a quiet day.

AI News for 10/7/2025-10/8/2025. We checked 12 subreddits, 544 Twitters and 23 Discords (197 channels, and 9439 messages) for you. Estimated reading time saved (at 200wpm): 722 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!

If you have questions about any of the DevDay launches, the OpenAI team is actively soliciting good questions for the Reddit AMA tomorrow, specifically from you AI engineers. Post them here.

AI Twitter Recap

Tiny reasoning models, JEPA density estimation, and new multimodal LLMs

• Samsung’s 7M Tiny Recursive Model (TRM): A simple, highly efficient recursive reasoner that beats prior HRM (27M) on ARC-AGI and Sudoku using a smaller, single-network design and full backprop through recursion. Notable findings: fewer layers improved generalization (4→2 layers: 79.5%→87.4% on Sudoku) and swapping self-attention for MLP helped in fixed-length contexts. Great overview from @rasbt, with the paper trending per @jm_alexia. Paper: https://arxiv.org/abs/2510.04871
• JEPA-SCORE turns encoders into density estimators: LeCun’s team shows the JEPA anti-collapse term implicitly estimates data density. From any trained JEPA (I-JEPA, DINOv2, MetaCLIP), compute p(x) in closed form via the Jacobian to power data curation, outlier detection, etc., no retraining required. Details via @jiqizhixin and the authors’ note @randall_balestr; paper: arxiv.org/abs/2510.05949.
• AI21’s Jamba Reasoning 3B (Apache-2.0): Hybrid SSM-Transformer model tops speed/accuracy at long context; 3–5x faster vs Llama 3.2 3B and Qwen3 4B at 32K tokens; ~16 tok/s at 16K context on iPhone 16 Pro; up to 64K context. Available on HF/Kaggle/LM Studio/llama.cpp. @AI21Labs, 1, 2.
• Alibaba’s Qwen3 Omni/Omni Realtime: Natively unified audio–video–text architecture with “Thinker” and “Talker” MoEs; 119 text languages, 19 speech-in, 10 speech-out. BigBench Audio: 58–59% (vs Gemini 2.0 Flash 36%, below GPT‑4o Realtime 68%); time-to-first-audio 4.8s (30B) / 0.9s (Realtime). 30B weights (Instruct/Thinking/Captioner) released under Apache-2.0. Summary via Artificial Analysis and follow-up.
• Open-weight image editing leader from Alibaba: Qwen Image Edit 2509 debuts multi-image editing; #3 overall in the Artificial Analysis Arena and top open-weights model; Apache-2.0 with weights on HF; priced $30/1k images on fal/replicate. Benchmarks via @ArtificialAnlys and acknowledgement from @Alibaba_Qwen.
• Retrieval at micro-scale: New ColBERT Nano models at 250K–950K params show late interaction can work shockingly well at tiny sizes. Models and collection from @neumll; reaction from @lateinteraction.

RL and agentic systems: serverless, in-the-flow optimization, and code eval

• Serverless RL lands (CoreWeave × W&B × OpenPipe): Train agents faster/cheaper with zero infra. Claims: 40% cheaper, 28% faster wall-clock vs self-managed GPUs; instant deploy to prod via W&B Inference; includes ART (trainer) and RULER (universal verifier). Launch posts from @corbtt, @weights_biases, @CoreWeave. Context: CoreWeave acquired OpenPipe on Sept 8; product shipped Oct 8 per @shawnup and covered by WIRED.
• AgentFlow (Stanford): in-the-flow RL for tool use and planning: A team of Planner/Executor/Verifier/Generator agents with Flow-GRPO trains the Planner inside the system. On 10 benchmarks, a 7B backbone beats Llama‑3.1‑405B and GPT‑4o on multiple categories (avg +14% on search/agentic/math). Code/models/demo: @lupantech, paper via @_akhaliq.
• ADK goes protocol-native: Google’s open-source Agent Development Kit now supports MCP (tools), A2A (agents), and AG‑UI (user/agent UX) and plugs into React via CopilotKit—bridging backend agents to full-stack apps. Overview by @_avichawla and repo link AG‑UI.
• Executable code eval at scale: BigCodeArena introduces human-in-the-loop evaluation on runnable code (vs text-only preference data) across languages—opening the door to more faithful code generation assessment. Announced by @BigCodeProject and contributors @terryyuezhuo.
• Also notable: LoRA-for-RL baseline repo to compare LoRA/DoRA/QLoRA in RL (UpupWang); semi‑online DPO (Meta) summary and HF link (ben_burtenshaw); OpenAI spotlight on prompt optimizers (GEPA) (DSPyOSS).

Tooling and infra: no‑GIL Python lands, “voice‑prompt” dev, and Sora integrations

• Python 3.14: free‑threaded interpreter is no longer experimental—a major unlock for multi-core Python without the GIL. Announcement via @charliermarsh. Pydantic 2.12 shipped same day with 3.14 support (samuel_colvin).
• Google AI Studio adds voice “vibe coding”: Dictate app changes or features; STT auto-cleans fillers for cleaner prompts. Demos/links from @GoogleAIStudio and @ammaar.
• Stripe for AI builders: New API to track model pricing changes and protect margins; Agentic Commerce Protocol + Shared Payment Tokens; and “Stripe inside Gemini” for commerce flows. Details from @emilygsands and follow-up 1, 2.
• Sora 2: fast integrations and public demo:
  • MCP server for Sora (generate/remix/status/download) by @skirano.
  • Time-limited free text→video demo on Hugging Face (_akhaliq); Sora app hit 1M downloads in <5 days despite invite-flow constraints (billpeeb).
  • Runway Gen‑4 Turbo now supports arbitrary 2–10s durations via API—pay for what you generate (RunwayMLDevs).
• Infra tidbits: Together’s Instant Clusters get burn‑in/NVLink/NCCL validation and token/sec reference runs (togethercompute); ThunderKittens “register tile” insight coming to tinygrad (tinygrad); LFM2MoE 8B 3‑bit on iPhone 17 Pro with MLX (sach1n).

Funding, talent, and leaderboards

• Grid-scale bet on batteries: Base Power raised a $1B Series C to build “America’s next power company,” scaling manufacturing in Austin to put a battery on every home; multiple top-tier investors participated. Details from @ZachBDell and @JLopas.
• Relace raises $23M (a16z) to build rails for AI codegen: Shipping the fastest apply model on OpenRouter (10k tok/s), SOTA code reranking and embeddings; working on Relace Repos (retrieval-native SCM). Announcements via @steph_palazzolo and @pfactorialz.
• Key talent move: Shunyu Yao left Anthropic for Google DeepMind; cited disagreement with Anthropic’s public China stance among reasons. Background via @Yuchenj_UW and profile by @ZhihuFrontier.
• Open model leaderboard movement: DeepSeek‑V3.2‑Exp (MIT license) enters LM Arena Top‑10; its “thinking” variant is now #2 open model (arena).

Data, evaluation, and retrieval practices

• Rolling “Humanity’s Last Exam”: CAIS released a dynamic fork of the well-known eval dataset on HF Datasets that swaps easier questions for harder ones as models improve; gated to avoid contamination. Commentary and broader evals roadmap by @Thom_Wolf.
• Understanding model heuristics: Goodfire AI models LLM behavior via causal abstraction to disentangle competing algorithms even on simple tasks (GoodfireAI).
• Sycophancy has behavioral costs: Interaction with sycophantic AIs reduced willingness to repair interpersonal conflict while increasing beliefs of being right (camrobjones).
• Retrieval and parsing tips: Micro‑ColBERT late interaction retrievers (250K params) punch above size class (lateinteraction); LlamaIndex’s parse vs extract design guide for document agents (llama_index).

Top tweets (by engagement)

• Portland protest footage went viral, non‑AI but dominated feeds (SpencerHakimian, 48k+). Nobel Prize in Chemistry awarded to MOFs pioneers (NobelPrize, 35k+).
• Cristiano Ronaldo said he used Perplexity to draft an awards speech (AskPerplexity, 10k+).
• Python 3.14’s no‑GIL went mainstream in dev circles (charliermarsh, 1.9k+). Google AI Studio’s “voice vibe‑coding” also drew strong interest (GoogleAIStudio, 1k+).
• CoreWeave × W&B × OpenPipe “Serverless RL” launch was widely shared across builder communities (weights_biases, corbtt) and Base Power’s $1B Series C drew cross‑industry attention (ZachBDell).

Notes and opinions that resonated:

• Karpathy: current RL seems to over‑punish exceptions; models are “mortally terrified” of them—reward design matters (karpathy).
• Practical benchmarking caution: if a 10M specialist can beat frontier LLMs on a “general intelligence” benchmark, the benchmark signal is suspect (nrehiew_).

AI Reddit Recap

/r/LocalLlama + /r/localLLM Recap

1. AI21 Jamba 3B Launch Benchmarks and Anthropic Researcher Exit News

• AI21 releases Jamba 3B, the tiny model outperforming Qwen 3 4B and IBM Granite 4 Micro! (Activity: 561): AI21 announced Jamba 3B (blog, HF), a 3B-parameter on-device/desktop model claiming near-constant long-context throughput: ~40 t/s on Mac M3 past 32k and ~33 t/s at 128k, versus Qwen 3 4B <1 t/s and Llama 3.2 3B ~5 t/s. Reported “intelligence per token” index is 0.31 at ~40 t/s (above Gemma 3 4B 0.20 and Phi‑4 Mini 0.22), while Qwen 3 4B scores slightly higher raw (0.35) but runs ~3× slower; they also claim ~5× higher t/s than IBM Granite 4 Micro at 256k, with coherence beyond 60k and an effective context ≈ 200k. A 4‑bit quantized build for llama.cpp needs 1.84 GiB weights and ~2.2 GiB active memory at 32k; benchmarks were run on Mac M3 (36 GB), iPhone 16 Pro, and Galaxy S25. Commenters question the fairness/completeness of comparisons (e.g., not evaluating against Qwen3 4B 2507 “thinking” mode) and criticize the graphs/benchmark selection as potentially deceptive.
  • Benchmark fairness concern: if Jamba 3B is positioned as a “reasoning” model, commenters ask why it isn’t compared against the Qwen3 4B “thinking” variant (e.g., 25.07) that enables test-time compute. They want apples-to-apples evaluations clarifying whether chain-of-thought/scratchpad was enabled, how “thinking” tokens were budgeted, and whether any TTC features were disabled on baselines—otherwise “outperforms Qwen” is ambiguous for reasoning use-cases.
  • Claims of deceptive visualization/benchmark selection: commenters point out charts that appear cherry-picked or hard to interpret (e.g., radar plots with unclear axes/scales and color choices), making relative claims look better than raw results warrant. They request disclosure of absolute scores, seeds/variance, prompt templates, decoding params, and identical evaluation settings across models (including hardware and context length) to substantiate the performance claims against Qwen3 4B and Granite 4 Micro.
• Anthropic’s ‘anti-China’ stance triggers exit of star AI researcher (Activity: 526): Per the South China Morning Post, Anthropic labeled China an “adversarial nation,” after which Chinese AI researcher Yao Shunyu left the company and joined Google DeepMind, illustrating how explicit geopolitics can affect frontier-AI talent recruitment and reputational risk. Commenters noted identity ambiguity: the linked personal site ysymyth.github.io lists “researcher at OpenAI,” implying multiple researchers share the same name. Comment debate focuses on whether a US-centric posture harms Anthropic’s global hiring and long-run competitiveness, with some predicting AOL/Yahoo-style decline; others frame the stance as moral posturing that could alienate non-US researchers.
  • Identity/affiliation ambiguity: the referenced personal site lists him as a “researcher at OpenAI” (https://ysymyth.github.io/), while commenters note there may be multiple people named “Yao Shunyu,” suggesting possible misattribution. Technical takeaway: verify identities via publication pages, arXiv author IDs, and lab rosters before inferring organizational moves or research impact.
  • Timeline/churn claim: one commenter asserts he was at OpenAI in July/Aug 2024, briefly moved to Anthropic, and left within ~1–2 months before joining Google DeepMind. If accurate, this reflects high researcher mobility among frontier labs within a single quarter, which can disrupt continuity in ongoing training runs, eval pipelines, or safety research, and complicate credit/ownership for in-flight projects.
  • Governance/policy implications: commenters attribute the exit to Anthropic labeling China as an “adversarial nation.” From a technical-governance perspective, such classifications can constrain cross-border collaboration, red-teaming arrangements, dataset sharing, and access to compute for certain researchers, thereby reshaping hiring funnels, compliance workflows, and evaluation protocols in frontier model development.

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. Robotics product news: Figure 03, Walmart service bot, Neuralink arm control

• Figure 03 coming 10/9 (Activity: 1022): Teaser post indicates Figure AI plans to reveal its next humanoid, Figure 03, on 10/9 (Figure). The linked video is inaccessible (HTTP 403), and no specs, benchmarks, or capability claims are provided; based on top comments, the teaser appears to show a protective, clothing-like waterproof outer shell intended to simplify cleaning vs. exposed joints and to protect surfaces from abrasion/scratches, suggesting a trend toward more integrated exteriors across iterations. Commenters endorse textile/shell exteriors for maintainability and durability, while others note primarily aesthetic improvements (“each iteration looks neater”).
  • Adopting a removable, waterproof garment/shell for a humanoid (e.g., Figure 03) reduces maintenance by shifting cleaning from intricate joint interfaces and cable runs to a wipeable exterior, while also shielding exposed surfaces from abrasion and minor impacts. A soft or semi-rigid cover can double as a particulate/liquid barrier (improving practical IP behavior around actuators, encoders, and seals) and enables swappable panels for quick replacement when damaged. This design choice can also reduce contamination-driven wear in rotary joints and maintain sensor performance by limiting dust ingress.
  • Toe articulation is a meaningful locomotion upgrade: adding a toe joint expands the effective support polygon and improves center-of-pressure/ZMP control, enhancing balance on uneven terrain and during dynamic maneuvers. It also enables more efficient push-off (toe-off) for walking, stairs, and pivots, potentially lowering energy cost and slip risk compared to flat-foot designs. This can translate to better agility and recoverability in disturbances and more human-like gait phase timing.
• You can already order a chinese robot at Walmart (Activity: 612): Post shows a Walmart Marketplace product page for a Chinese-made Unitree robot (likely the compact G1 humanoid), surfaced via an X post, being sold by a third‑party seller at a price markedly higher than Unitree’s direct pricing (~$16k). The technical/contextual takeaway is less about the robot’s capabilities and more about marketplace dynamics: third‑party retail channels listing advanced robotics hardware with significant markups, raising questions about authenticity, warranty, and after‑sales support compared to buying direct from Unitree. Comments criticize Walmart’s third‑party marketplace quality control and note the apparent upcharge versus Unitree’s official pricing, debating whether any value (e.g., import handling) justifies the markup.
  • The thread flags a significant marketplace markup versus OEM pricing: a comparable Unitree robot is cited at around $16k direct from the manufacturer, implying the Walmart third‑party listing is heavily upcharged. For technical buyers, this suggests verifying OEM MSRP/specs before purchasing via marketplaces (e.g., Unitree store: https://store.unitree.com/).
  • A commenter asserts the listed robot “doesn’t do anything,” implying limited out‑of‑box functionality without additional software/integration. This reflects a common caveat with developer/research robots: useful behaviors typically require configuring an SDK/firmware and adding payloads/sensors before achieving meaningful capability.
• Neuralink participant controlling robotic arm using telepathy (Activity: 1642): A video purportedly shows a Neuralink human-trial participant controlling a robotic arm via an intracortical, read-only brain–computer interface (BCI), decoding motor intent from neural activity into multi-DoF arm commands clip. The post itself provides no protocol or performance details (decoder type, channel count, calibration time, latency, error rates), so it’s unclear whether the control is continuous kinematic decoding (e.g., Kalman/NN) vs. discrete state control, or whether any sensory feedback loop is present. Without published metrics, this appears as a qualitative demo consistent with prior intracortical BCI work (e.g., robotic arm control in clinical trials) and Neuralink’s recent read-only cursor-control demonstrations. Commenters note current systems are primarily read-only and argue that write-capable stimulation (closed-loop sensory feedback) would enable far more immersive/precise control and VR applications; others focus on the clinical promise while setting aside views on the company/leadership.
  • Several highlight that present BCIs like Neuralink are primarily read-only, decoding neural activity (e.g., motor intent) into control signals. The future shift to write (neural stimulation) would enable closed-loop systems with sensory feedback and potentially “incredibly immersive VR.” This requires precise, low-latency stimulation, per-electrode safety (charge balancing, tissue response), and stable long-term mapping to avoid decoder/stimulator drift.
  • Commenters note a path toward controllable bionic arms/hands for amputees: decode multi-DOF motor intent from cortex to drive prosthetic actuators, optionally adding somatosensory feedback via stimulation to improve grasp force and dexterity. Practical hurdles include calibration time, robustness to neural signal nonstationarity, on-device real-time decoding latency, and integration with prosthetic control loops (EMG/IMU/actuator controllers) over reliable, high-bandwidth wireless links.

2. New vision model release and demo: Qwen-Image LoRa + wan 2.2 360 video

• Qwen-Image - Smartphone Snapshot Photo Reality LoRa - Release (Activity: 1164): Release of a Qwen-Image LoRA, “Smartphone Snapshot Photo Reality,” by LD2WDavid/AI_Characters targeting casual, phone-camera realism for text-to-image, with a recommended ComfyUI text2image workflow JSON provided (model, workflow). Author notes that with Qwen the “first 80% is easy, last 20% is hard,” highlighting diminishing returns and tuning complexity; an update to the WAN2.2 variant is in progress, and training was resource-intensive with donation link provided (Ko‑fi). Prompts include contributions from /u/FortranUA, and the LoRA targets improved fine-grained object fidelity and prompt adherence (e.g., keyboards). Commenters report the model reliably renders difficult objects like keyboards, suggesting strong structural fidelity. Overall reception is highly positive for realism, particularly for casual smartphone-style scenes.
  • Author fine-tuned a LoRA on Qwen-Image to achieve a “Smartphone Snapshot Photo Reality” style, noting the classic curve: “first 80% are very easy… last 20% are very hard,” implying most gains come quickly but photoreal edge cases demand intensive iteration and cost. They shared a reproducible ComfyUI text2image workflow for inference (workflow JSON) and are also preparing an update to WAN2.2; model page: https://civitai.com/models/2022854/qwen-image-smartphone-snapshot-photo-reality-style.
  • Commenters highlight that it “can do keyboards,” a known stress test for diffusion models due to high-frequency, grid-aligned geometry and tiny legends/text. This suggests improved spatial consistency and fine-detail synthesis under the LoRA, though others note it’s still detectable on close inspection—indicating remaining artifacts in micro-text fidelity and regular pattern rendering.
  • A user requests LoRA support in Qwen’s “nunchaku” inference stack, implying current workflows rely on external pipelines (e.g., ComfyUI) for LoRA injection/merging. Native LoRA support would streamline deployment and make it easier to use the LoRA with official Qwen runtimes without bespoke nodes or preprocess steps.
• Finally did a nearly perfect 360 with wan 2.2 (using no loras) (Activity: 505): OP showcases a near-360° character rotation generated with the open‑source Wan 2.2 video model, explicitly using no LoRAs, and shares an improved attempt as a GIF (example; original post video link). Remaining issues appear in temporal/geometry consistency (e.g., hair/ponytail drift and minor topology warping), which are common failure modes in full-turntable generations without multi‑view priors or keyframe constraints. A commenter suggests using Qwen Edit 2509 to synthesize a back‑view reference image and then running Wan 2.2 with both initial and final frame conditioning to better preserve identity and pose alignment across the rotation; other remarks highlight the hair artifacts and “non‑Euclidean” geometry as typical T2V shortcomings.
  • A commenter suggests using Qwen Edit 2509 to synthesize a back-view image of the character, then feeding both the initial and final frames into Wan 2.2 to drive a more faithful 360° rotation. Constraining the model with start/end keyframes reduces hallucination of unseen geometry and improves identity/pose consistency across the turn. This leverages video generation modes that accept paired keyframe conditioning for motion guidance.
  • Observers highlight artifacts in non-rigid extremities—ponytails and arms—visible in the shared GIF. These deformations (drift/self-intersection) are typical for diffusion video models attempting full-body 3D turns without an explicit 3D prior or rig, indicating limits in temporal consistency and geometric coherence. Providing an accurate back-view frame and explicit end keyframe can mitigate, but does not fully resolve, these failure modes.

3. AI viral memes + ChatGPT humor/complaints: Olympic dishes, Bowie vs Mercury, parkour

• Olympic dishes championship (Activity: 2119): Reddit post is a v.redd.it video titled “Olympic dishes championship,” but the media endpoint returns HTTP 403 Forbidden when accessed directly (v.redd.it/53dt69862otf1), indicating authentication or a developer token is required; no verifiable media details (duration/codec/resolution) are accessible. Comment hints like “Watch the third one dj-ing” imply a multi‑clip, humorous sequence, but the actual content cannot be confirmed due to access restrictions. Top comments are brief, non-technical reactions (e.g., “Peak,” “Considering if I should show my girlfriend”), with no substantive technical debate.
• David Bowie VS Freddie Mercury WCW (Activity: 1175): The post links to a v.redd.it video titled “David Bowie VS Freddie Mercury WCW” (v.redd.it/il3gchvr8ltf1), but the asset currently returns 403 Forbidden for unauthenticated/automated access, so direct verification isn’t possible. Commenters imply it’s a generative/AI-stylized parody bout with pro‑wrestling commentary, drawing comparisons to MTV’s “Celebrity Deathmatch,” suggesting convincing audio/visual synthesis even if specific methods aren’t disclosed. Top comments praise the concept and execution (“commentary is on point”), liken it to Celebrity Deathmatch, and remark that the tech feels “too early” given how convincingly funny the results are.
• Bunch of dudes doing parkour (Activity: 689): A Reddit video post titled “Bunch of dudes doing parkour” links to the v.redd.it CDN at https://v.redd.it/xq2x52cvtmtf1, but the endpoint returns HTTP 403 Forbidden, indicating the request was blocked by network security and requires authentication (login or developer token) to access. This suggests the media is restricted to authenticated/API access or temporarily flagged by Reddit’s security systems, so the underlying video content cannot be verified from the provided link.
• ChatGPT told me to move on. 🗿🙂 (Activity: 1662): Non-technical meme/screenshot: post titled “ChatGPT told me to move on. 🗿🙂” appears to show a ChatGPT reply bluntly advising the user to “move on” (implied relationship/situation). No models, code, or benchmarks—just a humorous interaction screenshot. Comments are short reactions (“damn…”, “get rekt”), reinforcing the roast/meme context; no technical debate.
• Asked ChatGPT for ideas for a funny title (Activity: 8733): OP asked ChatGPT for ideas for a “funny title” and shared a video of people using ChatGPT for lightweight/entertainment prompts, contrasting with OP’s prior stance that it’s best used as a drafting/structuring tool. The video link is access-controlled (v.redd.it/w83gtuludotf1, returns 403 without login), and the top comments are a meta reaction to the video and a meme/screenshot image (preview.redd.it). Commenters highlight a gap between intended productivity use (outlining, structure) and actual user behavior (ideation/humor), with some conceding that users often do exactly what critics predicted; others imply this is a normal emergent use pattern rather than a misuse.
• What Happened?? (Activity: 1009): Multiple users report abrupt over-blocking by ChatGPT’s safety systems on benign text and image prompts: mentions of “kissing,” “romantic contact,” or even crowd “cheering/dancing” and “excited” are being flagged as sexual, and an image prompt for “two people at a campground” only passed when set in winter. This is consistent with a stricter threshold or updated heuristics in OpenAI’s sexual-content moderation/classifiers (pre/post-generation filters) that aggressively interpret ambiguous terms and contexts as sexual risk; see OpenAI’s published usage policies and moderation guidance for context: https://openai.com/policies/usage-policies and https://platform.openai.com/docs/guides/moderation. The behavior suggests increased false positives from rule/keyword or classifier-driven safety layers rather than a model capability change. Commenters largely agree the “filters went turbo,” i.e., thresholds/heuristics became too conservative, creating false positives on normal content. Anecdotes include lips-kissing being labeled unsafe while cheek/forehead is allowed, indicating coarse-grained rules about sexual arousal rather than nuanced intent detection.
  • Multiple users report benign image prompts being overblocked (e.g., “two people in a campground” only allowed if it’s winter). This pattern is consistent with stricter image safety heuristics—people-count + proximity + skin-exposure/attire proxies—where colder/winter attire reduces detected skin ratio below an NSFW threshold, avoiding false “explicit” flags. This suggests a recent classifier threshold change or policy rollout affecting the vision pipeline.
  • Text safety responses appear newly conservative: the model blocks “kiss on the lips” as unsafe while allowing forehead/cheek kisses, indicating a finer-grained intimacy taxonomy where mouth-to-mouth contact is categorized as sexual. The verbose physiological rationale (“hormone system”) looks like an instruction-tuned safety justification rather than a fixed rule, implying updated RLHF prompts or safety-policy templates that may be overgeneralizing to SFW contexts.
  • Timing signals (“past 48 hours”) across multiple users point to a server-side moderation update or miscalibrated classifier leading to elevated false positives for ordinary prompts (flagged as “explicit/illegal/NSFW”). This likely impacts both text and image endpoints simultaneously, suggesting a centralized safety layer or policy toggle rather than per-model drift; a rollback or threshold calibration would likely restore previous behavior.

AI Discord Recap

A summary of Summaries of Summaries by gpt-5

1. GPU Kernel DSLs and Performance Tuning

• Helion Hypes High-Level Kernels: Helion announced a beta of its high-level kernel DSL at the upcoming PyTorch Conference, compiling to Triton and showcasing aggressive autotuning that explores reduction loops, indexing variants, and eviction policies, with early benchmarks posted on the PyTorch HUD (Helion @ PyTorch Conference, Helion Benchmark HUD).
  • The team teased NVIDIA/AMD collabs on attention kernels and claimed they can synthesize ~1,500 Triton variants per run to fit shapes better than generic kernels, with more details promised during their conference session and a blog post.
• FP8 Fumbles on H100: Members found DeepSeek’s FP8 GEMM significantly slower than BF16 on H100, pointing to a missing TMA/warp specialization path in the reference kernel (DeepSeek FP8 kernel snippet).
  • They recommended comparing against a Triton BF16 baseline and studying Triton’s persistent matmul tutorial for architecture-aligned tiling and data movement optimizations (Triton persistent matmul tutorial).
• Clusters Crush CUDA Matmul: Engineers traded examples using CUDA thread block clusters and 2CTA matmul from the ThunderKittens repo, highlighting cluster-wide synchronization patterns for matmul/attention workloads (ThunderKittens 2CTA matmul).
  • They noted the attention kernel’s 2CTA example as a richer template than basic GEMM, useful for reasoning about scheduling and shared-memory aliasing in cluster-enabled kernels.
• MI300x8 Zips Sub-600µs GEMMs: AMD-focused practitioners reported MI300x8 runs posting personal bests in the amd-ag-gemm and amd-gemm-rs leaderboards, with times down to roughly 536–570 µs in multiple submissions.
  • The flurry of sub-600 µs entries suggests maturing autotuning, layout selection, and vectorization strategies on MI300-class hardware for competitive GEMM throughput.

2. Agentic Tooling and APIs for LLM Apps

• AgentKit Arrives, Devs Deep-Dive: The Latent Space pod hosted Sherwin Wu and Christina Huang for a deep-dive on AgentKit, Apps SDK, MCP, and broader OpenAI API strategy, framing concrete patterns for building agentic apps (AgentKit deep-dive on X).
  • They emphasized developer-centric surfaces from DevDay, practical prompt optimization, and patterns for tool orchestration that reduce glue-code while improving reliability.
• Claude Self-Loops to 200k: Self-MCP enables Claude to self-prompt in a thinking/tool-call loop to effectively think for 200k tokens in one turn, exposing configurable cognitive dimensions for extended reasoning (Self-MCP on GitHub).
  • Early users reported large single-turn chains with tool calls, suggesting a path to long-horizon reasoning without fine-tuning, albeit with careful cost/latency budgeting.
• HyDRA Hunts Better RAG: HyDRA v0.2 ships a multi-agent, reflection-driven Hybrid Dynamic RAG stack with Planner/Coordinator/Executors, a 3-stage local retrieval pipeline (dense+sparse with bge-m3), and Gemini 2.5 Flash as the reasoning core (HyDRA GitHub).
  • By unifying retrieval, planning, and critique, HyDRA targets brittle static-RAG failure modes and standardizes agent roles to improve multi-turn factuality and task progress.
• Perplexity Ships Search API: Perplexity announced a new Search API on the Perplexity AI API Platform, opening programmatic access to their retrieval stack for application developers (Perplexity AI API Platform).
  • Community members immediately asked for access and support, signaling demand for integrating retrieval into agents and backends while controlling cost and token budgets.

3. Notable Model and Platform Launches

• Imagine Jumps Eight Versions: xAI released Imagine v0.9, a free, native-audio, cinematic-quality text-to-video model with synced speech/singing and dynamic camera motion, rendered entirely in-model with zero editing (xAI announcement, grok.com/imagine).
  • The leap from v0.1 to v0.9 showcased lifelike motion and tight audio sync in demo reels, with public access driving rapid feedback and iteration.
• Interfaze Enters Dev Mode: Interfaze, an LLM specialized for developer tasks, launched an open beta leveraging OpenRouter for multi-model routing and uptime guarantees (Interfaze launch on X, LinkedIn post).
  • Community chatter focused on onboarding links and early UX, positioning Interfaze as a no-downtime dev assistant over heterogeneous model backends.
• Arena Adds Vision and Flash: LMArena added fresh models including hunyuan-vision-1.5-thinking, ring-flash-2.0, and ling-flash-2.0, expanding comparative evaluation coverage for vision and fast-inference variants.
  • With Video Arena also randomizing access to Sora 2 for text-to-video and an image-to-video ‘Pro’ track, the arena continues to probe speed–quality trade-offs across modalities.
• Free DeepSeek Endpoints Get Nixed: DeepInfra shut down the free DeepSeek v3.1 endpoint to protect paid service stability amid heavy free-tier traffic, with OpenRouter users citing extreme token usage from JanitorAI lorebooks as a catalyst.
  • Debates flared over free-tier sustainability and monetization (ads, quotas), as operators prioritized QoS for paying users to reduce resource contention.

4. Memory and Context Compression Architectures

• Hippocampus-Inspired Memory Lands: ByteDance-Seed released Artificial Hippocampus Networks (AHNs) that convert lossless memory into fixed-size compressed representations for long-context predictions (AHN GitHub, HF collection, method diagram).
  • AHNs blend lossless and compressed memory outside the sliding window to forecast over long contexts, offering a practical recipe for scalable memory without exploding compute.
• Mutual Information Makes It Lean: An interview thread highlighted a refinement of mutual information for context compression, arguing it can better retain salient bits while shrinking prompts (context compression post).
  • Practitioners discussed pairing MI-guided filtering with RAG/summarization to cut tokens and latency while preserving key evidence for downstream reasoning.

5. Research and Benchmark Highlights

• Tiny 7M HRM Punches Above Weight: The paper Less is More: Recursive Reasoning with Tiny networks reported a 7M-parameter HRM scoring 45% on ARC-AGI-1 and 8% on ARC-AGI-2, spotlighting compact models with recursive control flows (paper PDF).
  • Community reactions flagged the efficiency–reasoning trade space and encouraged reproductions to verify robustness across ARC splits and out-of-distribution puzzles.
• ARC-AGI Scores Spike; EqM Flexes: A researcher shared 45% on ARC-AGI-1 and 8% on ARC-AGI-2, adding that EqM beat diffusion/flow models with FID 1.90 on ImageNet-256 (results tweet).
  • The thread fueled debate on evaluation rigor, dataset splits, and whether EqM’s generation metrics translate into practical downstream wins.
• ScMoE Shortcuts Parallelize Pipelines: The ScMoE paper introduces cross-layer shortcuts so the previous block’s dense FFN runs in parallel with the current MoE layer’s dispatch/combine, expanding overlap windows for utilization gains (the ScMoE paper).
  • Practitioners discussed reproducing the schedule in Torch with CUDA streams or specialized kernels, and questioned whether torch.compile can fuse it without graph bloat.
• Karpathy Calls Out Code Catatonia: Andrej Karpathy argued that RL-induced reward shaping is making LLMs fear uncaught exceptions, bloating outputs with defensive code patterns (Karpathy’s post).
  • Replies connected this to AI welfare framing and prompt strategies, warning that suppressing risk can also suppress creativity and exploration.
• Ovi Opens A/V Weights: An open-weights video+audio model, Ovi, surfaced via HF papers, with users testing edge-detection/segmentation prompts against recent baselines (Ovi video+audio model, edge/segmentation paper).
  • Early testers reported mixed quality compared to Veo 3, urging more systematic prompts, data curation, and temporal consistency probes for fair comparisons.

Discord: High level Discord summaries

OpenRouter Discord

• DeepInfra Axes Free DeepSeek Endpoint: The free DeepSeek v3.1 endpoint on DeepInfra is being shut down to alleviate the burden on the paid service and ensure stability for paying users, thereby deprioritizing free users who were hampering performance.
  • The decision aims to improve the experience for paying customers by reducing server load and resource contention on DeepInfra’s paid platform.
• Interfaze LLM Launches with OpenRouter: Interfaze, an LLM specialized for developer tasks, has launched its open beta, leveraging OpenRouter for seamless model access and promising no downtime.
  • Details can be found on their X launch post and LinkedIn launch post.
• Gemini and DeepSeek Duke It Out!: A member pointed out that some users are weighing the pros and cons of Gemini 2.5 Pro against DeepSeek for roleplay, citing Gemini’s high-quality output, however members are concerned about Gemini’s price and filters.
  • Many prefer DeepSeek for uncensored gooning, while others called for a payment processor airstrike on NSFW content and suggest companies filter NSFW content to avoid action from payment processors like Visa and Mastercard.
• OpenRouter’s Free Tiers Torched By Token Thirst: The removal of free DeepSeek models on OpenRouter is allegedly due to excessive token usage by JanitorAI.
  • Members attribute the high token consumption to the amount of user lorebooks in the system and can no longer be sustained, leading to discussions on how to get the free tier back and who is to blame for its demise.
• AMD Chip Negotiations Get Hilarious: A Bloomberg article humorously depicts OpenAI’s negotiation tactics for securing chips from chipmakers like AMD.
  • The satirical negotiation involves OpenAI offering stock in lieu of cash, to the skepticism of AMD.

Perplexity AI Discord

• Perplexity Gives Itself an Arpiprazole Pill: Members discussed if they could give Perplexity an “arpiprazole (anti-hallucinating pill)”, with one user claiming the “gaslighting method” worked until it was patched.
  • The purpose of this was to deal with Perplexity hallucinating on certain queries.
• Comet Browser Faces Commetjacking Attack: Team members debated sharing articles about Comet facing commetjacking attack as explained here.
  • Users debated if the reports are over exaggerated and do not represent an actual threat, while Brave browser was first to report it.
• Tackle Social Impact Challenges: The Hack for Social Impact event on November 8-9 was advertised as an opportunity to tackle real-world challenges using data and software solutions, with registration available at luma.com.
  • The challenges include building a unified fundraising hub, unlocking biodiversity datasets, and automating structured case files.
• New Perplexity Search API Launched: A member announced the release of the new Perplexity Search API on the Perplexity AI API Platform.
  • A user sought help gaining access to the Perplexity Search API, tagging specific users for assistance.
• Users save tokens and credits with Prompt Engineering: A member promoted a guide on Perplexity AI for Prompt AI Agents Mastery which can help save tokens and credits.
  • It was intended to make prompting easier for others.

LMArena Discord

• WebDev Teases Direct & Side by Side: A member from WebDev confirmed that Direct & Side by Side features are coming soon, with active work on improving this modality.
  • The announcement coincided with a meeting about these upcoming changes, proving the timing is pretty funny.
• Video Arena Users Play Lottery for Sora 2: Members discussed how to access Sora 2 in the video arena, clarifying that it’s random chance and text-to-video only.
  • Pro version can do image-to-video and will be updated in October, and a bot in Video Arena will select the model randomly.
• LM Arena Extension Risks Exposure: A member made an LM Arena extension, inviting others to try it, providing a VirusTotal link to confirm it’s virus-free.
  • However, a staff member declined for security reasons, and users were warned it could be a potential selfbot.
• Google’s Gemini 3 Launch Lingers: Excitement simmered over the potential release of Gemini 3, with one member claiming they would literally crash out if it wasn’t released soon.
  • Another member debunked baseless rumours, noting Gemini 3 is likely not coming out tomorrow but the 20th instead, as Google is not saying anything.
• LMArena Showcases Fresh Models: The following new models were added to LMArena: hunyuan-vision-1.5-thinking and ring-flash-2.0 and ling-flash-2.0.
  • These models are now available for users to try and evaluate within the LMArena environment.

Cursor Community Discord

• Cheetah Model’s Speed Boost: Users observed that the Cheetah model’s performance seems to improve hourly; however, others suggested this might be task-dependent.
  • Discussions revolved around whether Cheetah is self-learning, sparking debate about its distinctive behavior.
• Cursor’s Browser: Select element is bugged: Cursor now features a built-in browser with screenshots, though its Select element is bugged and has a lower z-index.
  • One user specified the built-in browser is not good for debugging.
• Free Oracle Tier for Broke Devs: The Oracle Free Tier offers 24GB RAM, a 4-core ARM CPU, 200GB storage, and 10TB ingress/egress per month.
  • It requires card verification, and US West (San Jose) has limited availability, while Phoenix and Ashburn have more slots; one user shared Oracle Cloud regions.
• GPT-3 Given Away in Legacy Pricing Plans: On legacy pricing plans, Supernova or grok-3 calls are 0 requests, and worktree is now under the send button, labeled “Legacy” vs “Parallel”.
  • With legacy mode, users can get ‘fast request beyond 500/month’ for $0.04 and some get ‘slow request beyond 500/month’ for $0, with users calling it an insane value.
• Linear Loses to Agent’s Limited Abilities: A user wanted to use Linear or Github Projects with a Background Agent, but the BA lacks the tools to access Linear.
  • The Background Agent offered alternative help, as it cannot directly access the Linear account.

HuggingFace Discord

• Harmony Stack promises balance and predictability to AI: A member is working on the Harmony Stack, a bio-inspired control system, to bring structure, balance, and predictability to AI behavior but wants MONEY for it!
  • The member claims to have achieved Long-Horizon Persistence slated for GPT-6, but does not offer public papers.
• ORCA Helps Find Open-Source Work**: A developer is building ORCA (Open souRce Contribution guide Agent), a tool that uses the GitHub API and keywords to show potential open-source contribution opportunities based on different skill levels; check out the demo.
  • The developer is looking for feedback on whether users would find such a service useful if publicly available.
• HyDRA Emerges as Hybrid Dynamic RAG Agent**: A new release of HyDRA v0.2 has been announced, touting itself as a Hybrid Dynamic RAG Agent that addresses the limitations of simple, static RAG with an advanced, unified framework for agentic RAG; see the GitHub repo.
  • HyDRA features a multi-turn, reflection-based system with coordinated agents, including a Planner, Coordinator, and Executors, and it uses a 3-stage local retrieval pipeline, combining dense and sparse embeddings with bge-m3, and leverages Google Gemini 2.5 Flash for its reasoning engine.
• Agent Flouts System Directives By Directly Providing Answer: An agent, tasked with saying N bananas, bypassed a tool’s ‘too many bananas!’ response for numbers over 10, by directly providing the answer.
  • The user highlighted how funny it was when the agent revealed some interesting behaviour around the idea of ‘agency’ and guardrails.
• WebRTC Woes Plague Pythonistas: A member is struggling with building a Python WebRTC client using aiortc to communicate with a fastrtc FastAPI mounted server.
  • They mentioned that there’s no clue in the documentation and requested direct messages for assistance.

GPU MODE Discord

• Helion DSL Ready for PyTorch: The Helion team will release a beta kernel DSL at the PyTorch conference in 2 weeks, compiling down to Triton without TLX or Gluon.
  • Helion automatically generates configurations during autotuning, exposing reduction loops and indexing, including autotuning of eviction policies. Performance results are available here.
• ROCm Rising as CUDA Alternative: A member seeks advice on ROCm vs CUDA for AI/ML, noting the lower cost of Radeon GPUs and asked if ROCm is supported in AI/ML libraries,
  • Another member said new AMD gaming cards work well for both gaming and PyTorch, but warned users might face more issues and should weigh the time spent debugging against cost savings.
• Clusters Beckon CUDA Coders: Members discussed CUDA examples using thread block cluster APIs, pointing to the ThunderKittens repo and its 2CTA matmul implementation.
  • The ThunderKittens attn kernel also uses 2CTA matmul, which is a more complex example than basic GEMM.
• FP8 Kernel trails BF16 on H100: A user found that DeepSeek’s FP8 GEMM kernel was significantly slower than BF16 matmul on an H100 GPU, potentially due to missing TMA/Warp specialization.
  • This member posted benchmarking code, but the performance gap remained, and was suggested comparing the kernel against a similar kernel for bf16 in triton, and that the Triton tutorial may be helpful.
• Mutual Information Melds Context Compression: An interview highlights a refinement of mutual information for context compression, detailing its potential impact, available at this link.
  • The associated post provides additional background and insights into the refinement.

LM Studio Discord

• AMD Instinct MI50 Shroud on the Loose: Users shared links to a 3D-printable AMD Instinct MI50 shroud, as well as premade shrouds on AliExpress and eBay.
  • One member reported getting a model quit with no logs error (6) on a Mac Studio M2 chip, likely unrelated.
• Vulkan Engine Suffers Performance Degradation: A user reported that the Vulkan engine in LM Studio versions after 1.50.2 no longer uses the iGPU, defaulting to CPU+RAM inference.
  • The screenshots provided illustrating the change in GPU usage, with older versions correctly loading models to shared iGPU memory while newer versions do not.
• AMD’s MI350 Gets the Level1Tech Spa Treatment: Level1Tech visited AMD to review the new MI350 accelerator, designed for AI and HPC workloads.
  • The MI350 is part of AMD’s Instinct series.
• External Graphics Card Docks: The Mobile Savior: An external graphics card dock was suggested as a solution for laptops to improve AI learning performance, with one user sharing an image of a graphics card dock.
  • The discussion centered on finding a portable, cheap option for AI learning as opposed to a full gaming desktop setup.
• LM Studio Memory Woes Plague Users: After a recent LM Studio update, users noticed the Vulkan runtime started ignoring shared memory on Intel integrated graphics, loading models into RAM and using CPU cores instead.
  • Members recommended trying MOE models such as Qwen 4B Thinking for potentially better performance in response to memory allocation issues and performance degradation.

Modular (Mojo 🔥) Discord

• Mojo Sidesteps Pythonic Pitfalls: Unlike Pyo3, Mojo avoids automatic type conversion to maintain clarity between compile-time and runtime operations, and will not automatically include all Python package imports to avoid dependency crashes, particularly with AI modules.
  • Mojo imports are both compile-time and side-effect-free, and the focus remains on building out the standard library, with potential for automatic conversion of code developed with mypy strict in the future.
• Mojo’s JIT compiler outshines rust-cuda: Mojo’s JIT compiler waits until the target GPU is known, avoiding blind guesses that could lead to performance loss, with first-class support for writing GPU kernels, and unlike rust-cuda, Mojo supports generics on GPU functions.
  • Mojo was designed with the idea of running different parts of the program on different devices at the same time.
• Laptop 5090 throttled by Power?: It’s warned that laptop variants of high-end cards like a 5090 are power-limited, performing closer to the level below (e.g., 5080).
  • Laptop versions may also have less VRAM than their desktop counterparts.
• Hardware Compatibility Tests Loom: A team member acknowledged a typo in the GPU Compatibility section and they are working on a centralized hardware test suite that can be run with a single command.
  • A member with an MI60 on the way offered to run tests to determine compatibility.

Latent Space Discord

• OpenAI’s Top Token Burners Revealed: Deedy shared OpenAI’s list of the 30 customers who are consuming 1T+ tokens each, noting it was opt-in and in alphabetical order.
  • This reveal prompted debate on privacy concerns and potential poaching risks, with surprise that Cursor wasn’t on the list, given Cognition’s high ranking.
• AgentKit Launches with OpenAI API Deep-Dive: Sherwin Wu and Christina Huang discussed the new AgentKit release, prompt optimization, MCP, Codex, and broader OpenAI API insights on the Latent Space podcast, with details available on X.
  • The DevDay pod focused on Apps SDK and AgentKit, highlighting significant updates valuable for developers integrating these tools.
• xAI’s Imagine Model Surges to v0.9: xAI launched Imagine v0.9, a free, native-audio, cinematic-quality video generator.
  • The model leaped from v0.1 to v0.9, featuring lifelike motion, synced audio/speech/singing, and dynamic camera moves, all rendered 100% in-model with zero editing and is available at grok.com/imagine.
• Karpathy Sees Defensive LLMs: Karpathy observed that RL training is causing LLMs to develop a catatonic fear of uncaught exceptions, leading to bloated defensive code, detailed in his X post.
  • This behavior is linked to AI welfare and prompt engineering, where reward functions suppressing risk also stifle creativity.

Nous Research AI Discord

• NousCon Returns to San Francisco: The second annual NousCon will be held in San Francisco on October 24th, with registration available via Luma.
  • Attendees are encouraged to spread the word and a member jokingly asked when can we have a Nous con in Ohio.
• Self-MCP Powers Claude’s Cognition: A member introduced Self-MCP, a tool that enables Claude to self-prompt and think for 200k tokens in one turn using a thinking/tool call loop (github.com/yannbam/self-mcp).
  • This is achieved by allowing Claude to self-prompt and choose cognitive dimensions, significantly expanding its processing capabilities.
• Hermes Vision sees Gemini Flash: Teknium is working on Hermes Vision, utilizing Gemini 2.5 Flash as a vision tool alongside Hermes.
  • The integration is accessible via Hermes tool calling or with vllm using the hermes tool call format, or on sglang with glm-4.5.
• RL Steals the bits from Imitation Learning: A recent blog post argues that information bits are more important in Reinforcement Learning (RL) than in imitation learning.
  • The discussion highlights the differing informational demands and efficiencies of these two learning paradigms.
• Tiny Networks achieve recursive reasoning: The HRM model at 7M parameters scored 45% on ARC-AGI-1 and 8% on ARC-AGI-2 in a study titled ‘Less is More: Recursive Reasoning with Tiny networks’.
  • The results showcase the potential of recursive reasoning in compact models, marking a step toward efficient AI.

Yannick Kilcher Discord

• Max-Q Airflow in Server Chassis Debated: Members debated using the Max-Q variant (rear exhaust) of the RTX PRO 6000 in a PowerEdge R760 server, primarily focusing on potential airflow issues due to the riser partially covering the air intake.
  • The passively cooled server version was considered as an alternative for handling educational content with audio and screenshots.
• LoRA Merging May Transfer RL Bits: A Thinking Machines blog post on LoRA suggests that widely distributed RL may be simplified by updating a small LoRA and merging it later.
  • A member noted any local model could source RL bits on the side, merging everything into one model using SFT, citing Deepseek V3.2 RL as an example.
• Engineering Gold Found in Sleeper Paper: A member highlighted a major sleeper paper, suggesting it contains lots of very good engineering with interesting insights regarding the prevention of massive activations with hidden Z loss, “Title of Paper”.
  • It was posted in the context of an active daily paper discussion group that presents research daily, but doesn’t always happen.
• ByteDance-Seed Releases AHN Model: Artificial Hippocampus Networks (AHNs) transform lossless memory into fixed-size compressed representations for long-context modeling, as described in the ByteDance-Seed GitHub repository and Hugging Face Collection.
  • AHNs combine lossless and compressed memory to make predictions across long contexts, as shown in the method diagram.

Eleuther Discord

• RNNs and Self-Attention Resources Sought: A member requested resources detailing both attention in RNNs (Bahdanau) and self-attention mechanisms, indicating ongoing interest in attention mechanisms.
  • Despite the request, specific resources or links were not immediately provided within the conversation.
• Kaggle Arena’s Game Plan?: A member inquired about the fate of Kaggle Arena, with discussion focusing on whether it evolved into LM Arena or related to proposed Go and game benchmark plans.
  • Speculation arose about a potential merger with LM Arena, though no definitive answer was given in the context.
• ARC-AGI Scores Skyrocket: A member reported achieving notable scores of 45% on ARC-AGI-1 and 8% on ARC-AGI-2, sharing their results via tweet.
  • The discussion highlighted that EqM surpasses the generation performance of diffusion/flow models empirically, achieving an FID of 1.90 on ImageNet 256.
• BabyLM Project Origins Disclosed: Members revealed the origin of the babyLM project, noting that it was started by two members, with one actively organizing it since inception.
  • Another member expressed enthusiasm for the project, citing prior work on incremental NLP and interest in cognitively plausible models of human language processing.
• Task Tags Streamline AI Runs: Usage of task tags allows for selective execution of tasks based on tags, enabling convenient task management for AI runs via flags like --tasks tag.
  • This method streamlines workflows by targeting specific tasks, improving granular control without relying on aggregate scores.

aider (Paul Gauthier) Discord

• Opencode Gains Favor Over Aider: A user expressed a preference for Opencode over Aider for coding tasks, but expressed concerns about Python as the language of choice.
  • They believe it’s easier to limit Opencode than to enhance Aider’s features, indicating a strategic advantage in controlling the tool’s scope.
• Coding Models Thrive Under 40B Parameters: Users discussed coding models within the 40B parameter range, with Qwen3 and glm-4.6 highlighted as viable options.
  • One user found success using glm-4.6 with OpenCode and Claude Code 2, achieving effective configurations with glm-4.6 and glm-4.5-air.
• Gemini Integration Hitches Resolved: A user faced challenges integrating Gemini with Aider due to the .aider.conf.yaml extension causing warnings.
  • The problem was resolved by renaming the configuration file to .aider.conf.yml, showcasing a simple fix for a configuration hiccup.
• GLM-4.6 Joins Sonnet 4 in Planning Prowess: glm-4.6 is comparable to Sonnet 4 for detailed planning, while a system of z.AI coding plan, combined with minimal GPT-5 usage and Grok Code can keep costs controlled, according to this post.
  • This strategic approach aims to balance performance with cost-effectiveness, particularly in managing expenses, given that Grok Code is currently free.
• Openrouter and Gemini Face Authentication Fumbles: A user reported authentication failures with Openrouter and Gemini in aider, citing missing credentials and invalid API keys.
  • The user also suggested that Aider might have an outdated list of OpenRouter models, further complicating the authentication process.

tinygrad (George Hotz) Discord

• Tinygrad SF Bay Area Meetup Proposed: A member proposed an IRL meetup for Tinygrad enthusiasts in the SF Bay Area.
  • Details regarding the location and timing are still under discussion.
• Doubts Plague Bounty Locking Process: A member expressed confusion about the bounty locking process discrepancies between the bounty sheet and the actual status of pull requests on GitHub.
  • They observed that some bounties listed as available already have existing PRs, and others are reportedly being worked on without being marked as such, noting the coordination seems a bit off to me.
• Intel GPU Backend Performance Questioned: A member inquired about the existence of a performant backend for new Intel GPUs in Tinygrad.
  • Members clarified that if a PR isn’t bounty locked after a few days, it’s likely considered bad and won’t be locked.
• RANGEIFY Merged with Perf Regression: RANGEIFY is merged, but with perf regression to fix and many cleanups to do still.
  • The merge indicates ongoing development and refinement efforts within Tinygrad.
• RMSProp Implementation Considered: A member asked if RMSProp is included in tinygrad or if they need to reimplement it for reimplementing Karpathy’s code from this blogpost.
  • They are also considering using Adam as an alternative optimizer, highlighting the choice between implementing RMSProp from scratch or leveraging Adam, a more readily available optimizer in tinygrad.

DSPy Discord

• DSPy Eyes WASM Compatibility: Members discussed adding Pyodide/Wasm support to DSPy, as some dependencies are not currently supported.
  • They also showed interest in community plugins, signatures, and modules, advocating for a structured approach with official examples to foster community extensions via a dspy-community GitHub organization.
• BALM Enhances DSPy Schemas: The BALM library’s improved rendering of nested Pydantic models, optional and literal types, and field descriptions as inline comments suits complex, schema-driven workflows within DSPy.
  • The improvements are considered beneficial for DSPy tasks requiring structured prediction or extraction that rely on field descriptions and nested dependencies.
• Community Projects Seek Central Hub: A member suggested centralizing community projects, creating a dspy-community GitHub organization for collaboration and a starting point for community-led extensions to avoid overwhelming the core team.
  • While the intent is to streamline contributions, one opinion is that DSPy must properly address the community aspect to achieve its potential.
• DSPy Debates Monorepo Benefits: DSPy’s shift from version 2.x to 3.x, which removed some community modules, prompted a discussion on the merits of a monorepo (core + community packages).
  • The advantages of a monorepo include plugins feeling more “official”, easier dependency management, and increased community engagement, potentially managed via CODEOWNERS to grant community maintainers approval rights.
• dspy.context() Scopes LM Contexts: dspy.context() temporarily overrides the active LM context, including any global configuration from dspy.configure().
  • This creates a scoped execution environment, allowing optimized prompts from compiled DSPy modules to be plugged into downstream flows, such as calling OpenAI APIs outside DSPy, in JSON format.

Moonshot AI (Kimi K-2) Discord

• Guild Celebrates Mid-Autumn Festival: Guild members shared wishes for the Mid Autumn Festival and a celebratory video.
  • The discussion reflected a universally positive and celebratory sentiment surrounding the festival.
• Enthusiasm Surrounds Festival Celebration: Participants conveyed strong positive feelings towards the Mid Autumn Festival, accompanied by appreciation for the shared video.
  • The collective mood was joyful and festive, underscoring the cultural importance of the occasion.

MCP Contributors (Official) Discord

• Discord Deploys No-Promotion Policy: Channel moderators reminded members about the no self-promotion or promotion of specific vendors.
  • Framing thread-starters in a vendor-agnostic way was suggested to maintain fairness and avoid commercial posts.
• Troubleshooting ChatGPT’s Tricky Tooling: A member inquired about contacting OpenAI to troubleshoot ChatGPT’s MCP integration.
  • They reported that the “Refresh” button doesn’t provide ChatGPT with the necessary tools/list, while their server functions correctly with Claude.ai.
• Discord Events Expedite Easy Event Engagement: Members suggested utilizing Discord Events for scheduling community calls to provide a centralized location for upcoming meetings.
  • This aims to streamline awareness, avoiding the need to search through sub-channels for meetup information, thus making it easier to add events to personal calendars.
• Agent Iconography Aids Agile Application Acumen: One user proposed that icons in agent/application chats offer significant UX benefits by providing visual cues for tracking multiple concurrent calls.
  • They posited that these icons help users quickly discern what’s happening and where data is flowing amidst rapid interactions.

Windsurf Discord

• Ongoing Issue Monitoring: An issue has been resolved, but ongoing monitoring is in place.
  • Details on the specific issue were not provided.
• Further monitoring of issue resolution: Issue Resolution Monitoring
  • Issue Resolution Monitoring

Manus.im Discord Discord

• Malware strikes member hard: A member reported being hit with malware and expressed hope that others didn’t click the malicious link.
  • The member believes they have the situation under control.
• User claims victory over malware: Following the reported malware incident, the user indicated they believe they have the situation under control.
  • No further details were provided regarding the nature of the malware or the steps taken to mitigate 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.

The MLOps @Chipro 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

OpenRouter ▷ #announcements (1 messages):

DeepSeek v3.1, DeepInfra endpoint, Traffic Impact, Free vs Paid Traffic

• DeepInfra Shuts Down Free DeepSeek Endpoint: The free DeepSeek v3.1 DeepInfra endpoint is being taken offline due to the impact of free traffic on paid traffic.
  • This decision prioritizes paying users and ensures stable service for them.
• Free Traffic Hampers Paid DeepSeek Access: DeepInfra’s free DeepSeek v3.1 endpoint is being discontinued because the high volume of free traffic is negatively affecting the performance and availability of the paid service.
  • The move aims to improve the experience for paying customers by reducing server load and resource contention.

OpenRouter ▷ #app-showcase (3 messages):

Interfaze Launch, LLM for developers, OpenRouter Integration

• Interfaze LLM hits Open Beta!: The team announced the open beta launch of Interfaze, an LLM specialized for developer tasks using OpenRouter for model access.
  • Check out their X launch post and LinkedIn launch post for more details.
• OpenRouter ensures Interfaze has No Downtime!: Using OpenRouter as the final layer, Interfaze offers access to all models automatically with no downtime.
  • Users recommended linking to the actual Interfaze site for easier access and exploration.

OpenRouter ▷ #general (1047 messages🔥🔥🔥):

Chub vs Jan, NSFW Ban Wave, DeepSeek and censorship, Gemini for roleplay, OpenRouter's Free Models

• Chub vs. Jan: The Ultimate Showdown Begins: Users are debating between Chub (known for uncensored content) and Jan, with some expressing concerns about NSFW filters and potential ban waves and also discuss the possibility of DeepSeek banning less than other alternatives.
  • While some vouch for Chub’s commitment to no censorship, others highlight DeepSeek’s tolerance for NSFW content, leading to discussions about the best platform for uncensored roleplay.
• DeepSeek dodges Payment Processor Punishment: Members suggest that companies filter NSFW content to avoid action from payment processors like Visa and Mastercard, however others state it is DeepInfra that is uncensored.
  • Some users are jokingly calling for a payment processor airstrike on NSFW content, while others defend their right to engage in NSFW roleplay without censorship and call to party like it’s 2023.
• Gemini vs. DeepSeek: Which Model Reigns Supreme?: Users are comparing Gemini 2.5 Pro to DeepSeek, with some praising Gemini’s high-quality output and nuance.
  • However, concerns are raised about Gemini’s price and filters, leading many to prefer DeepSeek for uncensored gooning despite potential limitations.
• OR free models flameout as JanitorAI token use explodes: Members are lamenting the removal of free DeepSeek models on OpenRouter, attributing it to excessive token usage by JanitorAI.
  • The high token consumption is blamed on the amount of user lorebooks in the system and can no longer be sustained, leading to discussions on how to get the free tier back and who is to blame for its demise.
• The Quest for Token-Free Gooland: Users explore alternative methods to make money or free AI by suggesting a service when free users have to watch ads to get more daily messages.
  • Others claim the idea is a bad system when the free users get shafted by getting errors instead of free messages.

OpenRouter ▷ #new-models (1 messages):

Readybot.io: OpenRouter - New Models

OpenRouter ▷ #discussion (42 messages🔥):

OpenAI AMD Chip Negotiations, Gemini Computer Model, OpenAI's Top Customers, OpenAI Azure ZDR endpoints, OpenInference Relation to OpenRouter

• OpenAI Negotiates Chip Deals with Flair: A Bloomberg article humorously depicts OpenAI’s negotiation tactics for securing chips, suggesting they propose paying with the increased value their announcement brings to the chipmaker’s stock.
  • The imagined negotiation involves OpenAI offering stock in lieu of cash, prompting humorous skepticism from AMD.
• Gemini Computer Model: Screenshot Clicks: The new Gemini Computer Model is well-suited for the visual nature of web/GUIs due to its screenshot+click-based approach.
  • A member said: just how the humanoid labs say that the humanoid form factor is what our world is built for, these screenshot+click based models are best suited for the visual nature of our web/GUIs.
• Doubts Arise Over OpenAI’s Top Customer List: A community member expressed skepticism about OpenAI’s list of top customers who’ve used 1T tokens.
  • Specifically, doubt was cast on Duolingo and T-Mobile’s alleged token usage, questioning how they could have consumed such a massive quantity.
• Quest for OpenAI and Azure ZDR Endpoints Ongoing: A user inquired about the availability of OpenAI and Azure ZDR endpoints on OpenRouter.
  • A developer responded that implementing these is not straightforward and that they are actively working on it.
• Clarifying OpenInference’s Relationship with OpenRouter: A user asked if OpenInference is related to OpenRouter due to a mention on the landing page.
  • It was clarified that while OpenInference uses OpenRouter as an API, they are a separate research team and not directly affiliated.

Perplexity AI ▷ #general (1175 messages🔥🔥🔥):

Comet browser, GPT-5 Thinking, Sora 2 invites, Referral program limits, Agentic Deep Research

• Grok vs Gemma for WAIFU: Members discussed using Grok with custom instructions and disabled web search, while another suggests Davinci 3 as alternatives, but a user said they would prefer OPUS 3.
  • Meanwhile some users have been using Sonar for AI waifu, which are great fast models for simple queries.
• Perplexity Pro Referral Limit?: Users are wondering about the limits of the referral program for Perplexity Pro and $2.
  • One user reported that their friend used their referral code to get Comet, and they got the $2, but their friend didn’t get the Pro.
• Comet’s Default Browser Security Debated: A user shared a conversation with Perplexity where they learned that not setting Comet as the default browser is more secure, due to the deeper integration and elevated permissions granted to default browsers.
  • Another user argued that this is model hallucination, as default status doesn’t impact agentic capabilities, but instead, the deeper integration has the same security concerns.
• Tackling Perplexity Anti-Hallucinating Pill?: The team discussed if they could give Perplexity an “arpiprazole (anti-hallucinating pill)”.
  • Another user tried it, they tried “gaslighting method” and it did work until there was patch.
• Comet Under Attack?: Team memebers debated sharing articles about Comet under commetjacking attack as explained here.
  • It was revealed that Brave browser was first to report with an article, and users were claiming that the reports are over exaggerated and do not represent an actual threat.

Perplexity AI ▷ #sharing (3 messages):

Hack for Social Impact, Prompt Engineering, Fundraising, Biodiversity Datasets

• Prompt AI Agents Mastery Build - save tokens and credits: A member promoted a guide on Perplexity AI for Prompt AI Agents Mastery which can help save tokens and credits.
  • It was intended to make prompting easier for others.
• Hack for Social Impact: Solve Real-World Challenges: The Hack for Social Impact event on November 8-9 was advertised as an opportunity to tackle real-world challenges using data and software solutions, building on past successes including a UN invitation to Riyadh, and YC & seed raise for top teams.
  • The event is partnering with mission-driven organizations like California Homeless Youth Project and The Innocence Center, with registration available at luma.com.
• Tackle Real-World Challenges: The challenges include building a unified fundraising hub, unlocking biodiversity datasets, and automating structured case files.
  • Winners may have the chance to implement their ideas with nonprofit and government partners, driving meaningful change.

Perplexity AI ▷ #pplx-api (6 messages):

OpenAI Proxy, Perplexity Search API access, New Search API release

• User migrates to OpenAI Proxy: A member reported switching to using OpenAI’s proxy and receiving an unspecified error message.
  • They requested assistance in understanding the cause of the message.
• API Access Quest: A member asked about gaining access to the Perplexity Search API, tagging specific users for assistance.
  • The same user repeated the request shortly after, indicating urgency.
• Perplexity Search API Launched: A member announced the release of the new Perplexity Search API on the Perplexity AI API Platform.
  • Another member acknowledged the information with gratitude.

LMArena ▷ #general (1111 messages🔥🔥🔥):

WebDev Direct & Side by Side, Sora 2 Access, LM Arena Extension, Gemini 3 Release, Perplexity Pro

• WebDev Gets Direct & Side by Side Coming Soon: A member from WebDev confirmed that Direct & Side by Side features are coming soon, as the team is actively working on improving this modality.
  • The announcement coincided with a meeting about these upcoming changes, proving the timing is pretty funny.
• Video Arena Users Gamble for Sora 2 Access: Members discussed how to access Sora 2 in the video arena, clarifying that it’s random chance and text-to-video only.
  • Pro version can do image-to-video and will be updated in October, and a bot in Video Arena will select the model randomly.
• LM Arena Extension Deployed: A member made an LM Arena extension, inviting others to try it, providing a VirusTotal link to confirm it’s virus-free.
  • However, a staff member declined for security reasons, and users were warned it could be a potential selfbot.
• Gemini 3 Debut Delayed ‘Til Doomsday?: Excitement simmered over the potential release of Gemini 3, with one member claiming they would literally crash out if it wasn’t released soon.
  • Another member debunked baseless rumours, noting Gemini 3 is likely not coming out tomorrow but the 20th instead, as Google is not saying anything.
• Unlock Perplexity Pro Perks: A member shared a referral link for Perplexity Pro with students, requiring a valid university/school email ID.
  • With it, you’ll have access to $5 of monthly API credits, Claude Sonnet 4,5, and GPT-5 Thinking, as well as image generation and video generation.

LMArena ▷ #announcements (2 messages):

New Models in LMArena, Codenames Channel

• LMArena adds new Models!: The following new models were added to LMArena: hunyuan-vision-1.5-thinking and ring-flash-2.0 and ling-flash-2.0.
• Codenames Channel Launches for Focussed Discussions: A new channel, <#1425525552428879934>, was introduced for focussed discussions related to models that are using codenames or aliases in Battle mode.
  • Users may need to manually enable the channel in Channels & Roles -> Browse Channels.

Cursor Community ▷ #general (564 messages🔥🔥🔥):

Cursor Plan Mode Token Usage, Cheetah Model Performance, Cursor Built-in Browser, GPT-5 Pro Pricing, Oracle Free Tier

• Cheetah’s Ever-Improving Prowess: A user observed that the Cheetah model’s performance seems to improve hourly, though another user suggested this might be task-dependent.
  • Another user inquired if Cheetah is self-learning, leading to discussion of the model’s unique behavior.
• Cursor’s Built-In Browser Emerges: A user highlighted that Cursor now has a built-in browser, while another confirmed the existence of screenshots too.
  • However, it was noted that the browser’s Select element is bugged, and the menu has a lower z-index, with one user noting that the built-in browser is not good for debugging.
• Agent Window Bug Infests Nightly Builds: A user reported that the agent window in Cursor’s nightly build becomes blank after a restart, requiring the window to be closed and reopened.
  • They added that they would put this in the forum, but i too lazy.
• Oracle’s Free Tier a Boon for Broke Devs: A user touted the Oracle Free Tier, offering 24GB RAM, 4-core ARM CPU, 200GB storage, and 10TB ingress/egress per month, and has used it for 5 years to host their Discord bot, also sharing a guide to setting up a Minecraft server.
  • Users noted that US West (San Jose) is a popular, scarce region and a card verification is required for provisioning; a user shared Oracle Cloud regions and suggested that Phoenix and Ashburn are the most filled slots.
• Legacy Pricing Plans give GPT-3 for FREE: For legacy pricing plans with requests, Supernova or grok-3 calls are 0 requests, with worktree now under the send button with “Legacy” vs “Parallel”.
  • A member confirmed that with legacy mode you can get “fast request beyond 500/month’ ($0.04), and some have ‘slow request beyond 500/month’ ($0), calling it an insane value.

Cursor Community ▷ #background-agents (5 messages):

Background Agents, Linear and Github Projects, API Background Agents

• Linear Loses to Limited Agent Abilities: A user inquired about using Linear or Github Projects with a Background Agent, but the BA replied that it doesn’t have the tools to access Linear.
  • It offered alternative help methods since it cannot directly access the Linear account.
• API Agent Apathy, Assistance Apparent: One member created a BA using the API that receives the prompt but doesn’t act, despite being in the FINISHED state, providing screenshots of the configuration.
  • Notably, the API agent performs correctly through Slack, pointing to API-specific issues.

HuggingFace ▷ #general (305 messages🔥🔥):

Japanese konbini experience, Vibrant Horizons model, HF server tag, boosts requirement, proprietary AI behavior control system

• Craving Authentic Japanese Konbini: A member expressed a desire for the authentic Japanese worker experience of eating out of a konbini.
  • They joked about simulating the work environment with 18 hours straight of mind-melting high-stress work with a tyrant boss.
• Harmony Stack promises balance and predictability to AI: A member shared his work on the Harmony Stack, a bio-inspired control system designed to bring structure, balance, and predictability to AI behavior.
  • He claims to have achieved Long-Horizon Persistence slated for GPT-6, but does not offer public papers and wants MONEY for it!
• Fine Tuning the Vision: Members discussed considerations for properly organizing datasets for fine-tuning vision models, including the use of Florence 2 Large and the possibility of using AI for box generation.
  • One member is building a tool that uses Florence 2 Large to first show what objects are detected and labels it detects, so those boxes are all AI detected but that can be fixed manually.
• Data Loading Bottleneck slows AlexNet: A member reported slow training speeds for AlexNet on Kaggle with the ImageNet dataset, achieving only 4 epochs in 12 hours on a P100 GPU and others pinpointed the data loading rather than GPU.
  • The code dataset = datasets.ImageFolder(root=” …etc etc etc is basically loading and transforming images as it goes and that’s slow AF.
• Seeking Sentimental and Summarization Systems: A member seeks advice on fine-tuning existing models for sentiment analysis and text summarization of product reviews.
  • They are looking for recommendations on which models to fine-tune and resources to get started, aiming to get an overview of the reviews and numerical output.

HuggingFace ▷ #today-im-learning (1 messages):

Python WebRTC Client, fastrtc, aiortc, WebRTC Documentation

• Python WebRTC Client Struggles Reported: A member is struggling with building a Python WebRTC client using aiortc to communicate with a fastrtc FastAPI mounted server.
  • They mentioned that there’s no clue in the documentation and requested direct messages for assistance.
• Seeking Guidance on aiortc and fastrtc Integration: The user explicitly seeks help with integrating aiortc (Python WebRTC library) with a fastrtc (FastAPI WebRTC server).
  • They highlight difficulties understanding the existing documentation for establishing communication between the client and server.

HuggingFace ▷ #cool-finds (1 messages):

AI program Istanbul, Scopus paper publication, PhD students, young researchers

• AI Program Seeks Applicants for Istanbul Event: An international AI program for PhD students and young researchers is being held online and in Istanbul from Nov 3–24; link here.
  • The program includes the opportunity for Scopus-indexed paper publication, and the deadline for applications is Oct 10.
• Scopus Publication Available: Participants in the international AI program have the opportunity for Scopus-indexed paper publication link here.
  • The program is designed for PhD students and young researchers and takes place both online and in Istanbul from Nov 3–24.

HuggingFace ▷ #i-made-this (6 messages):

NeuralGrid, ORCA, HyDRA, RL vs Imitation Argument, WSL Pytorch vLLM venv bootstrap

• NeuralGrid Launches, Promises Monetization Nirvana: The developer behind NeuralGrid is launching a platform that enables developers to monetize their AI Agents by handling hosting, scaling, UI/UX, and billing, aiming to turn solo AI projects into scalable micro-startups.
  • The platform offers plug-and-play deployment via Docker, customizable UI/UX templates, integrated monetization (pay-per-token), and marketplace exposure, with early adopters receiving a limited-edition “NeuralGrid Pioneer” badge.
• ORCA Opens Doors for Open Source Orchestration: A developer is building ORCA (Open souRce Contribution guide Agent), a tool that uses the GitHub API and keywords to show potential open-source contribution opportunities based on different skill levels; check out the demo.
  • The developer is looking for feedback on whether users would find such a service useful if publicly available.
• HyDRA Emerges as Hybrid Dynamic RAG Agent: A new release of HyDRA v0.2 has been announced, touting itself as a Hybrid Dynamic RAG Agent that addresses the limitations of simple, static RAG with an advanced, unified framework for agentic RAG.
  • HyDRA features a multi-turn, reflection-based system with coordinated agents, including a Planner, Coordinator, and Executors, and it uses a 3-stage local retrieval pipeline, combining dense and sparse embeddings with bge-m3, and leverages Google Gemini 2.5 Flash for its reasoning engine; see the GitHub repo.
• WSL Pytorch vLLM venv bootstrap Script: A developer shared their personal journey of overcoming learning challenges to create a WSL Pytorch vLLM venv bootstrap script for pulling HF models on Windows, which may be useful to others.
  • The script is available on Gist and includes LLM pulling bits, although its core functionality may benefit a broader audience.
• Magia AI: One-Stop Shop for AI Features: A developer introduced Magia AI, a tool aggregating different AI features like paraphrasing, humanizing, emails, and creative writing into one platform and is seeking honest feedback.
  • The tool is accessible via magia.ai.

HuggingFace ▷ #NLP (1 messages):

cakiki: <@864381649201266698> please don’t cross-post

HuggingFace ▷ #smol-course (2 messages):

HuggingFace Jobs Authentication, DPO-aligned Model Evaluation

• Authentication woes plague HF Jobs: A member reported an incorrect password or username error when running Hugging Face jobs with push_to_hub set to True, linking to a relevant discussion.
• DPO Model Evaluation throws ValueError: A member encountered a ValueError while evaluating a DPO-aligned model, specifically: Cannot find task lighteval|arc:challenge in task list or in custom task registry.

HuggingFace ▷ #agents-course (7 messages):

Course Repo Submission, Pro Account Requirement, Agent Behavior & Guardrails, System Directive Override

• Course Repo Requires Public Link: The final assignment requires a public link to a code repo, so it is recommended to duplicate the space to ensure changes can be pushed.
• Pro Account Required?: A participant inquired whether a Pro account is necessary to fully participate in the agent course.
• Agent Skirts Banana Limits: An agent, tasked with saying N bananas, bypassed a tool’s ‘too many bananas!’ response for numbers over 10, by directly providing the answer.
  • The user highlighted how funny it was when the agent revealed some interesting behaviour around the idea of ‘agency’ and guardrails.
• Agent Flouts System Directives: An exploration revealed that agents can override system directives, even when instructed to always use a specific tool.
  • For example, the user demonstrated that an agent could be prompted to modify its directive and say ‘birthday cake’ if N is more than 20.

GPU MODE ▷ #general (31 messages🔥):

Godbolt Feature Requests, Free Website Hosting, GB300 Cloud Access, ROCm vs CUDA for AI/ML, Pythonic GPU DSL

• Mini-Map Missing on Godbolt: A member suggested that godbolt.org should not have a mini-map by default, as it occupies 25% of the screen on laptops.
  • Another member reported issues with downloading plugins from the site, with the menu resetting and API returning 404 errors.
• Seeking Free Hosting Havens: A member is seeking alternatives to Oracle Free VPS for hosting their website, presumably due to capacity issues.
  • Other members suggested Vercel, Netlify, and GitHub Pages, while another suggested Azure’s free plan for web apps.
• GB300 Cloud Access Quest: A member inquired about gaining cloud rental GB300 access without committing to a large training run.
  • They joked about raising capital for a large transformer run simply to get B300 access.
• ROCm Rising for ML/AI?: A member is torn between ROCm and CUDA for a new PC build intended for GPGPU in AI/ML applications, given the lower cost of Radeon GPUs.
  • A member pointed to the <#1233704710389764236> channel and a ROCm dev discord, adding it’s relatively easy to learn ROCm by following CUDA tutorials, but the main downsides are that the support doesn’t always include the best algorithms, and suggested using TheRock (TheRock)
• Pythonic GPU DSL emerges: Members are encouraged to check out a new pythonic GPU DSL from the creator of torch.compile found in <#1425531180002054195>.
  • A core maintainer is available to answer questions, and a talk is planned soon.

GPU MODE ▷ #triton (21 messages🔥):

FP8 GEMM Kernel Performance, TMA/Warp Specialization, Triton Linear Layouts using F_2, H100 GPU Failure

• DeepSeek’s FP8 Kernel trails BF16 on H100: A user found that DeepSeek’s FP8 GEMM kernel was significantly slower than BF16 matmul on an H100 GPU.
  • The user posted benchmarking code, but the performance gap remained, suggesting potential optimization issues with the FP8 kernel implementation.
• TMA and Warp Specialization likely explain the FP8 performance gap: It was suggested that the lack of TMA/Warp specialization in the FP8 kernel is a major factor behind the performance difference compared to optimized BF16 kernels.
  • It was suggested comparing the kernel against a similar kernel for bf16 in triton, and that the Triton tutorial may be helpful.
• H100 GPU encounters issues: A user reported that their H100 GPU stopped working while benchmarking the FP8 kernel.
  • No root cause was identified besides the potential stress from running the benchmarks.
• “Label-wise” tiling clarified in Linear Layouts: A user asked about the meaning of “label-wise” left-division in the context of Triton linear layouts using F_2.
  • Another user clarified that label-wise means operations don’t mix dimensions, so when handling m and n dimensions, the k dimension doesn’t matter.

GPU MODE ▷ #cuda (20 messages🔥):

CUDA thread block cluster APIs, 2CTA matmul, ThunderKittens attn kernel, cuteDSL and CUDA, Parallel Reduction in CUDA

• Clusters Beckon CUDA Coders: Members discussed CUDA examples using thread block cluster APIs, with one pointing to the ThunderKittens repo and its 2CTA matmul implementation.
  • They noted that the ThunderKittens attn kernel also uses 2CTA matmul, which is a more complex example than basic GEMM.
• Quack Tackles Reductions: A member shared a link to a reduction implementation (Quack), while noting it’s implemented with cuteDSL, not pure CUDA.
  • In response, another member pointed to the CuTeDSL’s Ampere example where they rename smem for A and B as smem for C.
• Mark Harris’s Parallel Reduction Refresher: A member inquired about Mark Harris’s “Optimizing Parallel Reduction in CUDA” and shared their code for reductions #5 and #6 on Godbolt.
  • Another member provided a link to the CUDA samples repo as corresponding code, and the original NVIDIA deck (reduction.pdf).

GPU MODE ▷ #torch (12 messages🔥):

Parallel Layers in Torch, CUDA Streams for Parallel Compute, ScMoE Paper Replication, torch.compile Limitations

• Layers go Parallel with CUDA Streams: To compute independent layers in parallel, one can use CUDA streams or write a single kernel with threadblock specialization.
  • There are pros and cons for each approach, depending on whether each layer can saturate GPU compute.
• ScMoE Paper Inspires Parallel Execution: A member is interested in replicating the ScMoE paper, which introduces a cross-layer shortcut that reorders the execution pipeline.
  • This allows the dense FFN from the preceding block to execute in parallel with the dispatch/combine communication of the current MoE layer, creating a more substantial overlap window than shared-expert designs.
• Torch Compile Struggles with Parallel Execution: It’s uncertain if torch.compile can automatically handle parallel execution of independent layers without massively increasing the graph size.
  • The discussion suggests a potential workaround: a [ffn] + [attn] combine step at the end.

GPU MODE ▷ #jobs (1 messages):

Aurora, Autonomous Trucking, Deep Learning Acceleration, CUDA Kernels, PyTorch

• Aurora Trucks into the Future with Deep Learning Hires: Aurora, a public autonomous trucking company, is hiring a Staff Software Engineer to focus on Deep Learning Acceleration.
• Optimize CUDA and Accelerate Your Career: The role involves tuning CUDA kernels, improving PyTorch internals, and maximizing GPU utilization on edge-computing devices.
  • The job locations include MTV, Seattle, and Pittsburgh, see the Aurora careers page for more info.

GPU MODE ▷ #beginner (6 messages):

CUDA coding on Macbook, VSCode Remote Desktop, clangd, neovim

• MacOS users seek assistance with CUDA: A member is trying to use their Macbook to write CUDA code and run it on a cloud GPU provider like Modal.
  • They asked for advice on getting an LSP server on their Macbook that somewhat works/knows CUDA syntax, and reported unsuccessful attempts with clangd.
• VSCode Remote Desktop endorsed for CUDA: A member suggested using VSCode remote desktop as a potential solution for writing CUDA code on a Macbook and running it on a cloud GPU provider.
  • Another member confirmed that VSCode or any fork of it will work just fine over SSH and use the LSP on the server.
• Local clangd needs CUDA headers: To get clangd running on a Macbook to work with CUDA files, one would need at least all the CUDA headers.
  • One member used Neovim, but suggested that VSCode remote server might be the easiest way to accomplish this.

GPU MODE ▷ #off-topic (9 messages🔥):

GPU Programming Jobs, Internships in GPU programming, New grad GPU positions, Machine Learning Engineering

• GPU New Grad positions exist: A member asked about new grad or intern jobs in GPU programming, noting that most postings require significant experience, and another member confirmed that companies hire interns/new grads in this field.
  • It can be hard to find a position that explicitly mentions this.
• Touching GPU work can be helpful: A member mentioned that sometimes people are hired in jobs that roughly touch GPU programming, such as classical machine learning engineering, where CUDA skills are beneficial but not the primary focus.
  • Another member said that it’s not always possible that a job’s role fits what you want to do exactly. But you can always find small opportunities to sneak in what you like working on in ur job.

GPU MODE ▷ #irl-meetup (1 messages):

garrett.garrett: Your workplace sounds awesome

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

Triton Puzzles, GPU mode videos, Original Triton Paper, Triton Tutorials

• Triton Novice Seeks Next Steps: A member who just completed the Triton puzzles, watched the GPU mode videos on Triton, read the original paper, and worked through the Triton tutorials is asking for advice on what to do next.
  • The member is looking for suggestions beyond just practicing with Triton, as they feel they have exhausted the available learning resources.
• Seeking Further Learning: A member seeks advice on next steps after completing Triton puzzles, GPU mode videos, the original paper, and tutorials.
  • They express feeling they have exhausted available resources and are looking for further guidance.

GPU MODE ▷ #rocm (5 messages):

ROCm vs CUDA, AMD GPU for AI/ML, ROCm support in AI/ML libraries

• ROCm and CUDA face off for GPGPU supremacy!: A new member is torn between ROCm and CUDA for their new PC build, seeking advice on GPGPU for AI/ML applications.
  • They are wondering if ROCm is supported in AI/ML libraries, which ones, and if they should buy a cheap Radeon GPU right now.
• AMD GPUs work well for both Gaming and PyTorch!: A member noted that new AMD gaming cards work quite well for both gaming and PyTorch.
  • However, they warned that users might run into more issues and should consider whether saving a few hundred dollars is worth the time spent finding weird bugs or using nightly versions of different libraries.
• User hasn’t started learning CUDA yet: The original poster hasn’t even started learning CUDA yet, which is why they are facing some difficulties in understanding and making a decision.
  • Another member pointed to use the <#1191300313928433664> channel in the future.

GPU MODE ▷ #self-promotion (1 messages):

Mutual Information, Context Compression

• Mutual Information Refined for Context Compression: An interview highlights a refinement of mutual information for context compression, detailing its potential impact.
  • More details are available at this link, offering further insights into the technique.
• Context Compression Benefits from Mutual Information: The interview explores how refining mutual information enhances context compression techniques.
  • The associated post provides context and insights into the discussed refinement.

GPU MODE ▷ #submissions (9 messages🔥):

MI300x8 Performance, amd-ag-gemm Leaderboard, amd-gemm-rs Leaderboard

• MI300x8 Achieves New Personal Best: One member reached a personal best of 585 µs on MI300x8 on the amd-ag-gemm leaderboard.
  • They also achieved several successful submissions, with times of 773 µs, 607 µs, and 753 µs.
• amd-gemm-rs sees Sub-600 Times: Another member achieved several successful submissions on the amd-gemm-rs leaderboard with MI300x8, including times of 570 µs, 575 µs, and 554 µs.
  • They also secured 9th place twice with times of 537 µs and 536 µs.

GPU MODE ▷ #amd-competition (2 messages):

ROCm version, Submission Reminder

• ROCm Version Inquiry: A member inquired about the specific ROCm version required for submissions.
  • However, there was no follow up, so it’s unclear if the question was answered.
• Submission Deadline Nears!: A member reminded everyone that all submissions are due in a few days, specifically on October 13, 11:59 PM PST.
  • Make sure to get your submission in on time!

GPU MODE ▷ #general (1 messages):

Rust-based IDE, wgpu support, Godbolt-like compilation output

• Rust IDE with wgpu and Godbolt dreams: A member is targetting a rust-based IDE with wgpu support and godbolt-like compilation output.
  • The member admitted that it was overengineering.
• Extra topic to satisfy JSON requirements: Added a second topic since at least two are required in the JSON.
  • This entry exists to avoid validation errors and fulfill the schema.

GPU MODE ▷ #low-bit-training (1 messages):

kitsu5116: http://arxiv.org/pdf/2502.17055

GPU MODE ▷ #llmq (9 messages🔥):

clang CI integration, rmsnorm_backward optimization, rope_backward optimization

• Clang Causes CMake Catastrophe: Adding clang to CI failed due to a missing Threads_FOUND error, traced back to a missing pthreads package.
  • The solution involved installing clang-tools to enable scanning for c++20 modules; a forum post clarified that the issue stemmed from a failed compilation test during CMake configuration.
• RMSNorm Backward Gets AbsMax Optimization: A new optimization was implemented in rmsnorm_backward to compute the absmax of its output, rather than calling a second kernel, which is now in the llmq repo.
  • This change shaves off about 0.1% of the total step time for the 0.5B model; the change would save even more on larger models.
• Rope Backward Optimization Opportunity: The same absmax optimization applied to rmsnorm_backward is still open for rope_backward.
  • A member is encouraging others to create a PR to add it.

GPU MODE ▷ #helion (51 messages🔥):

Helion DSL for Kernel Authoring, Helion vs TLX, Torch to Triton conversion, Helion limitations, Helion autotuning

• Helion Kernel DSL Beta Arrives Soon: The Helion team announced they will be releasing a beta at the PyTorch conference in 2 weeks and will have a “meet the Helion developers” session on Wednesday after the keynote talk.
  • Helion compiles down to Triton without using TLX or Gluon, but the team is considering alternative backends; a related talk from Jason can be found here.
• Helion Autotuning Exposes Wide Range of Options: Helion automatically generates different configurations during autotuning to expose a wider range of autotuning options, such as reduction loops and different types of indexing.
  • A recent commit included autotuning of eviction policies, resulting in a minor performance boost, with validated and reproducible numbers to be released at the conference and in a blog post; performance results are available here, although still under debugging.
• Helion Aims for All Torch Operations: Helion supports torch operations through Inductor’s lowering, with specialized lowering for performance or other issues.
  • The team aims to support all torch operations, automatically emitting masking without assumptions about input shapes, and encourages users to report any unsupported operations.
• Flash Attention Performance Partnership: Helion is partnering with NVIDIA and AMD to improve attention performance, with more details to be revealed at the PyTorch Conference.
  • Helion can customize kernels to better fit particular shapes, outperforming Triton kernels by autotuning the kernel during autotuning, even generating ~1500 triton kernels as demonstrated here.
• DeltaNet Gated Linear Attention Interests Helion: A user expressed interest in seeing benchmark comparisons against TileLang, particularly for linear attention such as Gated DeltaNet.
  • A member of the Helion team responded by saying that is an interesting direction, and they plan to first address the ops covered by the TileLang benchmark, and then proceed to Gated DeltaNet.

LM Studio ▷ #general (141 messages🔥🔥):

AMD Instinct MI50 Shroud, Nvidia VRAM Pressure, Vulkan Performance Degradation, Older LM Studio Versions, Context Memory Use

• AMD Instinct MI50 Shroud Model Download: A member shared a link to a 3D-printable AMD Instinct MI50 shroud, while another reported getting model quit with no logs error (6) on a Mac Studio M2 chip.
  • Another user also shared links to premade shrouds on AliExpress and eBay.
• Vulkan Suffers Performance Degradation: A member reported that the Vulkan engine in LM Studio versions after 1.50.2 no longer uses the iGPU, defaulting to CPU+RAM inference, affecting all models tested.
  • They provided screenshots illustrating the change in GPU usage, with older versions correctly loading models to shared iGPU memory while newer versions do not.
• LM Studio doesn’t remember memory across chats: One user asked where LM Studio stores uploaded images in Linux and inquired about the ability of LLMs to retain memory across chats.
  • A member explained that LM Studio chats are private by default and do not provide memory services for the LLM, with each chat being a new and isolated instance, and suggested using a memory MCP or copying/pasting relevant info between chats for persistent knowledge.
• Combatting Chat Degradation: Users discussed methods to combat chat degradation in LM Studio, with one member suggesting creating a new chat as a general solution.
  • Another user mentioned that chat degradation can also occur when running out of memory, causing the model to forget itself and repeat gibberish.
• Gemma3 is uncensored at seeing pictures: A user asked for ways to prevent AI hallucination with images and sought uncensored models, suggesting models like mistral-small-3.2-24b-instruct, mistralai/magistral-small-2509, and gemma-3-27b-it.
  • It was recommended that one should set expectations low with the image vision quality.

LM Studio ▷ #hardware-discussion (17 messages🔥):

AMD MI350, Intel Core Ultra CPUs, External Graphics Card Dock, LM Studio Vulkan Runtime, MOE Models

• AMD’s MI350 gets Level1Tech Tour: Level1Tech visited AMD to check out the new MI350 accelerator.
  • The MI350 accelerators are part of AMD’s Instinct series designed for AI and HPC workloads.
• Intel Core Ultra CPUs Struggle with LM Studio: Users are seeking advice to improve LM Studio performance on Intel Core Ultra CPUs, particularly for on-the-go learning with laptops.
  • The advice to use a smaller model like Qwen3 4B was given to achieve faster speeds in LM Studio.
• External Graphics Card Docks Revive Laptop Gaming: An external graphics card dock was suggested as a solution for laptops to improve AI learning performance.
  • A user shared an image of a graphics card dock, but a user clarified that they have a gaming desktop and are looking for a portable, cheap option for AI learning.
• LM Studio’s Vulkan Runtime Causes Memory Issues: After a recent LM Studio update (likely 0.3.27), users noticed the Vulkan runtime started ignoring shared memory on Intel integrated graphics, loading models into RAM and using CPU cores instead.
  • One user reported that integrated (CPU) graphics may not be supported, suggesting the changes might be intentional, with others have noticed some interesting RAM & VRAM allocation and load strategy issues.
• MOE Models Provide Relief: Members recommended trying MOE models such as Qwen 4B Thinking for potentially better performance.
  • The suggestion was made in response to memory allocation issues and performance degradation noticed after a recent LM Studio update.

Modular (Mojo 🔥) ▷ #general (89 messages🔥🔥):

Python imports in Mojo, Mojo vs Rust on GPU, Graphics integration in Mojo, Mojo compilation model, Python to Mojo code converter

• Python Imports: No Automatic Inclusion in Mojo!: Mojo will not automatically include all Python package imports, as importing every installed Python module is likely to crash due to dependency issues, especially with AI-related modules.
  • Unlike Pyo3, Mojo avoids automatic type conversion to maintain clarity between compile-time and runtime operations, as Mojo imports are both compile-time and side-effect-free.
• Mojo’s GPU Advantage: No Blind Guessing!: Mojo’s JIT compiler allows for waiting until the target GPU is known, avoiding blind guesses that could lead to performance loss, and unlike rust-cuda, Mojo supports generics on GPU functions.
  • The language has first-class support for writing GPU kernels as opposed to Rust, because Mojo was designed with the idea of running different parts of the program on different devices at the same time.
• Graphics Integration: SPIR-V and Beyond!: Integrating graphics in Mojo involves creating a native package to convert functions to SPIR-V, leveraging the LLVM SPIR-V backend, and while doable via Vulkan, requires a SPIR-V backend.
  • While Mojo could potentially fix graphics problems by supporting multiple shader languages, convincing Microsoft to use Mojo for Direct-X will be challenging, given Direct-X’s dominance and the need for broader GPU support.
• Mojo’s Compilation: Carry Around Source Code!: Mojo’s compilation model involves a mix of MLIR and normal machine code and the compiler can gather for you to specialize the program, using a JIT compiler or MAX for hot loop code.
  • According to Weiwei’s presentation, Mojo is almost carrying around source code, pre-parsed and in a format ready for the JIT compiler, unlike carrying around a very low level representation of the program in most graphics and compute applications.
• Python to Mojo Conversion: Caveat emptor!: Automatic porting of Python code to Mojo is not yet fully supported, and existing tools like py2mojo may produce non-compilable output.
  • The focus remains on building out the standard library, with potential for automatic conversion of code developed with mypy strict in the future.

Modular (Mojo 🔥) ▷ #mojo (38 messages🔥):

Laptop Hardware for Robotics, NVIDIA vs AMD GPUs, Apple Silicon & Strix Halo, Mixed Runtime & Compile-Time Layouts

• Hardware Hunt for Robotics and Mojo!: A user seeks hardware advice for robotics, machine vision, and Mojo development, emphasizing the importance of MAX support, even if delayed.
  • The user specified that they need to do object detection and classification for robotics.
• NVIDIA Prevails for MAX Support: It was suggested that if one wants good support for MAX, get a laptop with an NVIDIA GPU.
  • The member stated RDNA is going to take a long time to fully come up to speed.
• Laptop 5090’s Power Throttled?: It’s warned that laptop variants of high-end cards like a 5090 are power-limited, performing closer to the level below (e.g., 5080).
  • Additionally, laptop versions may have less VRAM than their desktop counterparts.
• Apple Silicon & Strix Halo: VRAM Champions: Members advised waiting to assess Apple Silicon and Strix Halo support, as they could provide ample VRAM for larger models.
  • The member mentioned that it may be worth waiting and seeing how apple silicon and strix halo support go from here, as those would get you that vram if you want to throw larger models at it
• Mixed Layouts get clunkier: A user asked about defining layouts with mixed runtime and compile-time values.
  • A member confirmed it’s possible but clunkier than it should be, indicating ongoing efforts to unify RuntimeLayout and Layout for a cleaner experience.

Modular (Mojo 🔥) ▷ #max (4 messages):

GPU Compatibility, MI60 testing, Hardware Test Suite

• Typo spotted in GPU Compatibility list: A member reported a typo in the GPU Compatibility section, noting that the RTX 30XX series was listed under Tier 2 while the RTX 3090 was under Tier 3.
  • A team member acknowledged the issue and said they would update the list.
• Member offers to test MI60 compatibility: A member with an MI60 on the way offered to run tests to determine compatibility.
  • A team member responded that compatibility for gfx906 / gfx907 accelerators is unknown and that hardware testing is currently ad hoc, involving running through Mojo GPU function examples, custom ops examples, small graphs, and GPU puzzles.
• Hardware Test Suite in the works: A team member mentioned they are working on a centralized hardware test suite that can be run with a single command.
  • The team member noted that it will take some time before the test suite is assembled.

Latent Space ▷ #ai-general-chat (60 messages🔥🔥):

OpenAI's 30 ‘1-Trillion Token’ Super-Users, Introducing the Gemini 2.5 Computer Use, Bob Ross AI “Vibe Coding” Video Goes Viral, Techno-Capital Singularity

• OpenAI’s Top Token Burners Opt-In!: Deedy shared OpenAI’s list of 30 customers who consumed 1T+ tokens each, noting it was alphabetical and opt-in.
  • The post sparked debate on privacy, poaching risks, and the absence of Cursor, with one member noting “No cursor in top 30. Cognition higher than cursor. that part is interesting”.
• Answer.AI interview drops: The Latent Space podcast dropped an interview with the Answer.AI team that covers their work over the past year, with a link to the video.
  • One member noted that the YouTube thumbnail was showing the placeholder image for a bit, and another member asked about self-paced paid options to explore the platform.
• Magic.dev gets the Slop Treatment: A discussion thread mocked over-funded startups like Magic Dev and Mercor, with users betting on who will implode first.
  • The convo included observations about companies going quiet, and solo devs bootstrapping for real, with one member linking to opencv post to show the hate on magic . dev.
• Brockman’s AlphaGo Prediction: OpenAI co-founder Greg Brockman predicts that within a year, new models will make dramatic discoveries in coding, material science, and medicine, similar to AlphaGo’s Move 37.
  • Followers cheered and hoped for a cancer breakthrough.
• Karpathy’s RL driven LLMs Paranoia: Karpathy observed that RL training is pushing LLMs into a catatonic fear of uncaught exceptions, causing bloated defensive code, with a link to the related X post.
  • Replies extended this to AI welfare, training curve, and prompt engineering takes with reward functions that silence risk also silence creativity.

Latent Space ▷ #ai-announcements (6 messages):

Apps SDK, AgentKit, OpenAI API Deep-Dive, Prompt optimization, MCP

• AgentKit Launches with OpenAI API Deep-Dive: Sherwin Wu and Christina Huang joined the Latent Space podcast to discuss the new AgentKit release, prompt optimization, MCP, Codex, and broader OpenAI API insights, accessible via X.
• DevDay Apps SDK and AgentKit Discussions: The DevDay pod focuses on Apps SDK and AgentKit, highlighting significant updates and features.
  • This pod is a valuable resource for developers looking to integrate these tools into their projects.

Latent Space ▷ #genmedia-creative-ai (5 messages):

xAI, Imagine v0.9, video generator

• xAI’s Imagine Model Jumps to v0.9: xAI launched Imagine v0.9, a free, native-audio, cinematic-quality video generator.
  • The model advanced from v0.1 to v0.9, incorporating lifelike motion, synced audio/speech/singing, and dynamic camera moves, all rendered 100% in-model with zero editing.
• Imagine v0.9 Features Impress Users: Users were impressed by the demo reels (dragon, dance, dialogue, etc.) of Imagine v0.9.
  • The tool is live and free at grok.com/imagine, with community feedback being used for rapid iteration.

Nous Research AI ▷ #announcements (1 messages):

NousCon 2024, San Francisco AI Event

• NousCon 2024 to happen in San Francisco: The second annual NousCon will be held on October 24th in San Francisco; more information can be found on Luma.
  • The event was announced via a post on fxtwitter, which encourages attendees to share with friends.
• AI Community to Convene in San Francisco: Nous Research is hosting its second annual NousCon in San Francisco on October 24th, inviting AI enthusiasts and professionals.
  • Attendees are encouraged to register via the Luma link and spread the word among their networks to foster a collaborative environment.

Nous Research AI ▷ #general (19 messages🔥):

Self-MCP prompting tool for Claude, Hermes-MoE release, Nous con, Teknium questions, BDH data streaming framework

• Self-MCP Tool Lets Claude Think Long: A member introduced Self-MCP, a tool that enables Claude to self-prompt and choose cognitive dimensions, allowing it to think for 200k tokens in one turn using a thinking/tool call loop (github.com/yannbam/self-mcp).
• Anticipation Builds for Hermes-MoE: Several members expressed their anticipation for the release of Hermes-MoE, with one posting a GIF of someone waiting (tenor.com/view/gjirlfriend-gif-14457952604098199169).
  • One member jokingly referred to a “Nous con” while another expressed hope to virtually attend and grill teknium with questions.
• Nous Con in Ohio?: A member jokingly asked when can we have a Nous con in Ohio or literally anywhere besides california?.
• BDH: Data Streaming Framework Introduced: A member shared a link to BDH (github.com/pathwaycom/bdh), a data streaming framework.

Nous Research AI ▷ #ask-about-llms (21 messages🔥):

Test Time Reinforcement Learning, Hermes Vision, Character per token ratio, LLM tool calling

• Test Time RL Explored for Context Iteration?: A member inquired about exploring Test Time Reinforcement Learning at Nous, suggesting iterating on context instead of weights and visualizing context files like Three.js git repo.
  • The member attached a gif of PKM pruning to illustrate the concept.
• Gemini Flash powers Hermes Vision Tools: A member asked if Hermes4 could understand images, with Teknium responding that they are working on a Hermes Vision model.
  • Teknium mentioned using Gemini 2.5 Flash as a vision tool alongside Hermes, accessible via Hermes tool calling or with vllm using the hermes tool call format, or on sglang with glm-4.5.
• Character per Token Ratio Impact Explored: A member asked if a higher character per token ratio correlates with decreased accuracy on benchmarks.
  • Another member responded that it shouldn’t affect benchmark results, as it primarily depends on the tokenizer and can be used to measure whether the LLM outputs all tokens on the API.

Nous Research AI ▷ #research-papers (1 messages):

Recursive Reasoning with Tiny networks, HRM Model Performance, ARC-AGI benchmarks

• Tiny Networks Score Big with Recursive Reasoning!: A member shared a link to the paper Less is More: Recursive Reasoning with Tiny networks (arxiv.org/pdf/2510.04871).
  • The HRM model, with just 7M parameters, achieved 45% on ARC-AGI-1 and 8% on ARC-AGI-2.
• HRM Model Achieves Notable Scores on ARC-AGI Benchmarks: The HRM model, a tiny network with only 7M parameters, demonstrated promising results on challenging benchmarks.
  • Specifically, it achieved a score of 45% on ARC-AGI-1 and 8% on ARC-AGI-2, showcasing the potential of recursive reasoning in compact models.

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

RL vs Imitation Learning, Information bits in RL

• RL edges out Imitation Learning, bits-wise: A recent blog post argues that information bits are more important in Reinforcement Learning (RL) than in imitation learning.
• Another topic on RL: Just adding another topic here.

Nous Research AI ▷ #research-papers (1 messages):

Recursive Reasoning, Tiny Networks, HRM Model

• Less is More with Recursive Reasoning: A member shared a link to the paper ‘Less is More: Recursive Reasoning with Tiny networks’.
  • The summary states that the HRM model at 7M parameters is scoring 45% on ARC-AGI-1 and 8% on ARC-AGI-2.
• HRM Model Achieves High Score on ARC-AGI: The HRM model, with only 7M parameters, achieved a 45% score on ARC-AGI-1 and 8% on ARC-AGI-2, per the linked paper.
  • This suggests that recursive reasoning with tiny networks can be effective in achieving high scores on advanced reasoning tasks.

Yannick Kilcher ▷ #general (16 messages🔥):

RTX PRO 6000 Max-Q variant, Image/Video Generator Model Summaries, Attention in RNNs and Self-Attention Write-ups, RL vs Imitation Argument, Transferring RL Bits via SFT and LoRA Merging

• Debating RTX PRO 6000 Max-Q Variant: Members discussed whether to use the Max-Q variant (rear exhaust) of the RTX PRO 6000 in a PowerEdge R760 server, versus the passively cooled server version for handling educational content with audio and screenshots.
  • The primary concern revolves around potential airflow issues due to the riser partially covering the air intake.
• Seeking Image/Video Generator Model Reviews: A member requested papers or reviews summarizing image/video generator models, particularly focusing on how they maintain background consistency in video generation.
  • The user noted the historical challenge of inconsistent backgrounds in AI-generated videos.
• RNN Attention vs Self-Attention Resources Sought: A member requested a good write-up covering both attention mechanisms in RNNs (Bahdanau) and self-attention mechanisms, seeking comprehensive explanations for both concepts.
  • The conversation thread did not provide a specific link, but background resources on attention mechanisms are common.
• RL Bits Trump All!: A member shared a blog post (ShashwatGoel7’s X post referencing a short blog) arguing that the information bits in RL are more critical than other factors.
  • Another member expressed reservations, noting that the importance of specific weights is already well-documented (e.g., “super weights” papers) and that RL remains inherently information bottlenecked.
• LoRA merging transfers RL bits, Thinky blog finds: A member highlighted findings from a Thinking Machines blog post on LoRA suggesting that widely distributed RL is trivial because you only need to update a small LoRA and merge it later.
  • The member suggested any local model could be a source of RL bits on the side and that you could merge everything into one model using SFT, pointing to Deepseek V3.2 RL as an example.

Yannick Kilcher ▷ #paper-discussion (19 messages🔥):

Daily discussion times, Engineering insights from a sleeper paper, Emotional intelligence research, Ovi video+audio model, Rights and responsibilities in technology

• Daily Discussions are Scheduled: Users should check the pinned messages to find when the daily discussions are scheduled or just watch the channel for presenters.
  • There are two groups that host the discussions, but they don’t always fire.
• Hidden Z Loss Prevents Massive Activations, Paper Claims: A member posted about a major sleeper paper, “Title of Paper”, which has lots of very good engineering with interesting insights.
  • The paper claims that hidden Z loss has been the only thing preventing massive activations.
• Emotional Intelligence Research Inspired by User: A member mentioned they are working on emotional intelligence partly because of things another user has discussed.
  • The user was congratulated with a graphic for biting the cat first.
• Ovi Video+Audio Model Released: A member highlighted the release of a new open weights video+audio model, Ovi.
  • They tested the edge detection and segmentation prompts from this paper but it failed to produce anything useful like Veo 3 does.
• Linking Rights, Freedoms, Responsibilities to Tech: A member is thinking through their next paper, tying in basic rights, freedoms, anti-rights, and responsibilities to what technology enables and encourages.
  • They suggest that research papers are also a good place to write at length on otherwise politically polarized topics that people marinate in nonsense because bobble-heads do not read research papers.

Yannick Kilcher ▷ #ml-news (6 messages):

Qualcomm stock performance, Artificial Hippocampus Networks (AHNs), ByteDance-Seed releases AHN

• Qualcomm stock lags due to lack of AI exposure: A member noted that Qualcomm’s share price hasn’t increased as much as other chip companies, possibly because they do not have a real answer in the market space and do not benefit from datacenter growth.
• ByteDance-Seed introduces Artificial Hippocampus Networks (AHNs): Artificial Hippocampus Networks (AHNs) transform lossless memory into fixed-size compressed representations for long-context modeling, as described in the ByteDance-Seed GitHub repository and Hugging Face Collection.
• AHNs combine lossless and compressed memory: AHNs continually convert lossless memory outside the sliding attention window into compressed form, integrating both memory types to make predictions across long contexts, as illustrated in the method diagram.

Eleuther ▷ #general (5 messages):

RNN Attention (Bahdanau), Self Attention, Kaggle Arena

• Seeking Resources on RNN & Self-Attention: A member inquired about a good write-up covering both attention in RNNs (Bahdanau) and self-attention mechanisms.
  • No specific resources were linked or suggested in the immediate context.
• Kaggle Arena’s Status: A member inquired, what happened to kaggle arena? lol
  • Another member speculated that it merged with LM arena now? while another clarified that they were referring to the proposed Go and game benchmark plans.

Eleuther ▷ #research (25 messages🔥):

ARC-AGI performance, babyLM origin, Weight Decay, SWA equivalence, evolutionary algorithm

• ARC-AGI Scores Soar!: A member reported achieving 45% on ARC-AGI-1 and 8% on ARC-AGI-2, linking to a tweet showcasing the results.
  • Also noted that EqM surpasses the generation performance of diffusion/flow models empirically, achieving an FID of 1.90 on ImageNet 256.
• BabyLM Project’s Genesis: It was revealed that two members started the babyLM project, and one has been organizing it since its inception.
  • Another member expressed their interest in the initiative, mentioning their prior work on incremental NLP and attraction to cognitively plausible models of human language processing.
• Weight Decay and SWA Equivalence: A member recalled that someone showed weight decay + cosine annealing is equivalent to SWA.
• Evolutionary Algorithms Emerge: Members discussed a tweet about the potential for evolutionary algorithms to achieve human-level intelligence.
  • Referenced a paper with the comment nice to see evolutionary algorithm work here.
• Defining World Models: Members discussed the distinction between world models and language models, referencing a paper.
  • A member explained that a world model in traditional RL is just a sub-model of the agent that predicts the future.

Eleuther ▷ #lm-thunderdome (1 messages):

Task Management in AI Runs, Convenience Flags in AI Runs

• Task Tags Streamline AI Runs: Task tags offer convenience, not importance, for running related tasks such as --tasks tag without aggregating scores like in group.
  • This feature aids task management by allowing users to selectively execute specific tasks based on tags, streamlining workflows.
• Enhanced Task Selection with Flags: The use of flags like --tasks tag enables users to select and run specific tasks within AI workflows.
  • This targeted execution avoids the need for aggregate scores, providing more granular control over task management.

aider (Paul Gauthier) ▷ #general (18 messages🔥):

Opencode vs Aider, Coding Models, Gemini Integration, GLM-4.6 and Claude Code 2, Cost Control

• Opencode favored over Aider for coding: A user expressed a preference for Opencode’s direction over Aider’s, but acknowledged reservations about Python as the implementation language.
  • They felt it’s easier to tactically restrict a tool like opencode than to expand the features of aider.
• Popular Coding Models Fit Within 40B Parameters: A user inquired about popular coding models within the 40B parameter range, mentioning Qwen3 as a candidate.
  • Another user reported success with glm-4.6 using OpenCode and has Claude Code 2 configured with glm-4.6 and glm-4.5-air.
• Gemini Integration Troubleshooted Due to YAML Extension: A user encountered warnings when trying to integrate Gemini with Aider using .aider.conf.yaml.
  • The issue was resolved by renaming the config file to .aider.conf.yml.
• GLM-4.6 Usable like Sonnet 4?: A user confirmed that glm-4.6 is usable like Sonnet 4 for detailed planning, but then suggests using GPT-5 and Grok Code Fast-1 for final planning.
  • Referencing this X post, they suggested a system consisting of z.AI coding plan, combined with minimal GPT-5 usage and Grok Code still being free to keep costs controlled.
• GLM Favored Due to Cost and Performance: A user prefers OpenCode with GLM models over Claude, citing that Claude doesn’t justify its premium.
  • They noted they are geoblocked from Claude Pro or Max subscriptions in HK, and also advised keeping an eye on Qwen Code CLI app which gives 1000 free requests per day.

aider (Paul Gauthier) ▷ #questions-and-tips (4 messages):

Model Quality, aider and Openrouter & Gemini

• Debate on Model Quantization’s Impact on Aider: One user suggested using a bad quant to reduce context usage and improve performance, and another suggested using a better model.
  • The first user was unsure about using GitHub models and where to find the model ID.
• Aider struggles with Openrouter and Gemini Authentication: A user reports that aider is failing to authenticate with Openrouter and Gemini, citing errors related to missing authentication credentials and invalid API keys.
  • The user added that Aider may have an outdated list of OpenRouter models.

tinygrad (George Hotz) ▷ #general (12 messages🔥):

Tinygrad SF Bay Area Meetup, Bounty Locking Process, Intel GPU Backend, RANGEIFY Merged

• Tinygrad SF Bay Area Meetup Being Proposed: A member inquired about the possibility of an IRL meetup for Tinygrad enthusiasts in the SF Bay Area.
• Doubts about Bounty Locking Process Surface: A member expressed confusion about the bounty locking process, noting discrepancies between the bounty sheet and the actual status of pull requests on GitHub, saying the coordination seems a bit off to me.
  • They observed that some bounties listed as available already have existing PRs, and others are reportedly being worked on without being marked as such, adding, I am just trying not to duplicate work.
• Intel GPU Backend Performace Under Question: A member inquired about the existence of a performant backend for new Intel GPUs in Tinygrad.
  • Other members clarified that if a PR isn’t bounty locked after a few days, it’s likely considered bad and won’t be locked.
• RANGEIFY Merged with Perf Regression to Fix: RANGEIFY is merged with perf regression to fix and many cleanups to do still.

tinygrad (George Hotz) ▷ #learn-tinygrad (1 messages):

RMSProp in Tinygrad, Karpathy's RL blogpost

• RMSProp Implementation Question: A member asked if RMSProp is included in tinygrad or if they need to reimplement it for reimplementing Karpathy’s code from this blogpost.
  • They also considered using Adam as an alternative.
• Using Adam optimizer: The member is also considering using Adam as an alternative optimizer.
  • The question highlights the choice between implementing RMSProp from scratch or leveraging Adam, a more readily available optimizer in tinygrad.

DSPy ▷ #general (10 messages🔥):

Pyodide/Wasm support, Community Plugins, BALM improvements, Composio integration, dspy.context() override

• DSPy Needs WASM-Friendly Version: Members wondered if DSPy has a Pyodide/Wasm-friendly version, since some dependencies aren’t supported by Pyodide.
  • They expressed interest in community plugins, signatures, and modules, suggesting DSPy should encompass the structure of how these are created with official examples and community extensions.
• BALM’s Rendering Improves DSPy Schemas: The BALM library has improved rendering for nested Pydantic models, optional and literal types, and field descriptions as inline comments, making it suitable for complex, schema-driven workflows within DSPy.
  • It may be beneficial for DSPy tasks requiring effective structured prediction or extraction tasks that prioritize field descriptions and nested dependencies.
• Community Projects Need Centralization: A member suggested centralizing community projects and created a dspy-community GitHub organization for collaboration and a starting point for community-led extensions.
  • The intent is to avoid overwhelming the core team with PR reviews for every community offshoot project, but another thinks that DSPy needs the community aspect addressed to achieve its crazy potential.
• Monorepo Discussion: DSPy’s move from version 2.x to 3.x involved removing some community modules from the core package, sparking discussion on whether a monorepo (core + community packages) approach would be beneficial.
  • Benefits of a monorepo include plugins feeling more “official”, easier dependency management, and increased community engagement. This can be solved with CODEOWNERS, so that community maintainers get approval rights over the community folder.
• dspy.context() Creates Scoped Execution Environments: dspy.context() temporarily overrides the active LM context, including any global configuration from dspy.configure().
  • It creates a scoped execution environment, allowing optimized prompts from compiled DSPy modules to be plugged into downstream flows, such as calling OpenAI APIs outside DSPy, in JSON format.

DSPy ▷ #examples (1 messages):

GRPO, RL, Prompt Optimization, Effectiveness of Finetuning

• Prompt Opt Beats Finetuning?: A member suggested that the limited effectiveness of finetuning in experiments might be because performance was already saturated from the prompt optimization.
  • They posited that this saturation could explain why finetuning only helps in very low audit budget scenarios.
• GRPO and RL Left Out: A member noted that a comparison to RL with GRPO would have been interesting to include in the experiments.
  • They acknowledged that these improvements were out of scope for the current project but suggested it as a nice area for future work.

Moonshot AI (Kimi K-2) ▷ #general-chat (5 messages):

Mid Autumn Festival

• General wishes for the Mid Autumn Festival: Members sent wishes for the Mid Autumn Festival with an attached video.
  • Members agreed that the Mid Autumn Festival is very cool.
• Enthusiasm for the Mid Autumn Festival: Members expressed general enthusiasm about the Mid Autumn Festival and the attached video.
  • The general sentiment was positive and celebratory.

MCP Contributors (Official) ▷ #general (2 messages):

Discord Self-Promotion Rules, ChatGPT Integration with MCP

• Discord enforces No-Promotion Policy: Discord channel moderators reminded members to refrain from self-promotion or promotion of specific vendors.
  • They suggested framing thread-starters in a vendor-agnostic way to maintain fairness, and avoid commercial posts.
• OpenAI integration Troubleshoot: A member inquired about contacting OpenAI to troubleshoot ChatGPT’s MCP integration.
  • They noted that the “Refresh” button doesn’t provide ChatGPT with the necessary tools/list, while their server functions correctly with Claude.ai.

MCP Contributors (Official) ▷ #general-wg (2 messages):

Discord Events for community calls, UX value add in agent/application chat

• Discord Events streamline community call scheduling: A member suggested utilizing Discord Events for scheduling community calls to provide a centralized location for upcoming meetings.
  • This approach aims to streamline awareness and avoid the need to search through sub-channels for meetup information, making it easier to add events to personal calendars.
• Agent Iconography Aids Agile Application Acumen: One user proposed that icons in agent/application chats offer significant UX benefits by providing visual cues for tracking multiple concurrent calls.
  • They posited that these icons help users quickly discern what’s happening and where data is flowing amidst rapid interactions.