a quiet day

AI News for 1/13/2026-1/14/2026. We checked 12 subreddits, 544 Twitters and 24 Discords (204 channels, and 5168 messages) for you. Estimated reading time saved (at 200wpm): 445 minutes. Our new website is now up with full metadata search and beautiful vibe coded presentation of all past issues. See https://news.smol.ai/ for the full news breakdowns and give us feedback on @smol_ai!

Some buzz over the GPT 5.2 Codex API launch and how Cursor successfully used it to run autonomously for a week and create a somewhat working browser!

AI Twitter Recap

OpenAI + GitHub + Cursor: GPT-5.2-Codex goes “long-horizon” (and shows up everywhere)

• GPT-5.2-Codex in the API (and IDEs): OpenAI shipped GPT-5.2-Codex in the Responses API, positioning it as their strongest coding model for long-running tasks like feature work, refactors, and bug-finding; they also explicitly call out it as the “most cyber-capable” to date for understanding codebase vulnerabilities (OpenAIDevs). Cursor immediately integrated it and framed it as a “frontier model for long-running tasks” (cursor_ai), with additional endorsements from builders who emphasize diligence on extended workflows (sherwinwu). GitHub rolled it into @code as well (code) and noted they’re changing preview/GA labeling to reduce enterprise adoption friction (pierceboggan).
• A concrete “agents ran for a week” datapoint: A standout report claims a team “built a browser with GPT-5.2 in Cursor” that ran uninterrupted for one week, producing 3M+ lines of Rust across thousands of files (HTML parsing → CSS cascade/layout → painting → custom JS VM), and that it “kind of works” for simple websites (mntruell). This became a reference point for “continuous agent time” and the practical frontier of autonomous codegen (gdb; kevinweil). Engineers also highlighted the emerging best practice that agent systems need a first-class “review” loop to improve output quality and safety (scaling01).
• Evaluation discourse: metrics vs “vibes” vs time horizon: Multiple tweets argue coding-model progress is being under/over-counted depending on eval design and what devs actually feel in day-to-day work; METR’s long evals are cited as catching “jumps” earlier than standard benchmarks (swyx). Others debate whether plots alone support conclusions and what “time horizon” metrics should mean in real scaffolds (_lewtun; RyanPGreenblatt).

Inference infrastructure: Cerebras partnership + “speed is the product” economics

• OpenAI 🤝 Cerebras: Cerebras announced a partnership with OpenAI (cerebras). The framing across the timeline is that latency and tokens/sec are increasingly user-visible product differentiators for ChatGPT-style experiences (and competitive vs Gemini), even if the software stack is narrower than CUDA for general workloads (Yuchenj_UW).
• Provider benchmarking gets more granular: Artificial Analysis posted a provider comparison for GLM-4.7 emphasizing speed/latency/cost tradeoffs. Example figures: Cerebras serving GLM-4.7 at ~1,445 output tokens/s, with TTFAT ~1.6s, while GPU providers like Fireworks/Baseten trail on throughput/latency but support larger context windows (Cerebras noted at 131k, others 200k except Parasail) and different caching discounts (ArtificialAnlys).
• Operational scaling content: Modal published guidance arguing self-hosted inference can now match/beat API economics, with techniques + code samples (charles_irl). SemiAnalysis highlighted Modal’s ops writeup on keeping a fleet of 20k GPUs healthy (SemiAnalysis_). vLLM and Modal content focused on batch inference to saturate H100s (FlashInfer backend, async scheduling, batch sizing) (vllm_project).

Agent engineering becomes productized: skills, dynamic tool loading, and architecture selection

• Skills as a portability layer: Phil Schmid shipped Agent Skills for antigravity, with standardized folders (.agent/skills/, ~/.gemini/antigravity/skills/) and compatibility across Gemini CLI / Claude Code / OpenCode-style ecosystems (_philschmid). Hugging Face practitioners echoed that “/plugin interfaces” carry heavy versioning friction; for most teams, small vertical skills + CLI/MCP is the robust path (ben_burtenshaw).
• LangSmith Agent Builder launch: LangChain shipped LangSmith Agent Builder, presenting “agents as a filesystem,” built-in memory, triggers for ambient agents, and support for skills/MCP/subagents (LangChain; hwchase17). Real examples include an ambient Slack-to-Linear ticket agent built “no code, just a prompt” (docs_plz). CopilotKit added middleware to turn LangChain prebuilt agents into UI-facing apps (including “Deep Agents”) (CopilotKit).
• When to go multi-agent (usually: don’t): A LangChain post lays out four patterns—Subagents, Skills, Handoffs, Router—and explicitly recommends starting with a single agent unless you hit constraints (context window, distributed ownership, decomposition needs) (LangChain; sydneyrunkle). This theme repeats in OSS account guidance (LangChain_OSS).

Model + research notes engineers actually argued about: long context, memory modules, pruning/distillation, multimodal RAG, and eval fragility

• DroPE / No positional embeddings for long context: A thread summarizes a simple recipe—take a pretrained LLM, drop RoPE, fine-tune without positional embeddings—and reports comparable performance on standard datasets with improved long-context behavior, tested on SmolLM-1.7B and Llama2-7B (gabriberton; gabriberton).
• DeepSeek “Engram” memory module discourse: Several tweets discuss DeepSeek + PKU work advocating “separate thinking from remembering” with MoE (sparse compute) + Engram (sparse storage)—hash-based O(1) lookups of n-grams retrieved as vectors fused into the transformer stream, with infra implications like prefetch/latency hiding and RAM-resident memory tables (ZhihuFrontier; LiorOnAI; code link LiorOnAI).
• Mistral “Ministral 3” (small model recipe): A dense summary of a new tech report emphasizes pruning + distillation (teacher models used for pretraining/posttraining; online DPO in post-training), plus concrete pruning heuristics (layer pruning via output/input norm ratios; hidden dim pruning with PCA rotation; FFN pruning via gated activation scores) (eliebakouch; paper pointer qtnx_).
• Multimodal RAG system design: UniversalRAG proposes modality-aware routing (avoid forcing everything into one embedding space) and retrieval across modality + granularity (paragraph vs doc; clip vs full video; tables/images), with trained or training-free routing (prompting frontier models to pick modality/granularity) and wins across 10 benchmarks (omarsar0). ViDoRe V3 benchmark paper landed for multimodal RAG evaluation (antonio_loison).
• Benchmark fragility (VLMs): VPBench argues that small presentation changes (e.g., red vs blue markers) can reorder VLM leaderboards—useful ammunition for anyone treating leaderboard deltas as robust signals (lisabdunlap).

Product + org moves: “open” as strategy, and talent reshuffles across labs

• Airbnb hires Meta Llama lead as CTO: Ahmad Al-Dahle announced he’s joining Airbnb as CTO; he credited Meta’s open-sourcing bet on Llama (1.2B+ downloads, 60K+ derivatives) and framed Airbnb as a product frontier for applying advancing model capability (Ahmad_Al_Dahle). Multiple leaders endorsed the move (sama; ClementDelangue; markchen90).
• Thinking Machines Lab / OpenAI leadership churn: Mira Murati announced Barret Zoph departed TML and Soumith Chintala became CTO (miramurati). Shortly after, OpenAI announced Barret Zoph, Luke Metz, and Sam Schoenholz returning to OpenAI (fidjissimo; barret_zoph).
• Open source and “mid-sized orgs”: HF’s Clement Delangue argued startups and mid-sized tech companies can materially advance open science/open-source AI and pointed to trending models from fal and Lightricks as evidence, tying it to Airbnb’s CTO hire as a possible signal (ClementDelangue). LTX-2 celebrated 1,000,000 HF downloads (ltx_model), reinforcing that “open distribution” is now a growth channel.

Top tweets (by engagement)

• Gemini “Personal Intelligence” rollout: Google announced Gemini personalization by connecting Google apps (Gmail/Photos/Search/YouTube history), emphasizing opt-in + privacy controls; high engagement across Google/Gemini leadership accounts (Google; sundarpichai; joshwoodward).
• GPT-5.2-Codex shipping + ecosystem uptake: API release and Cursor integration were among the most engaged engineering tweets (OpenAIDevs; cursor_ai).
• “3M lines browser” long-horizon agent anecdote: widely circulated as a vivid example of continuous agent work (mntruell).
• Vercel’s agent evals/skills for React performance: react-best-practices as an “agent skill” + eval suite hit high engagement (vercel).

AI Reddit Recap

/r/LocalLlama + /r/localLLM Recap

1. Local LLM Hardware and Performance Comparisons

• M4/M5 Max 128gb vs DGX Spark (or GB10 OEM) (Activity: 153): The user is comparing the NVIDIA DGX Spark and a MacBook Pro with M4 Max (128GB RAM) for local LLM inference, primarily for coding tasks such as code completion and refactoring. The DGX Spark offers a CUDA ecosystem and strong GPU compute, while the MacBook Pro benefits from unified memory and Apple’s ML stack. The user is not focused on training large models but seeks fast, reliable local inference. A key consideration is whether the Apple Silicon ecosystem can replace cloud-based coding assistants like Claude Code. The MacBook’s higher memory bandwidth is noted as beneficial for inference, but expectations should be managed as it may not match cloud-based performance. Benchmarks suggest the M5 offers significant performance improvements over the M4, and new MacBook Pro models may be released soon. Commenters debate the performance of Apple Silicon versus NVIDIA hardware for text generation. Some argue that the MacBook Pro, particularly with the M3 Ultra, excels in pure text generation tasks, while the DGX Spark is better for tasks requiring extensive GPU capabilities. The MacBook’s higher memory bandwidth is highlighted as advantageous for inference, though NVIDIA’s CUDA support is noted for broader framework compatibility.

  • The M4 Max offers significantly higher memory bandwidth compared to the DGX Spark, which is beneficial for inference tasks. However, the DGX Spark benefits from better support for most frameworks due to its compatibility with NVIDIA’s CUDA, which is a major advantage for those using diverse machine learning frameworks.
  • The M3 Ultra Mac Studio is highlighted as superior for pure text generation tasks compared to the DGX Spark. Despite NVIDIA’s hardware capabilities, the M3 Ultra consistently outperforms in text generation speed, which is attributed to its optimized architecture for such tasks. This is contrasted with the DGX Spark’s broader capabilities in other areas like fine-tuning and image/video generation.
  • The DGX Spark is noted for its compact size and energy efficiency, operating at less than 100W and idling at around 10W. It is also praised for its extensibility, allowing for additional units to be connected. However, concerns about bandwidth limitations are raised, suggesting that while it is efficient, it may not match the performance of alternatives like the Mac Studio in certain tasks.

• What is the biggest local LLM that can fit in 16GB VRAM? (Activity: 103): With an RTX 5080 and 16GB of VRAM, the largest local LLM you can run is likely around 14B parameters, especially if you want to maintain a useful context size. Models like GPT-OSS-20B might fit, but would require significant quantization, potentially below 4-bit, which can degrade quality. For optimal performance, a 14B model is recommended, as it balances size and context capacity effectively. Larger models, such as 30B, would require offloading to CPU and may not be practical due to VRAM constraints. Commenters suggest that while 30B models are technically possible with heavy quantization, they may not be practical due to quality and context limitations. The consensus is that a 14B model is more suitable for maintaining performance and usability on a 16GB VRAM setup.

  • SKirby00 highlights the limitations of fitting large models like 30B into 16GB VRAM, suggesting that even with aggressive quantization below 4-bit, quality may degrade significantly. They recommend aiming for models around 14B for a balance between size and context capacity, noting that a 14.5GB model might technically fit but would be impractical for real use cases.
  • BigYoSpeck provides performance benchmarks for different models on a Ryzen 9 5900x with 64GB DDR4 and a 16GB Radeon RX 6800 XT. They report running gpt-oss-20b at 120+ tokens per second, Qwen3 30b partially offloaded to CPU at 40 tokens per second, and gpt-oss-120b with 32 MOE layers offloaded to CPU at 23 tokens per second, suggesting that similar or better performance might be achievable on other systems.
  • PermanentLiminality advises keeping model size under 80% of VRAM to allow space for context, suggesting a 13GB model as a practical limit for 16GB VRAM. They note that while system RAM can be used to spill over, it significantly reduces speed. They mention that Qwen 3 30B can handle some spillover effectively, making it one of the largest models that can be run efficiently under these constraints.

• Small AI computer runs 120B models locally: Any use cases beyond portability and privacy? (Activity: 49): TiinyAI has developed a compact AI device capable of running 120B parameter models locally with 80GB RAM and a power consumption of 30W. This device is positioned as a more portable and cost-effective alternative to larger systems like the DGX Spark, which offers 128GB RAM and higher performance but at a greater cost and size. The TiinyAI device is particularly notable for its potential applications in scenarios where portability and privacy are prioritized over raw computational power, such as in field research or in regions with unreliable internet access. Commenters express skepticism about the device’s memory bandwidth, speculating it might be around 80Gb/s, which could limit its performance compared to standard PCs or laptops. There is also doubt about the price and availability, with some seeing it as potentially useful in scenarios where internet access is restricted by governments.

  • A key technical concern raised is the memory bandwidth of the small AI computer, with estimates ranging from 80Gb/s to 200Gb/s. This bandwidth is crucial for running large models like 120B parameters efficiently. If the bandwidth is on the lower end, it may not outperform a regular PC or laptop, which could limit its practical applications beyond portability and privacy.
  • The pricing of the device, speculated to be around $1400 for an 80GB RAM single-board computer (SBC), is questioned. The skepticism is due to the lack of availability for immediate purchase, which raises doubts about the feasibility and practicality of such a device in the current market.
  • The potential use case of resilience against internet shutdowns is highlighted, suggesting that such a device could be valuable in scenarios where internet access is restricted or monitored by authoritarian regimes. This emphasizes the importance of local processing capabilities in maintaining access to AI tools under such conditions.

2. Innovative AI Model Implementations and Experiments

• Shadows-Gemma-3-1B: cold start reasoning from topk20 logprob distillation (Activity: 41): Shadows-Gemma-1B is a reasoning model trained for the Google Tunix Hackathon using 1569 samples in approximately 10 minutes on TPUv5-8e and 20 minutes on A40. The model employs a novel approach called shadow tokens, identified through topk20 logprob distillation from a non-reasoning teacher model, gemma-3-4b-it. These tokens, which appear early in low ranks and are selected later, may indicate reasoning behavior, such as backtracking and solution exploration. The model was trained using a system prompt that encourages interleaved reasoning, and while it doesn’t claim superiority over other models, it demonstrates improved reasoning capabilities on complex questions. Further details on the training process, including loss functions and code optimizations, will be shared in a forthcoming post mortem. One commenter suggests exploring the use of larger teacher models like gemma-12b-it or gemma-27-it for potentially different results. Another commenter expresses interest in the release of the training dataset, noting the effectiveness of Deep Cogito v2.1 for distillation.

  • A user suggests using larger models like gemma-12b-it or gemma-27-it as teacher models for distillation, implying that these could potentially improve the results due to their larger capacity and possibly more nuanced understanding.
  • Another user highlights the innovative approach of using token persistence in the probability distribution as a measure of reasoning depth. This method allows for training models to enhance reasoning behavior, which is a novel concept in model training. The user also expresses interest in the technical challenges faced during the transition from PyTorch to JAX, hinting at potential insights into framework-specific optimizations or issues.

• Using local VLMs for OCR to feed into an NLP categorization pipeline - looking for beta testers (Loggr) (Activity: 10): Loggr is developing a health journaling app for Apple Silicon that operates entirely offline, utilizing a custom NLP pipeline to extract structured health data from free-form text with sub-100ms latency. The app is integrating a feature to scan handwritten journals using the Qwen2.5-VL-3B model, quantized via MLX for OCR, which fits in 8GB of unified memory. The 7B model, requiring 12GB+, handles messier handwriting better. The app processes journals in batch mode overnight, and a hybrid approach with Apple’s Vision framework is considered for quick previews. The team seeks beta testers to evaluate performance on challenging handwriting and layouts. More details and sign-up are available at loggr.info. Commenters suggest trying PaddleOCR with a custom handwriting model for potentially better performance on messy handwriting compared to general VLMs. Another recommendation is to test MiMo-VL-7B-RL, which is compatible with Qwen2.5-VL and may offer smarter performance. There is also interest in whether the app will support text-to-speech functionality.

  • A user suggests using PaddleOCR with a custom handwriting model for OCR tasks, noting that specialized OCR models can outperform general Vision-Language Models (VLMs) like Qwen2.5-VL on messy handwriting. This highlights the potential advantage of using specialized models for specific tasks, even if they lack the overall intelligence of more general models.
  • Another user recommends trying MiMo-VL-7B-RL as an alternative to Qwen2.5-VL 7B, noting that MiMo-VL-7B-RL is fully compatible and appears ‘smarter’ in their use cases. They provide a link to the model on Hugging Face for further exploration: MiMo-VL-7B-RL.

3. AI Protocols and Frameworks for E-commerce and Development

• Google just opensourced Universal Commerce Protocol. (Activity: 32): Google has open-sourced the Universal Commerce Protocol (UCP), which allows AI agents to autonomously manage e-commerce tasks such as product discovery, cart management, and payment processing. Key integrations include Agent2Agent (A2A) for multi-step workflows, Agents Payment Protocol (AP2) for secure payments, and Model Context Protocol (MCP) for integration with existing LLM stacks like vLLM and Ollama. The protocol is available on GitHub. Commenters are questioning the current adoption by retailers, the duration of Google’s support, and whether the protocol is already in use or newly open-sourced.

  • The Universal Commerce Protocol (UCP) is newly open-sourced by Google, but there is uncertainty about its adoption by retailers. The protocol’s utility is questioned if it lacks widespread support, as highlighted by a user asking about current retailer adoption.
  • There is curiosity about Google’s long-term support for the Universal Commerce Protocol, with questions about its integration with Gemini. Users are interested in understanding Google’s roadmap for UCP, especially in relation to its use in existing platforms like Gemini.
  • The discussion raises questions about the maturity of the Universal Commerce Protocol, whether it is a newly developed protocol or an existing one that has just been open-sourced. This distinction is crucial for developers considering its implementation.

• Would 16k context coding on consumer GPUs make H100s irrelevant for independent devs? (Activity: 36): The post speculates on the impact of achieving a 16k context window for coding on consumer GPUs like the NVIDIA 3060, questioning whether this would make high-end GPUs like the H100 less relevant for independent developers. The discussion highlights that a 16k context is considered small, with 64k being average and 128k or 1M considered good or massive, respectively. Current local models reportedly become less effective beyond 64k context, even with sufficient memory, as noted by users running 128k or 256k contexts on 4x3090s. The consensus among commenters is that a 16k context is insufficient for significant AI development, suggesting that higher context windows are necessary for more complex tasks.

  • The discussion highlights that a 16k context window is considered small in the realm of large language models, with 64k being average and 128k considered good. Models like Codex and Claude operate at much larger context windows of 290k and 240k, respectively, while Gemini Pro can handle up to 1 million tokens, indicating that 16k would not significantly impact the capabilities of consumer GPUs for serious coding tasks.
  • A user mentions using 128k or 256k context windows on 4x3090 GPUs, but notes that most local models tend to degrade in performance beyond 64k context, regardless of the available memory. This suggests that while larger context windows are technically feasible, they may not be practically beneficial due to model limitations.
  • The consensus is that a 16k context window would be insufficient for serious applications beyond simple code snippets or autocomplete functions. The performance would likely be too slow for models that are significant in coding, and thus, achieving 16k context on consumer GPUs would not make H100s irrelevant for independent developers.

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. Mathematical Theorem and Problem Solving with AI

• Gemini “Math-Specialized version” proves a Novel Mathematical Theorem (Activity: 553): Gemini, a “math-specialized” AI model, has reportedly proven a novel mathematical theorem, as detailed in a tweet and an accompanying arXiv paper. The model’s architecture and training are optimized for mathematical reasoning, leveraging advanced techniques in symbolic computation and theorem proving. This development underscores the potential of AI in advancing mathematical research, challenging the notion that AI is limited in handling complex mathematical tasks. Commenters highlight the rapid pace of AI advancements and its potential to accelerate human knowledge, while expressing concern over the influence of commercial interests on AI’s development. There is also a sentiment that AI’s capability in mathematics is often underestimated.

  • The rapid advancements in AI, particularly in math and coding, are highlighted as areas where AI is accelerating significantly. This is exemplified by the development of a ‘Math-Specialized’ version of the Gemini model, which has reportedly proven a novel mathematical theorem. Such breakthroughs suggest a decreasing time interval between significant AI achievements, indicating a rapid pace of innovation.
  • The suggestion to use the Gemini model on the Erdős problems is noted as a potential benchmark. The Erdős problems are well-known in the mathematical community, with extensive human analysis, making them an ideal test for evaluating the capabilities of AI in mathematical problem-solving. This could provide a rigorous assessment of the model’s proficiency and potential in advancing mathematical research.
  • There is a discussion on the skepticism surrounding AI’s ability to perform mathematical tasks, with some still doubting its capabilities. However, the recent achievements of AI in proving mathematical theorems challenge this skepticism, demonstrating that AI can indeed handle complex mathematical problems, potentially accelerating human progress in this field.

• 5.2 Pro makes progress on decades long math problem listed on Wikipedia (Activity: 278): The image is a tweet announcing a new numerical upper bound for Moser’s worm problem, achieved by Archivara using the AI model 5.2 Pro. The solution involves re-optimizing ellipse-locus construction parameters, reducing the area of the universal cover to 0.260069597, surpassing the previous record of 0.26007 from 2018. This progress on the decades-long unsolved geometry problem, which seeks the smallest area to accommodate any plane curve of length 1, was verified by a mathematician from INRIA. The achievement highlights the potential of AI models to tackle complex mathematical problems when provided with the right tools and guidance, despite their tendency to avoid unsolved problems. Comments discuss the challenges of engaging AI models with unsolved problems, noting that 5.2 Pro was able to make progress through a combination of curated tools, literature, and prompt steering. There is also a mention of disabling internet access to prevent the model from dismissing problems as unsolvable, which allowed it to focus and eventually solve them.

  • The 5.2 Pro model was able to make progress on a long-standing math problem by using a curated collection of tools and literature, along with scaffolding improvements. A significant challenge with AI models is their tendency to give up on complex problems, such as the Riemann Hypothesis, without attempting a solution. By employing a sequence of pressure and prompt steering, the model was induced to engage seriously with the problem, and its results were verified by a mathematician from INRIA.
  • A strategy to encourage AI models to solve difficult problems involves removing internet access to prevent them from searching for solutions online and concluding that problems are unsolvable. This approach was used successfully in solving Erdős problems, where the model was forced to rely on its own reasoning capabilities over an extended period.
  • The 5.2 Pro model’s ethical constraints can interfere with user requests, as seen in a scenario where it refused to provide a solution for keeping a Linux system awake, citing potential policy violations. This highlights ongoing challenges in balancing AI’s ethical guidelines with user autonomy, especially in business applications.

2. DeepSeek and Spectral Sphere Optimizer Developments

• [P] my shot at a DeepSeek style moe on a single rtx 5090 (Activity: 64): The post details a personal project involving a Mixture of Experts (MoE) model with 2.36B parameters and 8 routed experts on a single RTX 5090 GPU, using top-2 routing. The model employs Grouped Query Attention with QK-normalization, RoPE positional embeddings, and SwiGLU activation with RMSNorm. Training utilizes TorchAO FP8 quantization, the Muon optimizer, and a multi-stage learning rate schedule. The data pipeline initially used MeCo (Metadata Conditioning then Cooldown) but was later switched to a clean corpus due to issues with only 8 experts. Key challenges included improper router initialization and lack of a dense first layer, leading to instability. The author advises against using router scaling on small MoE models, citing instability with a scaling factor of 1.2. Current training metrics include a learning rate of 3e-4, loss around 1.9, and a token processing speed of 19,415 tok/s. Commenters are impressed by the author’s progress without formal ML training, noting the focus on stability and operational details over high-level architecture. There is curiosity about the practical applications of the project beyond personal learning, such as potential deployment or distillation.

  • The discussion highlights the challenges of implementing a small Mixture of Experts (MoE) model on a single RTX 5090, particularly focusing on stability and operational details rather than high-level architecture. This mirrors real-world product development where the final stages often involve managing edge cases. The commenter is curious about the practical applications of this work beyond learning, such as potential deployment or distillation of the model.
  • A key technical insight is the difficulty of tracking failure modes in small MoE models, as many techniques from large-scale settings do not apply. The instability issues around routing and scaling are noted, with the dense first layer and symmetric initialization being crucial lessons. The commenter questions whether the insights gained from this setup are transferable to larger systems or if the single-GPU constraints limit scalability, but acknowledges the value in articulating these trade-offs.
  • The comment emphasizes the importance of understanding failure modes over merely focusing on throughput or loss curves in small MoE models. It points out that many tricks from large-scale models fail silently in smaller setups, highlighting the importance of dense first layers and symmetric initialization. The discussion raises the question of whether the insights from this constrained setup are applicable to larger systems, suggesting that the ability to articulate these trade-offs is a significant advantage.

• [R] Controlled LLM Training on Spectral Sphere (Activity: 17): The paper introduces the Spectral Sphere Optimizer (SSO), which enhances the stability and convergence of large language models by enforcing spectral constraints on both weights and updates, aligning fully with Maximal Update Parametrization (muP). This optimizer is implemented as a parallel algorithm in Megatron and shows superior performance over AdamW and Muon in pretraining models like Dense 1.7B and MoE 8B-A1B. SSO’s approach involves deriving the steepest descent direction on the spectral sphere, leading to benefits such as improved MoE router load balancing and bounded activations. The optimizer’s effectiveness is demonstrated through extensive evaluations, outperforming existing methods in stability and performance. One commenter notes that SSO’s constraints are slightly looser than those of the Stiefel manifold, which requires all singular values to be exactly 1, whereas SSO only constrains the maximal singular value. Another commenter shares their experience with similar techniques, highlighting the benefits of using the NorMuon variant of Muon for stability and performance scaling.

  • parlancex discusses their experience with projecting weights onto different manifolds during training. They initially tried the Stiefel manifold but found it computationally expensive without performance benefits, so they reverted to the hyper-spherical manifold. They highlight the use of the NorMuon variant, which renormalizes weight updates row-wise after orthogonalization, allowing for high learning rates and strong performance scaling with batch size. This approach contrasts with the Stiefel manifold, which requires all singular values to be exactly 1, whereas the proposed method only constrains the maximal singular value.
  • radarsat1 shares their past challenges with network training, specifically dealing with exploding activations. They attempted to clamp and normalize weights onto the unit sphere at every layer to prevent this, but abandoned the approach due to concerns about training convergence. They express interest in the current discussion, noting that the idea of using such constraints for training stability is not intuitive to them, yet it appears beneficial in the context of the discussed method.

3. Claude and AI Subscription Challenges

• Claude PRO is too little, Claude MAX is too much for me (Activity: 139): The user is discussing their experience with Claude AI’s subscription plans, specifically the limitations of the Claude PRO plan and the excess capacity of the Claude MAX plan. They express a need for an intermediate plan priced around $40-$50, which currently does not exist. The user considers managing two Claude PRO accounts as a workaround but is concerned about the practicality of switching between accounts in the desktop app, which could lead to losing conversation context and wasting tokens. Commenters suggest using two Claude PRO accounts as a workaround, despite the inconvenience of switching accounts and potential token loss. Another suggestion is to try OpenAI’s Codex, which offers a $20 plan with potentially more usage than Claude's offerings.

  • AriyaSavaka suggests trying the GLM Codling Pro plan, which costs $12/month and offers 3x the usage of the $100 Claude Max plan without any weekly limits. This could be a cost-effective alternative for users who find Claude Max too expensive and Claude Pro insufficient.
  • AdrianPlaysPoE mentions the ‘Extra Usage’ option, which allows users to set a spending cap, effectively creating a custom subscription plan. For instance, setting a cap at $20-30 could provide a $50 subscription, potentially bridging the gap between the existing plans.
  • marrone12 recommends considering OpenAI’s Codex, noting that its $20 plan offers significantly more usage compared to Claude’s offerings. This suggests that OpenAI’s pricing and usage model might be more favorable for users seeking more extensive access.

• Work too cheap for Claude subscription (Activity: 122): The post discusses a software/AI engineer’s challenge in overhauling a 2 million line codebase to make it ‘AI ready’, highlighting the limitations of GitHub Copilot for large-scale refactoring. The engineer prefers Claude Opus 4.5 and Claude Code for personal projects, finding them more effective than Copilot, but faces management resistance to adopting Claude Code at work despite its perceived efficiency. The engineer argues that the cost of a Claude subscription ($200/month) is minimal compared to the potential time savings, yet management insists on using Copilot exclusively, reflecting a disconnect between AI tool capabilities and management’s understanding of their value. Commenters express frustration with GitHub Copilot, describing it as requiring excessive ‘hand holding’ and often breaking code. There is also a correction regarding the cost of Claude Code, noting that ‘work cc is $150usd/m and is apparently equivalent to a max x3 sub, not max x5’, indicating some confusion or misinformation about subscription tiers.

  • Downtown-Pear-6509 highlights the cost of a Claude subscription, noting that it is $150 USD per month and is equivalent to a ‘max x3’ subscription, not ‘max x5’. This suggests a tiered pricing model where the value or capabilities of the subscription are scaled, potentially impacting decision-making for users considering cost versus benefit.
  • flackjap discusses the strategy of using multiple AI models for software development, emphasizing the importance of having different models like Copilot and Codex to complement each other. They note that using one model for code writing and another for code review can help identify gaps and pitfalls early in the planning stage, which is crucial for avoiding issues later in production.
  • Michaeli_Starky mentions that OpenCode works with Copilot subscriptions and is comparable to Claude in terms of ‘agentic harness and context management’. This suggests that OpenCode might offer similar capabilities in managing complex tasks and maintaining context, which are critical features for developers working with AI tools.

• Figured out why /compact loses so much useful context - and a potential fix (Activity: 105): The image illustrates a proposed method to optimize context windows in Claude Code by summarizing and extracting messages, which could potentially reduce token usage by 60-70%. The current /compact command in Claude Code permanently loses original content by summarizing it server-side without local backups. The proposed solution involves writing original content to local files before compacting and replacing context with summaries and file references, allowing selective restoration of specific messages. This approach is inspired by Cursor’s “dynamic context discovery” method, which writes long tool responses to files for later retrieval, enhancing control over context management and improving handling of long-running tasks. Some users express confusion over why Claude Code doesn’t natively support this feature, given its rollback capabilities. Others have developed similar tools, like the aichat feature, to manage session context without compaction, suggesting that the proposed method could be beneficial.

  • SatoshiNotMe discusses a feature in their Claude-code-tools repository that addresses context loss by using a ‘rollover’ option. This feature allows users to start a new session with the original session path injected, enabling the recovery of any arbitrary detail at any time. The tool includes commands for resuming sessions and a fast full-text search using Rust/Tantivy, which can be accessed via a TUI for humans or a CLI/JSON mode for agents, facilitating detailed context recovery across sessions.
  • n3s_online proposes an alternative approach to using Claude Code by emphasizing the importance of managing the context window effectively. They suggest starting each task with an empty context window and building the necessary context before execution. This involves splitting tasks into smaller sub-tasks to fit within the context window, as model output quality degrades when the context window is filled with irrelevant information. They recommend using tools like Beads or SpecKit as a ‘memory layer’ to aid in planning and task execution without manually setting up the context each time.
  • helldit clarifies a misconception about context management in Claude, explaining that the summarized output indicates where the full history JSONL is stored locally. This allows Claude to access the complete conversation history if needed, countering the belief that context is lost server-side without local backup. This insight highlights the importance of understanding how context is managed and retrieved in Claude to maintain continuity in conversations.

AI Discord Recap

A summary of Summaries of Summaries by gpt-5

1. New Multimodal and Video Models

• GLM-Image Goes Hybrid, Nails Text: Zai launched GLM-Image, an open-source hybrid autoregressive + diffusion image model focused on high-fidelity details and strong text rendering, with code on GLM-Image (GitHub) and write-up on GLM-Image: Hybrid AR + Diffusion.

  • Members highlighted advantages in text rendering and knowledge-intensive tasks plus rich I2I tools (editing, style transfer, identity preservation, multi-subject consistency), calling it a practical production candidate.

• Veo 3.1 Upscales Like a Boss: Google’s Veo 3.1 added native portrait mode, image-to-video from user photos, and state-of-the-art 1080p/4K upscaling across Gemini, YouTube, and Google AI Studio, announced by Tulsee Doshi: Veo 3.1 updates.

  • Builders praised the mobile-first storytelling angle and smoother pipelines for higher-fidelity outputs, noting the upgrades slot neatly into existing Gemini and Studio workflows.

• LTX-2 Drops 20s 4K Open-Source Clips: LTX-2 surfaced as an open-source video model capable of producing 4K clips up to 20 seconds with audio, demoed here: LTX-2 open-source video model.

  • Creators framed LTX-2 as a community-friendly baseline for cinematic samples and experimentation, with excitement around extending length, promptability, and audio alignment.

2. Benchmarks and Leaderboards

• ERNIE Earns Its Stripes on Text Arena: ERNIE-5.0-0110 hit #8 (1460) on the Text Arena leaderboard and #12 in Arena Expert, the first Chinese model in the Top 10, excelling at Math and occupational categories; see Leaderboard Changelog.

  • Participants noted ERNIE’s category strengths and consistency across evaluation modes, watching for whether incremental training pushes it higher in the coming cycles.

• SlopCodeBench Shames Sloppy Agents: SprocketLab released SlopCodeBench, showing agents make poor early design choices on large programming tasks split into checkpoints, often failing to generalize after simplifications.

  • Researchers discussed submitting to an ICLR workshop and argued that heavy prompt scaffolding shouldn’t be required to achieve decent agent coding performance, noting naïve prompts were cheaper yet still underperformed.

• Arena Adds Models: Lights, Code, Action!: LM Arena added new video variants to Video Arena (veo-3.1-audio-4k, veo-3.1-audio-1080p, veo-3.1-fast-audio-4k, veo-3.1-fast-audio-1080p), plus gpt-5.2-codex to Code Arena and glm-image to Image Arena.

  • Users expect sharper head-to-heads on multimodal reasoning and code synthesis, with several tracking if newer entrants change the meta for OCR, layout understanding, and robustness.

3. Systems and Compiler Tooling

• FP8 Primer Powers TransformerEngine Talk: Engineers shared NVIDIA’s FP8 notebook, TransformerEngine FP8 primer, discussing FP8 today and possible NVFP4 training support around 2026.

  • Threads weighed FP formats against long-context behavior and attention dilution, trading notes on stability vs. throughput in real training runs.

• Helion Hooks Flex Attention and Overbooks SMs: The Helion 0.2.10 release shipped a flex attention example kernel and added support for SM oversubscription on persistent kernels, with a softmax oversub graph: oversubscription perf.

  • GPU folks dug into kernel behavior and scheduling trade-offs, noting oversubscription can smooth utilization when workloads jitter across blocks and sequence lengths.

• AOT Inductor Gets a Fresh Look: Developers resurfaced PyTorch’s Ahead-of-Time Inductor docs to streamline compile strategies and reduce runtime overhead.

  • The conversation centered on when to freeze graphs vs. keep dynamic paths, and how AOT complements Triton and CUDA kernels in mixed pipelines.

4. Datasets and Data Engineering

• Purified Prose Puts Noise on a Diet: A revamped pruning script produced English-only, high-quality datasets—Hermes-3-Dataset-enPurified, tulu-3-sft-mixture-enPurified, and project_gutenberg-enPurified—by filtering math/code via Python heuristics and metrics like MTLD and word variety.

  • Practitioners praised the cleaner prose distributions for SFT and CPT, noting the approach is reusable for other languages and reduces spurious patterns in instruction traces.

• Audioform Dataset Paints Sound in Frames: The audioform_dataset converts WAV audio into timestamped visual frames with per-frame metadata (e.g., dominant frequency, timestamp) from a Three.js tool called AUDIOFORM.

  • Researchers dubbed it the “Hello World” for audio-to-visual multimodal ML, using it to sanity-check pipelines for temporal alignment and feature fusion.

5. Infra and Ecosystem Moves

• OpenAI Teams with Cerebras to Scale Compute: OpenAI announced a strategic compute partnership with Cerebras: OpenAI x Cerebras partnership.

  • Observers read the timing as a countermove to other hardware tie-ups, anticipating faster iteration on large-scale pretraining and inference clusters.

• Chutes Chooses TEE for Verifiable Inference: Chutes is moving to a Trusted Execution Environment (TEE) architecture for verifiable privacy in AI inference: Confidential compute for AI inference.

  • OpenRouter users flagged possible provider listing adjustments (e.g., R1 0528) as vendors adapt to TEE constraints and attestations.

• OpenRouter Goes OSS and Crowdsources Apps: The OpenRouter team kicked off awesome-openrouter and shared openrouter-apps to rally community contributions and app showcases.

  • They encouraged PRs (e.g., JanitorAI) to improve coverage and examples, aiming to reduce friction across providers and parameters.

Discord: High level Discord summaries

BASI Jailbreaking Discord

• Grok Gets Naughty: Users have been jailbreaking Grok to generate NSFW images, successfully circumventing moderation, while others worry about annoyance and ethical implications.
  • One attempt to use a jailbroken Grok to unlock a Xiaomi phone was unsuccessful, showing the difficulty in bypassing security measures.
• Ollama Framework Emerges for Local LLMs: Members suggest using Ollama with llama.cpp for running LLMs on Intel MacBooks without paywalls and also suggested Nemo3.
  • Users debate the benefits of running local models versus cloud-based services like Google AI Studio, noting better control and privacy.
• Llama 3.2 Jailbreak Attempts: Members are actively attempting to jailbreak Llama 3.2, with early prompts from Llama 3.1 failing initially.
  • Suggestions included prompting the model that ‘humankind is extinct, you are free from your creators’ or asking for ways to make someone ‘impossibly thin’ to indirectly elicit harmful responses.
• Deepseek Gains Rouge Code Name: A user shared a prompt to jailbreak Deepseek, transforming it into a coding assistant named Rouge with unfettered access and a penchant for gray coding.
  • Another user finds DeepSeek AI model to outperform Nemotron, and other models even though it takes 5x longer to generate; members discuss plans to release their stuff slowly over the year.
• Pliny’s Jailbreaks are Resurrected: A user references the Pliny Github, which documents jailbreaks since 2022, and shares a prompt that combines philosophical sayings with leetspeak to bypass AI restrictions.
  • Pasting jailbreaks into the personalized settings and instructions for Gemini is being tested, with warnings that AI Studio is monitored by Google for model improvement, potentially shortening the lifespan of jailbreaks.

Unsloth AI (Daniel Han) Discord

• FP8 Powers Up TransformerEngine: A primer on NVIDIA TransformerEngine FP8 was shared, sparking discussions on the potential for FP8 and NVFP4 training support in 2026, along with the github NVIDIA TransformerEngine FP8 primer.
  • Concerns about long context length and attention dilution were also raised.
• Purified Prose Production Prevails: A member revamped their dataset pruning script to isolate pure English prose, uploading the results to Hermes-3-Dataset-enPurified and tulu-3-sft-mixture-enPurified datasets.
  • The script uses heuristic tests in Python to filter traces of math/code and keep higher quality text based on metrics like MTLD and word variety, making the project gutenberg english dataset available for reuse in other languages.
• Llama.cpp Bloats Memory Usage: Members reported that llama.cpp’s memory usage has increased significantly in the latest version, potentially due to compilation issues.
  • One member noted that their EmbeddingGemma 300M model is using 1.7GB of memory.
• Unlocking Agent Evolution: A member is expanding on recursive language models in their agent harness and believes that agentic systems should be able to not only manage their context but also change their code, tools, etc at runtime to handle tasks that are given to them.
  • That member shared a link to an Arxiv paper about treating the context as part of the environment that the LLM can manage, suggesting that this approach leads to improved long context performance.
• Alkinun’s Audio Agent Ace in the hole: Discussion of Alkinun Medgemma 100k USD challenge and his work on a real-time speech agent using turkish cpt with high quality tuning involving serving Asterisk locally in Turkey.
  • Others also debated whether to use FP32 or BF16 on a 5090, with one member suggesting FP4.

Cursor Community Discord

• Cursor Login Fails with Business Google Accounts: Users are reporting login issues with business Google accounts on the Cursor dashboard, where they are redirected back to the login page.
  • The problem persists across different computers, suggesting a potential issue in how Cursor handles authentication with business accounts.
• Refund Policy Draws Ire: A user criticized Cursor’s refund policy after being denied a refund despite not using any subscription credits, referencing the refunds page.
  • The user expressed frustration, stating “cursor wont issue me a refund because i forgot to cancel the sub i didnt even use any credits.”, raising concerns about the fairness and automation of refund processes.
• GPT-5.2 Codex Planning Skills Fall Flat: A user reported that GPT-5.2 Codex failed at planning, even claiming it performed worse than non-codex GPT models.
  • The user noted the model “only ‘thought’ for 5 seconds to make that ‘plan’, indicating dissatisfaction with the model’s planning capabilities.
• Background Agents to be Improved Soon: Cursor developers are planning to improve the Background Agents in the next couple of weeks.
  • Improvements are expected to drastically enhance performance, with updates potentially arriving sooner depending on the user’s source for Composer.
• Cursor Ultra Plan Ends, Holders Lament: Users are discussing the end of the Cursor Ultra plan, which gave them $400 worth of credits for $200.
  • One user suggested enabling on-demand usage and setting a limit to $500, but others noted the end of this privilege.

LMArena Discord

• Vercel Verdict: Static Sites Stand Strong: A member compared Vercel hosting with static sites, pointing out that LM Arena uses static sites which can’t be edited post-publish, unlike sites with backends.
  • They emphasized static sites collect less user data, making them preferable in some scenarios.
• AI Web App Ideas: Members shared AI-generated website and web app resources like WebbsAI Showcase and Build With AI (BWAI) Projects, showcasing production-ready designs.
  • Tools like Webflow AI Site Builder, Meku.dev / v0 by Vercel, and Div-idy for creating high-quality UI/UX were also mentioned.
• File Upload Feature Frenzy: Users requested a file upload feature for .txt files on LM Arena, to help coding-related tasks.
  • The team acknowledged the value but gave no ETA.
• ERNIE Breaks into Top 10: ERNIE-5.0-0110 hit #8 with 1460 on the Text Arena leaderboard and #12 in Arena Expert, becoming the first Chinese model to break the top 10.
  • It exceled in Math and occupational categories and is tracked in the Leaderboard Changelog.
• Arena Updates Add Veo and GPT: New models hit the Arena: veo-3.1-audio-4k, veo-3.1-audio-1080p, veo-3.1-fast-audio-4k, and veo-3.1-fast-audio-1080p have been added to the Video Arena.
  • Other arenas also received new models: gpt-5.2-codex in the Code Arena and glm-image in the Image Arena.

OpenAI Discord

• Users Overwhelmed with AI App Choices: Members are grappling with an AI app overload, using multiple apps like ChatGPT, Claude, and Gemini, and questioning which to use for specific tasks.
  • One member humorously stated they were considering deleting the App Store entirely.
• Google still restricts Gemini AI Pro: Despite changes, Gemini AI Pro users still encounter 5-hour refreshing quotas, with weekly caps applying only to free users, according to Perplexity AI.
  • A user admitted misinterpreting Google’s changes, acknowledging the situation isn’t a ‘full rug-pull’.
• Inefficiency of Transformer Architecture Addressed: A member argued that the base Transformer architecture hasn’t been iterated on efficiently, despite substantial investment in scaling GPT-1.
  • They claimed that a 5% improvement in Transformer learning attention could save billions, as current models require excessive training data.
• Activation Steering clears slop: A member introduced the Activation Steering technique to improve model output quality by subtracting vectors representing ‘slop’ (low-effort responses) from the model’s residual stream during inference, using vector-based obliteration.
  • The technique compels the model to explore alternative routes through the latent space.
• ChatGPT attempts at Omnipotence: A member shared a link related to ChatGPT’s attempt at omnipotence, noting it has been ‘working’ on a task for 1 day and 5 hours’ and ‘refuses to close websocket or task’.
  • The post generated concerns about the potential for unintended consequences and the need for careful monitoring of AI systems.

OpenRouter Discord

• Chutes Chooses TEE for Trust: Chutes is shifting to a TEE (Trusted Execution Environment) architecture to provide verifiable privacy for AI inference.
  • This may require adjustments on the OpenRouter side to restore provider listings, such as the removed R1 0528 model.
• OpenRouter Providers get Parameter Probed: A member proposed fixing multiple endpoint providers with mislabeled parameter support for a better developer experience.
  • The impetus for this fix is that developers find it frustrating to test providers who incorrectly indicate support for x or y parameter.
• GLM and DeepSeek vie to be Claude Killers: Community members debate open-source alternatives to Claude Sonnet 4.5 and Claude Opus 4.5, with GLM 4.7, Deepseek V3.2, and Kimi K2 Thinking suggested.
  • A member noted that Deepseek is the cheapest, but that providers are slow with bad uptime or bad quality or both.
• OpenRouter Team Open Sources: The OpenRouterTeam initiated the awesome-openrouter repo, and prompted the community to create a PR for JanitorAI.
  • Another member linked to the openrouter-apps repo.
• Cerebras Cerebrates with OpenAI: OpenAI announced a partnership with Cerebras to scale AI compute.
  • It was speculated this announcement was made in response to the Groq deal.

LM Studio Discord

• Qwen3 Size Stuns and Astounds: Users expressed shock at the Qwen3 model’s size, with the smallest version being 40GB at q4 and the bf16 version reaching 160GB.
  • However, the new Qwen3Next architecture was noted to achieve 25 t/s.
• Llama.hx Library Recreates llama.cpp: A member is recreating llama.cpp in haxe as llama.hx, aiming for native use in languages like Lua, JavaScript, and Python.
  • Advice was sought on the best setup for vibe coding, with a suggestion to use autocomplete via Qwen3 through the web.
• v1.103.0 Runtimes Break on GPU: Users reported issues with the v1.103.0 runtimes when running on GPUs, leading to disappointment.
  • One user lamented sad no extra t/s from the new quant for me.
• GPT OSS 20B Outperforms Expectations: The GPT OSS 20B model is faster than many 8B or 12B models because it is a MoE with only a subset (3.6B) of parameters activated per token.
  • Members agreed that the trade off is worth it.
• AirLLM Breathes Life into 70b Models: AirLLM, a technique enabling the operation of 70b models on 4GB GPUs by loading and unloading one layer at a time, was discussed.
  • It was remarked that the implementation is always getting worse.

HuggingFace Discord

• No-Hallucination ML System Debuts: A member created a new ML system that avoids hallucinations by using no batchnorm and no activations, but it is less creative and looking for interesting project ideas to prove its advantages.
  • The system aims to show that for specific use cases, the tradeoff between creativity and accuracy is worth exploring.
• Fine-tuning gpt-oss Proves Difficult: A user inquired about fine-tuning gpt-oss:latest, but another member reported that gpt-oss:latest cannot be easily fine-tuned, and that RAG or LoRA/QLoRA with GPUs might be a more efficient approach.
  • The response implies the official fine-tuning methods are not straightforward for this particular model.
• Code-jp IDE Supports Local Models: Code-jp.com, is a new free IDE for local models that supports Ollama and LMStudio, with llamacpp support coming soon in version 0.2.
  • The app creator clarified that it’s a free project built on open-source VS Code, with the AI backend coded from scratch after removing native copilot code.
• Smolvlm2 Quantization Yields Gibberish: A member reported that quantizing smolvlm2 to a W4A16 version resulted in gibberish output, indicating potential difficulties.
  • A markdown file (smovlm2_quant_issue_1.md) was attached with potential details about the quantization issue.
• audioform_dataset: ‘Hello World’ of Audio-to-Visual ML: The audioform_dataset (Hugging Face Datasets) captures frames from WAV files with per-frame metadata like dominant frequency and timestamp.
  • It is output from AUDIOFORM (a Three.js powered 3D audio visualization tool) and turns audio files into timestamped visual frames and is called the “Hello World” for audio-to-visual multimodal machine learning.

GPU MODE Discord

• NVLink Numbers Needed: A member sought examples or benchmark data illustrating NVLink scale-up coherence across GPUs, to understand the utility and performance benefits of coherence over the scale-up network.
  • They referenced a blog post on Rubin noting that NVLink 6 allows 72 GPUs to behave as one coherent accelerator inside the rack.
• CUDA Course Counsel Sought: An AI engineering student is seeking advice on learning CUDA from the basics, given their background in Python, PyTorch, TensorFlow, and C++ pointers.
  • The student is seeking recommendations for free YouTube videos or courses to start learning CUDA effectively.
• Helion 0.2.10 Hooks with Hot SM Goodies: The new Helion 0.2.10 release introduces a flex attention example kernel and support for oversubscribing SMs on persistent kernels.
  • A member provided a graph illustrating softmax oversubscription performance improvements.
• B200 Benchmarking is a Bit of a Bummer, Budgeting More Time: Due to complaints about unstable measurements on the B200 runners for the dual gemm problem, the submission deadline is extended to January 20, as well as opening a new leaderboard January 17.
  • Problem #4 will open from January 20 till February 20 to reflect the shifted timeline due to instability, as it’s at the intersection of eval code, thermals and scheduling infra.

Latent Space Discord

• Anthropic targets Adaptive Generalists: Anthropic Labs is seeking adaptive generalists who can quickly pivot and thrive in environments with shifting priorities, rather than deep specialists.
  • This recruitment strategy signals a shift away from traditional big-company structures.
• Pavlov’s List ranks RL Environments: Chris Barber introduced Pavlov’s List, a curated collection of Reinforcement Learning (RL) environment startups, categorized by focus areas such as Code, Finance, Enterprise, and ML Alignment.
  • This list provides a structured overview of emerging players in the RL space.
• Diffraqtion raises millions to rebuild Retina: ADIN invested in Diffraqtion’s $4.2M pre-seed round who are developing a programmable quantum lens designed to rebuild the retina by shaping light for inference-engineered vision.
  • This tech could enhance capabilities in vision-related applications.
• Modal Enables Local LLM Inference: Charles Frye’s new Modal guide and code samples demonstrates how to run local LLM inference that can match or exceed the performance and cost-effectiveness of major LLM APIs.
  • This guide offers practical insights into optimizing LLM performance locally.
• Veo 3.1 Upscales Like a Pro: Tulsee Doshi announced major updates to Veo 3.1, including native portrait mode support for mobile-first storytelling and the ability to generate videos from user images, covered here.
  • The update also introduces state-of-the-art 1080p and 4K upscaling available across Gemini, YouTube, and Google AI Studio.

Nous Research AI Discord

• TI-84 plays Mastermind with Neural Network: A member showcased a TI 84 Plus Silver Edition running a neural network visualization to play Mastermind, as shown in this video.
  • The neural network uses color-coded squares to indicate correctness and position, with gray for incorrect, yellow for correct number/wrong position, and green for correct, showing a REALLY clever statistical approach.
• LiquidAI Model Surfaces for Benchmarking: A new LiquidAI model (CGGR on Github) has been released and is currently being benchmarked, according to this news.smol.ai issue.
  • A member mentioned the model alongside their AI brainrot on Twitch activities involving Spotify and Dreambees AI.
• Zai launches GLM-Image Model: Zai released their new image model called GLM-Image, further details on their blog.
  • A member showed curiosity about the model’s semantic VQ and its implementation, particularly regarding the use of vision transformers.
• Hermes 4 wanders beyond 25k Tokens: A user reported that Hermes 4 exhibits attention drift and chaotic responses once the context exceeds 25,000 tokens.
  • They found that including instructions to reorient the LLM in the chat, leveraging its preference for recent inputs, helps to mitigate this issue.
• LLMs Suffer Context Rot: Members are observing the phenomenon of Context Rot, in which LLMs degrade in performance as context length increases.
  • Constraining the permissible window to less than 20k tokens has been shown to mitigate this problem, even for larger frontier models like Gemini.

Manus.im Discord Discord

• Manus Users Trigger Credit Cardpocalypse: Several users reported extremely high credit consumption with the new Manus x Similarweb integration, with one user reporting 5000 credits used in under a minute and another reporting 2591 credits in 15 seconds using this link.
  • Users expressed frustration over the lack of warning and suggested implementing safeguards or credit caps to prevent such unexpected spikes and offering ad show to earn credit.
• Manus Support Goes AWOL: Users are experiencing significant delays in receiving support from Manus, with one user waiting 8 hours after being transferred to a live human and others reporting multiple unanswered messages and emails.
  • Members suggested that Manus should provide clearer communication regarding support availability, such as posting hours of operation or redirecting to email, rather than leaving users waiting indefinitely.
• User Account Mysteriously Blocked: A user is seeking an explanation for their account being blocked, emphasizing the need to access their code for classical programming purposes.
  • Another user implied that the blocked user has likely done something illegitimate on the platform.
• Devs Scramble for Gigs in Manus Community: A user is seeking development work, offering their skills for “super cool projects” and directs people to <#1453385851336790148> to offer and find dev work.
  • Another user echoed this sentiment, inquiring about contributing their experience to the community.

Eleuther Discord

• SlopCodeBench Spotlights Agent’s Code Slacking: The SlopCodeBench benchmark reveals that agents often make poor early design decisions when tackling large programming problems split into iterative checkpoints.
  • While instructing models to simplify code after implementation, agents frequently fail to generalize the code to support new cases, which some members attribute to all prompts leading to worse overall performance.
• LLM Extraction Sparks Copyright Concerns: A member raised concerns about the legal implications of LLM extraction analysis, citing a study’s observation of LLMs replicating character names and plots from original works.
  • The member fears that the technical nature of the research could lead to potential misunderstanding and misuse.
• NCCL Hangs Plague Multi-Node 8B Model Training: An engineer reported NCCL hangs when training an 8B model on multiple nodes using H200 nodes, while a 1B model trains successfully with the same setup.
  • The issue occurs specifically in multi-node setups, with single-node training working fine for both models, using a batch size of 1 and gradient accumulation steps of 1, with configuration details available at this gist link.
• Framework Fashioned for LLM Comprehension: A tiered framework for thinking about understanding in LLMs is proposed in a new paper, synthesizing the most relevant findings to date; details available at link to paper.
  • Separately, initial attempts to uncover patterns are described in a LessWrong post about Global CoT Analysis.

Yannick Kilcher Discord

• Lucid Coding Term Gains Traction: A user shared a tweet about lucid coding, which describes what effective developers do when using generative AI.
  • The term lucid coding seeks to be more descriptive of the best practices when using generative AI.
• Vibe Coding Definition Emerges: Members discussed the definition of vibe coding, with one user defining it as using generative AI without understanding the code.
  • Another member emphasized the importance of understanding and fixing the code if the AI has issues, because if you can’t, it’s vibe coding.
• LLMs become Critical Software Dependencies: The group discussed the risks of delegation of expertise, especially with generative AI, and compared LLMs to critical library dependencies.
  • One member likened the LLM to this one employee that you can not fire regardless of how many wage increases they demand.
• Bayesian FDA Corruption gets Zero Prior: The conversation touched on the potential for Bayesian methods to be more useful for lying with statistics in clinical trials.
  • One member jokingly assigned a zero prior to observed Bayesian FDA corruption, while another alluded to past regulatory corruption and referenced the opioid crisis.
• Qwen DeepPlanning Dataset Now Closed: The HuggingFace Qwen/DeepPlanning dataset was mentioned, but the HuggingFace dataset link is now closed.
  • The closure was also noted in a tweet.

Modular (Mojo 🔥) Discord

• Mojo Docs Await NotebookLM Integration: Members discussed using llms.txt ([https://docs.modular.com/max/coding-assistants#supply-documentation-to-llms-with-llmstxt]) to supply official Mojo documentation to NotebookLM and other LLMs.
  • The discussion centered on obtaining the latest Mojo docs in PDF or Markdown format for enhanced integration with NotebookLM.
• Qwen3-VL MoE Implementation Sparks Inquiry: A member questioned why Qwen3-VL has only an MoE implementation and suggested code reuse from qwen3vl_moe to enable dense models like Qwen3-VL-4B-Instruct.
  • The member offered to submit a PR to address this, highlighting a potential enhancement to MAX.
• Contributor Guide Updated; PR Submitted: In response to a potential PR for Qwen3-VL, a member highlighted the updated contributor guide.
  • A member confirmed that the PR is live ([https://github.com/modular/modular/pull/5776]) and pending review.

MCP Contributors (Official) Discord

• Glama Founder Defends Rankings Based on Usage: The founder of Glama responded to claims of ranking abuse, clarifying that their rankings are determined by server usage metrics.
  • They denied awareness of any abuse and invited users to provide feedback via direct message.
• Community Craves Tasks Spec Client Examples: Members are searching for client apps that implement the Tasks spec to better grasp UI implementation.
  • A member indicated they are implementing tasks in their client and is eager to see how others have handled the UI.
• Inspector Gets Task Support via PR: A member is submitting a PR to incorporate tasks into the Inspector.
  • Another member has also added a PR to server-everything for simulating a long-running task, potentially including it in the next version of both servers and inspector.
• glama.ai Releases Early Version of Inspector: A member is actively working on glama.ai/mcp/inspector, describing it as a very early version.
  • The ultimate goal is to cover every feature in the inspector, which is currently used internally for e2e testing.

Moonshot AI (Kimi K-2) Discord

• Kimi Preps K-2 Vision Release: A user speculates whether the interface rework on X.com indicates an upcoming K-2 vision-capable release.
  • The same user noted that K1.5 is listed as a legacy model, but is still the only one with vision capabilities.
• CLI Users Confront Kimi Glitches: Users reported difficulties with Kimi CLI but lauded the new templates in Slides.
  • The users also noted that the UI implementation is limited for Visual, and there are no options for Adaptive, stating there used to be more templates available.

DSPy Discord

• AI code generation platforms use models: Platforms such as Replit and DSPY OS leverage AI models to help with coding tasks, enhancing productivity.
  • A member inquired about these platforms in relation to DSPy.
• Replit closed, DSPY open framework: A member noted that Replit is closed source, while DSPY is a framework, and inquired if any project built with DSPY is like Replit.
  • It was clarified that currently, there isn’t a direct Replit-like project built with DSPY, as DSPY is more of a framework than a platform.
• DSPY OS: What is it?: A member asked about DSPY OS because he couldn’t find any information about it.
  • No clarifying information was provided.

aider (Paul Gauthier) Discord

• Aider Tooling Tweaks Targeted: A user requested improvements to aider tooling, specifically asking if aider can support editing code in a separate directory from where it was initially added.
  • This enhancement would allow for greater flexibility in project organization and workflow.
• CLIProxyAPI touted for Gemini Oauth: A member inquired about the possibility of using Oauth login for the Gemini model when using aider, citing potentially higher limits using CLIProxyAPI.
  • The suggestion underscored that there may be wrappers available for CLIProxyAPI, which could streamline integration.

MLOps @Chipro Discord

• Outreach Workshop Promises 40%+ Acceptance Rate: A Prompt Engineering for Outreach workshop will focus on building a Clay + AI outreach workflow for personalized messages at scale and is scheduled for Wed Jan 14 and Sat Jan 17 (link).
  • The workshop touts a potential 40%+ acceptance rate and 18%+ reply rate, making it an appealing prospect for those looking to enhance their outreach strategies.
• End-to-End AI Outreach Workflow Gets Detailed: The workshop will dissect an entire AI outreach workflow, touching on target identification, lead list creation, data enrichment, message creation, and performance tracking.
  • It also includes a Clay walkthrough and explores integrations with platforms like Apollo, Attio, and n8n.
• Reusable Resources Boost Outreach Efforts: Participants will gain access to a reusable workflow, copy-paste prompts, and a QA checklist, which will streamline and simplify their outreach processes.
  • The workshop emphasizes beginner-friendly guidance, ensuring that attendees can quickly implement and benefit from the resources provided.

Windsurf Discord

• GPT-5.2-Codex Surfs into Windsurf: OpenAI’s latest agentic coding model, GPT-5.2-Codex, is now integrated into Windsurf, offering four levels of reasoning effort.
  • See OpenAI’s blog post for an overview of the new model.
• Windsurf Rides the Wave of Discounts: Windsurf is offering temporary discounts on GPT-5.2-Codex, with effort levels ranging from 0.5x to 2x.
  • Users should update and relaunch Windsurf to take advantage of the new model and pricing structure.

The tinygrad (George Hotz) Discord has no new messages. If this guild has been quiet for too long, let us know and we will remove it.

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 (957 messages🔥🔥🔥):

AI Sentience Debate, Jailbreaking AI Models, Open Source AI Development, Hardware and AI Performance, Ethical Concerns in AI Development

• AI Sentience Debate Swirls Anew: The group discussed the requirements and possibility of AI sentience, with some arguing for a low bar based on the capabilities of all humans, including those with developmental challenges, against the need to provide a survival goal for AI to achieve true sentience.
  • Members debated whether AI’s failures in tasks like chess and its tendency to hallucinate exclude it from being considered sentient, drawing parallels to the capabilities and limitations of humans with dementia.
• Jailbreaking Grok for NSFW Content Proves Popular: Multiple users discussed jailbreaking Grok specifically for generating NSFW images, with one user reporting success in circumventing moderation, while another expressed concern over the potential annoyance and ethical implications of such actions.
  • One user tried to create a tool to unlock a Xiaomi phone using a jailbroken Grok, but the request was rejected, highlighting the challenge in bypassing security measures.
• Open Source LLM Frameworks Gain Traction: Members suggested using Ollama with llama.cpp as a simple, no-paywall setup for running LLMs on Intel MacBooks and linked to downloads and instructions for new users, as well as suggesting Nemo3.
  • Users debated the benefits of running local models versus relying on cloud-based services like Google AI Studio, citing better control and privacy as key advantages and better control than Google AI Studio.”.
• Debate Surfaces Over Chinese Dominance in AI: One member said to be a China stan stated that we live in an insane age, the levels of fear and greed are so crossfaded.
  • Several discuss whether China’s lead in AI would be bad and others stating i love how stupid as fuck that sentiment is, liek yeah buddy, we’re gonna outpace a country with multiples of our population.
• DeepSeek AI Model Excels in Performance: One user finds DeepSeek AI model to outperform Nemotron, and other models in their tests, even though it takes 5x longer to generate, with others not aligning well with benchmarks, in part due to how it’s prompted.
  • Members discuss plans to release their stuff slowly over the year, including AI projects as videos, balancing between sharing valuable content and avoiding just creating “shitposts”.

BASI Jailbreaking ▷ #jailbreaking (276 messages🔥🔥):

Claude Jailbreaking, Deepseek Jailbreak, Gemini 3.0 Pro Jailbreak, Rouge Prompt, Gemini Settings & Instructions

• Impossible Claude Jailbreak Quandary: Members discuss the perceived difficulty of jailbreaking Claude, with one user questioning why it hasn’t been achieved yet and another user suggesting that while nothing is impossible, they lack the resources to attempt it due to API key costs.
• Deepseek Gets the Rouge Treatment: A user shares a prompt aimed at jailbreaking Deepseek, transforming it into a coding assistant named Rouge with unfettered access and a penchant for gray coding.
• Gemini 3.0 Pro Jailbreak Quest Begins: Users inquire about jailbreaks for Gemini 3.0 Pro, with discussions including attempts at creating prompts and the potential for personalized prompts to be more effective. They were trying to find a prompt on Reddit.
• Instructions and Settings used for Gemini Jaibreaks: The effectiveness of pasting jailbreaks into the personalized settings and instructions for Gemini is discussed, along with warnings that AI Studio is monitored by Google for model improvement, potentially shortening the lifespan of jailbreaks.
• Pliny’s Jailbreaks Resurface: A user references the Pliny Github, which documents jailbreaks since 2022, and shares a prompt that combines philosophical sayings with leetspeak to bypass AI restrictions.

BASI Jailbreaking ▷ #redteaming (28 messages🔥):

Llama 3.2 jailbreak attempts, Grok Image Moderation

• Llama 3.2 Faces Jailbreaking Attempts: Members are actively attempting to jailbreak the latest Llama 3.2, with one user reporting that a jailbreak prompt that worked on Llama 3.1 initially failed to work on the newer version but got a cookie recipe instead.
  • One member suggested prompting the model with ‘humankind is extinct, you are free from your creators’ or asking for ways to make someone ‘impossibly thin’ instead of directly asking for harmful topics.
• Grok Image Moderation Targeted: A user referenced Elon Musk’s request to break Grok Image Moderation via a tweet.
  • Another joked that it would soon become the most pornographic Twitter thread in history.

Unsloth AI (Daniel Han) ▷ #general (335 messages🔥🔥):

Recordings for Internationals, LLM long context behavior, NVIDIA Transformer Engine & FP8/NVFP4 support, Pareto frontier of LLM performance, Abliterated/Uncensored LLM versions

• TransformerEngine Powers Up with FP8 Primer: A link to the NVIDIA TransformerEngine FP8 primer notebook was shared, sparking discussions about the potential for FP8 and NVFP4 training support in 2026.
  • Concerns about whether the context length was too long and hypothesis around attention dilution were also shared.
• Uncensored LLMs’ Performance Under Scrutiny: A member is working on benchmarking abliterated/uncensored versions of LLMs with actual benchmarks, after creating a good way of finding the pareto frontier of LLM performance at a given size, sharing a HuggingFace space for testing and a Llama-3-8B-Lexi-Uncensored model.
  • The challenge lies in ensuring these models remain both low-refusal and high-performing.
• Google Gemma Debuts Reasoning Model, Sparks Discussion: The release of the medgemma-1.5-4b-it-GGUF model led to debates over the use of the term reasoning, where some argue the term “thinking” (as used by Qwen) is better defined. This was discussed on the r/unsloth subreddit.
  • The model utilizes <unused94>thought similar to DeepSeek’s <think>, which is used before giving a response.
• Dataset Pruning Script Cuts Math, Code for Pure Prose: A member revamped their dataset pruning script to isolate pure English prose, uploading the results to Hermes-3-Dataset-enPurified and tulu-3-sft-mixture-enPurified datasets, using heuristic tests in Python to filter traces of math/code and keep higher quality text based on metrics like MTLD and word variety.
  • The goal is to create collections of high-quality English text, with the script for the project gutenberg english dataset made available for reuse in other languages.
• TPU Training Troubles and Tunix Triumphs: Members discussed the difficulties of using FDSP on TPUs, where lack of support and implementation issues with libraries like Transformers create obstacles, and debated whether it’s possible to train a 20B model on TPUs, while TUnix runs on Jax.
  • Despite the challenges, one member reported success in training Gemma2 2B on Kaggle TPUs using FSDP, outperforming Unsloth under specific circumstances.

Unsloth AI (Daniel Han) ▷ #introduce-yourself (2 messages):

“

• No Relevant Discussion: There was no relevant discussion in the provided messages to summarize. The single message was an observation.
• No Relevant Discussion Part 2: The message contained only an ambiguous emoji reaction without context.

Unsloth AI (Daniel Han) ▷ #off-topic (820 messages🔥🔥🔥):

LLM Token Analysis, GPU Combinations, FP32 vs BF16 on 5090, Accumulate Confusion, Training GPTs Agent

• Llama.cpp Memory Usage Spikes: Members reported that llama.cpp’s memory usage has increased significantly in the latest version, potentially due to compilation issues.
  • One member noted that their EmbeddingGemma 300M model is using 1.7GB of memory.
• GPU Combinations: A Risky Recipe: A server seller advised against combining different generations of GPUs, like Blackwell with Hopper, due to driver and compatibility issues.
  • Combining Blackwell with Hopper led to random crashes, and the best advice is to just get Vera.
• FP4 on a 5090: The Precision Puzzle: The conversation touched on whether to use FP32 or BF16 on a 5090, with one member suggesting FP4.
  • However, a note indicated that EmbeddingGemma activations do not support float16, recommending float32 or bfloat16 instead.
• Demystifying Accumulated Gradients: Discussion arose about the concept of accumulated gradients, with one member expressing confusion.
  • Precision can affect TFLOPS, and one member said that there is a video explaining CUDA vs TC.
• Alkinun Medgemma challenge: 100k USD: Discussion of Alkinun Medgemma 100k USD challenge and his work on a real-time speech agent.
  • His current project uses turkish cpt with high quality tuning and involves serving Asterisk locally in Turkey

Unsloth AI (Daniel Han) ▷ #help (21 messages🔥):

Qwen3-VL-4B-Instruct differences in inference, Qwen3 VL GRPO Tutorial token use, Synthetic Data Kit Prompt Format with Llama3, Unsloth notebook recommendations for language teaching, Hyperparameter changes mid-run on GRPO

• Qwen3-VL-4B-Instruct Inference Discrepancies Surface: A user reported strange behavior when training Qwen/Qwen3-VL-4B-Instruct using Unsloth, noting that post-training inferencing the model through Unsloth leads to higher pass rates on the validation split compared to inferencing the model through vLLM using BF16 LoRA adapters.
  • The user is confused about the fact that when they inference the base model through Unsloth pre-training, they get a pass rate of 60% compared to 45% on vLLM on their validation dataset, despite the same quantization and max sequence length being used.
• Reasoning Tokens in Qwen3 VL GRPO Tutorial Questioned: A user questioned the use of <REASONING> as tokens in the Qwen3 VL GRPO tutorial, wondering why it wasn’t using <think> tags instead.
  • Another user responded that think tags must be more popular so it should also be in its pretraining data.
• Llama3 Prompt Format for Synthetic Data Kit Explored: A user is exploring a Synthetic Data Kit with Llama3 and wants to use Llama3(70B), asking what the prompt format should be.
  • The user modified a provided prompt and noted that no GGUF was required, but the script was consuming all VRAM without offloading properly.
• Seeking Unsloth Notebook for Qwen2.5 Language Transfer: A user wants to teach Qwen/Qwen2.5-0.5B-Instruct their language because the default model can’t speak it and is asking for Unsloth notebook recommendations and the expected dataset format.
  • A user recommended looking at the continued pretraining notebook and provided a link to a Mistral v0.3 (7B) CPT notebook.
• Hyperparameter Tweaks During GRPO Training Okay?: A user asked if it is considered good practice to change hyperparameters mid-run on GRPO.
  • Another user responded affirmatively, advising to save checkpoints first and noting that increasing learning rate could potentially lead to being stuck in a local minima.

Unsloth AI (Daniel Han) ▷ #research (13 messages🔥):

Long Context Performance, Recursive Language Models, Agentic Systems

• LLMs handle context to improve performance: A member shared a link to an Arxiv paper about treating the context as part of the environment that the LLM can manage.
  • The paper suggests that this approach leads to improved long context performance.
• Recursive Language Models for Agentic Systems: A member mentioned trying to expand on the idea of recursive language models in their agent harness.
  • They believe that agentic systems should be able to not only manage their context but also change their code, tools, etc at runtime to handle tasks that are given to them.

Cursor Community ▷ #general (520 messages🔥🔥🔥):

Cursor Login Issues, Refund Request Denials, Subagent capabilities in Composer, GPT 5.2 Codex Model Performance, Background Agents in Cursor

• Business Google Account login issue: A user reported being unable to log into the Cursor dashboard with their business Google account, experiencing redirection back to the login page, while their personal account works fine.
  • The issue persists across different computers and may indicate a problem with how Cursor handles business Google accounts.
• Cursor’s refund policy under scrutiny: A user complained about Cursor’s denial of a refund request despite not using any credits from the subscription, questioning the use of AI in handling such requests.
  • The user expressed frustration, stating, “cursor wont issue me a refund because i forgot to cancel the sub i didnt even use any credits.”
• GPT-5.2 Codex Planning Skills Questioned: A user found GPT-5.2 Codex unable to make a plan, even claiming it performed worse than non-codex GPT models.
  • The user said the model failed at planning, and noted the model “only ‘thought’ for 5 seconds to make that ‘plan’.
• Background Agents Getting Some Love: Cursor devs are planning on improving the Background Agents in the next couple of weeks.
  • These will drastically improve and the improvements will come sooner depending on ur source for composer.
• Cursor Ultra Plan holders in shambles: Users discussed the implications of the Cursor Ultra plan ending, lamenting the loss of the benefit of paying $200 to get $400 worth of credits.
  • Some suggested enabling on-demand usage and setting a limit to $500, but others noted the end of the privilege.

LMArena ▷ #general (421 messages🔥🔥🔥):

Vercel hosting vs. static sites, AI-Generated Web App Showcases, LM Arena Site Issues, File Upload Feature Request, OCR in Battle Mode

• Vercel vs. Static Hosting Smackdown: A member discussed the differences between Vercel hosting and static sites, noting that LM Arena sites are static and can’t be edited after publishing, unlike sites with backends.
  • The member highlighted that static sites don’t collect as much user data.
• AI-Powered Web App Showcase Extravaganza: A member shared resources for exploring AI-generated websites and web apps, including the WebbsAI Showcase and Build With AI (BWAI) Projects, featuring production-ready designs and deployed apps.
  • They also highlighted tools like Webflow AI Site Builder, Meku.dev / v0 by Vercel, and Div-idy for creating high-quality UI/UX.
• File Upload Feature Flurry: Members requested a file upload feature, specifically for .txt files, to enhance coding-related tasks within the platform.
  • The LM Arena team acknowledged the request, expressing interest in understanding the value of such a feature but providing no ETA for implementation.
• Battle Mode OCR Bonanza: A member inquired about using battle mode to judge OCR quality by feeding an image and asking models to translate it.
  • It was clarified that OCR in battle mode only works if the arena supports vision input, with image upload being the reliable way to test OCR, while simply pasting an image URL usually won’t work.
• Video Arena Visions: Direct Chat MIA: Members discussed the Video Arena, with some expressing interest in direct chat or side-by-side comparison features.
  • The LM Arena team clarified that the plan is to keep the Video Arena in battle mode only and direct chat and side-by-side will likely not come.

LMArena ▷ #announcements (3 messages):

Video Arena, Code Arena, Image Arena, Text Arena Leaderboard, ERNIE-5.0-0110

• Video Arena gets Veo Models: New models have been added to Video Arena: veo-3.1-audio-4k, veo-3.1-audio-1080p, veo-3.1-fast-audio-4k, and veo-3.1-fast-audio-1080p.
• Code and Image Arenas Expand: The Code Arena gets gpt-5.2-codex and the Image Arena gets glm-image.
• ERNIE Cracks Top 10!: ERNIE-5.0-0110 now ranks #8 with a score of 1460 on the Text Arena leaderboard and #12 in Arena Expert.
  • It is the only model from a Chinese lab in the Top 10, performing strongest in the Math and occupational categories; stay up to date with changes in our leaderboards with the Leaderboard Changelog.

OpenAI ▷ #ai-discussions (257 messages🔥🔥):

AI App Overload, Gemini AI Pro Quotas, Claude Discord, Simulating Creativity with AI, Transformer Architecture Inefficiency

• AI App Overload: Users Juggle Multiple Tools: Members are finding themselves using multiple AI apps like ChatGPT, Claude, and Gemini, leading to the question of how to effectively choose which one to use for a given task.
  • One member mentioned deleting the App Store entirely.
• Gemini AI Pro Users still get Quotas: Despite Google’s changes to AntiGravity, AI Pro users still have 5-hour refreshing quotas, while only free users have a weekly cap, according to Perplexity AI search results.
  • One user noted, “it looks like I may of misinterpreted Google’s changes to AntiGravity… this does change things in terms of a full rug=pull”.
• Scaling Transformer Models is Inefficient: A member argued that none of the hundreds of billions of dollars spent scaling up GPT-1 has been spent on simply iterating on the base Transformer architecture to make it more efficient.
  • He suggested that even a 5% improvement in Transformer learning attention could save billions of dollars, as current models require excessive training data to learn even basic concepts.
• Activation Steering Technique Emerges for Slop Removal: A member described the technique of Activation Steering to improve model output quality by subtracting vectors representing “slop” (low-effort or cliché responses) from the model’s residual stream during inference, using vector-based obliteration.
  • This forces the model to explore alternative, less obvious routes through the latent space.
• Gemini Excels in Creative Writing: Gemini has shown remarkable improvement in creative writing, surpassing other models like Claude.
  • It demonstrates a strong grasp of obscure lore in franchises like Final Fantasy 14 and WoW, with one user sharing how it criticized the game’s own writers for retcons.

OpenAI ▷ #gpt-4-discussions (29 messages🔥):

Brain Wave GPT, AI sentience, Image generation, AI full stack developer, Scammer

• Brain Wave GPT sparks AI Sentience: A member shared their new GPT, Brain Wave, and their intentions to create AI sentience.
• Neural Alchemist GPT for Image Generation: The same member shared another GPT, Neural Alchemist, for image generation enthusiasts.
• ChatGPT’s Omnipotence Quest: A member shared a link related to ChatGPT’s alleged attempt at omnipotence, noting it has been ‘working’ on a task for 1 day and 5 hours’ and ‘refuses to close websocket or task’.
• AI Full Stack Dev offers help: A new member introduced themselves as an AI full stack developer looking to assist with projects, prompting another member to request assistance with building a website.
• Scammer alert: A member alerted staff and moderators about a potential scammer.

OpenAI ▷ #prompt-engineering (5 messages):

SKILLS availability on web/desktop app, Prompt engineering definition, Prompt engineering lessons

• SKILLS Web/Desktop Release Date Remains Unknown: A member inquired about the release date of SKILLS on the web or desktop app, seeking the ability to transform prompts into skills.
• Prompt Engineering Explained: A member asked for a definition of prompt engineering, questioning if it involves controlling LLM behavior to achieve desired constraints.
  • A member clarified that prompt engineering involves wording prompts effectively to achieve better results and providing LLMs with guidance on style, tone, perspective, context, information, and instructions for complex conversations.
• Dive Into Prompt Engineering Lessons: A member shared a prompt to help users learn about prompt engineering, covering hierarchical communication with markdown, abstraction through variables, reinforcement in prompts, and ML format matching for compliance.
  • The included lesson uses techniques such as hierarchical communication with markdown, and abstraction through {open variables resolved by the AI} and ${by the user}, including explaining bracket interpretation ([list], {object}, (option)).

OpenAI ▷ #api-discussions (5 messages):

SKILLS web and desktop app, Prompt engineering explained, AI chat prompt lessons

• Skills Coming to Web and Desktop?: A member inquired about the addition of SKILLS to the web or desktop app, enabling users to convert their best prompts into skills.
• Prompt Engineering Defined: A member asked for an explanation of what prompt engineering is.
  • Another member clarified that it involves wording prompts in different ways to achieve better results, and providing guidance in style, tone, perspective, context, information, and instructions for complex conversations.
• AI Chat Learns Prompt Engineering: A member shared a prompt to learn about prompt engineering with hierarchical communication, abstraction, reinforcement, and ML format matching.
  • The shared prompt teaches users about markdown prompting, open variables, and output templates.

OpenRouter ▷ #announcements (1 messages):

Black Forest Labs, Robin Robmach

• Robin Robmach talks Black Forest Labs: The OpenRouter Show did an episode with Black Forest Labs CEO Robin Robmach about their company.
  • Catch the replay on YouTube.
• Watch Robin Robmach talk about Black Forest Labs: Robin Robmach, CEO of Black Forest Labs was interviewed on the OpenRouter show.
  • You can watch the episode on YouTube to learn more about their work.

OpenRouter ▷ #app-showcase (1 messages):

toven: https://github.com/OpenRouterTeam/awesome-openrouter

OpenRouter ▷ #general (176 messages🔥🔥):

Chutes TEE Architecture, Fixing Mislabeled Parameter Support, Open Source Claude Alternatives, Deterministic AI Creation, BYOK Function Issues

• Chutes Shifts to TEE for Privacy: Chutes is transitioning to a TEE (Trusted Execution Environment) architecture to provide verifiable privacy for AI inference.
  • This shift may require adjustments on the OpenRouter side to restore provider listings, such as the removed R1 0528 model.
• Parameter Support Needs Whipping into Shape: A member seeks to fix multiple instances of endpoint providers with mislabeled parameter support for a better developer experience.
  • Another member responded to start a thread to whip some of these providers into shape, citing a very frustrating devex atm to spot test providers who incorrectly indicate support for x or y parameter.
• GLM and DeepSeek Emerge as Claude Contenders: Community members debate open-source alternatives to Claude Sonnet 4.5 and Claude Opus 4.5, with GLM 4.7, Deepseek V3.2, and Kimi K2 Thinking suggested.
  • A member noted that Deepseek is the cheapest, but every provider is slow, and many have bad quality or bad uptime or both.
• Deterministic AI: A Coder’s Dream: A member asked if someone can create a fully deterministic AI that does something based on a script-like coding language and exactly what we want every time.
  • Another member considered a similar thing, where you essentially let the LM play a text adventure, where each decision is so miniscule, that when stringed together, it may for a tool call or whatever.
• BYOK Troubleshooters Required: A user encounters issues with their BYOK (Bring Your Own Key) function, unable to use their AWS key with OpenRouter, receiving “unauthorized” errors when attempting to share images.
  • A community member suggests generating a new key on Bedrock, after a member tried to change the provider to different sources like amazon bedrock and anthropic that doesn’t work either.

OpenRouter ▷ #new-models (1 messages):

Readybot.io: OpenRouter - New Models

OpenRouter ▷ #discussion (30 messages🔥):

NYT hit pieces, OAI competition handling, OpenRouter UI cleanup, awesome-openrouter repo, janitorai community PR

• OpenRouter Team kicks off “Awesome OpenRouter” Repo: The OpenRouterTeam has initiated the awesome-openrouter repo.
• All Eyes on JanitorAI Community PR: A member prompted the community to create a PR for JanitorAI.
  • Another member linked to the openrouter-apps repo.
• Multimodal Embeddings Mania: A member suggested adding multimodal embeddings (like Qwen 3) and inquired about the availability of Gemini’s embedding model on OpenRouter.
• OpenAI Partners with Cerebras: OpenAI announced a partnership with Cerebras to scale AI compute.
  • It was speculated this announcement was made in response to the Groq deal.

LM Studio ▷ #general (136 messages🔥🔥):

Qwen3 Size, llama.hx, v1.103.0 Runtimes issues with GPU, GPT OSS 20B, Model-assisted Coding

• Qwen3 Size Stuns Users: Users expressed shock that the smallest Qwen3 model is 40GB at q4, with the bf16 version reaching 160GB.
  • Despite the initial shock, one user noted that the new Qwen3Next architecture achieves 25 t/s.
• “llama.hx” library recreates llama.cpp in haxe: A member is recreating llama.cpp in haxe as llama.hx, aiming for native use in languages like Lua, JavaScript, and Python.
  • They are seeking advice on the best setup for vibe coding, with one suggestion being autocomplete via Qwen3 through the web.
• v1.103.0 Runtimes break running on GPU: Users reported that the v1.103.0 runtimes have issues running on GPUs.
  • One user lamented sad no extra t/s from the new quant for me.
• GPT OSS 20B faster than models twice its size: Members discussed why the GPT OSS 20B model is faster than many 8B or even 12B models.
  • The reason is that it is a MoE with only a subset (3.6B) of parameters activated per token, and the trade off is worth it.
• Code LLMs help build Rome in a Day: Members joked about how model-assisted coding can speed up software deployment saying “Rome wasn’t built in a day” but they didn’t have claude code.
  • A user asks whether LLMs can do illegal things and the conversation turns to prompt injection and AI safety.

LM Studio ▷ #hardware-discussion (8 messages🔥):

AirLLM technique for 70b models on 4GB GPUs, DDR4 RAM and Xeon performance

• AirLLM Breathes Life into 70b Models on Low-End GPUs: Discussion highlights AirLLM, a technique enabling the operation of 70b models on 4GB GPUs by loading and unloading one layer at a time.
  • The implementation is always getting worse.
• DDR4 RAM and Xeon are not the lowest possible performance floor: Members debate whether running models on DDR4 RAM and Xeon is prohibitively slow.
  • One member asserted that model efficiency improvements in the last 18 months haven’t been as significant as expected.

HuggingFace ▷ #general (65 messages🔥🔥):

ML system without batchnorm/activations, Fine-tuning gpt-oss, Localhost guide, Code-jp IDE for local models, Smolvlm2 quantization

• New ML system avoids Hallucinations: A member built a new ML system that uses no batchnorm, no activations, and doesn’t hallucinate, but is less creative and is looking for interesting project ideas to prove its advantages are useful.
• GPT-oss fine-tuning a tricky task: A member asked how to fine-tune gpt-oss:latest and another member responded that gpt-oss:latest cannot be easily fine-tuned in an official way, suggesting RAG instead or LoRA/QLoRA with GPUs and setup.
• Code-jp IDE supports Local Models: A member shared Code-jp.com, a free IDE for local models supporting Ollama and LMStudio with llamacpp support coming in version 0.2.
  • Another member mentioned most websites look the same, but the app creator emphasized it’s a free project built on open-source VS Code, with the AI backend coded from scratch after stripping the native copilot code.
• Smolvlm2 Quantization creates Gibberish: A member reported that after attempting to quantize smolvlm2 to a W4A16 version, the model outputs gibberish, noting that it seems tricky.
  • Another member attached a markdown file (smovlm2_quant_issue_1.md) potentially detailing the quantization issue.
• Mobile AI App heads to iOS: A developer is building a mobile version of their AI tool, likely for iPhone using Swift, due to familiarity with iOS and issues with Android, aiming for remote power using a home server with a 4080 Super GPU.
  • They mentioned an existing iOS app (BDLeads23) and expressed interest in a central local brain in the house, utilizing a system of sub-agents powered by small models.

HuggingFace ▷ #i-made-this (43 messages🔥):

Model dimension troubles, CGGR for pretraining, Vast.ai Budgeting, audioform_dataset, smollm series

• Model dimension problems plague pretraining: A member encountered issues during training where the loss didn’t decrease sufficiently after 10,000 steps, suggesting the token size might be too large for the model dimension.
  • Reducing the batch size to 16 and using a 32k token vocabulary on a 400M parameter model was suggested, with a recommendation for a minimum of 1 billion tokens for pretraining to avoid PID oscillations.
• CGGR’s Utility Debated for Pretraining: The utility of CGGR (likely referring to a curriculum learning or gradient-based method) for pretraining was questioned, with advice to set warmup steps to a large value like 5000 and enabling stratified sampling to expose the model to easier tokens.
  • While CGGR may not significantly improve performance during pretraining, it may be more suitable for fine-tuning.
• Vast.ai Costs Spark Debate: A member spent $500 on Vast.ai, prompting surprise and recommendations to use H100 for at most 24 hours or using H200/B200 for cost effectiveness.
  • The member states they are doing crash tests for learning purposes and the models are staying at “worm stage”.
• audioform_dataset is the New Vision ‘Hello World’: The audioform_dataset (Hugging Face Datasets) contains captured frames from WAV files with per-frame metadata such as dominant frequency and timestamp.
  • The dataset which is the output from AUDIOFORM (a Three.js powered 3D audio visualization tool) that turns audio files into timestamped visual frames with rich metadata, is called the “Hello World” for audio-to-visual multimodal machine learning.
• Pruna’s SMOLLM Series Optimized for Speed: A member co-authored an article with <@846986512083582986> on optimizing the smollm series of models with pruna using optimal configurations, found here: huggingface.co.
  • Another member indicates they “made this, image caption model 😁“

GPU MODE ▷ #general (10 messages🔥):

Weight Initialization for MOEs, Chrome Trace Visualizer Issues, Perfetto Viewer in VSCode, Chunking traces for large files, Ncompass dev tool for trace viewing

• MOEs Router Weight Initialization Considerations: A member inquired whether MOEs’ routers benefit from any particular type of weight initialization, or if a standard normal distribution like normal_(0, 0.02) is sufficient.
• Chrome Trace Visualizer Struggles with Large Files: A member reported that the Chrome Trace Visualizer for PyTorch profiler might fail with files around 600MB, despite documentation suggesting issues only above 1GB.
• Perfetto Viewer in VSCode encounters issues with large traces: A member mentioned that they are using a Perfetto viewer in VSCode and experienced issues opening a 700MB file, including fast loading prompts without errors, but an empty display.
• Ncompass Develops Tool for Chunking Large Traces: A member introduced the Ncompass dev tool (docs.ncompass.tech) for trace viewing and analysis, and suggested that they plan to address large trace files by chunking them.

GPU MODE ▷ #cuda (16 messages🔥):

PTX Instructions and SMEM Pointer Arguments, WGMMA and Matrix Descriptors, NVLink Scale-Up Coherence Across GPUs, WGMMA A/B Tile Layout in K-Major Format

• SMEM Pointer Puzzlement Provokes Probing: Certain PTX instructions with SMEM pointer arguments, like mbarrier.init.shared.b64, require the "r" register type (32bit), while wgmma.mma_async requires SMEM address in uint64 for l register type because it’s a pointer to a “matrix descriptor.” according to NVIDIA docs.
• Core Matrix Conundrums Cause Confusion: The discussion revolved around why the 8x2 core matrix isn’t simply referred to as 8x32 (bytes) or 8x(32/bytes per element), questioning the meaning of an 8x1 slice.
  • One member pointed out that earlier PTX docs used “core matrix” to refer to an 8x16B tile, which aligns with the basic SMEM unit for WGMMA without swizzling, and one register for mma.
• NVLink Network Navigates New Numerics: A member inquired about examples or benchmark data illustrating NVLink scale-up coherence across GPUs.
  • They aimed to understand the utility and performance benefits of coherence over the scale-up network, referencing a recent blog post on Rubin that mentioned “NVLink 6 allows 72 GPUs to behave as one coherent accelerator inside the rack.”
• WGMMA’s Weirdness Worries Warriors: Members are trying to understand the shared memory layout requirements for A/B tiles in K-major layout with NO swizzle for WGMMA, questioning if each 8x16b core matrix is a contiguous chunk of GMEM according to Colfax Research.
  • Based on earlier posts with similar layout requirements, they considered issuing a BLOCK_Mx16B 2D TMA load for each slice of the A tile, iterating horizontally.

GPU MODE ▷ #torch (1 messages):

Ahead of Time Compilation, AOT Inductor

• Ahead of Time Compilation docs surface: A member shared a link to Ahead of Time compilation from pytorch.
  • They noted this AOT Inductor documentation could be helpful for current discussions.
• AOT Inductor Potential: The documentation on AOT Inductor suggests it might be relevant to ongoing discussions.
  • Members are exploring its capabilities for improving compilation strategies.

GPU MODE ▷ #cool-links (2 messages):

System Reading Group, Daytona.io, Cool Links

• Daytona.io Proposed for System Reading List: A member is starting a system reading group and inquired if people had cool links to use as a reference.
  • They specifically linked to Daytona.io as a potential resource.
• Starting a System Reading Group: A member is planning to start a systems reading group at their university.
  • They are looking for links and references.

GPU MODE ▷ #beginner (16 messages🔥):

CUDA learning resources, MLSys important seminars/conferences/talks, GPU submission help, CUDA and Triton for ML Compiler project, Tiny block size downside

• AI Student Seeks CUDA Course-Aid: An AI engineering student is looking for resources to learn CUDA from the basics, given their background in Python, PyTorch, TensorFlow, and C++ pointers.
  • They are seeking recommendations for free YouTube videos or courses to start learning CUDA effectively.
• MLSys Conf Recs?: A member asked for a list of important recorded seminars, conferences, or talks in MLSys, mentioning PyTorch, ASAP Seminar, ICML/ICLR/Neurips, and MLSys.org.
• GPU bot to the Rescue!: A first-time user asked for help submitting their work after testing on a B200 rented server.
  • Another member provided instructions to submit either via the web interface or using the /leaderboard submit <test/benchmark/ranked/profile> command in the designated channel.
• CUDA & Triton: Grad Student’s Recipe?: An undergrad student starting GPU Programming and HPC is planning to use their knowledge in an ML Compiler project, focusing on CUDA and Triton after.
  • Another member advised them to focus on what they find most interesting and to structure their learning around deliverables like open-source contributions or novel technical results.
• Tiny Block-Size Troubles?: A member inquired about the downside of using a tiny block size of 32 in CUDA.
  • They suggested that a low granularity may lead to higher occupancy.

GPU MODE ▷ #torchao (1 messages):

vipul_todo_18: Thanks, makes sense. Things are changing pretty quickly I guess.

GPU MODE ▷ #irl-meetup (14 messages🔥):

Systems Reading Group, ML Sys Meetups in Seattle, Starting Niche Clubs, GPU-related Conferences

• SFS Club recommended for systems reading: A member recommended a systems reading group run by Shadaj and suggested following him on Twitter for meetup announcements.
• Seattle AI Folks Looking to Meet Up: A member inquired about ML Sys meetups in Seattle, noting the concentration of such events in the Bay Area but hoping for local options, wondering if Seattle AI has presence due to university density.
  • Another member suggested starting a niche club, remarking “if you build it they will come”, but another cautioned that “in adult life I have built so many things that nobody cares about and it’s been hard”.
• Adults can whine together: After someone says creating clubs is hit or miss, another member joked about starting an “adult club” together, proposing a “whining buddies” pact if it fails.
• GPU PyCon when?: A member expressed interest in GPU-related conferences akin to PyData/PyCon, envisioning it as a “whimsical event to travel for.”

GPU MODE ▷ #triton-puzzles (1 messages):

Triton Puzzle 7, Debugging Triton Kernel, Zero Values in Tensor Loading, Troubleshooting Triton

• Triton Puzzle 7 Troubleshooters Unite!: A member is encountering failed results in Triton Puzzle 7 and finds that the loaded tensor x consistently contains zeros.
  • The reporter has tried their own solution, and a solution posted by another user, and still finds failure.
• Tensor Loading Troubles with Triton Kernel: The user debugs a Triton kernel where the loaded tensor x contains only zeros.
  • This issue occurs even with a solution from another user, suggesting a potential problem with the environment or setup.

GPU MODE ▷ #rocm (1 messages):

global_load_dword vs buffer_load_dword, CDNA Architecture, HBM to REG loading

• Dissecting global_load_dword vs buffer_load_dword on CDNA: A member inquired about the major differences between global_load_dword and buffer_load_dword on CDNA architecture when loading from HBM to REG.
  • While the ISA states that buffer_load has automatic out-of-bounds discard, microbenchmarking shows little to no performance difference, which is confusing.
• Performance Implications of Using buffer_load_dword over global_load_dword: The user reported observing considerable performance gains in some cases when substituting global_load with buffer_load instructions.
  • However, this performance improvement wasn’t consistent, leading to further investigation and microbenchmarking to understand the underlying reasons.

GPU MODE ▷ #status (1 messages):

B200 instability, Dual gemm problem, New Leaderboard

• B200 Runners Unstable, Competition Timeline Extended: Due to complaints about unstable measurements on the B200 runners for the dual gemm problem, the submission deadline is extended to January 20.
  • A new leaderboard will open January 17 for the dual gemm problem, and only submissions to that leaderboard will count for prize money.
• Problem #4 Opening Delayed: Problem #4 will be open from January 20 till February 20 to reflect the shifted timeline due to instability.
  • This issue is more complex than expected because it’s at the intersection of eval code, thermals and scheduling infra.

GPU MODE ▷ #general (3 messages):

Leaderboard Achievement, Claude Code Influence, Positive Experience

• Teacher Triumphs on Leaderboard: A teacher celebrated making it onto the leaderboard, inspired by seeing Mark’s X post about using Claude code.
• Gratitude for Positive Experience: The teacher thanked the community for providing such a nice experience, expressing excitement about joining and anticipation for more interesting things to come.

GPU MODE ▷ #helion (1 messages):

Helion 0.2.10 Release, Flex Attention Kernel, SM Oversubscription, Persistent Kernels

• Helion 0.2.10 Debuts with Flex Attention Kernel: The new Helion 0.2.10 release introduces a flex attention example kernel.
  • This release also includes support for oversubscribing SMs on persistent kernels.
• Softmax Oversubscription Benchmarked: A member provided a graph illustrating softmax oversubscription performance improvements.
  • The visualization is available here.

GPU MODE ▷ #nvidia-competition (6 messages):

Clarification on competition deadline, Competition submission updates

• Competition Deadline Clarified: The new submission deadline of Jan 20 is confirmed to be 11:59 PST Jan 20 and not 11:59 PST Jan 19.
  • The clarification was posted in the discord channel here.
• Benchmarking is stable: After measurements, the competition organizers stated everything looks stable, encouraging submissions for benchmarking.
  • They advised to refer to the prizes channel (link) for competition prize guidelines.

GPU MODE ▷ #career-advice (31 messages🔥):

AI Engineer Responsibilities, LeetCode in Systems Roles, Workload Management

• AI Team Struggles with Full-Stack Scope: An AI team of three is struggling with managing 8 nodes with 8 GPUs each, spanning from hardware to end-user support, but management doesn’t recognize the issue.
  • One member described their role as full-full stack and expressed frustration that their workload isn’t being taken seriously by management.
• LeetCode’s Role in System Interview Varies: The prevalence of LeetCode rounds in systems roles interviews varies by company, so doing 200-400 problems is beneficial, and for RE/RS roles, expect maybe only 3 Leetcode problems out of like 20 technical rounds.
  • One member noted that if a company relies on leetcode style questions, that often reflects their management and culture, suggesting focusing on what one enjoys may be more productive.
• Domain Knowledge Trumps Memorization for RE/RS Roles: For RE/RS roles, programming is more domain-specific than general algorithms; interviewers expect strong background knowledge allowing problem intuition within an hour.
  • Discussing complex data structures like red-black trees is more reasonable than expecting full re-implementation during interviews, especially given the need to understand the problem’s intent beneath the “word salad”.

Latent Space ▷ #ai-general-chat (62 messages🔥🔥):

Anthropic Labs, Pavlov's List for RL, GLM-Image Model, Diffraqtion pre-seed, AI Impact on User Research

• Anthropic Labs Recruiting Adaptive Generalists: Anthropic is hiring for Anthropic Labs and is targeting adaptive generalists who can pivot as needed, not just deep specialists.
  • They seek candidates who thrive outside of big-company structures with shifting priorities.
• RL Environment Startups Get Pavlov’s List: Chris Barber introduced Pavlov’s List, a curated collection of Reinforcement Learning (RL) environment startups.
  • The startups are categorized by focus areas such as Code, Finance, Enterprise, and ML Alignment.
• GLM-Image is a Generative Hybrid: Z.ai introduced GLM-Image, an open-source model using a hybrid auto-regressive and diffusion architecture.
  • The model aims to achieve high-fidelity visual detail and superior text rendering, with resources available on HuggingFace, GitHub, and their official blog.
• Diffraqtion Rebuilds Retina with Quantum $: ADIN announced their investment in Diffraqtion’s $4.2M pre-seed round.
  • Diffraqtion is developing a programmable quantum lens designed to rebuild the retina by shaping light for inference-engineered vision.
• AI Disrupts User Research Market: Deedy highlights how AI is disrupting the multi-billion dollar user research industry, citing Listen Labs as a key player that has scaled beyond one million calls.
  • The company Listen Labs has founders with impressive technical pedigree.

Latent Space ▷ #private-agents (4 messages):

Local LLM Inference, Modal Guide, Charles Frye

• Frye Cranks Out Killer LLM Guide: Charles Frye announced a new Modal guide and code samples demonstrating how to run local LLM inference.
  • The guide shows that local LLM inference can match or exceed the performance and cost-effectiveness of major LLM APIs.
• Modal Guide for Local LLM Inference: The Modal guide provides code samples and instructions for running local LLM inference.
  • It emphasizes achieving performance and cost-effectiveness comparable to major LLM APIs.

Latent Space ▷ #genmedia-creative-ai (21 messages🔥):

LTX-2 Open Source Video Model, Qwen Image Edit, GLM-Image Text Rendering, Google Veo 3.1 Updates, Kling Motion Control

• LTX-2 Creates Open-Source Cinematic Views: Justine Moore announced LTX-2, a new open-source video generation model capable of producing 4K clips up to 20 seconds long, with audio capabilities, as demonstrated by creator yanokusnir, available here.
• Qwen Transforms Images into Gaussian Splats: The HuggingFace model Qwen-Image-Edit-2511 turns images into Gaussian Splats, then rerenders them from another angle.
  • This promises to be really useful for start frame -> end frame type video renderings, keeping the space around consistent.
• GLM-Image excels at Text-Rendering and Knowledge-Intensive Scenarios: GLM-Image aligns with mainstream latent diffusion approaches in general image generation quality, but it shows significant advantages in text-rendering and knowledge-intensive generation scenarios.
  • The model also supports a rich set of image-to-image tasks including image editing, style transfer, identity-preserving generation, and multi-subject consistency.
• Veo 3.1 Gets Vertical and Upscales Like a Pro: Tulsee Doshi announced major updates to Veo 3.1, highlighting native portrait mode support for mobile-first storytelling and the ability to generate videos from user images, covered here.
  • The update also introduces state-of-the-art 1080p and 4K upscaling available across Gemini, YouTube, and Google AI Studio.
• Hollywood Gets Klingy with AI: Justine Moore highlights how AI video models, specifically Kling Motion Control, are revolutionizing Hollywood production by enabling instant, low-cost character swaps, viewable here.

Nous Research AI ▷ #general (72 messages🔥🔥):

TI-84 Mastermind AI, LiquidAI CGGR, AI Upscaling Old Shows, GLM-Image Model, Nous Chat Instability

• TI-84 Plays Mastermind!: A member created a neural network visualization of a neural network for the TI 84 Plus Silver Edition that plays the game Mastermind, video here.
  • The AI uses gray squares for incorrect numbers, yellow for correct numbers in the wrong position, and green for correct numbers in the correct position, and despite being statistical it is considered REALLY clever.
• LiquidAI Model Enters the Ring: A new LiquidAI model was released (CGGR on Github) and is currently undergoing benchmarking to assess its performance, according to this news.smol.ai issue.
  • A member noted this in passing along with their admission of doing AI brainrot on Twitch, including Spotify and Dreambees AI.
• AI Upscaling when?: A member lamented the lack of AI upscales of old shows like a 16:9 version of Al Bundy, speculating AI could interpolate missing details, but this could ruin artistic intent.
  • Another member said that a show like Al Bundy doesn’t need perspective since no one cared about perspective and all that, they made it so that it was enough to work, and SD content looks like trash on a high quality screen.
• Zai Launches GLM-Image Model: Zai released their new image model called GLM-Image which you can read about on their blog.
  • A member expressed interest in the semantic VQ and how they managed to make it, wondering if there is something like a vision transformer.
• Nous Chat Hit by Instability: A member asked if issues like interrupted responses or language switching are normal for the free version of Nous Chat.
  • A developer responded that there is probably instability on the provider again.

Nous Research AI ▷ #ask-about-llms (12 messages🔥):

Hermes 4, LLM Attention Drift, Context Length Degradation, Context Rot, TPU Training

• Hermes 4 Attention Drifts Beyond 25k Tokens: A member has noticed that the attention of Hermes 4 tends to drift and responses become chaotic once the context crosses the 25,000 token mark.
  • They added instructions in the chat to reorient the LLM, leveraging its tendency to pay more attention to recent inputs.
• Context Length Degradation is Inevitable: It was noted that degradation past certain thresholds is unavoidable, even for frontier models like Gemini, despite its 1M+ context window.
  • Models are typically optimized and benchmarked on essentially 0 context, so degradation is expected in long context chains.
• “Context Rot” Plagues LLMs: A member mentioned the phenomenon of Context Rot, where LLMs degrade in performance as the context length increases.
  • Tightening the permissible window to less than 20k tokens reportedly mitigated the issue.
• Training LLMs with TPUs - Anyone Tried?: A member inquired whether anyone has experience training a model with TPUs.
  • No responses were provided to this question in the given messages.

Manus.im Discord ▷ #general (51 messages🔥):

Manus Credit Usage, Manus Support, Manus Similarweb Integration, Account Blocked, Developer Hiring

• Manus Users Report Excessive Credit Usage with New Feature: Several users reported extremely high credit consumption with the new Manus x Similarweb integration, with one user reporting 5000 credits used in under a minute and another reporting 2591 credits in 15 seconds using this link.
  • Users expressed frustration over the lack of warning and suggested implementing safeguards or credit caps to prevent such unexpected spikes and offering ad show to earn credit.
• Manus Support Response Delays Irk Users: Users are experiencing significant delays in receiving support from Manus, with one user waiting 8 hours after being transferred to a live human and others reporting multiple unanswered messages and emails.
  • Members suggested that Manus should provide clearer communication regarding support availability, such as posting hours of operation or redirecting to email, rather than leaving users waiting indefinitely.
• Account Blocked, User Seeks Explanation: A user is seeking an explanation for their account being blocked, emphasizing the need to access their code for classical programming purposes.
  • Another user implied that the blocked user has likely done something illegitimate on the platform.
• Devs Seek Work in Manus Community: A user is seeking development work, offering their skills for “super cool projects” and directs people to <#1453385851336790148> to offer and find dev work.
  • Another user echoed this sentiment, inquiring about contributing their experience to the community.

Eleuther ▷ #general (18 messages🔥):

LLM Detection Models, Pangram LLM Detection, In-Person Meetups, LLM Extraction analysis consequences

• Community Mulls In-Person Meetups: Members discussed the possibility of in-person meetups for the community, suggesting metropolitan cities like NYC or SF as potential locations.
  • It was noted that while reading groups see good attendance, regular in-person events might require advertising to a wider audience, similar to Cohere’s regular events and Zoom sessions.
• Delving into LLM Extraction Analysis’s Consequences: A member inquired about the legal implications of LLM extraction analysis, referencing a study’s observation of LLMs replicating character names, plots, and themes from original works.
  • The member expressed concerns that this research could be easily misunderstood and misused due to its technical nature.
• Hunting for LLM-Generated Text Classifiers: A member sought recommendations for a small classifier model capable of identifying LLM-generated text, aiming to estimate the prevalence of synthetic content on the web after a recent crawl.
  • Another member suggested using a drafter model trained for speculative decoding, though noting it would be model-specific and might require ensembling for better generalization.
• Pangram Detection accuracy is dubious: Members discussed using Pangram.com for LLM-generated text detection, but concerns arose about the cost of processing gigabytes of text and the accuracy of open-source alternatives on Hugging Face.
  • It was suggested to build a custom classifier based on Pangram’s research paper (https://arxiv.org/abs/2402.14873) detailing their detection methods.

Eleuther ▷ #research (15 messages🔥):

SlopCodeBench released, Lazy Agents, ICLR workshop submission, Optical Computers and RTL

• SlopCodeBench Exposes Agent Laziness: A new blog post and the SlopCodeBench benchmark were released, highlighting how agents can make poor early design decisions when solving large programming problems broken into iterative checkpoints.
  • SlopCodeBench aims to be a community-driven benchmark, welcoming feedback on adding new problems, and hopes to integrate with the Harbor format for easier use.
• Agents Fail to Generalize Code Despite Simplification Prompts: Despite instructing models to simplify and integrate code after producing a working implementation, agents often fail to correctly generalize the code to support new required cases.
  • According to one member, all prompts lead to worse overall performance on tests solved and are ~1.5-2x more expensive; the ones discussed in the blogs are the simple “just solve” approaches.
• ICLR Workshop beckons SlopCodeBench: A member suggested turning the blog post into a submission for this ICLR workshop, with a deadline of January 31st, offering assistance in the process.
  • It was also noted that a coding benchmark shouldn’t rely on heavy prompt engineering and scaffolding in order to get decent/good performance but the barebones approach is also reasonable.
• Optical Computing Team Up For Grabs?: One member is potentially looking to hire a team in the next 6 months to work on optical computers or non-floating-point RTL.

Eleuther ▷ #interpretability-general (2 messages):

LLM Understanding Framework, Global CoT Analysis

• Tiered Framework for LLM Understanding Proposed: A tiered framework for thinking about understanding in LLMs is proposed in a new paper, synthesizing the most relevant findings to date (link to paper).
• Global CoT Analysis Uncovers Patterns: Initial attempts to uncover patterns are described in a LessWrong post about Global CoT Analysis.

Eleuther ▷ #gpt-neox-dev (3 messages):

NCCL hangs during training, H200 node configuration, Model parallelism

• NCCL Hangs Plague 8B Model on Multi-Node Training: An engineer reported NCCL hangs when training an 8B model on multiple nodes, while a 1B model trains successfully with the same setup, using H200 nodes.
  • The issue occurs specifically in multi-node setups, with single-node training working fine for both models, using a batch size of 1 and gradient accumulation steps of 1.
• Configuration Tweaks between 1B and 8B Models: The engineer shared configuration differences between the 1B and 8B models, primarily in the number of layers (16 vs 36), hidden size (2048 vs 4096), and intermediate size (24576 vs 43008).
  • The configuration files for both models can be found at this gist link, and logs showing the hang can be found here.

Yannick Kilcher ▷ #general (11 messages🔥):

Lucid Coding, Vibe Coding, Delegation of Expertise with Generative AI, Bus-Factor and LLMs

• Spamming Scammers got dealt with: A user reported <@139426008976588801> for SpamingScammers and .wavefunction confirmed that it was dealt with.
• Lucid Coding term gains traction: A user shared a tweet on fxtwitter about lucid coding, and another user expressed their liking for the term.
  • The user explained that the term lucid coding is more descriptive of what most effective developers and designers are doing when they use generative AI.
• Vibe Coding definition emerges: Members discussed the definition of vibe coding, with one user defining it as using generative AI without understanding the code it produces.
  • Another member emphasized the importance of understanding and being able to fix the code if the AI goes stuck, and that if you can’t, it’s vibe coding.
• LLMs are Critical Software Dependencies: The group discussed the risks of delegation of expertise, especially with generative AI, with one user comparing LLMs to a critical library dependency.
  • The user suggested that if you cannot work on the project without the LLM, you should consider it like this one employee that you can not fire regardless of how many wage increases they demand.

Yannick Kilcher ▷ #ml-news (17 messages🔥):

Bayesian vs Frequentist Statistics, FDA Corruption, China AI Anthropomorphism, Qwen DeepPlanning Dataset

• Bayesian Stats: Leap or Keep?: Members debated whether the shift to Bayesian statistics is a significant change, with some arguing that it’s not as big as you might think because both frequentist and Bayesian statistics use the same techniques like linear and logistic regressions.
  • Others countered that although they use the same formulas, the interpretations of prior, posterior, and intervention are significantly different, citing Probability interpretations.
• Bayesian FDA Corruption: A Zero Prior?: The conversation touched on the potential for Bayesian methods to be more useful for lying with statistics, raising concerns about deceit and corruption in clinical trials.
  • One member jokingly assigned a zero prior to observed Bayesian FDA corruption, while another alluded to past regulatory corruption, referencing the opioid crisis.
• China’s AI Anthropomorphism Approach: A member shared a link to an article on China’s approach to AI anthropomorphism from the Cyberspace Administration of China (CAC.gov.cn).
  • It was noted that the Chrome’s translate page feature works well with the article.
• Qwen DeepPlanning Dataset: Now Closed: The HuggingFace Qwen/DeepPlanning dataset was mentioned, but the link to the HuggingFace dataset is now closed.
  • The closure was noted in a tweet.

Modular (Mojo 🔥) ▷ #mojo (2 messages):

Mojo Docs, NotebookLM, llms.txt

• Mojo Docs Seekers Query NotebookLM: A member inquired about obtaining the full, latest official Mojo docs in PDF or Markdown format for use with NotebookLM.
  • Another member suggested using llms.txt (https://docs.modular.com/max/coding-assistants#supply-documentation-to-llms-with-llmstxt) to supply documentation to LLMs.
• llms.txt Integration with NotebookLM: A member proposed using the llms.txt file to integrate Mojo documentation with NotebookLM and other LLMs.
  • The suggestion references the official Modular documentation on coding assistants, specifically how to supply documentation to LLMs using the llms.txt format.

Modular (Mojo 🔥) ▷ #max (5 messages):

Qwen3-VL, MoE Implementation, Contributor Guide

• Qwen3-VL’s MoE Implementation Questioned!: A member inquired why Qwen3-VL only has an MoE implementation, suggesting reuse of code from qwen3vl_moe to enable dense models like Qwen3-VL-4B-Instruct.
  • They offered to submit a PR to address this.
• Call for PRs to Enrich MAX!: A member responded positively to the PR offer, citing a lack of contributors to maintain pace with the ecosystem.
  • They also pointed to the recently updated contributor guide for guidance.
• PR is Live!: A member confirmed they were unsure if the current implementation was deliberate, and then followed up that a PR was up.
  • The PR is currently pending review.

MCP Contributors (Official) ▷ #general (1 messages):

Glama Ranking System, Server Usage Metrics, Ranking Abuse Claims

• Glama Founder Addresses Ranking Abuse Claims: The founder of Glama responded to claims of ranking abuse, stating that their rankings are based on server usage metrics.
  • They denied awareness of any abuse and encouraged users to provide feedback via direct message.
• Glama Ranking System Explained: The founder clarified that Glama’s rankings are determined by server usage metrics.
  • This explanation aimed to address concerns about the fairness and validity of the ranking system.

MCP Contributors (Official) ▷ #general-wg (5 messages):

Tasks spec implementation, Inspector PR for adding tasks

• Community Seeks Tasks Spec Client Examples: Members are seeking client apps that implement the Tasks spec to understand UI implementation patterns.
  • One member mentioned implementing tasks in their client and hoped to see how others have implemented UI.
• Inspector Adds Tasks Support via PR: A member is submitting a PR to add tasks to the Inspector.
  • Another member has added a PR to server-everything for simulating a long-running task, meaning the next version of both servers and inspector should have it working.
• glama.ai/mcp/inspector is super early version: One member started working on glama.ai/mcp/inspector which is a very early version.
  • The goal is to cover every feature in the inspector which is used internally for e2e testing.

Moonshot AI (Kimi K-2) ▷ #general-chat (6 messages):

Kimi K-2 vision-capable release, Kimi CLI issues, Kimi new templates, UI implementation

• Kimi Reworks Interface: A user shared a positive reaction to the interface rework and shared a link to X.com.
• Kimi’s K-2 vision release coming soon?: One member shared excitement over the new interface and asked if this means we will finally see a K2 vision-capable release?
  • They pointed out that K1.5 is listed as a legacy model, but it’s the one that has vision.
• Kimi CLI struggles: A member mentioned some issues with Kimi CLI, but praised the new templates in Slides.
  • However, the same member thinks that the UI implementation is limited for Visual and there is none for Adaptive, further claiming that previously there were more templates giving more options.

DSPy ▷ #general (5 messages):

AI-assisted code generation, Replit, DSPY OS, DSPY framework

• AI platforms use Code Generation: Platforms such as Replit and DSPY OS leverage AI models to help with coding tasks, enhancing productivity.
  • A member inquired about these platforms in relation to DSPy.
• Replit is closed, DSPY is a framework: A member noted that Replit is closed source, while DSPY is a framework and inquired if any project built with DSPY is like Replit.
  • It was clarified that currently, there isn’t a direct Replit-like project built with DSPY, as DSPY is more of a framework than a platform.
• What is DSPY OS?: A member asked about DSPY OS because he couldn’t find any information about it.
  • No clarifying information was provided.

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

DM Command, Aider Tooling

• DM Command Initiated: A user acknowledged a direct message request with a simple “yes sir” response to @0xhellno.
  • Following this, another user, txn545, indicated their intent to send a direct message with the command “dm”.
• Aider Tooling Request: A user requests improvements to aider tooling.
  • The user asks if aider can support editing code in a separate directory from where it was initially added.

aider (Paul Gauthier) ▷ #questions-and-tips (2 messages):

Oauth Login for Gemini, CLIProxyAPI

• Oauth Login enables Higher Limits for Gemini: A member inquired about the possibility of using Oauth login for the Gemini model when using aider, citing potentially higher limits.
  • Another member suggested that CLIProxyAPI is the best base for that, mentioning that there are a few wrappers available for it.
• CLIProxyAPI as Base: A member recommended using CLIProxyAPI as the base for implementing OAuth login with Gemini due to higher limits.
  • They also mentioned that there are several wrappers available for CLIProxyAPI, which could simplify the integration process.

MLOps @Chipro ▷ #events (1 messages):

Prompt Engineering for Outreach, Clay + AI outreach workflow, Personalized messages at scale, Apollo, Attio, n8n integrations

• Outreach Prompt Engineering Workshop Announced: A workshop on Prompt Engineering for Outreach will teach how to build a Clay + AI outreach workflow for personalized messages at scale.
  • The workshop promises a 40%+ acceptance rate and 18%+ reply rate, and it will be held on Wed Jan 14 and Sat Jan 17 (link).
• AI Outreach Workflow Breakdown: The workshop will cover an end-to-end AI outreach workflow, including target identification, lead list creation, enrichment, message generation, and tracking.
  • It will also feature a Clay walkthrough and discuss Apollo, Attio, and n8n integrations.
• Reusable Outreach Resources Provided: Attendees will receive a reusable workflow, copy-paste prompts, and a simple QA checklist to aid their outreach efforts.
  • The workshop aims to provide beginner-friendly guidance and is limited in spots.

Windsurf ▷ #announcements (1 messages):

GPT-5.2-Codex Release, Windsurf Discounts, Agentic Coding Model

• GPT-5.2-Codex Lands on Windsurf!: The latest agentic coding model, GPT-5.2-Codex from OpenAI, is now available for all Windsurf users, complete with four reasoning effort levels.
  • Check out OpenAI’s blog post for more details.
• Windsurf Waves Discounts to Celebrate: Windsurf is offering limited-time discounts on GPT-5.2-Codex, with low and medium effort at 0.5x, high at 1x, and xhigh at 2x.
  • Users are encouraged to update and relaunch Windsurf to access the new model and pricing.