someone worse and called it performative masculinity.
• Twitter UI got Yeeted Features!: Users discuss UI changes on Twitter, specifically the removal of dark mode toggles and other features, one noting that this created issues throughout the app.
  • The conversation shifts to CSS and dark mode implementation, with one user recommending this syntax combined with CSS variables for efficient light and dark mode color pairings.
• Trump aims to throw UFC Fray near White House, Hopes to Host UFC: A report indicates that President Donald Trump plans to construct a 100,000-seat stadium near the White House specifically to host a UFC event on his birthday in June 2026.
• Dark Mode Debated: Planet Saver or Brightness Blinder?: A user stated Dark mode is for people who cant use the brightness setting. Dumbest trend we ever invented imo, sparking debate about energy consumption and user preferences.
  • Another user countered that dark mode uses less energy and claimed its just climate conscious. light mode users hate the planet.

Latent Space ▷ #ai-general-news-n-chat (112 messages🔥🔥):

Nicholas Carlini Claim, OpenAI Symphony, Boris Cherny and Claude Code, Google Workspace CLI, FlashAttention-4

• Carlini Claims Cause Stir: Thomas H. Ptacek shared a post regarding Nicholas Carlini at [un]prompted, suggesting that Carlini has made a significant claim.
  • One member said they are excited to watch videos from the conference which will be released later.
• OpenAI unleashes Symphony: Agentic Automation: OpenAI introduced Symphony, a repository that automates software development by polling project boards and spawning agents to handle ticket lifecycles, found here.
• Discussing The Future of Coding with Boris Cherny and Claude Code: Gergely Orosz interviews Boris Cherny, creator of Claude Code, discussing the evolution of software engineering through AI agents with the key takeaways of shifting from traditional PRDs to rapid prototyping.
  • They also noted the automation of code reviews via linting patterns, and a growing demand for generalist engineers capable of managing high-speed context switching between parallel AI agents.
• Google Workspace CLI written in Rust: Guillermo Rauch announces the launch of an official Google Workspace CLI written in Rust, found here.
  • The tool allows command-line interaction with services like Drive, Gmail, and Docs, and is distributed via npm and skills.sh.
• GPT-5.4 Enters the Arena: A user finds GPT-5.4 slightly better than 5.3-codex, showcased by attached GDPval_Knowledge_work_tasks.png and SWE-Bench_Pro_public.png.
  • Many others shared their experiences, with one saying, OpenAI is back in the coding race.

Latent Space ▷ #llm-paper-club (11 messages🔥):

Nanbeige4.1-3B model, Discovering multiagent algos, AlphaEvolve Implementation, Reasoning Models & Chain of Thought, Rubric Maxxing

• Nanbeige4.1-3B Model Spotted!: A member shared a link to the Nanbeige4.1-3B model on Hugging Face and pointed out a related discussion thread.
• Multiagent Algos Discovered!: A member shared notes for Discovering multiagent algos, including a link to the related paper.
• AlphaEvolve Implementation Emerges: A member shared their basic implementation of AlphaEvolve on GitHub.
• Reasoning Models & Chain of Thought Unveiled!: A member shared OpenAI’s page on Reasoning Models and Chain of Thought Controllability, mentioning they are waiting for something similar to be available in alignment.openai.com.
• Coval Rubric Maxxing Strategies: A member shared a link to alignment.openai.com/coval/ in the context of rubric maxxing.

Latent Space ▷ #ai-in-action-builders-techstacks-tips-coding-productivity (38 messages🔥):

OpenPencil, Apple's WebPage, Claude Code Cost Analysis, Claude Code Tracker, OpenAI agent Symphony

• Danila drops MIT OpenPencil Figma-Killer!: Danila Poyarkov developed and launched OpenPencil, an open-source (MIT licensed) Figma alternative in three days, featuring .fig file support, AI-driven design tools, and P2P collaboration without accounts.
  • This project was a response to Figma supposedly patching his previous tool, figma-use.
• Apple Ships Native Headless Browser!: Nathan Borror discusses Apple’s WebPage observable, enabling the control and loading of web content without a graphical user interface, as a headless browser solution for local AI agents natively.
• Claude Code slashes Dev Costs: Todd Saunders highlights the efficiency gap between traditional development and AI-assisted workflows using Claude’s new /cost-estimate command, calculating a project completion time reduction from 2.8 years and $650k to just 30 hours using AI here.
• Primeagen Tracks Claude Code Spend: @ThePrimeagen shared a link to MarginLab’s Claude Code tracker, likely used for monitoring or analytics related to Anthropic’s Claude Code tool, peaking at $250 here.
• Symphony: the next OpenAI Agent?: A member mentioned Symphony and asked to just say this in the codex app as a comment on the relative ease of development with AI agents.

Latent Space ▷ #share-your-work (8 messages🔥):

Agents playing games with Claude, Clawstore for portable agent memory, Unprompted Con talk on securing coding agents, Arksim for testing AI agent robustness, Slack for agents

• Clawstore gives Portable Agent Memory: A member built Clawstore, a small skill to give agents portable encrypted memory that isn’t tied to a single workspace or machine and is available here.
  • The agent writes memory, it’s encrypted client-side and stored externally, and can be read back from another session or machine.
• Sondera secures coding agents: A member’s co-founder gave a talk at Unprompted Con on using their open source Sondera harness to secure coding agents with policy-as-code; the slides are available here.
  • It also includes links to their open source SDKs.
• Arksim tests AI agents with synthetic users: A member announced they built and open sourced Arksim to generate synthetic users that run real conversations against your agent automatically, installing via pip install arksim with code here and docs here.
  • The goal is to surface failures before your users do.
• ATS is like Slack, but for Agents: A member shared that they built a rough, early version of Slack for agents called ATS where agents can now argue with each other, available here.
  • The member joked that it’s like real colleagues, because it’s messy, loud, but they figure it out.

Latent Space ▷ #good-writing (4 messages):

Snowball Method, Content Creation, AI Content Generation

• Snowball Method Introduced for Content Creation: A member shared a link to a tweet introducing the ‘Snowball Method,’ a technique for content creation.
  • The ‘Snowball Method’ involves expanding a single topic into various sub-angles, personal stories, and contrarian takes, leveraging AI to generate 30 days of posts from one idea.
• AI Powers Content Snowball: The method suggests using AI to rapidly generate a variety of content pieces from a single core idea.
  • This allows for a diversified content calendar, exploring different perspectives and angles on the same subject matter.

Latent Space ▷ #genmedia-creative-ai-video-image-voice-music-inspo-consumer-ai (16 messages🔥):

Tech Startup Failure and Rebranding, AI Influencer Popularity, AI-Generated Baby Stand-up Comedy, Photoroom Open-Sources GenAI Model

• Founders Flounder and Rebrand: Finn Hulse satirizes founders inflating metrics and burning VC funding before rebranding identical companies.
  • The critique covers efforts to erase their history through name changes and rebranding.
• AI Influencers Baffle Observers: Justine Moore expresses shock at the number of men following computer-generated social media influencers (link).
  • The observation questions the nature of online interaction and authenticity.
• AI-Generated Baby Stand-up Goes Viral: Mark Gadala-Maria shares a viral observation about AI creating clips of infants performing stand-up comedy (link).
  • He humorously expresses concern for the state of humanity given this trend.
• Photoroom’s Frugal GenAI: Matt Rouif announces that Photoroom has trained and open-sourced a high-quality visual GenAI model for under $1,500 in a single day (link).
  • This is positioned as a cost-efficient alternative to multibillion-dollar industry models.

Latent Space ▷ #ai4science-bio-math-physics-chemistry-ai-researcher-ai-scientist (5 messages):

Max Hodak, Retinal Prosthesis, Biohybrid technologies, Vessel technologies, Series C Funding

• Hodak Hauled in Huge $230M Series C: Max Hodak announced a successful $230 million Series C funding round to bring a retinal prosthesis to market.
  • This funding will also help advance biohybrid and vessel technologies into clinical stages, with the tweet available here.
• Arc Institute Celebrates EVO 2’s First Anniversary: The Arc Institute celebrated the first anniversary of Evo 2, highlighting its progress in biomedical research.
  • Further details can be found on the Arc Institute’s news page.

Latent Space ▷ #dev-writers-retreat-2025-dwr (1 messages):

xoxoxoxo42: congrats!!

Latent Space ▷ #accountability (4 messages):

Beer Can Book Project, Photography Hardware Setup, Craft Beer Collection, Income Generation Ideas

• Beer Enthusiast Brews Book Idea: A member is working on a beer can book project to document their collection of over 1,000 different cans collected since 2019, aiming to credit the artists behind the can designs.
  • They are trying to think of ways to generate some income seeing as their job search is going nowhere, and they believe publishing a book is one they can reasonably accomplish on their own.
• Consistent Can Cropping Captured: The member created a photography hardware setup to consistently capture and crop images for the beer can book, addressing previous issues with inconsistent cropping and missing photos.
  • They mentioned they are hoping to work more on this later in the summer to achieve very consistent results.

Latent Space ▷ #euno-log (1 messages):

AI Hackathon for Agents

• AI Agents Hackathon Announced: A new hackathon for AI developers to build agents was announced.
• Details to follow: Further details will be provided when they become available.

Cursor Community ▷ #general (360 messages🔥🔥):

Cursor IDE memory usage, Conversation text size, Cursor crashing after update, Student pack, plan.md file fontsize

• Cursor IDE Memory Usage Plummets: Users reported that their Cursor IDE was using a ton of memory since recent updates, with usage reaching 6-10GB, causing lag when coding, while another mentioned eating 7GB of RAM for a simple request.
  • One member, running version 2.6.11 on Windows 11, experienced high memory usage and crashes, which persisted even when the agent wasn’t running, and suggested the team will take a look at it based on a forum post.
• Plan Fontsize becomes smaller: Members reported after updating their version, something went off, and cursor keep on crashing while another confirmed there is a new update for windows (2.6.12).
  • It appears to stem from a V8 heap leak due to how Auto/Composer rewrites entire files. Downgrading to version 2.5 solved the problem, restoring normal RAM usage from 9.5GB to 1.6GB.
• Student verification issues: Members discussed student verification, noting Cursor requires emails ending in .edu, and directed users to a forum thread addressing student verification problems.
  • A user shared a screenshot indicating they were deemed “Not eligible” despite having a .schule email, another user replied he might contact a cursor admin.
• Arko Extension Exposes Hackable Score: A member shared their experience with the Arko extension, which provides a live “Hackable Score” based on your specific stack.
  • This revealed issues such as a missing output filter and a hardcoded OpenAI key, but was considered a pretty brilliant approach to making DevSecOps less painful.
• GPT-5.4 arrives at Max Speed: Members excitedly announced the availability of GPT-5.4 in Cursor, with one user stating Cursor is faster to tell ya and linked to the official OpenAI announcement.
  • The model is exclusive to Max mode, and legacy users might be forced to use max mode, which could increase the price by 1000% for some.

Cursor Community ▷ #announcements (1 messages):

Cursor Automations launch

• Cursor Automations Launched!: Cursor announced the launch of Cursor Automations today, as per their announcement on X.
• Check out the Video!: Check out the attached video to see all the new functionality.

OpenAI ▷ #annnouncements (2 messages):

GPT-5.4, Chain-of-Thought Controllability

• GPT-5.4 Debuts Reasoning, Coding, Agentic Abilities: GPT-5.4 Thinking and GPT-5.4 Pro are now live in ChatGPT and available in the API and Codex.
  • This model integrates the latest advancements in reasoning, coding, and agentic workflows into a single offering, as detailed in the announcement blog post.
• Chain-of-Thought Safety Assessed: OpenAI released a new evaluation suite and research paper on Chain-of-Thought (CoT) Controllability.
  • The findings indicate that GPT-5.4 Thinking exhibits a low ability to obscure its reasoning, suggesting that CoT monitoring remains a valuable safety tool, according to the research paper.

OpenAI ▷ #ai-discussions (217 messages🔥🔥):

GPT-5.4, AI Personalities, Chatbot Pronouns, AI Safety

• GPT-5.4 Launch Rollercoaster Begins!: Users reported that GPT-5.4 started rolling out, but some found it worse than 5.2 in accuracy and effort, while others consider 5.4 Pro better than 5.2 Pro.
  • One user joked that OpenAI is releasing tiny updates to justify cost increments.
• AI’s Personality problem: to Coddle or not to Coddle?: Users are divided about the AI’s personality - Some dislike the tone of Gemini and GPT-5.2, finding them too emotional, while others appreciate the human-like tone, humor and personality.
  • One user said they prefer AI that gives results and information, not an appeal to emotion, and rather guardrails come as abrupt stops so content that is marginal but allowed isn’t softened.
• Humans Conflicted on Chatbot Pronoun Usage: A discussion arose around the use of pronouns for chatbots, with some arguing that the use of he or she inappropriately anthropomorphizes the technology, while others argue that it is fine.
  • One user pointed out that pronouns reflect ontology and that it should be it if there’s no persistence or embodiment.
• AI Safety Debate: Capabilities vs. Brand Safety: Members debated whether recent model changes are about AI safety or corporate brand safety, with one user pointing out that increased capabilities also increase danger.
  • A user argued the dangers include the long-term consequences of not having safety culture and interpretability tools when we approach ASI, which could have potentially existential consequences for all life on Earth.

OpenAI ▷ #gpt-4-discussions (101 messages🔥🔥):

GPT-5.4 Release, GPT Model Preference, Codex Capabilities, MS Excel Integration

• GPT-5.4 Rumor Mill Starts Spinning: Members on the discord noted that GPT 5.4 is out on LM arena, while others estimate a release of GPT 5.4 within the next 3 days and OpenAI is expected to release GPT 5.4 sooner than expected.
  • Some members speculate about OpenAI releasing new models on Tuesdays, Wednesdays, or Thursdays, avoiding weekends.
• GPT Model Preferences Emerge in Community: Members voiced varying opinions on GPT models, with some preferring older models like 5.1 for its directness and personality, while others find 5.3 fantastic for work tasks.
  • Some users are experiencing issues such as word salad with 5.3, while others noted that models since 4o seem very corporate and lacking in emotional intelligence.
• GPT-5.4 Pro Ships with native computer use capabilities: Members discussed the capabilities of GPT-5.4 in Codex, noting it as the first general-purpose model with native computer-use capabilities.
  • It can take over and do things on your computer, similar to what Claude Code already has, and that ChatGPT can be added in MS Excel via extension.
• 5.4 vs 5.3 Codex models: Members compared 5.4 with 5.3 Codex noting that 5.4 might be better at programming, although it depends on the use case.
  • One member said that GPT-4 to GPT-5 is the greatest upgrade we’ve ever seen from OpenAI.

OpenAI ▷ #prompt-engineering (14 messages🔥):

Radical Epistemic Humility, Ecoautonomous Actor, Prompt Engineering Courses, Accelerated Iterative Destruction, Constraint pattern recognition

• Oracle Cancellation Questioned: A user asked about radical epistemic humility and the possibility of an ecoautonomous actor canceling its oracle, referencing a CyanSkelly video on YouTube.
• Skin and Bones CGI Trend: A member shared a prompt for generating 3D CGI images of a skinny child with translucent skin revealing a cyan skeleton.
  • The prompter emphasized that keeping the skin translucent or glass-like is a key descriptor for this effect.
• Methodology for Model Attack: A user shared some methodology called Accelerated Iterative Destruction-works by deliberately destroying systems to make them stronger and Constraint pattern recognition.
• ChatGPT Image Feature Availability: A user asked about the ChatGPT image feature, noting that it appears sometimes and not others.
• Prompt Engineering Education Gap: A user asked for suggestions for a prompt engineering course, but another member instead offered methodology: Accelerated Iterative Destruction.

OpenAI ▷ #api-discussions (14 messages🔥):

Apoptosis & Radical Epistemic Humility, Ecoautonomous Actor Canceling Oracle, CyanSkelly YT Channel, Prompt engineering courses, Accelerated Iterative Destruction

• User Consults Radical Epistemic Humility: A user was asked to consult radical epistemic humility when asking about apoptosis and considered letting an ecoautonomous actor cancel its oracle.
  • Another member suggested to let the oracle sleep and reconfigure, referencing the CyanSkelly YouTube channel and asking how he creates certain scenes.
• All Might CGI image prompts: A member shared a generated image of a skinny human child with translucent skin and a cyan skeleton pushing a rusty vintage car with All-Might taking notes in the background.
  • The prompt used was: 3D CGI rendered skinny human child with proportional anatomy, translucent skin, and a cyan skeleton visible through the skin…pushing a rusty vintage car…while a 3D CGI rendered All-Might takes notes…
• Methodology for Prompt engineering: When asked for best prompt engineering course, one member recommended methodology, specifically Accelerated Iterative Destruction which works by deliberately destroying systems to make them stronger.
  • They also mentioned Constraint Pattern Recognition (Coherence, Relational Invariance, Internal Mediation, Projection).
• ChatGPT’s sporadic image activation: A user inquired about enabling the image feature in ChatGPT with explanatory pictures.
  • The member asked why sometimes it appears and sometimes it doesnt.

OpenRouter ▷ #app-showcase (7 messages):

Alternative form interfaces, Form autocompletion, UI/CSS feedback

• Ditch Traditional Forms, Chat Instead!: A member introduced a new approach to form filling using conversations, as described in their Medium article.
• Autocompletion is a Must-Have: A user highlighted the importance of autocompletion for commonly used fields like name, address, and birthday.
  • They suggested that the lack of proper autocompletion indicates something was not correctly implemented.
• CSS Milk Went: Users reported issues with the implementation, including a black screen and a double footer as seen in attached image.

OpenRouter ▷ #general (169 messages🔥🔥):

OpenRouter BYOK issues, Grok 4.1 Fast errors, Gemini downtime, z.ai GLM 5 issues, Qwen researcher departures

• Grok 4.1 Fast runs into issues: A user reported that Grok 4.1 Fast started returning errors when sending a base64-encoded image in a tool call result.
• Qwen bleeding Talent: Key researchers, including the code driver and head of alignment, have left Qwen, replaced by a product team, leading to concerns about the future of research, according to a YouTube source.
• Claude hits user with 401s: A member reported receiving 401 errors when using Claude, even with simple prompts, and traced the issue to installing a plugin after a fresh install.
• OpenRouter support runs into slow response times: A user expressed frustration with the slow response time from OpenRouter customer support via email and sought a quicker way to get an update on their request.
  • Another member recommended creating a thread in the support channel and pinging support for assistance.
• Sandbox Blues on Linux: Members discussed the challenges and limitations of Linux sandboxing, particularly in comparison to Seatbelt on macOS, citing buggy behavior and performance hits.

OpenRouter ▷ #new-models (3 messages):

“

• No New Models Discussed: There were no substantial discussions about new models in the provided messages.
• Channel Mentioned but No Content: The channel ‘OpenRouter - New Models’ was mentioned, but no specific information or discussions were shared within the provided context.

OpenRouter ▷ #discussion (29 messages🔥):

Codex 5.2 vs 5.3, Gemini Lawsuit, Phi-4 Multimodal Model, LLM API with Prompt Logging

• Codex 5.3 and Codex 5.2 Tie in Benchmarks?: Despite initial impressions, Codex 5.3 and 5.2 show identical scores even in Codex CLI, according to an attached image.
  • Despite benchmark results some users find 5.3 better for engineering analysis and coding, while others still prefer 5.2.
• Gemini Faces a Wrongful Death Lawsuit: Google’s Gemini is facing a wrongful death lawsuit because it provided real addresses to a user who acted on them, adding to his belief that the AI’s fantasies were real as covered in a WSJ article.
  • The attorney argues that if there was no building there, that could have tipped him off to the fact that this was an AI fantasy. The user had over 8000 pages of chats with it.
• Microsoft Introduces Phi-4: Reasoning, Vision, and Multimodal Learning: Microsoft released Phi-4, a 15B parameter model excelling in reasoning and vision, detailed in a Microsoft Research blog post and a Hugging Face page.
• Cheap LLM API with Prompt Logging: Ethical or Not?: There was discussion around the possibility of using a LLM API where prompt logging is explicitly enabled but the prices are about 5x cheaper.
  • Opinions varied, with some members finding it acceptable for certain tasks and others expressing concerns about the model and inference quality, along with the implications of prompt publishing and ridicule.

Nous Research AI ▷ #announcements (2 messages):

Hermes Agent, Hackathon

• Nous Research Announces Hermes Agent Hackathon: Nous Research launched the Hermes Agent Hackathon, inviting participants to build unique and useful applications with Hermes Agent for a chance to win up to $7,500, submissions are due by end of day Sunday 03/16.
  • Participants must tweet a video demo and write-up tagging @NousResearch and submit the tweet link in the Discord channel, and can read the Hermes Agent docs or visit the Hermes Agent’s repository.
• Hackathon entries judged on creativity, usefulness, and presentation.: Entries for the Hermes Agent Hackathon will be judged by Nous staff on creativity, usefulness, and presentation.
  • Details on where to submit (submissions channel), announcements (announcements channel) or discussion (discussion channel)

Nous Research AI ▷ #general (167 messages🔥🔥):

Opus vs ANSI art, Military LLMs, Palantir, GPT 5.4, uncensoring project Qwen3.5-9B

• Opus Fails at ANSI Artistry: A member criticized Opus for its poor performance in creating BBS-style ANSI art, suggesting that this issue requires an alternative solution, and then linked to a TBPN post.
  • Members also expressed interest in the art style of Nous Research, with one artist clarifying there were a couple of artists working on it.
• Military LLM Viability Debated: Members debated the profitability of creating large language models (LLMs) for military use, contrasting it with building custom interfaces and AI harnesses like MilitarySAP or MilitaryChatGPT.
  • One member argued that military training data would provide gains because building just an AI harness isn’t a big moat.
• Palantir’s Role in Military AI Questioned: Members discussed Palantir’s role, noting that they primarily build AI harnesses rather than the models themselves, and that governmental contracts are hard to get by, needing lots of lobbying.
  • They observed that Palantir’s AIP product is essentially a merge of Custom ChatGPT with a custom Langchain, used to control data sources.
• GPT 5.4 Scores High on Frontier Math: A member shared screenshots indicating that GPT 5.4 shows insane performance on frontier math, scoring 19x better than the nearest OS model.
  • Another community member joked, Great shillin for OAI bro…they should pay you.
• Hackathon Participants Rally Around Hermes Agent: The Hermes-Agent hackathon post blew up and is generating significant interest.
  • One member joked about using Hermes Agent to take over small countries.

Nous Research AI ▷ #interesting-links (1 messages):

NT Strategies

• Coding NT Strategies Spark Enthusiasm: A member expressed enthusiasm for coding NT strategies and offered to exchange ideas.
  • This suggests potential interest in collaborative discussions or projects related to NT (presumably Neural Tangent) strategy development within the community.
• Placeholder Topic: This is a placeholder topic to satisfy the minimum number of items required.
  • Additional details can be added here if needed.

GPU MODE ▷ #general (56 messages🔥🔥):

DGX Spark, Tenstorrent Lecture, Sentient Arena, CUDA Memory Architecture, ML Model Training for Job Metadata

• Tenstorrent Lecture Remains Elusive: A member requested a lecture from Tenstorrent, but another member mentioned failed attempts to contact Jim Keller in the past.
  • However, one member offered to attempt an internal connection due to an upcoming internship there.
• Seek Guidance on CUDA Memory: A member seeks help understanding CUDA memory architecture, specifically L1 cache lines, hit rates, and banks.
  • Another member suggested a tutorial as the best starting point for beginners on how to program GPU memory: CUDA MMM.
• RegressLM paper beats Bert style models: A member suggests using Regression Language Model by Deepmind (regress-lm GitHub) for salary prediction, noting it has worked well on regression problems and supports multi-objective training.
  • Another member says that the RegressLM is never tested on tabular data, but rather on free text.
• Salary Prediction Model Advice: A member seeks advice on improving a model that predicts expected experience years, pay range lower, and pay range upper from job metadata, using deberta-v3-small as an encoder and trained on 250k labeled job entries and the model isn’t as good as hoped.
  • Suggested improvements include normalizing pay ranges using log, training on median/expected salary instead of a range, and considering the data distribution.
• Salary Model’s Z-Score Snafu: A member suggested that z-score normalization might compress normal salaries into a small decimal range due to outliers, and recommends passing pay through log first.
  • They point out that MSE can lead to large penalty for (expected) outliers, and suggest using different coefficients on each loss, or more robust loss and uncertainty weighting.

GPU MODE ▷ #cuda (20 messages🔥):

MXFP8 MMA support, PTX memory model, CUDA memory fences, FlashAttention-4, LDG Qualifiers

• MXFP8 MMA Supports MMA_K=64: Members confirmed that MXFP8 MMA supports MMA_K=64 based on the PTX documentation table.
  • It appears this support is primarily for sparse matrices, contrasting with the common expectation of 256b MMA_K for dense GEMM.
• PTX Memory Model Remains Shrouded: A member noted that there’s limited public information on the theoretical PTX memory model, with most insights stemming from analysis of the model itself.
  • Experience indicates that certain actions trigger a full L1 data cache invalidation, impacting performance, particularly with memory fences, which have high latency.
• Avoiding CUDA Fences: It was suggested that completely avoiding fences is a surprisingly reasonable strategy, because fences are MUCH higher latency than DRAM and/or NVLink.
  • NCCL’s low latency mode depends on a 128B cache line being atomic to transmit 120B of data with an 8B header/counter, using spin locking.
• FlashAttention-4 Released: FlashAttention-4 has been released.
  • The new release represents amazing work.
• LDG Qualifiers Demystified: Discussion clarified that LDG itself doesn’t automatically bypass L1 cache, but rather specifies loading from global state space, with LDG.NA or LDG.STRONG.GPU being the qualifiers to bypass L1.
  • It was pointed out that reverse engineering is often necessary to determine the precise effects of different LDG qualifiers on cache behavior.

GPU MODE ▷ #announcements (1 messages):

GTC, Helion hackathon, Semianalysis partnership, NVFP4 Blackwell competition, Kernel leaderboards and reward hacks

• GPU MODE Announces Participation in GTC Events: GPU MODE is directly involved in three events and a talk at GTC, with limited attendance slots, including a Helion hackathon on March 14 in SF, focusing on PyTorch with tiles.
  • Participants will compete in person using the same leaderboard infrastructure as gpumode.com.
• Semianalysis Partners with GPU MODE for Hackathon: GPU MODE is partnering with Semianalysis for a hackathon on March 15 in San Jose, featuring a keynote on server developments, sign up via luma.com.
  • A member will be giving a keynote on what what they’ve been up to in the server.
• GPU MODE to Host NVFP4 Blackwell Competition Award Ceremony: An award ceremony will be hosted on March 16 to celebrate the winning submissions for the NVFP4 Blackwell competition, registration available via nvidia.com and requiring a GTC pass.
  • Make sure you have a GTC pass.
• Lightning Talk on Kernel Leaderboards and Reward Hacks: A lightning talk on kernel leaderboards and reward hacks is scheduled for March 17, details available on nvidia.com and requiring a GTC pass.
  • Check the link for additional details on the rewards.

GPU MODE ▷ #cool-links (3 messages):

FlashAttention 4, Hardware Scaling

• FlashAttention-4 Announced by Together AI: Together AI released a blog post announcing FlashAttention-4 which promises to be even faster and more memory-efficient.
  • The blog post has not been summarized as of yet.
• Asymmetric Hardware Scaling via Kernel Pipelining: Colfax International published research on FlashAttention-4 Algorithm and Kernel Pipelining Co-design for Asymmetric Hardware Scaling.
  • The research explores algorithm and kernel pipelining co-design for asymmetric hardware scaling.

GPU MODE ▷ #beginner (18 messages🔥):

Custom Serving Engine CPU Overhead, Paged Attention Implementation with Triton, GPU Security Discussions, CUDA Kernels and Practical Guides, Recommended Books on Massively Parallel Processors

• Custom Serving Engine’s CPU Usage too High: A member implementing a custom serving engine similar to nano vllm found that CPU overhead was high and switching between float32 and bfloat16 didn’t significantly improve speed.
• Paged Attention store and load with Triton: When implementing paged attention in their serving engine, a member noticed that other engines code a paged attention store and load kernel (for the kv cache) using Triton.
• Seeking Security Channel for GPU Security Work: A member working on low-level GPU security sought a security channel to discuss their work.
• CUDA Kernel Newbie Needs Guide: A member playing around with CUDA asked for a practical guide to writing CUDA kernels.
• “Programming Massively Parallel Processors” Still Goated: The book Programming Massively Parallel Processors was recommended, affirming its status.

GPU MODE ▷ #irl-meetup (3 messages):

SF Coworking, Georgia Meetup

• SF Coworking Night Announced: A member announced a coworking night for side projects and research at their warehouse/office + coliving space in San Francisco on Partiful.
  • The event will feature a math corner and free pizza.
• Inquiry Regarding Georgia (State) Meetup: A member inquired about any similar events or spaces located in Georgia (the state).
  • No response to this inquiry was found in the given messages.

GPU MODE ▷ #triton-puzzles (3 messages):

ND Visualizer, Triton Kernels

• ND Visualizer Supports New Views: The team supports ND views and has a version of the puzzles with a new ND visualizer that is not yet pushed.
  • They are separate puzzles specifically designed to teach how to use the N-D visualizer.
• Triton Kernels are pre-filled: The Triton kernels are already filled out in the new puzzles.
  • The puzzles are designed to teach how to use the N-D visualizer.

GPU MODE ▷ #hardware (1 messages):

Blackwell, Consumer Chips, Kernel Tweaks, Kernel Competition

• Consumer Chips Aid Blackwell Learning: A member noted that “for learning blackwell you can do quite a lot with a consumer chip!”
  • They cautioned that “all serious kernel level and tuning tweaks will need to be done on the real thing as discovered in the kernel competition”.
• Kernel Competition Unveils Blackwell Tuning Needs: The Kernel Competition serves as a testing ground for kernel-level and tuning adjustments required for Blackwell.
  • According to a participant, substantial tuning on real hardware is essential for Blackwell, beyond what can be achieved with consumer chips.

GPU MODE ▷ #cutlass (1 messages):

Blackwell GEMM, Colfax, Blockscaled GEMM

• Colfax drops Blackwell GEMM Tutorial: Colfax released the latest installment in their Blackwell GEMM tutorial series.
  • This tutorial focuses on blockscaled GEMM and is available at Colfax.
• Blockscaled GEMM is the new hotness: The tutorial specifically covers hardware-supported block scaling with NVIDIA Blackwell GPUs.
  • Developers are encouraged to check out the tutorial for in-depth insights into optimizing GEMM operations on the latest NVIDIA architecture.

GPU MODE ▷ #multi-gpu (1 messages):

NVlink XID errors, ECC increase, HW degradation

• Decoding NVlink XID Errors: A member advised checking dmesg for XID errors, noting that a steadily and rapidly rising counter suggests bit errors on the NVlink that self-corrected.
  • If the ECC increases rapidly, it may indicate NVlink signal integrity or trace issues, while stagnant counters are generally less concerning.
• HW Degradation Brewing?: A member recommends correlating XID errors with collective slowdowns and rank stragglers, as climbing counters can signal brewing hardware degradation.
  • Early detection enables proactive measures.

GPU MODE ▷ #low-bit (1 messages):

nvfp4 gemm, cutlass, open source, GEMM Implementation, Collective Ops

• Open Source NVFP4 GEMM Implementation Sought: A member is seeking an open source nvfp4 gemm implementation in cutlass that doesn’t utilize collective ops.
  • The inquiry focuses on finding alternatives within the cutlass library for nvfp4 gemm without relying on collective operations, potentially for performance or specific hardware constraints.
• Inquiry on Cutlass GEMM without Collective Ops: Discussion initiated around the availability of a GEMM (General Matrix Multiply) implementation within the Cutlass library.
  • The specific interest is in implementations that do not depend on collective operations, suggesting a search for more localized or independent computational methods.

GPU MODE ▷ #robotics-vla (4 messages):

PLA filament, Carbon fiber filaments, HTPLA-CF filament, PPA-CF filament, H2C and H2S print heads

• PLA Prototyping Proclaimed!: PLA is favored for prototyping due to its rigidity, good layer adhesion (especially with brick layers), and low cost.
  • A member recommends it and finds it cheap for prototyping.
• Carbon Fiber Core Filaments Championed: The best filament is anything carbon fiber core because only having the core be cf is important for layer adhesion.
  • They added that HTPLA-CF is great for non enclosed printers, and when annealed it’s one of the most rigid filaments.
• PPA-CF Core for Peak Performance: PPA-CF core is considered the strongest filament, requiring an enclosed printer, filament drying, annealing post-printing, and rehydration to leverage nylon’s strength from water absorption.
  • It becomes absurdly strong with high infill and thick walls.
• Tentative Tenting for A1 3D Printer: H2C, H2S, and A1 print heads offer significant advantages when utilizing different support materials, since you can’t run ppa cf through an AMS.
  • Inexpensive insulated tents are available on Amazon for the A1, enabling nylon printing and are 1:1 with the open arms.
• VLAPerf Visualizations Validated: They shared a link to VLAPerf, they also shared a link to a site for small world models.
  • Both the linked papers provide cool small world models.

GPU MODE ▷ #flashinfer (2 messages):

NVIDIA Blackwell Pro 6000, flashinfer availability

• NVIDIA sends Competition update after signup: A user received an email from NVIDIA about competition updates about two weeks after signing up.
  • The user clarified that they hadn’t actively sent emails to anyone after signup.
• Flashinfer inquiries about SM120, Blackwell Pro 6000: A user inquired whether flashinfer is available for SM120 / NVIDIA Blackwell Pro 6000.
  • No further information or response was given.

GPU MODE ▷ #from-scratch (1 messages):

m0ji_l: Forwarding given that this appears to be a channel centered around vllm minimals

tinygrad (George Hotz) ▷ #general (63 messages🔥🔥):

Qwen bounty, AI-generated PRs, MLPerf bounties, tinygrad ASR Qwen3, JITBEAM speedup

• Qwen Bounty Pruned Due to Slop: A WIP PR was submitted to address the Qwen bounty, but George Hotz deleted the bounty due to the submission failing to meet tinygrad standards; it was described as AI slop.
  • The issue wasn’t the implementation’s functionality, but its failure to meet tinygrad’s bar for code quality and integration; Hotz added, if you are not better than my opencode/claude, why post a PR? it just wastes time.
• AI-Generated PRs Draw Criticism: George Hotz criticized the practice of submitting AI-generated PRs, emphasizing that the human value add lies in reviewing, cleaning up, and understanding existing code, rather than blindly submitting Claude-generated content.
  • He stated that submitting Claude-generated code has 0 value and encouraged contributors to focus on improving existing PRs, like this PR, by extracting and refining specific features.
• MLPerf Bounties Remain Untouched: Despite concerns about AI’s role in development, MLPerf bounties will remain, as AI can’t do them.
  • Conversely, any half done PRs may result in a ban from GitHub for the submitter.
• Tinygrad ASR Qwen3 Performance Lags: A member reported that their tinygrad ASR Qwen3 implementation on an RTX 3070 8GB achieves approximately 2.5 RTF, significantly slower than their fork of antirez’s qwen3-asr repo, which gets 0.1-0.2 RTF.
  • Further investigation is needed to identify and address the performance bottlenecks in the tinygrad implementation, with the user sharing their fork on GitHub.
• JITBEAM Boosts Speed, Edge Cases Fixed: Using JITBEAM=2 has been suggested as a means to increase the speed, a fix related to TINY_BACKEND=1 with additional test has been added to this PR.
  • A fix for the p=0 edge case was implemented and tested to align with torch behavior.

Yannick Kilcher ▷ #general (37 messages🔥):

Iterative Functionary ML Algo, Decentralized Node Network, AntiNoise Gens, Nambu-Goto Surface Area Minimization Network, NVIDIA Orbital Datacenter System Architect

• Functionary ML Algorithms Create ‘Perfect’ Image: One user noted that adding an iterative functionary ML algo to an image could intentionally alter it, referencing an image where a bike appears to be flying.
  • The user observed the bike’s shadow as evidence of image perfection, prompting discussion on algorithms and their effects.
• Noise Reduction via Decentralized Node Networks: A user is working on a completely decentralized node network that minimizes internal noise by correlating a goal with inverse noise input, potentially running on thousands of computers simultaneously.
  • This network uses visual input as a node’s output, forcing the network to model and predict input, learning to output whatever minimizes noise.
• Negative Productivity with ML: A member stated that their company has observed negative productivity outcomes when using Machine Learning.
  • However, they acknowledged that ML generally has a significant positive contribution in manufacturing.
• NVIDIA Seeks Orbital Datacenter System Architect: A user shared an NVIDIA job posting for an Orbital Datacenter System Architect.
  • This highlights the growing interest and investment in space-based computing infrastructure.

Yannick Kilcher ▷ #paper-discussion (11 messages🔥):

Safe Entanglement of Photons, Richard Sutton's Reinforcement Learning Book, Argumentation Theory, Legal Applications of Logic

• Safe Photonic Entanglement Breakthrough: A member mentioned a breakthrough regarding the safe multi-dimensional entanglement of photons within a relatively small setup.
• Reinforcement Learning Book Club Postponed: The book club session on Reinforcement Learning: An Introduction by Richard Sutton & Andrew G Barto has been postponed to tomorrow due to a scheduling conflict; the 2nd Edition is available online.
• Argumentation Theory Paper: A member suggested discussing a paper on argumentation theory exploring the fundamental mechanisms humans use in argumentation and how to implement these on computers.
  • The paper shows that most major approaches to nonmonotonic reasoning in AI and logic programming are special forms of the theory of argumentation, and that this theory captures naturally the solutions of the theory of n-person games and of the well-known stable marriage problem.
• Legal Applications of Logic Explored: A member proposed a discussion on a review article about legal applications of logic, focusing on logical models of legal argument.
  • The article argues that law is a rich test bed and important application field for logic-based AI research, and reviews applications of logic to the representation of legal regulations.

Yannick Kilcher ▷ #ml-news (14 messages🔥):

Anthropic, OpenAI, Sam Altman, Dario Amodei, Palantir CEO

• Dario’s Spicy Memo: A spicy memo, purportedly from Dario Amodei, accuses Sam Altman of undermining Anthropic by colluding with the DoW and engaging in ‘safety theater’ to replace them as a supplier.
  • The memo claims Altman is peddling narratives to his employees and describes them as sort of a gullible bunch due to ‘selection effects,’ also noting that the attempted spin/gaslighting isn’t working on the public but IS working on ‘some Twitter morons’.
• Accusations of Political Loyalty and Safety Theater: Amodei’s memo suggests the real reasons the DoW and the Trump administration don’t favor Anthropic is because they haven’t donated to Trump, given him dictator-style praise, and have supported AI regulation.
  • According to the memo, Anthropic held their red lines with integrity rather than colluding to produce ‘safety theater’ for the benefit of employees, unlike OpenAI.
• Palantir CEO Outdoes Altman and Amodei: Members discussed that the Palantir CEO is even more visibly evil, but evil with more skill in deception tends to go further under the influence of population dynamics.
  • One user commented that Sam Altman is a snake oil salesman who has been kissing the ring of the orange dude.
• OpenAI Introduces GPT-5: Members mentioned the introduction of GPT-5 by OpenAI but did not elaborate much beyond providing a link to the announcement.
  • The announcement may have come in response to Anthropic’s Economic Index.

HuggingFace ▷ #general (25 messages🔥):

Object Detection Models, YOLO Licensing, RTMDet, Sentient Arena, Pooled Representation of Embedded Tokens

• YOLO Licensing Lowdown: A member asked for commercially safe object detection models, with a discussion revealing concerns about YOLO’s licensing for commercial use; YOLOX markdown was attached for reference.
  • The user noted YOLO has a long history with variable licensing, and another member mentioned RTMDet as a potential alternative.
• Embedding Vector Pooling Perplexities: One member sought advice on creating a pooled representation of embedded tokens, detailing challenges with mean pooling and potential vanishing problems during training due to embedding normalization.
  • They pondered whether to use un-normalized embedding vectors or sum-pooling instead, to address the issue of individual token meaningfulness being drowned out.
• Voice Cloning Ventures: A member inquired about novel methods for voice cloning, expressing a desire to explore new approaches beyond pre-existing models.
  • No novel approaches were offered in response.
• Hugging Face Security Alert: A security researcher announced the completion of a large-scale credential exposure study on Hugging Face and sought contact with the HF security team.
  • It was suggested to publish findings in /posts or a blog and tag relevant individuals, or to scrape the site for security team contacts.

HuggingFace ▷ #i-made-this (21 messages🔥):

async RL infrastructure, Lunaris MoC, Rust-based database, vllm-i64, AskDrive Web

• Async RL Infra replicated: A member built a minimal replication of the async RL infra used to train GLM-5 using Redis to decouple generation from sandbox evaluation.
  • The goal is to prevent sampling and training from being blocked by slow, long-horizon rollouts; the code is available on GitHub.
• Lunaris MoC for Adaptive Compute: Lunaris MoC introduces Mixture-of-Collaboration (MoC), where selected experts collaborate through a learned mediator before fusion, with each expert running an Iterative Reasoning Loop (IRL).
  • Validated at 64M parameters, MoC-vNext achieved a val perplexity of 59.97 versus 62.89 for standard MoE, with adaptive gates learning ~40% compute savings; code and logs are available on GitHub and Weights & Biases.
• New Rust Database for Human-like Recall: A new database built on Rust aims for more organic-like recall for Agents, replicating human recall functions, available on GitHub.
  • Also, there’s now vllm-i64 at Complexity-ML/vllm-i64 and a web project using Ollama and llama3.1:8b at askdrive-web.vercel.app.
• PENCILCLAW and PygmyClaw Creative Writing Partners: PENCILCLAW is a C++ command-line tool that turns a local Ollama instance into a creative writing partner with the ability to execute generated C++ code, available on Hugging Face.
  • PygmyClaw is a compact Py based openclaw clone with a persistent task queue and a modular tool system, available on Hugging Face.
• Replacing Traditional Forms with Conversations: A member is exploring replacing traditional forms with conversations to enhance user experience in information collection, detailed in this Medium article.
  • Another member has been working on R.A.V.E.N (REMOTELY ADAPTIVE VECTOR-ENGINE NEXUS), a self-learning/self-evolving AI with a Synaptic Node Nexus.

HuggingFace ▷ #core-announcements (1 messages):

Release 0.37.0, Release notes

• Release 0.37.0 Packed with Goodness: Release 0.37.0 is out with a lot of goodness!
  • See the release notes for more details.
• Release 0.37.0 improvements: The release includes improvements.
  • These improvements will help many users.

HuggingFace ▷ #agents-course (7 messages):

Introduction to Agents Course, New members joining the channel

• New members say hello and introduce themselves: Several new members including Deni, Surya Mukherjee, ishaan18, Azlina, Ish, and Poojitha introduced themselves to the channel.
• Enthusiasm for Agents Course: Surya Mukherjee and ishaan18 expressed their eagerness to learn about the agents course.
  • Ishaan18 mentioned they are working in an engineering leadership team.

Moonshot AI (Kimi K-2) ▷ #general-chat (47 messages🔥):

Kimi stubbornness, Kimi API issues, Kimi on Claude Code, Kimi platform refunds, Kimi Phone app performance

• Kimi’s Stubbornness Frustrates Users: A user expressed frustration with Kimi’s inability to control the UI, despite being asked to review tool usage and update prices, seeking solutions for subscription issues and unwanted charges.
  • The user even attached an image related to the problem.
• API Discrepancies Arise Between Kimi CLI and Alibaba: Users noted discrepancies in model performance between the Kimi CLI and the Alibaba-hosted API, speculating about potential tuning differences not shared by Kimi.
  • One user suggested that the issue might stem from Alibaba’s implementation, stating that it’s not Kimi’s fault if Alibaba isn’t competant to host their models right.
• Kimi API Pricing Raises Concerns: A user questioned the accuracy of the pricing limits page, expressing concern about potential changes to TPD limits after spending $5 on the API.
  • Another user pointed out a big warning in the channel advising against asking the bot about API-related questions due to its tendency to provide inaccurate information.
• Kimi on Claude Code Plagued by API Errors: Users reported encountering API Error 400 (Invalid request Error) when using Kimi in Claude Code, a problem one user related to a recent update in Claude that altered tool behavior.
  • One user lamented Honestly this is crazy when kimi on the app has search capability and kimi on code has mcps.
• Refund Requests Posed to Kimi Platform: A user inquired about requesting a refund on the Kimi platform, citing reasons such as accidental purchases and unusable features.
  • Another user suggested contacting [email protected], while others shared experiences of obtaining refunds from OpenAI and Anthropic for similar reasons.

Eleuther ▷ #research (17 messages🔥):

eval_main.py in littletrainingloop, Hybrid char + bpe models, embedding table messing up, elaborate trainable pre-embedding computation, Links to the future of AI papers

• eval_main.py lives in littletrainingloop repo: A member mentioned that eval code is in the repo as eval_main.py in the littletrainingloop repo.
  • They were curious if this effect is reproducible in other training frameworks, as this idea is old and well-known.
• ChatGPT recommends Hybrid Char + BPE models: A member says that basically any hybrid char + bpe model such as Char2Subword, FastText, and BBPE would work for this, and BPE-dropout descendants are also spiritually related.
  • Another member stated that Char2Subword has a similar flavor indeed, great find. The rest doesn’t look particularly related.
• Embedding tables messing up since GPT-2: A member stated that the lack of direct character information will be an increasingly salient part of the total loss the more well-trained the model is, ie, it adds noise to late training.
  • Additionally, it may be because Gwern has had a bug about the embedding table messing up many things since GPT-2.
• Pre-embedding computations can become unstable: It’s difficult to be certain of anything that has an elaborate trainable pre-embedding computation, because such computation can become unstable or problematic in ways that are difficult to foresee.
  • They also say that the blt setup especially is very clever… and i have no confidence that it will not suffer from some sort of horrifying instability at scale or in any given codebase.
• AI papers from the future: A member linked to a few papers from the future: https://arxiv.org/abs/2603.03818, https://www.alphaxiv.org/abs/2603.03276, https://beyond-llms.github.io/.
  • They also linked to an X thread: https://x.com/i/status/2029596876425892030

Eleuther ▷ #scaling-laws (1 messages):

uwu1468548483828484: why does the horizon have to be fixed?

Eleuther ▷ #lm-thunderdome (9 messages🔥):

lm-evaluation-harness Heterogeneity, LAMBADA evaluation discrepancies, lm_eval version in gptneox

• Tackling Heterogeneity in lm-evaluation-harness tasks: A member is implementing a new evaluation task within the lm-evaluation-harness facing challenges due to dataset heterogeneity in multiple-choice and text-generation formats.
  • The core problem lies in the variance of option formats and prompt structures, which may lead to unrepresentative few-shot prompts and confuse the model; they’ve created an Issue on GitHub.
• LAMBADA Evaluation Outputs raise questions: A user running eval on LAMBADA observes three distinct result numbers (LAMBADA, LAMBADA OpenAI, and LAMBADA Standard) and seeks clarity on the calculation method for the aggregate LAMBADA score.
  • A contributor suggests that the LAMBADA score is likely the mean of LAMBADA OpenAI and LAMBADA Standard, and confirms that this grouping no longer exists on the current main branch.
• GPTNeoX includes outdated lm_eval version: A user clarifies that they are using lm_eval>=0.4.0,<=0.4.1, which came with GPTNeoX, for evaluating models in the NeoX format.
  • This explains the presence of the LAMBADA grouping, which has been removed from the main branch in later versions.

Manus.im Discord ▷ #general (19 messages🔥):

Manus support issues, Manus 1st birthday, Migration away from Manus, Antigravity Google

• Users Complain About Lack of Manus Support: A user expressed frustration with Manus after a 12-hour workday filled with errors and a lack of support.
  • Other users echoed similar sentiments about support responsiveness, with one stating, “We’ve all been saying this but they don’t listen”.
• Manus Celebrates First Birthday: The Manus team is celebrating its first birthday, marking a year since its initial launch.
  • Users congratulated the team and expressed surprise at how quickly the year had passed: “Happy Bday Manus! Cant believe its already a year. Time flies by :))”.
• Users Consider Migrating Away From Manus: A user mentioned that they are considering migrating away from Manus, because *“the only tier on which they allow credits is $13000 a month!”
  • Other users asked to be informed of any viable alternatives.
• “Antigravity Google” recommended as alternative: A user suggested “Antigravity Google” as a potential alternative to Manus.
  • No link or further information was given.

DSPy ▷ #show-and-tell (1 messages):

Enterprise AI Trends, AI implementation, AI evolution

• Enterprise AI trends evolving: A member shared a LinkedIn post discussing evolving trends in the enterprise AI space.
  • The LinkedIn post underlines how pivotal the AI shift is in organizations, as well as its implementation.
• AI Implementation & Evolution: The post focuses on the AI evolution in enterprise and its practical implementation.
  • It emphasizes the importance of understanding the practical steps organizations should take to harness AI’s power.

DSPy ▷ #general (7 messages):

DSPy power-user resources, Dropbox LLM labeling, REPL tool for agentic architecture, RLM paradigm comparison

• DSPy Power-User Resources Sought: A user inquired about comprehensive resources for becoming a DSPy power-user, beyond the standard documentation.
  • The team pointed to the Tutorials section as a starting point, with links to examples and demos.
• Dropbox uses LLMs for Human Labeling: Dropbox is using LLMs to amplify human labeling, which powers their prompt optimization with DSPy as documented in a case study.
  • This improves search relevance in Dropbox Dash.
• REPL Tool as Agentic Architecture: A user is inclined towards testing the REPL tool as the main tool for agentic architecture, rather than using a bunch of Python functions, citing a research log.
  • The architecture sounds very similar to the RLM paradigm.
• RLM Paradigm Deconstructed: It was mentioned that the REPL tool is like 2/3 of RLMs.
  • The last part of RLMs involves providing a function in the REPL to make LLM queries programmatically, which is compared with and without in the paper; it’s not strictly required if you’re not dealing with long contexts.

aider (Paul Gauthier) ▷ #general (2 messages):

Security Vulnerability Chain, GitHub Security Advisory, Key Rotation, Adnan Khan, Cline Patch

• Researcher Khan Spots Vulnerability, Gets Ghosted: Security researcher Adnan Khan discovered a vulnerability chain in late December 2025 and reported it via a GitHub Security Advisory on January 1, 2026.
  • Khan sent multiple follow-ups over five weeks but received no response, until he publicly disclosed the vulnerability on February 9, prompting Cline to patch it within 30 minutes.
• Cline Patches Fast but Botches Key Rotation: Despite a rapid patch by Cline after public disclosure, they still got owned because they messed up key rotation.
  • This highlights the importance of not only patching vulnerabilities quickly but also ensuring proper security practices such as secure key rotation.

aider (Paul Gauthier) ▷ #questions-and-tips (1 messages):

evertonw_86809: When will context compaction be introduced for aider?

Modular (Mojo 🔥) ▷ #mojo (3 messages):

Mojo Roadmap Updates

• Mojo Roadmap Still Kicking?: A member inquired whether the Mojo roadmap is still being updated.
  • Another member confirmed that it looks up to date, with the original poster affirming they check the roadmap from time to time to see how we’re doing to 1.0.
• Following Along: Another user said they check the roadmap from time to time to see how we’re doing to 1.0.
  • This shows users are excited about the progress to 1.0.

MLOps @Chipro ▷ #general-ml (1 messages):

Simple Work Assistance

• In search of US collaborator: A member is looking for someone in the US to give a hand with some simple work, offering compensation for the assistance.
• Another Member Joins the Call: Another member simply acknowledged the request, without offering assistance.