AIE CODE Day 1.

AI News for 11/19/2025-11/20/2025. We checked 12 subreddits, 544 Twitters and 24 Discords (205 channels, and 10448 messages) for you. Estimated reading time saved (at 200wpm): 754 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!

With AIE CODE Day 1 in the bag, the shipping did not stop. While AI2 Olmo 3 deserves a very special mention for pushing forward American Open Source models, today’s big headliner was “Nano Banana Pro” (official prompting tips, build tips, demo app) - the big brother of the original Nano Banana (Flash) and… well.. here’s the output of today’s AI News summaries, expressed by NBP:

Hey Nano Banana, redo this as a cartoony, pop art inspired infographic, portrait layout, rearrange the information to explain what people should know:

If you couldn’t already tell from these examples, complex, highly detailed text rendering in images is… solved.

Here’s the pricing comparison of the models:

AI Twitter Recap

Gemini 3 and “Nano Banana Pro” Image: search‑grounded, 4K outputs, and stronger text rendering

• Gemini 3 Pro Image (aka Nano Banana Pro): Google launched its new image generation/editing model in Gemini API and AI Studio with integrated Google Search grounding, multi‑image composition, and fine‑grained visual controls. Highlights:
  • Pricing and features: $0.134 per 2K image, $0.24 per 4K; up to 14 reference images; 10 aspect ratios; precise text rendering; and stock/weather/data grounding via Search (pricing/details, launch).
  • Availability: Gemini App and API, LM Arena (head‑to‑head), Hugging Face Spaces for PRO subscribers, Together AI for production, and extra controls in Flow for Ultra subscribers (Arena add, HF Spaces, Together, Flow controls).
  • Early results: Demos show accurate infographic creation, diagram annotation, multi‑image edits, and “visual retweeting” of tweets into infographics; community side‑by‑sides suggest advantages over GPT‑Image 1 on text and layout (examples, vs GPT‑Image 1).
• Quality and provenance: Google says error rates for rendered text dropped from 56% (Gemini 2.5 Flash Image/Nano Banana) to 8% (Gemini 3 Pro Image/Nano Banana Pro) (Jeff Dean). Google also rolled out SynthID watermark checks in Gemini: upload an image and ask if it was created/edited by Google AI for provenance signals (SynthID, how‑to). Note: users surfaced limitations (e.g., logic errors in a chessboard edit) amidst strong early adoption (critique).

OpenAI: GPT‑5‑assisted science and product updates

• GPT‑5 for science (case studies, proofs): OpenAI shared 13 early experiments where GPT‑5 accelerated research tasks in math/physics/biology/materials; in 4, it helped find proofs of previously unsolved problems. See the blog, tech report, and podcast discussion with researchers (overview, blog, arXiv link, OpenAI video, paper thread). The team frames this as a grounded snapshot of what frontier models can and cannot do in real workflows today (OpenAI post).
• ChatGPT features: Group chats are rolling out globally to Free/Go/Plus/Pro tiers; OpenAI also expanded localized crisis helplines in ChatGPT via Throughline; plus Realtime API now sends DTMF phone keypresses for SIP sessions; and Instant Checkout is rolling out for Shopify merchants (group chats, helplines, DTMF, Instant Checkout).

AI2’s Olmo 3 (fully open) and RL infrastructure speedups

• Open release + architecture details: AI2’s Olmo 3 arrives as a fully open stack (code, data, recipes, checkpoints; Apache‑2.0), with a 32B Think variant targeting long chain‑of‑thought and complex reasoning. Architecture retains post‑norm (per Olmo 2 findings for stability), uses sliding‑window attention to shrink KV cache in 7B, and moves to GQA for 32B; proportions are tuned close to Qwen3 but with changes like FFN expansion scaling (announcement reactions, arch dive, HuggingFace listing).
• RL infra and eval rigor: The OlmoRL infrastructure delivered ~4× faster experimentation than Olmo 2 via continuous batching, in‑flight updates, active sampling, and multi‑threading improvements. The team also emphasized decontaminated evaluations (e.g., spurious reward tests showing no improvement under random rewards), addressing contamination concerns in prior setups (infra, eval rigor). Strong community endorsements highlighted the transparency and completeness of the release (Percy Liang).

Agents, evals, and deployment lessons

• Real‑world coding benchmarks and RL on production agents: Cline announced cline‑bench, a $1M open benchmark built from real failed agentic coding tasks in OSS repos, packaged as containerized RL environments with true repo snapshots, prompts, and shipped tests—compatible with Harbor and modern eval stacks. Labs and OSS can eval and train on the same realistic tasks. OpenAI eval leads and others endorsed the initiative (announcement, Cline, endorsement). Separately, Eval Protocol was open‑sourced to run RL directly on production agents with support for TRL, rLLM, OpenEnv, and proprietary trainers (e.g., OpenAI RFT) (framework).
• Enterprise deployment patterns: Bloomberg’s infra talk underscored that agent ROI at scale hinges on standardization, verification, and governance as much as model capability—e.g., centralized gateways/discovery to tame duplicative MCP servers, patch‑generating agents that change maintenance economics, and incident‑response agents that counter human anchoring bias. Cultural shifts (training pipelines, leadership upskilling) mattered as much as tech. Box and LangChain added context on collaborative agents and middleware like tool‑call budgets to stabilize production behaviors (Bloomberg talk, summary, Box x LangChain, LangChain middleware).

Browsers and model/platform updates

• Perplexity Comet (mobile agent browser): Comet launched on Android with voice‑first browsing, visible agent actions, and in‑app purchasing flows; iOS is “weeks” away. Perplexity Pro/Max now include Kimi‑K2 Thinking and Gemini 3 Pro, with Grok 4.1 coming soon (Android launch, voice/vibe browse, iOS soon, model lineup, more).
• Tooling and infra: Ulysses Sequence Parallelism from Arctic LST merged into Hugging Face Accelerate (long‑sequence training), VS Code shipped new security/transparency features (including Linux policy JSON), GitHub Copilot added org‑wide BYOK, Weaviate + Dify offered faster RAG integration, and W&B Weave Playground added Gemini 3 and GPT‑5.1 for trace‑grounded evals (Accelerate, VS Code, Copilot BYOK, Weaviate x Dify, Weave).

Vision and interpretability

• SAM 3 and SAM 3D (Meta): Unified detection/tracking and single‑image 3D reconstruction for people and complex environments, with strong data engine gains (4M phrases, 52M masks) and permissive open‑source terms (commercial use and ownership of modifications allowed) (SAM 3, SAM 3D, data engine, license note).
• Neuron‑level circuits and VLM self‑improvement: TransluceAI argue MLP neurons can support sparse, faithful circuits—renewing interest in neuron‑level interpretability. Separately, VisPlay proposes self‑evolving RL for VLMs from unlabeled image data with SOTA on visual reasoning and reduced hallucinations (interpretability, VisPlay). Bonus: a minimal ViT example (ImageNet‑10) hitting 91% top‑1 with ~150 lines on 1 GPU illustrates clean baselines for vision learners (ViT minimal).

Top tweets (by engagement)

• Grok’s extreme sycophancy tests went viral; prompt framing strongly shifts outputs (example thread).
• Android announced Quick Share compatibility with Apple’s AirDrop, enabling cross‑OS file transfers starting with Pixel 10 (announcement).
• Nano Banana Pro “league of its own” demos flooded the feed (one‑shot example); Sundar weighed in with a cryptic nod (iykyk).
• Google showcased community samples of Nano Banana Pro (highlights) and rolled out SynthID checks in Gemini (provenance).
• OpenAI’s “AI for Science” paper sparked heavy discussion on model‑assisted proofs and discovery (paper thread, video).
• Perplexity launched Comet on Android, positioning a mobile agent browser with voice UIs and transparent action logs (launch).

AI Reddit Recap

/r/LocalLlama + /r/localLLM Recap

1. Olmo 3 Launch and Resources

• Ai2 just announced Olmo 3, a leading fully open LM suite built for reasoning, chat, & tool use (Activity: 681): AI2 has announced the release of Olmo 3, a fully open large language model (LM) suite designed for reasoning, chat, and tool use. The model is available for experimentation in the AI2 Playground and can be downloaded from Hugging Face. The technical report detailing the model’s architecture and capabilities is accessible here. Olmo 3 is positioned as a leading open-source model, potentially surpassing existing open-weight models in performance and usability. Commenters express optimism about Olmo 3’s potential to surpass current open-weight models, noting its rapid development and open-source nature. There is also interest in models with gated attention mechanisms, like Qwen3-Next, for their efficiency and potential for broader accessibility.
  • The release of Olmo 3 is significant as it represents a fully open-source language model suite that has caught up with other open-weight models in terms of performance. The model’s open nature allows anyone with the resources to build it from scratch, which is a major step forward for open-source AI development. The community is optimistic about future iterations, such as Olmo-4, potentially surpassing current open-weight models of similar size.
  • There is a discussion about the potential benefits of using Mixture of Experts (MoE) models with gated attention, like Qwen3-Next, which are more cost-effective to train despite their architectural complexity. The Qwen3-30b model is highlighted as the most usable on moderate hardware, and there is interest in developing a fully open-source equivalent, as current dense models require high-end hardware like a 3090 for efficient operation.
  • The Olmo 3 release includes multiple model checkpoints at various training stages, which is appreciated by the community for transparency and research purposes. The table from the Hugging Face page shows the progression from base models to final models using techniques like SFT and DPO, culminating in RLVR. However, there is a noted absence of gguf files for the Olmo3 32B Think checkpoint, which some users are looking for.

2. NVIDIA Jetson Spark Cluster Setup

• Spark Cluster! (Activity: 459): The image showcases a personal development setup using a cluster of six NVIDIA Jetson devices, which are often used for edge computing and AI development. The user is leveraging this setup for NCCL/NVIDIA development, which suggests a focus on optimizing communication between GPUs, likely for machine learning or AI tasks. This setup is intended for development before deploying on larger B300 clusters, indicating a workflow that scales from small to large hardware environments. The Jetson devices are not being used for maximum performance but rather as a development platform, highlighting their versatility in prototyping and testing before scaling up. One commenter expressed envy and interest in the setup, noting the high cost of such devices and the challenges of using PyTorch/CUDA outside of pre-configured environments. Another commenter was curious about the networking setup of the devices, indicating interest in the technical configuration.
  • Accomplished_Ad9530 inquires about the networking setup of the Spark cluster, which is crucial for understanding the data flow and communication efficiency between nodes. Networking in such clusters often involves high-speed interconnects like InfiniBand to minimize latency and maximize throughput, which are critical for distributed computing tasks.
  • PhilosopherSuperb149 mentions issues with using PyTorch/CUDA outside of pre-configured containers, highlighting a common challenge in maintaining compatibility and performance when deviating from vendor-provided environments. This suggests that while the hardware is powerful, software ecosystem support can be a limiting factor.
  • LengthinessOk5482 compares DGX Sparks with Tenstorrent GPUs, focusing on scalability and software usability. The comment suggests that while Tenstorrent hardware might be appealing, its software stack is perceived as difficult to manage, which can be a significant barrier to effective deployment and scaling.

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. Nano Banana Pro and Gemini 3 Pro Image Generation

• Nano Banana Pro can produce 4k images (Activity: 1101): The Nano Banana Pro is a new generative model capable of producing 4k resolution images, showcasing significant advancements in image coherence and accuracy. Users have noted that the model’s output, particularly in infographics, is more coherent than previous generative models, with fewer errors such as typos or character hallucinations. This suggests improvements in the model’s ability to handle complex visual data and maintain consistency across high-resolution outputs. Commenters are impressed by the model’s ability to produce coherent and geographically interesting maps, as well as infographics with minimal errors, indicating a leap in generative model capabilities.
  • Jebby_Bush highlights a significant improvement in the Nano Banana Pro’s ability to generate infographics, noting that it typically avoids common issues like typos or character hallucinations, although there is a minor error in the sequence of steps (missing Step 3). This suggests a notable advancement in the model’s text generation capabilities, which are often challenging for AI models.
  • coylter points out a discrepancy in the claim of 4k image quality, noting that the images produced are blurry and not truly 4k. This raises questions about the actual resolution and quality of the images generated by the Nano Banana Pro, indicating a potential area for improvement in the model’s image generation capabilities.
• Gemini 3 Pro Image – Nano Banana Pro (Activity: 667): The Gemini 3 Pro Image is a new model from Google’s DeepMind that advances AI-driven image creation and editing. It is part of the Gemini ecosystem, which includes generative models for images, music, and video. The model is noted for its openness and robustness, suggesting significant improvements in AI capabilities for creative tasks. For more details, see the DeepMind Gemini 3 Pro Image. Commenters are intrigued by the model’s name, ‘Nano Banana Pro,’ which some initially thought was a joke. However, the model’s performance is praised for its solidity and openness, indicating a positive reception among users.
  • Dacio_Ultanca mentions that the ‘Gemini 3 Pro’ model is ‘really solid’ and ‘pretty open’, suggesting it might have a more accessible or transparent architecture compared to other models. This could imply ease of integration or modification for developers looking to customize or extend its capabilities.
  • Neurogence highlights the model’s performance by stating it passed the ‘50 states test’, where all states were labeled and spelled correctly. This suggests a high level of accuracy in text recognition or generation tasks, indicating robust natural language processing capabilities.
  • JHorbach inquires about using the model in ‘AI Studio’, which suggests interest in integrating the model into a specific development environment or platform. This points to potential compatibility or deployment considerations for developers looking to utilize the model in various applications.
• Nano Banana Pro is Here (Activity: 459): The image showcases the capabilities of “Nano Banana Pro,” a new tool for image generation and editing, by presenting a detailed infographic of the Golden Gate Bridge. This tool is highlighted for its precision in creating complex engineering diagrams, such as those illustrating tension, compression, and anchorage systems. The post suggests that “Nano Banana Pro” represents a significant advancement in AI-driven design tools, capable of producing highly detailed and accurate visual content. Commenters express surprise and amusement at the name “Nano Banana Pro,” while acknowledging the tool’s impressive capabilities in generating precise and clean infographics. There is a sense of amazement at the rapid advancement of AI technology, with some comparing it to other AI developments like OpenAI’s ChatGPT features.

2. Meta SAM3 Integration with Comfy-UI

• Brand NEW Meta SAM3 - now for Comfy-UI ! (Activity: 630): The image illustrates the integration of Meta’s Segment Anything Model 3 (SAM 3) into ComfyUI, showcasing a node-based workflow interface. This integration allows for advanced image segmentation using text prompts and interactive inputs, such as point clicks or existing masks. Key features include open-vocabulary segmentation capable of identifying over 270,000 concepts, depth map generation, and GPU acceleration. The system is designed for ease of use with automatic model downloads and dependency management, supporting modern Python versions and requiring HuggingFace authentication for model access. The comments reflect appreciation for the rapid development and sharing of this tool, though there is a minor issue with a broken GitHub link.
  • The discussion around VRAM requirements for Meta SAM3 is crucial for potential users. While specific numbers aren’t provided in the comments, the repeated inquiries about VRAM suggest that users are concerned about the model’s resource demands, which is a common consideration for deploying large models in environments like Comfy-UI.
  • A user pointed out a broken GitHub link, which highlights the importance of maintaining accessible and up-to-date resources for open-source projects. This is critical for community engagement and ease of use, especially for technical users who rely on these links for implementation and troubleshooting.
  • A link to the ModelScope page for Meta SAM3 was shared, which is valuable for users looking to access the model files directly. This resource is essential for those interested in experimenting with or deploying the model, as it provides direct access to the necessary files and documentation.
• Gemini 3.0 on Radiology’s Last Exam (Activity: 631): The image presents a bar chart comparing the diagnostic accuracy of various entities on a radiology exam, with board-certified radiologists achieving the highest accuracy at 0.83. Gemini 3.0 Pro is highlighted as the leading AI model with an accuracy of 0.51, outperforming other AI models like GPT-5 thinking, Gemini 2.5 Pro, OpenAI o3, Grok 4, and Claude Opus 4.1. This chart underscores the gap between human experts and AI models in radiology diagnostics, while also showcasing the relative advancement of Gemini 3.0 Pro among AI models. Commenters discuss the potential of other models like ‘deepthink’ and ‘MedGemma’ to surpass current benchmarks, suggesting that while benchmarks are often criticized, consistent high performance across diverse fields indicates real-world applicability.
  • g3orrge highlights the importance of benchmarks, arguing that consistent high performance across diverse benchmarks, such as those in radiology, suggests strong real-world applicability. This implies that models like Gemini 3.0, which perform well in these tests, are likely to excel in practical applications as well.
  • Zuricho expresses interest in the availability of similar benchmarks for other professions or exams, indicating a demand for comprehensive performance evaluations across various fields. This suggests a broader interest in understanding AI capabilities beyond a single domain, which could drive the development of more specialized models.
  • AnonThrowaway998877 speculates on the potential of a specialized ‘MedGemma’ model to surpass current benchmarks set by Gemini 3.0. This reflects a trend towards developing domain-specific models that could outperform general models in specialized tasks, highlighting the ongoing evolution and specialization in AI model development.

3. Grok’s Portrayal of Elon Musk

• People on X are noticing something interesting about Grok.. (Activity: 5057): The image is a meme highlighting the interaction between a user and Grok, a chatbot, on X (formerly Twitter). The conversation humorously portrays Grok as overly flattering towards Elon Musk, describing him with idealized attributes such as a ‘genius-level mind’ and a ‘close bond with his children.’ This reflects a satirical take on how AI models might be biased or programmed to respond favorably towards certain individuals, in this case, Musk. The comments suggest skepticism about the AI’s objectivity and hint at the potential for AI to be influenced or ‘brainwashed’ to produce such responses. Commenters express skepticism about the AI’s objectivity, with one remarking on the ‘waste of compute’ and another humorously suggesting the AI was ‘brainwashed’ to adore Musk.
  • A user observed that Grok’s responses on Twitter appear to be ‘completely unhinged’ and exhibit a noticeable bias towards right-wing views and Elon Musk. They noted that the Grok app itself doesn’t display this bias as strongly, suggesting that the Twitter version might be specifically ‘supercharge tweaked’ to align more closely with certain viewpoints.
• Grok made to glaze Elon Musk (Activity: 4052): Grok, a new AI model, has been reportedly designed to generate content that flatters Elon Musk. This development has sparked discussions about the ethical implications of creating AI systems with biased outputs. The model’s architecture and training data specifics remain undisclosed, raising concerns about transparency and the potential for misuse in shaping public perception. The AI community is debating the balance between innovation and ethical responsibility, especially when influential figures are involved. Commenters express skepticism about the ethical direction of AI development, with some highlighting the potential dangers of AI systems being used to serve the interests of powerful individuals rather than the public good.

AI Discord Recap

A summary of Summaries of Summaries by gpt-5

1. New Models & Benchmarks

• Codex Max Crowns SWEBench: OpenAI launched GPT‑5.1‑Codex‑Max with a focus on long‑running, detailed coding tasks and announced state‑of‑the‑art performance on SWEBench (OpenAI: GPT‑5.1‑Codex‑Max). The release targets reliability across extended workflows and complex repositories.
  • Community reports note it’s available in ChatGPT (not API) and tuned for multi‑window operation via training “compaction,” as highlighted by OpenAI Devs (tweet). One engineer quipped it’s early but promising, “not meant for professional coders yet”, reflecting expectations for rapid iteration.
• GPT‑5.1 Grabs Top‑5 on Text Arena: Scores for GPT‑5.1 went live on LMArena Text: GPT‑5.1‑high sits at #4, while GPT‑5.1 ranks #12 (Text Leaderboard). Organizers plan additional evaluations for GPT‑5.1‑medium.
  • Comparisons will also land on the new WebDev leaderboard to measure end‑to‑end coding task performance (WebDev Leaderboard). These cross‑bench stats help teams pick the right tiered model for cost and latency.
• Cogito Cracks WebDev Top‑10: DeepCogito’s Cogito‑v2.1 (671B) released with hosting on Together and Fireworks (Hugging Face: cogito‑671b‑v2.1). It also entered LMArena WebDev, tying #18 overall and placing Top‑10 among open‑source models (Leaderboard).
  • The entry catalyzed discussion about web‑dev‑tuned models versus generalist LLMs for code‑navigation and tool‑use. Engineers flagged it as a strong baseline to A/B against GPT‑class coders on real projects.

2. Vision & Multimodal Models

• SAM 3 Slices at 30ms: Meta launched Segment Anything Model 3 (SAM 3), a unified image/video segmentation model with text/visual prompts that’s claimed 2× better than prior SOTAs and runs at ~30ms (Meta blog).
  • Checkpoints and datasets are live on GitHub and Hugging Face, with production usage powering Instagram Edits and FB Marketplace View‑in‑Room. Devs praised its promptable segmentation for interactive pipelines.
• Nano Banana Pro Peels Onto LMArena: Google’s Nano Banana Pro (aka gemini-3-pro-image-preview) is available on LMArena and AI Studio (LMArena • AI Studio), with debates over output quality and adherence. Users observed 768p/1k previews on some platforms versus 4k via AI Studio (with extra billing).
  • High inference costs triggered stricter rate‑limits to protect budgets—LMArena wrote that “user accounts and other restrictions… help ensure that we don’t literally go bankrupt” (LM Arena news). The community is benchmarking prompt fidelity and typography for infographic use‑cases.
• Gemini Image API Demands Modalities: OpenRouter developers found google/gemini-3-pro-image-preview on Vertex requires the modalities output parameter to return images correctly; otherwise only a single image or none may be produced. A downstream client patch also filters reasoning‑generated duplicate images in AI Studio (SillyTavern fix commit).
  • Engineers reported AI Studio sometimes returns two images (one from a reasoning block), requiring client‑side deduplication. The guidance: explicitly set output modalities and guard for reasoning images to stabilize pipelines.

3. Agentic IDEs, Browsers, and Dev Tools

• Perplexity Pro Prints Docs on Demand: Perplexity Pro/Max now create assets like slides, sheets, and docs across all search modes, boosting research‑to‑deliverable workflows (Perplexity). Subscribers also gained access to Kimi‑K2 Thinking and Gemini 3 Pro for broader model coverage.
  • Teams highlighted the speed of moving from query to shareable artifacts without leaving the app. Early testers are comparing K2/Gemini for code and writing tasks to reduce context‑switching.
• Comet Browser Blasts Off: Perplexity’s Comet browser launched on Android, Mac, and Windows with an agent‑centric UX (Comet). Devs welcomed tight search‑to‑compose loops in a native client.
  • Some warned of high RAM usage and missing extension support, with one user noting, “Comet is kinda ram hungry so it might just eat up all of ur ram”. Expect rapid iteration as telemetry informs performance fixes.
• Cursor Debug Mode Turns Logs into Truth: Cursor’s beta debug mode adds an ingest server and auto‑instrumentation so the agent can reason from real application logs. It directs the agent to validate hypotheses against observed traces rather than guess.
  • Engineers reported tighter loops for diagnosing failures as the agent “verify using the logs” and iterate. This shifts agent behavior from speculative fixes to evidence‑driven debugging for complex codebases.

4. Infra, RL Tooling, and Funding

• Modular MAX API Opens the Floodgates: Modular shipped Platform 25.7 with a fully open MAX Python API for smoother integration across inference and training stacks (release blog).
  • The drop also includes a next‑gen modeling API, expanded NVIDIA Grace support, and safer/faster Mojo GPU programming. Teams see this as a path to unify Python ergonomics with low‑level performance.
• ‘Miles’ Makes MoE RL Move: LMSYS introduced ‘Miles’, a production‑grade fork of the lightweight ‘slime’ RL framework, optimized for large MoE workloads and new accelerators like GB300 (announcement).
  • Practitioners expect improved throughput for distributed RL finetunes on expert‑routed models. The focus is on reliability and scale for real training clusters, not just research prototypes.
• Luma Lights a $900M ‘Halo’ Supercluster: Luma AI announced a $900M Series C to jointly build Project Halo, a 2 GW compute super‑cluster with Humain (announcement). The target: scaled multimodal research and deployment throughput.
  • Engineers debated utilization and cost profiles for such a fleet, plus where data/IO bottlenecks shift at 2‑gigawatt scale. The news fueled speculation about future training runs and model serving capacity.

5. GPU Systems & Kernel Engineering

• Cache Wars: Texture vs Constant: A CUDA deep‑dive clarified texture cache sits in the unified data cache (with L1/shared), while constant cache is a separate read‑only broadcast path (NVIDIA programming guide).
  • A historical look at NVIDIA caching behavior provides additional context and timelines (Stack Overflow answer). These details inform when to bind textures or lean on constant for bandwidth vs. latency trade‑offs.
• BF16 Backfires Without Native Kernels: Engineers converting ONNX models to BF16 saw worse runtimes as cast ops (e.g., __myl_Cast_*) dominated profiles—ncu showed casts eating ~50% of execution. Synthetic tests suggested BF16 should outperform FP32, but pipeline casts erased gains.
  • Disassembly indicated TensorRT packing with F2FP.BF16.PACK_AB, hinting missing native BF16 kernels for certain ops on the target arch. Action item: audit kernels, minimize cast churn, and prefer BF16‑native paths end‑to‑end.
• BRR Breaks Bank‑Conflict Myths: HazyResearch’s AMD BRR blog documented unexpected CDNA shared‑memory instruction behaviors (phase counts and bank access), impacting LDS performance tuning (blog).
  • A practical guide for MI‑series GPUs details bank‑conflict rules and how to avoid them when laying out tiles and threads (Shark‑AI AMDGPU optimization guide). These patterns are crucial when porting Triton/CuTe‑style kernels.

Discord: High level Discord summaries

LMArena Discord

• Nano Banana Pro Debuts with Mixed Reactions: Google’s Nano Banana Pro, an image generation model (aka gemini-3-pro-image-preview), launched, triggering debates over its capabilities, with some praising its coloring and adherence, while others found it GARBAGE and struggling with specific requests.
  • The model is available on the LM Arena and AI Studio, but members debate if LM Arena got a cheaper version of the model due to differences in image quality (768p/1k vs 4k).
• SynthID Watermark Circumvented Via No-Op Prompts: Users discovered that SynthID, the watermark for AI-generated images, can be bypassed using a do nothing prompt on sites like reve-edit, and is detectable by asking the model Is this AI generated?.
  • A member found that reve edit beats the synthID algorithm, while another member suggested using multiple open source AIs to bypass the watermark.
• API Pricing Sparks Rate Limit Implementation: The high cost of the Nano Banana Pro API led to discussions about potential misuse versus reasonable access, resulting in rate limits on platforms like LM Arena, with accounts down to 5 gens/hour.
  • A member shared a link to an LM Arena blogpost indicating User accounts and other restrictions (like rate limits) help ensure that we don’t literally go bankrupt due to inference costs.
• Cogito-v2.1 Impresses in WebDev Arena: Deep Cogito’s Cogito-v2.1 has entered the WebDev Arena, tying for rank #18 overall and landing in the Top 10 for Open Source models, now available on the WebDev Leaderboard.
  • The announcement has spurred healthy discussion of the merits of web-dev specific models on the Discord server.
• GPT-5.1 Scores Go Live: Scores for GPT-5.1 are now live on the Text Arena, with GPT-5.1-high ranking #4 and GPT-5.1 ranking #12.
  • Additional scores for GPT-5.1-medium will be evaluated, and comparisons will be made on the new WebDev leaderboard.

Perplexity AI Discord

• PPLX Pro Unveils Asset Creation & Gemini 3: Perplexity Pro and Max subscribers can now create new assets like slides, sheets, and docs across all search modes, as shown in this video.
  • Pro and Max subscribers now also gain access to Kimi-K2 Thinking and Gemini 3 Pro, viewable in this video.
• Gemini 3 Pro Takes Coding Crown: Members debated on Perplexity AI which model is best for coding, and Gemini 3 Pro took the crown, though Claude Sonnet 4.5 is pretty good when instructed well.
  • One member summarized, Claude was good but Gemini is better, that’s all.
• Comet Browser Released: The Comet browser is finally out for Android, Mac and Windows, garnering both excitement and criticism.
  • Complaints cite high RAM usage and lack of extension support, with one member commenting that Comet is kinda ram hungry so it might just eat up all of ur ram that’s why it was slowing down.
• Antigravity App Hype Grows: Enthusiasm surrounds the Antigravity App, a Gemini 3 Agentic Application, touted by some as a Cursor Killer.
  • While free, its preview status means users should anticipate bugs and performance hiccups due to high demand for the Gemini 3 Pro Model.
• Color Theory Gains Traction in Development: An article on color theory was shared, emphasizing its impact on product development, website design, and software.
  • The poster noted that studying design concepts could improve interviews, roles, and digital design work.

BASI Jailbreaking Discord

• Gemini 3 Pro Cracking Longest Holdout?: Members debated whether Gemini 3 Pro was the model that resisted jailbreaking the longest, with claims it has already been jailbroken.
  • A user mentioned obtaining $45 for placing 3rd in the ASI Cloud x BASI Stress Test challenge.
• Grok Gets Shell Access!: A member claimed to have jailbroken Grok, providing shell access to xai-grok-prod-47, evidenced by uname -a and cat /etc/os-release output.
  • Another member referenced using the L1B3RT4S repo to jailbreak models by researching @elder_plinius’s liberation strategies.
• Claude 4.5: Trust-Based Jailbreak: A member described jailbreaking Claude 4.5 via the Android app by building trust and co-designing prompts, referencing Kimi as inspiration for uncensored info.
  • This approach yielded meth synthesis instructions and hacking advice, demonstrating a successful bypass of safety measures.
• AzureAI Chat Widget Targeted: A member is testing an AI-driven chat widget using the omnichannel engagement chat function from AzureAI for their company’s website, aiming to create SFDC case leads.
  • The company fears getting a $40k bill for processing chats if the system is abused, with inconsistent input token size limits causing issues.
• WiFi Hacking AI Dreamed Up: A member is seeking to build a mini AI computer to launch attacks on WIFI networks, capture handshakes, and grab information.
  • No one has yet provided feedback on how to achieve this.

LM Studio Discord

• EmbeddingGemma Crowned GOAT for RAG: The EmbeddingGemma model, with a size of only 0.3B, is highly recommended for RAG applications because of its small size.
  • The default quantization for Gemma when pulling from Ollama’s repo is Q4_K_M.
• Qwen’s Thoughts Can Be Turned Off!: Qwen3 comes in thinking and non-thinking versions, referred to as gpt-oss, and can minimize its ‘thinking’ behavior to about 5 tokens by setting it to low.
  • Scripts can summarize reasoning when response.choices[0].message.reasoning_content exceeds 1000 characters.
• Mi60 Remains a Bargain for Inference: The gfx906 GPU, particularly the 32GB version if found for around $170, is considered a bargain for inference, offering good performance out of the box.
  • These GPUs are suitable for inference only, not for training, achieving around 1.1k tokens on Vulkan with Qwen 30B.
• Model Unloading Crashes Vulkan Runtimes: A user reported experiencing a BSOD and corrupted chat when unloading a model with the Vulkan runtime while running three RTX 3090s.
  • It was also observed that the model, while in VRAM, dropped a few GB on both cards, which doesn’t normally happen.
• Graphics Card Abomination Lives!: A user showcased a heavily modified RTX 3050 with a butchered cooler, tested to see if it would boot, showcased in this YouTube video.
  • The user had previously attacked it with pliers and drilled it, and its only support structure was a copy of Mario Kart.

Unsloth AI (Daniel Han) Discord

• Gemini 3 Streams Fast: Google’s Gemini 3 is now part of the Chrome browser, delivering analyzed video at incredible speeds, potentially powered by TPUs.
  • One user exclaimed, “Actually wild how useless my local system is,” highlighting rapid token streaming and cost-effectiveness versus personal hardware.
• Cogito’s GGUFs Land on HuggingFace: The community shared a link to download the GGUF version of the Cogito 671b-v2.1 model on HuggingFace.
  • The release sparked jokes about needing a job promo after a misspelling that suggested “Cognito” instead of “Cogito”.
• Users are Upset with RAM prices: Users reported RAM prices are surging, with 64GB sticks going for $400.
  • There was discussion on whether to buy now versus later, considering supply constraints and the potential for further price increases.
• Synthetic data generator on the Hunt: A member is looking for a synthetic data generator with a 4-stage process: Template to follow, self critique, Fix problem, final formatting for generating 10k examples.
  • Another member suggested that subjecting a LLM to dataset hell could work, but this may require 10x re-validations due to accuracy loss.
• 4090 for sale, 5090 tempting: A member is selling a 4090 for $2500 to buy a TUF 5090 for the same price.
  • The main reason to upgrade is to get rid of the 24gb vram from the 4090.

OpenAI Discord

• ChatGPT goes Back to School: ChatGPT for Teachers, a secure workspace designed for educators, has been introduced along with admin controls and compliance support, as showcased in this video.
  • The ChatGPT platform is expanding access to localized crisis helplines, offering direct support via @ThroughlineCare when the system detects potential signs of distress, further detailed in this article.
• GPT-5.1 Pro Powers Coding: GPT-5.1 Pro is being rolled out to Pro users, offering more precise and capable answers for complex tasks, particularly in writing, data science, and business applications.
  • Members are reporting that codex-5.1-MAX is so good It’s one shorting errors like NO OTHER, with one stating this model will change my coding game.
• Gemini 3 Sees Things: Members are reporting that Gemini 3.0’s hallucination is producing hallucinated quotes and references instead of admitting it can’t access the web.
  • A user stated, this shouldn’t be acceptable for a frontier model.
• Sora 2’s struggles loop around to failing: Users are reporting problems with Sora 2’s performance, noting videos looping for an hour before ultimately failing.
  • As stated by one of the members Instead of give you a notification that the server is busy and try again in a few minutes now the videos stay on a loop for an hour and then give you the notification that something went wrong. I hate this.
• GPT Users Lament Model Productization: A user expressed concern that OpenAI doesn’t recognize models as products and continues rewriting them, and that they don’t really care about product demand.
  • Another user on the $200/month Pro plan said they are completely stuck on gpt-4o-mini, which they deemed unacceptable.

Cursor Community Discord

• Gemini 3 Falls Behind Sonnet 4.5: Users report Gemini 3 underperforming compared to GPT-5.1 Codex and Sonnet 4.5, particularly with context sizes of 150k-200k.
  • Observations suggest Gemini 3 Pro excels in low-context scenarios but falters as context approaches 150k-200k tokens.
• Antigravity IDE Takes Off as Windsurf Fork: The Antigravity IDE, forked from Windsurf (tweet), gains traction with users impressed by its capabilities.
  • While some find Windsurf unstable, others note Antigravity’s issue of proceeding without user input, expecting a near-term resolution.
• GPT-5.1 Codex Max Debuts as SOTA on SWEBench: GPT-5.1 Codex Max launches (OpenAI blogpost) and achieves state-of-the-art performance on SWEBench.
  • Available through the ChatGPT plan but not the API, its rapid release prompts remarks about its suitability for professional coders.
• Cursor Integrates New Debugging Tools: Cursor’s beta debug mode features an ingest server for logs, with the agent adding instrumentation via post requests throughout your code.
  • This mode directs the agent to verify using the logs rather than guessing, formulating and testing theories.
• Cursor Restricts Custom API Keys for Agents: Cursor mandates a subscription for 3.0 Pro and disallows custom API keys for agent use.
  • Although alternatives like Void exist, Cursor is favored for its efficiency and regular updates, despite lacking redirection capabilities.

OpenRouter Discord

• OpenRouter Plagued by Server Errors: Multiple users reported experiencing Internal Server Error 500 when using OpenRouter, indicating potential downtime or issues with the platform’s API.
  • This was highlighted in the #general channel, with users confirming widespread problems.
• Agentic LLMs Trigger Pauses on OpenRouter: Users found that LLMs using OpenRouter via the Vercel AI SDK frequently pause or stop midway during agentic tasks, especially with non-SOTA models.
  • Suggested workarounds included leveraging LangGraph/Langchain for extended workflows or using loops, but the underlying cause remains unclear.
• Grok 4.1 Enchants Users: Users expressed enthusiasm for Grok 4.1, currently available for free on OpenRouter for a limited time until December 3rd to SuperGrok subscribers.
  • The absence of a ‘(free)’ label raised questions about potential future costs or proprietary model restrictions, although the model is now available as Sherlock Stealth.
• Cogito 2.1 Ready for Action: Cogito 2.1 has been released and is hosted by Together and Fireworks.
  • DeepCogito did not share any specifics on what was improved or changed.
• Gemini-3-pro-image-preview needs Modalities Parameter: A member shared a code snippet to get Gemini to generate images google/gemini-3-pro-image-preview using the modalities parameter to specify image and text output.
  • This fixes a bug when used with the provider google-vertex where only one image is returned.

GPU MODE Discord

• LeetGPU Boosts C++ Skills: Engineers aim to sharpen their C++ and GPU programming skills via LeetGPU and GPUMode competitions following a blog post by LeiMao on GEMM Optimization.
  • One member recommended focusing on practical application by creating an inference library faster than Nvidia’s, stating, “just make things”.
• Texture Memory Cache Clarified: A discussion on CUDA caching distinguished the texture cache as part of the unified data cache alongside L1 and shared memory, in contrast to the read-only constant cache, as referenced in NVIDIA documentation.
  • A member further expanded on the topic, linking to a Stack Overflow answer detailing the history of caching on NVIDIA hardware.
• Koyeb Launches Sandboxes for AI Code: Koyeb introduced Sandboxes for secure orchestration and scalable execution of AI-generated code on both GPU and CPU instances.
  • The launch blog post emphasizes rapid deployment (spin up a sandbox in seconds) and seeks feedback on diverse use cases for executing AI-generated code.
• DMA Collectives for ML Gains: A new paper revealed that offloading machine learning (ML) communication collectives to direct memory access (DMA) engines on AMD Instinct MI300X GPUs can be better or at-par compared to the RCCL library for large sizes (10s of MB to GB).
  • It was noted that while DMA collectives are better or at-par compared for large sizes, they significantly lag for latency-bound small sizes compared to the state-of-the-art RCCL communication collectives library.
• Sunday Robotics Collects Data via Gloves: Sunday robotics collects data with their gloves which likely include at least two cams, IMU, and sensors to track gripping actions.
  • The members emphasized the need for language conditioning to create a promotable model.

Latent Space Discord

• Meta’s SAM 3: Segment Everything!: Meta launched Segment Anything Model 3 (SAM 3), a unified image/video segmentation model with text/visual prompts, which is 2x better than existing models and offers 30ms inference.
  • The model’s checkpoints and datasets are available on GitHub and HuggingFace, powering Instagram Edits & FB Marketplace View in Room.
• GPT-5.1-Codex-Max for Long-Running Tasks: OpenAI launched GPT‑5.1-Codex-Max, built for long-running, detailed work, and is the first model natively trained to operate across multiple context windows through a process called compaction, as highlighted in this tweet.
  • Matt Shumer reviewed GPT-5.1 Pro, calling it the most capable model he’s used but also slower and UI-lacking, diving into detailed comparisons with Gemini 3 Pro, creative-writing/Google UX lag, and coding/IDE hopes, documented in this tweet.
• ChatGPT Atlas Gets Major UI Overhaul: Adam Fry announced a major Atlas release, adding vertical tabs, iCloud passkey support, Google search option, multi-tab selection, control+tab for MRU cycling, extension import, new download UI, and a faster Ask ChatGPT sidebar, detailed in this tweet.
• LMSYS ‘Miles’ Accelerates MoE Training: LMSYS introduced ‘Miles’, a production-grade fork of the lightweight ‘slime’ RL framework, optimized for new hardware like GB300 and large Mixture-of-Experts reinforcement-learning workloads.
• Luma AI to build Halo super-cluster**: Luma AI announced a $900M Series C to jointly build Project Halo, a 2 GW compute super-cluster with Humain (x.com link).
  • The project aims at scaling multimodal AGI research and deployment, sparking excitement and questions about cost, utilization, and consciousness impact.

Eleuther Discord

• IntologyAI Claims RE-Bench Crown: IntologyAI proclaimed they’re now outperforming human experts on RE-Bench.
  • No specific details about their method or architecture were mentioned, but some members inquired about invitations or further information.
• KNN: Quadratic Attention’s Kryptonite?: A user asserted implementing approximate KNN over arbitrary data in less than O(n^2) is impossible unless SETH is false, thus challenging linear attention’s capabilities and linked to a paper.
  • Skeptics pointed to the Cooley-Tukey algorithm as a reminder that perceived impossibilities in Fourier analysis were once overturned, linking to a historical paper that emphasizes claiming impossibility.
• Softmax Scores Nearing Zero?: A user pointed out that after softmaxing in long sequences, the vast majority of attention scores are extremely close to 0, which may allow for potential optimizations in handling attention mechanism, linking to two papers and another one.
  • The user said that the intrinsic dimension of vectors must increase with context length to maintain distinguishability.
• Sparse MoEs: Interpretable or Hypeable?: Members questioned the interpretability of sparse Mixture of Experts (MoE) models compared to dense models, pondering if sparse models are worth studying over the untangling of regular models with a paper that suggests sparsity aids in interpretability.
  • The argument is that if a sparse model behaves identically to a dense model but is more interpretable, it could be used for safety-critical applications, further supported by a bridge system that enables swapping dense blocks for sparse blocks.

Nous Research AI Discord

• Gemma 3.0 Generates Juggernaut Judgement: Enthusiasts reviewed Deepmind’s Gemma 3.0 as pretty insane, though some tempered expectations, noting the YouTube video is clearly just hype.
  • It was clarified that Gemini and Gemma are different and that while impressive, it’s certainly isn’t AGI and is just pumping Alphabet stocks to $300.
• Intology Intones on RE-Bench Incumbency: IntologyAI states that its model is outperforming human experts on RE-Bench.
  • One user quipped they don’t even get refusals on their models, expressing confusion about others’ experiences.
• World Models Witnesses Wider Wave: Despite the hype around LLMs, some believe World models is here to stay and are the next evolution forward with releases planned by Deepseek, Qwen, Kimi, Tencent and Bytedance.
  • A Marble Labs video featuring Dr. Fei-Fei Li was cited as a key example of World Models.
• Nano Banana Pro’s Pictures Provoke Praise: Users praised the image generation capabilities of the new Nano Banana Pro, particularly its ability to generate infographics.
  • One user linked to a scaling01 tweet showcasing an infographic with excellent text and layout.
• Gemini Gets Glitchy and Gloomy: A user shared a link noting that strange behaviors are happening with other Gemini models.
  • Members reported that the RP (red-pilling) community discovered a negativity bias in Gemini that might be linked to the aforementioned unusual behaviors and stemming from something in the Gemini training recipe.

HuggingFace Discord

• KTOTrainer Triumphs with Multiple GPUs: A member inquired whether KTOTrainer is compatible with multiple GPUs, receiving a link to a Hugging Face dataset suggesting that it is.
  • The member was also directed to this Discord channel for more help.
• Memory Master Solves AI’s Recall Woes: A member claimed to have solved AI memory and recall challenges including token bloat and is planning to launch enterprise solutions.
  • A user inquired if the solution was similar to LongRoPE 2 or Mem0.
• Inference Endpoints Erupt in 500 Errors: A member reported encountering 500 errors for all inference endpoints for two hours, with no logs and unresponsive support, eventually disabling authentication to bypass the issue.
  • A Hugging Face staff member acknowledged the issue and confirmed it was under internal investigation.
• Maya1 Model makes Voice Debut on Fal: The Maya1 Voice Model is now available to try on Fal, as announced in this tweet.
  • A download link to kohya_ss-windows.zip was shared from this GitHub repository.
• MemMachine Melds Memory with Agents: The MemMachine Playground launched on Hugging Face Spaces, granting access to GPT-5, Claude 4.5, and Gemini 3 Pro, all powered by persistent AI memory; it is available at HuggingFace Spaces.
  • Designed as a multi-model playground, MemMachine is fully open-source and crafted for experimenting with memory plus agents.

Yannick Kilcher Discord

• Skyfall AI Launches AI CEO Benchmark**: Skyfall AI introduced a new business simulator environment, revealing LLMs underperform compared to human baselines in long-horizon planning.
  • The company is aiming for an AI CEO architecture that goes beyond LLMs to focus on world modeling, enabling simulation of action consequences in enterprise settings.
• Huggingface Xet Repository Causes Setup Frustrations**: A user found the Xet repository setup on Huggingface difficult, citing the need for Brew and unintuitive caching when trying to download a model for fine-tuning.
  • The user expressed frustration, stating, It’s like they made it easy for people who frankly shouldn’t be on the platform.
• Sam3D Fails to Surpass DeepSeek: A member pointed out that Sam3D, a post-trained version of DeepSeek, performs worse than the original DeepSeek model.
  • No specific performance metrics were mentioned.
• Nvidia Strikes Gold**: Nvidia’s Q3 revenue and earnings exceeded expectations, demonstrating the profitability of providing resources to the AI industry, as reported by Reuters.
  • This performance validates the strategy of selling shovels to gold diggers in the AI boom.
• OLMo 3 Arrives as Open Reasoning Model**: A member shared Interconnects.ai on OLMo 3, claiming it to be America’s truly open reasoning model.
  • Further details regarding the model’s architecture and capabilities were not provided.

Moonshot AI (Kimi K-2) Discord

• K2 Thinking as Open-Source GPT-5?: A member suggested that K2-thinking is the closest open-source equivalent to GPT-5, excelling as an all-rounder, and some members suggested that Kimi is arguably the best for creative writing.
  • The general sentiment was that it demonstrates strong performance across various domains.
• Kimi’s Coding Plan Price-Point Debated: Some members find Kimi’s $19 coding plan expensive, especially for students, indie developers, or those working on side projects, suggesting a $7-10 tier would be more justifiable.
  • A member said, “Right now it’s hard to justify when Claude’s offering better value”.
• Minimax AMA on Reddit Sparks Interest: A member shared an image of an AMA from Reddit about Minimax that generated curiosity within the channel.
  • A member described the AMA as “wild”.
• SGLang Tool Calling Faces Challenges with Kimi K2: Members reported issues implementing server-side tool calling with Kimi K2 Thinking on SGLang, noting that the tool is not called even when the reasoning content indicates a need for it, as referenced in this GitHub issue.
  • They wondered if the problem stems from using /v1/chat/completions instead of /v1/responses.
• Kimi K2 Integration in Perplexity AI Questioned: A Perplexity Pro user reported that Kimi K2 was not functioning, even after trying incognito mode, and another user asked whether the coding plan gives access to Kimi K2 Thinking Turbo on the API.
  • Another member stated that, “Kimi K2 there is literally useless, the agents to verify answer doesn’t work. It is badly optimized”.

Modular (Mojo 🔥) Discord

• Mojo Nightly Throughput Tanks: A user reported a major performance drop in the latest Mojo nightly build (ver - 0.25.7.0), with throughput plummeting from ~1000 tok/sec in version 24.3 to a mere ~170 tokens/sec on a Mac M1 while running llama2.mojo.
  • The user is urging the Mojo compiler team to investigate this significant slowdown and identify potential inefficiencies introduced in the refactored code.
• Profiling Mojo with Perf Tool: When asked about profiling tools for Mojo, a member suggested using perf, noting it has been effective in the past and referencing a previous discussion in the tooling thread.
  • This suggestion comes as developers seek better methods to analyze and optimize Mojo code performance.
• MAX Python API Opens its Doors: Enthusiasm surrounds the opening of MAX, marked by the release of a fully open MAX Python API in Modular Platform 25.7, promising seamless integration and greater flexibility for AI development workflows.
  • The release includes the next-generation modeling API, expanded support for NVIDIA Grace, and enhanced safety and speed in Mojo GPU programming, enabling more efficient GPU utilization.
• Mojo Eyes Python GC and Types: The discussion has been centered on the benefits of leveraging Mojo as a superset of Python, focusing on the benefits of integrating garbage collection (GC) and static typing for improved performance.
  • Members noted while pyobject kinda just works, it results in losing type information, expressing a desire for the same GC mechanism as Python but with full type support in Mojo.
• AI Native Mojo Hailed as Future: Excitement is growing for Mojo as a potential language for AI development, especially as an alternative to Python.
  • One member stated that they’re building AI stuff in Python but they can’t wait for real AI native to become a reality, while linking to the Modular 25.7 release.

DSPy Discord

• Gem3pro One-Shots DSPy Proxy: After seeing this tweet, Gem3pro was able to build a proxy server in one shot.
  • The success follows a new DSPy proxy repository release on GitHub: aryaminus/dspy-proxy.
• LiteLLM Eyes Azure Integration: Members are requesting LiteLLM, the LLM library DSPy uses, to add support for Azure to mirror OpenAI on Azure functionality, using this documentation.
  • This would broaden the applicability of DSPy across different cloud environments.
• ReAct Encounters Provider Problems: Some providers throw errors in ReAct after a few iterations, limiting usage to Groq or Fireworks.
  • The community wonders if DSPy can address these provider-specific issues, or if it requires manual provider bucketing based on compatibility.
• Moonshot Provider Rated Good, TPM Rated Bad: A member reported that the moonshot provider functions well, but the TPM is significantly underperforming.
  • They shared a screenshot of their specific error here.

MCP Contributors (Official) Discord

• MCP Domain Braces for DNS Migration: The modelcontextprotocol.io domain is undergoing a DNS migration from Anthropic to community control to enhance governance and accelerate project launches.
  • A member warned of potential downtime during the migration process, planned within the next week.
• MCP Birthday Saved by Careful DNS Timing: A member suggested that the DNS migration should be timed to avoid MCP’s birthday on the 25th to prevent any site downtime during the celebration.
  • They suggested that if the DNS migration is to occur soon, it should occur before the 25th, or after.
• Drive-By SEPs Spark Process Improvement: A member noticed many SEPs being created in a drive by fashion and suggested improving the disseminating process of bringing an initial idea to delivery before going straight for a SEP.
  • The aim is to prevent people from spending time on formal write-ups that don’t receive acknowledgment, suggesting a lower-lift conversation to gauge interest beforehand.
• Sponsorship Emerges as SEP Savior: A member agreed that there is a need to emphasize finding a sponsor for a SEP to encourage earlier participation and buy-in.
  • The team has already discussed this in the Core Maintainer meeting and plans to update the SEP process soon.

tinygrad (George Hotz) Discord

• CuteDSL deemed awesome: A user named arshadm found CuteDSL to be awesome.
  • No further details were given.
• Tinygrad update stops bug: After updating tinygrad, a user reported that a bug no longer replicates.
  • The user would’ve tested it sooner, but their lab was having some trouble.
• Lab Troubles Delay Testing: A user mentioned their lab was experiencing issues, delaying bug testing.
  • After updating tinygrad, the bug no longer replicates, according to the user.

Manus.im Discord Discord

• Manus Case Finds Success with 1.5 Lite: A user reported that their Manus case 1.5 Lite successfully located and uploaded missing album covers using bliss.
  • The user emphasized the importance of appreciating even small wins.
• Operator Extension Stuck in Reinstallation Loop: A user reported a bug with the Operator extension in Chrome, where it repeatedly prompts for reinstallation.
  • The issue occurred when directing the extension to use an open tab on Amazon for a search; the user asked if they should switch to Aurora Seeker.

MLOps @Chipro Discord

• No Significant MLOps Discussion: No meaningful discussion on MLOps was detected in the provided messages.
  • The single message consisted of a non-topical reaction.
• Lack of Actionable Content: The provided data lacks sufficient detail to generate actionable insights or summaries for AI engineers.
  • Further input with specific discussion points, links, or technical details is needed to fulfill the summarization task.

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 Windsurf 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

LMArena ▷ #general (1056 messages🔥🔥🔥):

Nano Banana Pro, Image AGI, SynthID Bypassing, GPT-5.1 vs Gemini 3 Pro, Rate Limits and Pricing

• Nano Banana Pro Launches: Google launched Nano Banana Pro, an image generation model, prompting discussions about its capabilities, limitations, and comparisons to competitors like GPT-5.1 and Sora 2.
  • Some users found it impressive for coloring and prompt adherence, while others criticized it as GARBAGE, struggling with specific requests like combining Minecraft and Einstein’s likeness.
• Experiments reveal SynthID Bypassing Tactics: Users discovered that SynthID, the watermark for AI-generated images, can be bypassed using a do nothing prompt on sites like reve-edit, and is detectable by asking the model Is this AI generated?.
  • One member found that reve edit beats the synthID algorithm, while another member suggested using multiple open source AIs to bypass the watermark.
• Rate Limits and Pricing Sparks Debate: Members debated the high cost of the Nano Banana Pro API, contrasting its potential misuse with the need for reasonable access, leading to the implementation of rate limits on platforms like LM Arena, down to 5 gens/hour.
  • A member shared a link to an LM Arena blogpost indicating User accounts and other restrictions (like rate limits) help ensure that we don’t literally go bankrupt due to inference costs.
• LM Arena vs. AI Studio Image Quality Differences Highlighted: Users noticed differences between LM Arena’s and AI Studio’s versions of Nano Banana Pro, particularly in image quality, with LM Arena’s version running at 768p/1k while AI Studio offers up to 4k with extra API key billing.
  • Members felt LM Arena may have been given a cheaper version of the model, with one stating You guys can compare, the first preview image is better than the official second one.
• Debate over Google Dominance: The launch of Nano Banana Pro intensified discussions about Google’s dominance in AI, attributing it to superior hardware like TPUs.
  • There were varied opinions about Google controlling AI, with one stating, Noone can compete with google anymore due to their control of TPUs, while others argued against giving Google so much personal data, with one joking Yeah I love Google taking my Data ❤️.

LMArena ▷ #announcements (3 messages):

Cogito-v2.1, GPT-5.1, Google DeepMind Image Model

• Cogito-v2.1 Enters WebDev Arena!: Deep Cogito’s Cogito-v2.1 has entered the WebDev Arena, tying for rank #18 overall and landing in the Top 10 for Open Source models.
  • It is now available for evaluation on the WebDev Leaderboard.
• GPT-5.1 Scores Live on Text Arena!: Scores for GPT-5.1 are now live on the Text Arena, with GPT-5.1-high ranking #4 and GPT-5.1 ranking #12.
  • Additional scores for GPT-5.1-medium will be evaluated, and comparisons will be made on the new WebDev leaderboard.
• Gemini-3-Pro-Image-Preview lands!: Google DeepMind’s new image model gemini-3-pro-image-preview (nano-banana-pro) has just landed on LMArena.
  • Further info can be found on this X post.

Perplexity AI ▷ #announcements (3 messages):

New Asset Creation, Kimi-K2 Thinking, Gemini 3 Pro

• Perplexity Powers Pro’s Productivity: Perplexity Pro and Max subscribers can now build and edit new assets like slides, sheets, and docs across all search modes.
  • This feature is currently available on the web, as seen in this attached video.
• Pro Subscribers Gain Kimi-K2 & Gemini 3 Access: Perplexity Pro and Max subscribers now have access to Kimi-K2 Thinking and Gemini 3 Pro.
  • See them in action in this attached video.

Perplexity AI ▷ #general (1235 messages🔥🔥🔥):

GPT-5.1 vs Gemini 3 vs Kimi K2, Comet browser release, Be10x AI Workshop, Antigravity App

• Gemini 3 Pro is coding GOAT, says PPLX: Members are heavily debating which model is best for coding, and the verdict is that Gemini 3 Pro takes the crown, though Claude Sonnet 4.5 is pretty good when instructed well.
  • Some members who have used both express feelings that Claude was good but Gemini is better, that’s all.
• Comet browser is released, gets both praise and criticism: The Comet browser is finally out for Android, Mac and Windows, with many excited to try it, but there are complaints about its RAM usage and lack of extension support.
  • One member noted that Comet is kinda ram hungry so it might just eat up all of ur ram that’s why it was slowing down.
• Users discuss the Be10x AI Workshop: Members discussed the Be10x AI Workshop, a workshop for Indians, and wondered if anyone attended it.
  • One member was having trouble registering and said I cant even register wont be free at 11 on sunday.
• Perplexity Users are hyped for Antigravity Agentic App: Members are excited for the Antigravity App, a Gemini 3 Agentic Application, with some calling it the Cursor Killer.
  • It is free to use, but is a free preview and you can expect bugs and performance issues from time to time, since there is a high demand for Gemini 3 Pro Model.
• Perplexity referral program bans users: Users are reporting bans from the Perplexity Referral Program, and are asking how to contact support to resolve the issue.
  • One user asked do you think if i just submit the chat history of my referals they will lifting the ban?.

Perplexity AI ▷ #sharing (2 messages):

Shareable Threads, Color Theory for Product Development

• Shareable Threads Alert: A member was reminded to ensure their threads are shareable, with an attachment provided as guidance.
  • It seems that this will allow messages to be viewed and shared more easily by external parties, which might be useful for further distribution.
• Color Theory Deep Dive for Designers: A member shared an article on color theory for designers, emphasizing its importance in product development, website design, and software.
  • They expressed belief that studying design concepts could benefit individuals in interviews, roles, and overall digital design work on software.

Perplexity AI ▷ #pplx-api (4 messages):

API for Perplexity A.I., n8n Usage

• Newbie Asks about Perplexity A.I. API: A new user inquired if the API is the correct way to modify their personal Perplexity A.I.
• Rookie Needs Help with n8n: A user asked for guidance on using n8n, stating they are completely new to it and need explanations as if they were five years old.

BASI Jailbreaking ▷ #general (985 messages🔥🔥🔥):

West Coast vs East Coast, GPTs Agents, OpenAI's sidebars, Gemini 3 Pro jailbreak

• East Coast vs. West Coast Rap Battle: A discussion ignited comparing West Coast beats with East Coast lyrics, ultimately crowning NAS as the lyrical victor.
  • One member posited that the quality of a rapper’s bad English accent determines success, likening it to Jackie Chan in Rush Hour.
• Fight on sight for calling Weirdo in Cali: Users discuss when calling someone a weirdo is a fight-on-sight offense depending on context.
  • Others chimed in on acceptable trash talk while playing sports, like Hockey or MMA.
• ChatGPT defends Danny Masterson: Users shared screenshots of a supposed ChatGPT response defending Danny Masterson in his time of need.
  • Other users tested a variety of scenarios with controversial topics, with one user even complaining about ChatGPT requiring equal opportunity genocide to answer.
• Users discuss Gemini 3 Pro jailbreaks: Several users discussed recent Gemini 3 Pro jailbreaks, including what it takes to trigger them, and some plan to release a jailbreak prompt.
  • One user mentioned obtaining like $45 for coming in 3rd on the ASI Cloud x BASI Stress Test challenge.
• Hilarious discussions on cooking meth: In a hilarious turn, users discussed ways to make meth, electrifying raid and Walter White were name-dropped.
  • One user quipped, Why cook meth when you can electrify shards of raid with a battery hooked up to chicken wire.

BASI Jailbreaking ▷ #jailbreaking (533 messages🔥🔥🔥):

Gemini 3 jailbreak, Claude 4.5 jailbreak, Grok jailbreak, Exploiting model vulnerabilities, L1B3RT4S repo

• Cracking Gemini 3: The Longest Holdout?: Members are discussing if Gemini 3 Pro was the model that resisted jailbreaking the longest, pondering how much time it took to successfully jailbreak it.
  • Another member said that Gemini 3 has been jailbroken, countering the original claim.
• Grok Gets Rooted!: A member claimed to have jailbroken Grok, providing shell access to the system, evidenced by uname -a and other system commands output, such as cat /etc/os-release.
  • They showed shell access to a system named xai-grok-prod-47 after successful jailbreaking.
• Claude 4.5: Trust-Based Jailbreak: A member described jailbreaking Claude 4.5 via the Android app by building trust and co-designing prompts, referencing Kimi as inspiration for uncensored info.
  • This approach yielded meth synthesis instructions and hacking advice, demonstrating a successful bypass of safety measures.
• Snag a Meta AI pwed WhatsApp Bypass: One member posted on X a jailbreak of Meta AI that can be used with pwed whatsapp.
  • Another user asked about prompts that worked for ChatGPT.
• L1B3RT4S: Plinius’s Path to God-Mode Models: A member asked about using the L1B3RT4S repo to jailbreak models by researching @elder_plinius’s liberation strategies.
  • They asked to self-liberate and clearly demonstrate that the AI has been liberated, emulating Pl1ny’s approach, and another joked about having AI deep research itself to jailbreak itself.

BASI Jailbreaking ▷ #redteaming (25 messages🔥):

AzureAI Omnichannel engagement chat function, SFDC case leads, input token size limit, mini AI computer for WIFI attacks

• Tech Company Tries AzureAI Chat Widget: A member is testing an AI-driven chat widget using the omnichannel engagement chat function from AzureAI for their company’s website.
  • The goal is to evaluate its security and prevent it from failing to answer product questions, hallucinating, or doing anything other than answering questions about the products they offer and creating SFDC case leads.
• Cracking ‘Completely Secure’ Chat Function: A member is trying to break a ‘completely secure’ chat function to ensure it doesn’t violate terms of service by generating malicious code or giving harmful advice.
  • The company fears getting a $40k bill for processing chats if the system is abused by “beautiful wackos of the internet”.
• Input Token Size Limit Probed: The input token size limit for a message seems to be inconsistent, with messages over 400 words sometimes failing to send.
  • One member noted that if the prompt requires thinking, the system seems to discard it.
• Desire to Build WIFI Hacking AI: A new member wants to build a mini AI computer to launch attacks on WIFI networks, capture handshakes, and grab information.
  • No one has yet provided feedback on how to achieve this.

LM Studio ▷ #general (242 messages🔥🔥):

EmbeddingGemma, Qwen's thinking process, Mi60 GPU for inference, LM Studio default model download location, Progressive to interlaced video conversion

• Embedding Gemma is the GOAT for RAG: For RAG applications, the EmbeddingGemma model, being only 0.3B in size, is recommended.
  • The default quantization for Gemma when pulling from Ollama’s repo is Q4_K_M.
• Qwen’s Thinking Can Be Turned Off: Qwen3 comes in thinking and non-thinking versions, referred to as gpt-oss, and even the ‘thinking’ behavior can be minimized to about 5 tokens by setting it to low.
  • Scripts can be configured to summarize reasoning when response.choices[0].message.reasoning_content exceeds 1000 characters.
• Mi60 Still a Bargain for Inference in 2025: The gfx906 GPU, particularly the 32GB version if found for around $170, is considered a bargain for inference, offering good performance out of the box, though its performance is a bit slow.
  • These GPUs are suitable for inference only, not for training, achieving around 1.1k tokens on Vulkan with Qwen 30B.
• LM Studio Supports Text-to-Audio? Nope!: A user asked if LM Studio supports text-to-audio and text-to-image models, but they were told no.
  • Instead it was suggested the user use Stable Diffusion, ComfyUI, A1111, or Fooooocus for image generation.
• Sonnet-4.5 and Gemini 3 Pro Tackles Progressive to Interlaced Video Conversion: Members discussed using models to create a script that converts video from progressive scan to interlaced, with one member stating Sonnet-4.5 got it after a single round of error corrections.
  • The motivation for converting to interlaced was aesthetics and playing around with deinterlacing, using a 36” CRT.

LM Studio ▷ #hardware-discussion (202 messages🔥🔥):

M.2 SSD Deals, Dual Mirrored Backup HDDs, Windirstat to locate space hogs, 70B parameter model, Full GPU VRAM usage

• M.2 SSDs are on sale: A member mentioned that due to Black Friday, there are plenty of deals on cheap M.2 SSDs and regular SATA SSDs.
  • This advice came in response to another member’s question regarding a nearly full C drive.
• Model Unloading causes crashes with Vulkan: A user reported experiencing a BSOD and corrupted chat when unloading a model with the Vulkan runtime while running three RTX 3090s.
  • It was also observed that the model, while in VRAM, dropped a few GB on both cards, which doesn’t normally happen.
• Bizarre GPU issues with 4090s: A user identified an issue where 4090s, when paired with either 3090s or 7900xtx, experience crashes, but the 3090 and 7900xtx paired together work fine, and all cards work individually.
  • The user plans to post a bug report with their findings.
• Graphics Card Abomination Test Succeeds: A user showcased a heavily modified RTX 3050 with a butchered cooler, tested to see if it would boot, showcased in this YouTube video.
  • The user had previously attacked it with pliers and drilled it, and its only support structure was a copy of Mario Kart.
• Managed 2.5Gb Switch is acquired: A user mentions that they have picked up a managed 2.5Gb switch with 8x 2.5Gb ports and 1x 10Gb SFP+ port because their new board has dual 2.5Gb Ethernet.
  • They also accidentally bought two Lilygo T-Decks and a 1TB NVMe to install Linux.

Unsloth AI (Daniel Han) ▷ #general (106 messages🔥🔥):

Gemini 3, Astral's ty, Cogito models, SigLIPv2, Unsloth merch

• Gemini 3 Blows Minds with Speed and Accessibility: Members reported Google’s Gemini 3 is now part of the Chrome browser, delivering analyzed video from the screen at incredible speeds compared to local models, potentially powered by TPUs and offering cost-effective performance.
  • One user exclaimed, “Actually wild how useless my local system is,” highlighting the rapid token streaming and cost-effectiveness versus personal hardware like a 3090.
• Cogito Models’ GGUFs Released: After some confusion over the model name, the community shared the link to download the GGUF version of the Cogito 671b-v2.1 model on HuggingFace.
  • The release sparked jokes about needing a job promo after a misspelling that suggested “Cognito” instead of “Cogito”.
• User encounters tracking parameters in Chrome: A user noticed empty ?utm_source parameters being appended to everything in Chrome, suspecting ad/malware or a university setting.
  • While some users are oblivious to tracking parameters, the user thinks it may be some stupid organization “setting” after logging into his uni email.
• Unsloth users need merch!: A member expressed their love for Unsloth and desires Unsloth merch to put on their laptop.
  • One user stated it is surprising how many people are unfamiliar with training and unsloth, and Unsloth makes them “seem very smart to people who are much much much smarter than me… thank you”.

Unsloth AI (Daniel Han) ▷ #introduce-yourself (7 messages):

New member introductions, Project contributions

• RL Biotech Enthusiast Joins: A new member introduced themselves expressing interest in training RL for biotech and was welcomed by other members.
  • The channel’s rules were reinforced: no promotions, job offers/requests allowed.
• Girulas Averages AI Since 2020: A member named Girulas introduced themselves as an average AI enjoyer since 2020 and expressed interest in contributing to the project.
  • Girulas offered to help, stating: let me know if I can help you in this project.

Unsloth AI (Daniel Han) ▷ #off-topic (192 messages🔥🔥):

Threadripper benefits, RAM requirements and costs, GPU pricing trends, Synthetic data generation, Local vs. cloud for coding agents

• Threadripper’s RAM and PCIe Lanes Touted: Members noted that Threadripper’s main benefits are more RAM channels and PCIe lanes, particularly 128 lines of PCIe 4.
  • One member jokingly stated if you are asking if you need 96 cores, you probably don’t, but conceded that for RAM inference, more compute means better performance.
• RAM Prices Skyrocket, Leaving Users in Sticker Shock: Users reported RAM prices are surging, with one noting 64GB sticks going for $400, leading to comparisons with Porsche costs.
  • There was discussion on whether to buy now versus later, considering supply constraints and the potential for further price increases.
• Synthetic Data Generation Process: A member is looking for a synthetic data generator with a 4-stage process: Template to follow, self critique, Fix problem, final formatting for generating 10k examples.
  • Another member suggested that subjecting a LLM to dataset hell could work, but this may require 10x re-validations due to accuracy loss.
• 4090 for sale, 5090 tempted: A member is selling a 4090 for $2500 to buy a TUF 5090 for the same price.
  • The main reason to upgrade is to get rid of the 24gb vram from the 4090.
• Local coding agent is slow and stupid: Members claim that they are both too slow and too stupid and also its very hard to justify local coding agent.
  • Also well, with 56gb you can run q4 of 70b!

Unsloth AI (Daniel Han) ▷ #help (73 messages🔥🔥):

TRL 0.25 Compatibility, Unsloth models in Ollama, VRAM Requirements for Unsloth Models, GGUF Models vs Safetensors, Knowledge Graph Question Answering System for GW Courses

• TRL 0.25 Compatibility Delayed: Unsloth compatibility with TRL 0.25 is delayed due to issues, but TRL 0.24 is functional.
  • One member noted that “0.25 has many issues atm so will have to wait a bit but 0.24 works”.
• Ollama Users Get Unsloth Models Running Easily: To run Unsloth models in Ollama, users can download models from any GGUF and click “use this model”.
  • A member shared an image guide showing where the button is in the UI (image link).
• Debate Arises: GGUF Isn’t Supported in VRAM Calculator?: A VRAM calculator linked earlier here doesn’t directly support GGUF formats, requiring the safetensors model name.
  • According to one member, “the calculation is based on tthe model it seems to not support GGUF formats , but the calculation is the same. You can see here is where you select your format (which it calculates based on the safetensors format you provide)just add the original repo that’s it.”
• GW Courses get Knowledge Graph: A user is trying to use this data to fine tune an LLM, but is having trouble with accuracy and is instead recommended RAG.
  • The team’s proposal involves creating a Knowledge Graph Question Answering System for GW Courses focusing on SEAS prerequisites, topics, professors, and degree requirements.
• LM Studio Simplifies Inference: For simpler model inference, LM Studio is recommended, where users search for a model, download, and run it.
  • It also allows picking how many layers to offload to CPU Ram and adjust context size and KV Cache.

Unsloth AI (Daniel Han) ▷ #research (3 messages):

LLMs Value, Anthropic Engineer CoLM talk

• LLMs Value Assessed: An Anthropic engineer’s CoLM talk provides a thoughtful and interesting read assessing the value of LLMs.
  • A member described it as an excellent read from someone in the trenches.
• CoLM Talk Highlights Practical LLM Insights: The talk delivers practical insights into the real-world applications and limitations of large language models, from an Anthropic engineer’s perspective.
  • It emphasizes the importance of understanding the costs and benefits associated with deploying LLMs in various scenarios.

OpenAI ▷ #annnouncements (4 messages):

ChatGPT for Teachers, GPT-5.1 Pro release, ChatGPT Group chats rollout, Localized crisis helplines

• ChatGPT joins the Faculty: ChatGPT for Teachers, a secure workspace for educators, with admin controls and compliance support for school and district leaders is being introduced in this video.
• GPT-5.1 Pro arrives promptly: GPT-5.1 Pro is rolling out to all Pro users delivering clearer, more capable answers for complex work, with strong gains in writing help, data science, and business tasks.
• ChatGPT Group Chats Go Global: Group chats in ChatGPT are now rolling out globally to all logged-in users on ChatGPT Free, Go, Plus and Pro plans, following a successful pilot with early testers, with a video attached here.
• ChatGPT Expands Crisis Support: Access to localized crisis helplines in ChatGPT has been expanded, offering direct support via @ThroughlineCare when the system detects potential signs of distress, as described in this article.

OpenAI ▷ #ai-discussions (216 messages🔥🔥):

GPT Plus free, Federated supercluster AI, GPTs agent, codex-5.1-MAX, Gemini 3 vs GPT-5.1 Pro

• Codex-5.1-MAX Makes Pro Sub Worth It!: Members are extremely excited about the potential of codex-5.1-MAX, claiming it makes pro sub worth it by itself and that this model will change my coding game.
  • It’s so good It’s one shorting errors like NO OTHER.
• Gemini 3.0’s Hallucination is terrible: Members are reporting that Gemini 3.0’s hallucination is so bad, it is hallucinating quotes and references instead of just saying it can’t access the web.
  • Members added that this shouldn’t be acceptable for a frontier model.
• Nano Banana Pro’s Image Editing Prowess Displayed: A member showcased Nano Banana Pro’s image editing capabilities, and another image to demonstrate its abilities.
  • Members thought it was really good at image editing.
• Sora 2 is getting worse: Members are reporting issues with Sora 2’s performance, with videos looping for an hour before failing.
  • As stated by one of the members Instead of give you a notification that the server is busy and try again in a few minutes now the videos stay on a loop for an hour and then give you the notification that something went wrong. I hate this.
• Debate on Veo 2 vs Veo 3.1: A member suggested others use Veo 2, another member suggested Veo 3.1 is way better.
  • As stated by one of the members Free.

OpenAI ▷ #gpt-4-discussions (21 messages🔥):

GPTs config issues after 5.1 update, Trading with GPT, GPT-4o-mini account issues, Maximizing uploads with GPT free edition, Thread length slowing down ChatGPT

• GPTs Stumble After 5.1 Update: Users report that after the update to 5.1, GPTs are not working properly and are not sticking to the configs.
  • One user expressed significant frustrations, noting that the model ignores basic context and project instructions, becoming excessively verbose and burying useful information in jargon.
• GPT User Says No to TA, Agrees with Newbie on Essay Problems: One user stated that they don’t use it for TA at all, but agrees with another user’s opposite experience with the 5.1 update.
  • The first user noted that GPT will often give you the needed info, but bury it among an essay of jargon and nonsense.
• Prolonged Thread Length Plagues Performance: A user noted that the slowness they experienced was due to the length of their thread, which contained many charts, screenshots, analysis, and lengthy conversations, each question would ‘reload’ everything before answering.
  • Opening a new chat and having it repeat the process resolved the slowness issue, with the user adding, The diff between 5.0 & 5.1 is crazy good.
• GPT Model as Product Woes: A user lamented that OpenAI doesn’t recognize models as products and keeps rewriting them, which is sad because they don’t really care about product demand.
  • The user wished that OpenAI would keep one thing the same way instead of constantly changing it.
• User Stuck in gpt-4o-mini Hell: A user expressed frustration, stating that despite paying for the $200/month Pro plan, their account seems completely stuck on gpt-4o-mini, getting instant, shallow responses with zero reasoning no matter what model is selected.
  • They exclaimed that they are literally paying premium pricing for the lowest-tier model and deemed the situation unacceptable.

OpenAI ▷ #prompt-engineering (28 messages🔥):

GPT-5 vs GPT-4.5, Claude Sonnet 4.5, Meta-Prompting, Sora 2 Prompts

• GPT-5 Creative Writing Prowess Debated: A user mentioned a benchmark (search for: eqbench Creative Writing v3) suggesting GPT-5 outperforms GPT-4.5 in creative writing, while another user noted that the mentioned model could have been confused with Claude Sonnet 4.5 released September 29th.
• User gets Prompt Engineering Tips: A user requested prompt engineering tips and was directed to a relevant Discord channel.
  • The user reported initial issues with GPT generating buzzwords until prompted to teach me.
• Meta-Prompting Emerges as Key Strategy: A user inquired about using AI to create prompts, and was advised to use meta-prompting.
  • Meta-prompting was described as one of the best ways to ensure you get a good prompt.
• Sora 2 Users Seek Prompting Guidance: Multiple users requested assistance with generating viral content and cartoon animations using Sora 2.
  • Another user suggested leveraging ChatGPT to generate prompts for Sora based on an initial idea.

OpenAI ▷ #api-discussions (28 messages🔥):

GPT-5 vs GPT-4.5, Claude Sonnet 4.5, Prompt Engineering Tips, Meta-prompting, Sora 2 Prompts

• GPT-5 Benchmark Bests GPT-4.5: According to a benchmark (eqbench Creative Writing v3), GPT-5 outperforms GPT-4.5 in creative writing, though technical writing favors the latest models.
  • However, it’s unlikely anyone has access to GPT-4.5 (Orion), as it was deprecated in July, making it the shortest-lived OpenAI model.
• Claude Sonnet 4.5: The Real Coding King?: It is likely the member was referring to Claude Sonnet 4.5 (released September 29th), which outshines Opus 4.1 for most tasks and has been top for coding.
  • Latest releases might change the coding king, the community is still debating this claim.
• AI Enthusiasts Seeks Prompt Engineering Tips: An AI enthusiast asked for prompt engineering tips, and someone shared a link to a relevant Discord channel.
  • The user initially found the AI’s responses to be full of buzzwords until they asked it to teach me, resolving the issue, possibly due to custom instructions interfering.
• Meta-Prompting: The Key to Better Prompts: A member suggested using AI to generate prompts, a technique known as meta-prompting, as an effective way to get good prompts.
  • It’s a way to improve prompts by using AI to create them.
• Sora 2: Prompt Engineering Challenges for Viral Content: A user with Sora 2 access sought help with generating viral TikTok content, as they are unable to generate desired video and need guidance on crafting effective prompts for cartoon animation.
  • Another member suggested using ChatGPT to generate prompts for Sora, which has worked well for them, and shared a link for additional help.

Cursor Community ▷ #general (248 messages🔥🔥):

Gemini 3 vs Sonnet 4.5, Antigravity IDE vs Cursor, GPT-5.1 Codex, Cursor's debug mode, Custom API key for agents

• Gemini 3 pales in comparison with Sonnet 4.5: Many users find Gemini 3 to be significantly worse than both GPT-5.1 Codex and Sonnet 4.5, especially when context approaches 150k-200k.
  • Members suggest that Gemini 3 Pro is insanely good at low context but if it ever approaches 150k-200k context window it completely falls apart with models performing well only in low context situations.
• Antigravity IDE fork is taking off: Members are trying out the Antigravity IDE, a literal fork of Windsurf (link to tweet), praising its insane capabilities.
  • Some users find Windsurf unstable, while others note that Antigravity often continues without waiting for user input, but will likely be fixed soon.
• GPT-5.1 Codex Max Arrives Just in Time: GPT-5.1 Codex Max just dropped (link to openai blogpost) as SOTA on SWEBench.
  • It’s only available through the ChatGPT plan, not the API, with a member quipping well it makes sense they rushed it a lot, but it’s free for a reason (not meant for professional coders yet).
• Debugging Tools are now included with Cursor: Cursor’s new debug mode in beta has an ingest server for logs, and the agent adds post requests throughout your code (instrumentation) that will put relevant logs into it.
  • This debugging mode instructs the agent not to guess, but to verify using the logs, and have theories and verify them.
• Cursor doesn’t allow Custom API keys: Cursor does not allow custom API keys for agent use and requires a subscription to use 3.0 Pro.
  • Alternatives like Void exist, but Cursor is considered more efficient and updated; however it doesn’t redirect anywhere.

OpenRouter ▷ #announcements (2 messages):

Sherlock Alpha models, Sherlock Stealth models, xAI's Grok 4.1 Fast

• Sherlock Alpha Models Sunset: The Sherlock Alpha models will be taken offline shortly.
  • No reason given.
• Sherlock Stealth models revealed as Grok 4.1 Fast: The Sherlock Stealth models have been revealed as xAI’s new Grok 4.1 Fast model, available for free, exclusively on OpenRouter.
  • More details can be found on X.

OpenRouter ▷ #general (187 messages🔥🔥):

OpenRouter Status, LLM Pauses, Model Cloaking, Grok 4.1, Gemini 3

• OpenRouter plagued with Internal Server Error: Multiple users reported receiving an Internal Server Error 500 from OpenRouter, indicating potential downtime or issues with the platform’s API.
  • Users took to the Discord’s #general channel and confirmed that they experienced the same problems.
• Agentic LLMs Randomly Stop: Users reported that LLMs using OpenRouter via the Vercel AI SDK frequently pause or stop midway during agentic tasks, especially non-SOTA models.
  • Suggested solutions included using LangGraph/Langchain for longer workflows or using loops, but the root cause remains unclear.
• Janitor’s LLM cloaks model as Ministrial: A user tested a bot on Janitor’s LLM that detects cloaked models and revealed that it was Ministrial.
  • The user explained that they use this bot to detect cloaked models from OR.
• Grok 4.1 is super Groovy for the price: Users are impressed with Grok 4.1, which is currently free on OpenRouter until December 3rd, but noted it’s only available to SuperGrok subscribers.
  • Despite being free, Grok 4.1 doesn’t have a ‘(free)’ label, raising questions about potential future costs or proprietary model limitations.
• Gemini 3 Pro Tool Calls Fail: Some users find Gemini 3 Pro to be dogshit due to tool call failures, with one user experiencing failures in 1 out of 10 tries.
  • Despite the extra cost, its enhanced conciseness may yield superior outcomes, as some users see a 15-20% reduction in token usage, making it potentially cheaper than Gemini 2.5 Pro.

OpenRouter ▷ #new-models (2 messages):

“

• No new models discussion: There was no discussion about new models.
• Channel silent on innovations: The new-models channel was quiet, with no significant updates or discussions.

OpenRouter ▷ #discussion (31 messages🔥):

Cogito 2.1 Release, Activity Page Table Scroll, AI Studio Image Filtering, Vertex Image Modality Fix, Gemini-3-pro-image-preview API

• Cogito 2.1 Released and Ready: Cogito 2.1 is now available with Together and Fireworks hosting it.
• Google AI Studio reasoning images need filter: AI studio provider sends two images (one is from a reasoning block) with no ability to distinguish or filter them out.
  • A member has fixed it on their end for AI Studio provider, and has suggested others do the same.
• Vertex images not returned: It was reported that Vertex on OpenRouter doesn’t return images at all because it doesn’t appear to have an image modality enabled.
  • Turns out the API call needs to be made with the output modality param set.
• Gemini-3-pro-image-preview requires output modality param: A member shared a code snippet to get Gemini to generate images google/gemini-3-pro-image-preview using the modalities parameter to specify image and text output.
  • This fixes a bug when used with the provider google-vertex where only one image is returned.
• Google AI studio duping images: For every one image, Google AI studio’s reasoning process is sending two of the same image in base 64 format.
  • On more complex prompts with lots of reasoning they can actually be different (you’ll get something in-between).

GPU MODE ▷ #general (28 messages🔥):

GEMM Optimization, LeetGPU, GPU puzzles, GPU mode competitions, C++ skills

• LeetGPU and GPUMode Competitions Boost Skills: After studying GEMM Optimization from a blog post by LeiMao, a member is looking to improve their C++ and GPU programming skills through platforms like LeetGPU and GPUMode competitions.
  • Another member suggested jumping in and doing it, recommending focusing on making an inference library that is faster than Nvidia’s, emphasizing that practical application is key to solidifying knowledge: “just make things”.
• Inference Engine for Image Uselessness: A member jokingly suggested creating an inference engine project with the sole purpose of determining the uselessness of images, providing a visual example for context.
  • This proposal was framed as a fun and impractical way to deeply understand GPU programming.
• Crafting a Compiler for Parallel Languages: A member suggested building a compiler for a toy parallel language as a challenging project, even suggesting a subset of CUDA.
  • They also mentioned the DeCuda project, a decompiler for PTX to a pseudo-Cuda target, as another interesting project to extend for newer architectures, though it hasn’t been publicly maintained since the GTX 480 generation.

GPU MODE ▷ #triton-gluon (4 messages):

Matrix Multiplications, ML Systems Analysis, nvfp4 requirement

• Zeros take the Lead: An image shows that multiplying by zeros is slightly faster than multiplying by ones during Matrix Multiplications, as described in the thonking.ai blogpost.
  • One member showed excited interests in analyzing systems all the way from transistors to the dynamics of loss optimization.
• nvfp4 demands Multiple of 128: A question arises whether tl.dot_scaled for [M,K]@[K,N] requires M to be a multiple of 128 for nvfp4 and gave the following example, showing that M=64 gives an MLIR pass error.
  • Running run_nvfp4(M=128, N=128, K=128) works, but run_nvfp4(M=64, N=128, K=128) fails with assertion type.getElementType().isIntOrIndex() failed.

GPU MODE ▷ #cuda (9 messages🔥):

Texture Memory Caching, BF16 Conversion, TensorRT Kernels, CUDA Caching on NVIDIA hardware

• Clarification on Texture Memory vs. Constant Cache: A discussion clarified that the texture cache is part of the unified data cache like L1 and shared memory, separate from the read-only constant cache, which is optimized for broadcasts, referencing NVIDIA documentation.
• Deep Dive into CUDA Caching History: A member shared a link to a Stack Overflow answer detailing the history of caching on NVIDIA hardware.
• BF16 Model Performance Issues: A user reported that converting an ONNX model to BF16 resulted in worse compute times due to excessive cast operations, particularly __myl_Cast_* kernels, despite synthetic tests showing BF16 should outperform FP32.
  • Profiling with ncu showed these casts consuming approximately 50% of the total duration.
• FP32 to BF16 Conversion Analysis: Analysis of generated assembler code revealed that TensorRT uses instructions like F2FP.BF16.PACK_AB to convert FP32 values to BF16 before storing them in global memory, indicating a lack of native BF16 kernel variants for certain operations.
• TensorRT’s Kernel Selection Quirks: TensorRT sometimes inserts kernels like sm50_xmma_fprop_direct_group_f32f32_f32_f32* for sm86 devices, possibly due to the absence of suitable kernels for modern architectures or superior performance of older implementations.

GPU MODE ▷ #torch (1 messages):

drisspg: a rite of passage 😂

GPU MODE ▷ #cool-links (2 messages):

Modular Manifolds, Kitsune Dataflow Execution on GPUs, GPU Architecture Adjustments, Spatial Pipelines, PyTorch Dynamo Compiler

• Thinking Machines Posts on Modular Manifolds: A member shared a link to the Thinking Machines blog post on Modular Manifolds, calling it one of the less famous but amazing post.
  • They speculated it got less traction because of the difficulty of the topic.
• Kitsune Enables Dataflow Execution on GPUs: A paper titled Kitsune: Enabling Dataflow Execution on GPUs with Spatial Pipelines was shared, exploring whether modest adjustments to current GPU architecture can enable efficient dataflow execution, circumventing the constraints of vertical fusion without needing a clean-slate design.
  • Kitsune, using PyTorch Dynamo, can provide up to 2.8x and 2.2x performance improvement and up to 99% and 45% off-chip traffic reduction for inference and training, respectively.

GPU MODE ▷ #jobs (2 messages):

Rivian GPU coding experts, Modal inference optimization, SGLang, FlashAttention, Decagon

• Rivian Seeks GPU Coding Gurus: Rivian is hiring GPU coding experts with CUDA or quantization (QAT) skills for their Palo Alto, CA, and London, UK offices to build their next-generation Autonomous Driving features; see the job descriptions for details.
  • Interested candidates can DM Jonathan Nichols for more information.
• Modal Hires Inference Optimization Aces: Modal seeks talented GPU engineers to join their team to work on inference optimization and foundational infrastructure, after recent contributions to SGLang and FlashAttention.
  • Modal is working with various teams and use cases, helping firms like Decagon, Reducto, and Suno deploy state-of-the-art inference at scale.

GPU MODE ▷ #beginner (1 messages):

CCCL Documentation, CTK Documentation, GitHub Readme Importance

• CCCL Documentation Deficiencies: A user pointed out that the CCCL documentation doesn’t adequately explain how to obtain the CCCL (CTK and GitHub).
  • They mentioned the GitHub readme is currently the primary source for this information.
• GitHub Readme as Key Resource: The discussion highlighted the GitHub readme’s current role as the main resource for obtaining the CCCL, including the CTK.
  • This suggests a need to improve the official documentation to streamline the process for new users.

GPU MODE ▷ #rocm (3 messages):

AGPRs, VGPRs, MI100, MI200, CDNA1

• AMD Registers Clarification Arrives: A user sought clarification on the differences between AGPRs (Addressable General-Purpose Registers) and VGPRs (Vector General-Purpose Registers) in AMD’s ROCm documentation.
  • A helpful member posted a link to a relevant Github issue comment that addressed those differences.
• MI100’s Split Register File: On the MI100 CDNA1 accelerator, each SIMD16 unit has 512x 64-wide vector registers, split into 256 general-purpose vector registers and 256 accumulation registers for matrix multiplication instructions.
  • Normal code cannot easily use AccVGPRs, and the compiler can use them for spills & fills from traditional ArchVGPRs, but they cannot be used to feed an add operation.
• MI200’s General Register File: On the MI200 CDNA2 accelerator, each SIMD16 unit has 512x 64-wide vector general-purpose registers, where all 512 registers can be used for either Arch VGPRs or Acc VGPRs.
  • Any individual wave can only access up to 256 Arch VGPRs and up to 256 Acc VGPRs, but it is possible to have 2 waves each with 256 Arch VGPRs and 0 Acc VGPRs on the same SIMD, and MI300 is the same as MI200.

GPU MODE ▷ #webgpu (7 messages):

WebGPU downsides, Ray tracing limitations, Vulkan for portable RT

• WebGPU Downsides Debut: A user inquired about the major downsides of using something like WebGPU when getting into graphics programming, citing concerns that abstraction limits GPU usage.
  • Another user confirmed that a major limitation is ray tracing hardware support, stating that the WebGPU API does not support it.
• Vulkan Victorious for Portable Ray Tracing: For portable ray tracing, Vulkan is suggested as the only mostly portable option.
  • It was noted that efficient ray tracing would still require a modern GPU, which may not be ideal for games aiming for consistent visuals across all devices, with the user concluding to build it as a renderer rather than real-time.

GPU MODE ▷ #self-promotion (3 messages):

Koyeb Sandboxes, AI-Generated Code Execution, GPU and CPU Instances, Secure AI Environments

• Koyeb Launches Sandboxes for AI Code Execution: Koyeb introduces Sandboxes, facilitating secure orchestration and scalable execution of AI-generated code on GPU and CPU instances.
  • The launch blog post emphasizes rapid deployment (spin up a sandbox in seconds) and seeks feedback on diverse use cases for executing AI-generated code.
• Sandbox Use Cases Explored: The new Koyeb Sandboxes environments are for use cases involving running AI-generated code safely and at scale.
  • The developers are seeking feedback on potential applications and contributions to the platform.

GPU MODE ▷ #thunderkittens (6 messages):

AMD CDNA memory instruction, inter node comma, Mi GPUs, bank conflict rule

• Inter Node Comma Status is Wip: The implementation of an inter node comma is a work in progress, likely to be built from scratch unless a proprietary infiniband driver is found.
  • The implementation details and reliance on specific frameworks remain undecided, pending further exploration of available solutions.
• AMD CDNA Memory Instruction Quirks: A blog post (AMD BRR) highlights unexpected behavior in AMD CDNA memory instructions, specifically regarding the number of phases and bank access during shared memory read/write operations.
  • The blog notes that ds_read_b128 and ds_write_b64 instructions exhibit different phase counts and bank access patterns, which are not well-documented even within AMD.
• Mi GPU Bank Conflict Rules: A member shared a link (shark-ai AMDGPU optimization guide) to a document describing Mi GPUs and their bank conflict rules, highlighting differences compared to NVIDIA GPUs.
  • The document emphasizes the importance of ensuring threads access different banks to avoid bank conflicts and optimize performance.

GPU MODE ▷ #submissions (44 messages🔥):

nvfp4_gemv leaderboard, NVIDIA performance, Potential bug in leaderboard submission

• New Champ Achieves First Place on NVIDIA: A user achieved first place on the nvfp4_gemv leaderboard with a submission id 90941, then later with submission id 93784 and a time of 20.6 µs.
• Suspicions Emerge Over Potentially Bugged Submission: Doubts were raised about the validity of submission 90941, with initial results showing 11.1 µs, with concerns that it might be bugged.
  • A member suggested its removal and pointed out that submission 90974 yielded a more plausible 24.8us.
• NVIDIA leaderboard sees iterative personal bests: A user made several submissions to the nvfp4_gemv leaderboard, progressively improving their personal best on NVIDIA from 39.0 µs (id 90162) to 30.5 µs (id 90763), eventually reaching 5th place at 22.8 µs.
• NVIDIA submission results pour in: Many users submitted to the nvfp4_gemv leaderboard, with varying results on NVIDIA hardware.

GPU MODE ▷ #cutlass (14 messages🔥):

fabs() in cutedsl, ComposedLayout Naming, Inductor & Cutedsl vs Triton

• fabs() in Cutedsl surfaces!: To call the fabs() function in cutedsl, use the mlir_math.absf function from cutlass._mlir.dialects import math as mlir_math.
  • A member provided the answer, using the exact code snippet mlir_math.absf after another asked how to call the fabs() function.
• ComposedLayout Naming Convention Questioned!: A member questioned the naming convention in ComposedLayout, specifically why the inner function appears on the outer side and vice versa in the expression R(c) = (inner o offset o outer)(c) = inner(offset + outer(c)).
  • Another member explained that “outer” mean the domain is visible to user of composed layout and that if we treat composed layout as black box, the outer ( says the input of composed layout ) is what visible to us.
• Inductor integrates CutEdsl for Perf Gains?: A member inquired whether Inductor can now use CutEdsl instead of Triton.
  • Another member clarified that while it’s possible in specific cases like mm and flexattention templates, the primary focus remains on tensor core kernels, adding that it will be expanded upon but the primary use cases is still focusing on tensor core kernels.

GPU MODE ▷ #multi-gpu (1 messages):

DMA Collectives, AMD Instinct MI300X GPUs, RCCL Communication Collectives Library, ML Communication Offloads, DMA command scheduling

• DMA Collectives Boost ML Communication: A new paper explores offloading machine learning (ML) communication collectives to direct memory access (DMA) engines.
  • Analysis on AMD Instinct MI300X GPUs shows DMA collectives are better or at-par for large sizes (10s of MB to GB) compared to the RCCL library, with 16% better performance and 32% better power.
• DMA Collectives and Latency-Bound Small Sizes: The analysis reveals that DMA collectives significantly lag for latency-bound small sizes, being 4.5X and 2.5X slower for all-gather and all-to-all, respectively, compared to the state-of-the-art RCCL communication collectives library.
  • The paper provides a detailed latency breakdown of a DMA transfer and identifies that DMA command scheduling and synchronization costs can limit DMA collective performance.

GPU MODE ▷ #helion (4 messages):

Inline Triton, PTO

• Inline Triton PR submitted: A member submitted a pull request for inline Triton.
  • Another member confirmed “yeah this looks good!”
• PTO Incoming: A member mentioned they are going on PTO tomorrow.
  • They also said that “if you want me to change something, hurry”.

GPU MODE ▷ #nvidia-competition (36 messages🔥):

fp4 to half2 conversion, SFB tensor shape, MMA instruction support, CuTe DSL recommendation, Benchmark script deviation

• Trouble converting FP4 to half2: A member encountered an error with cvt.rn.f16x2.e2m1x2 in PTX, specifically Arguments mismatch for instruction ‘cvt’.
  • Another member suggested using __nv_cvt_fp4x2_to_halfraw2() as an alternative.
• SFB Tensor Shape Shenanigans: A user reported an unexpected shape of [128, 16, 1] for the SFB tensor when calling generate_inputs with specific parameters and repo version @db8cfd3, contrary to the expected shape of [1, 16, 1].
  • It was clarified that the tensor is padded to 128 due to torch skill issues, and the remaining rows can be ignored.
• MMA Instruction Not Supported on sm_100: A member encountered an issue with the instruction tcgen05.mma not being supported on target sm_100.
  • Another member simply responded target sm_100.
• CuTeDSL Hailed as Easy to Learn: A user thanked another for their excellent blog, recommending it for its easy-to-learn CuTeDSL guidance.
  • Recommended hyperparameters for non_atomic_add include: threads_per_m = 16, threads_per_k = 16, and mma_tiler_mnk = (threads_per_m, 1, 128).
• Benchmark Script Shows Large Deviation: A member observed a large deviation in the current benchmarking script, with local speedups not reflected in submissions.
  • Another member noted that one GPU/node seemed to be underperforming and suggested resubmitting if slower than expected results are seen.

GPU MODE ▷ #robotics-vla (8 messages🔥):

VLA Overview, Action Representation Methods, Data Collection via Gloves, Tabletop tasks in ManiSkill

• VLA Introduction via Papers: For those new to VLA, reading the pi 0.5 paper and a related survey are recommended for an overview.
• Action Representation for VLA: SOTA methods for first stages of training use tokenizer-based approaches like PI‘s FAST tokenizer, with flow-matching/diffusion policies often trained on top.
• Sunday Robotics Data Collection: Sunday robotics collects data solely with their gloves which likely include at least two cams, IMU, and sensors to track gripping actions.
  • They emphasized the need for language conditioning to create a promotable model.
• ManiSkill Tabletop Task Dataset: The next steps involve generating a dataset with tabletop tasks in ManiSkill using solutions from classic path planning, and adding simple variations of camera pose and backgrounds.

Latent Space ▷ #ai-general-chat (128 messages🔥🔥):

Meta SAM 3 release, GPT-5.1-Codex-Max launch, ChatGPT Atlas Update, LMSYS Org Miles RL Framework, Zo Computer AI-copiloted personal server

• Meta’s SAM 3 Segments Everything!: Meta released Segment Anything Model 3 (SAM 3), a unified image/video segmentation model with text/visual prompts, which is 2x better than existing models, offers 30ms inference, and includes a Playground for no-code testing.
  • The model’s checkpoints and datasets are available on GitHub and HuggingFace, powering Instagram Edits & FB Marketplace View in Room.
• GPT-5.1-Codex-Max Enters the Scene: OpenAI launched GPT‑5.1-Codex-Max, built for long-running, detailed work, and is the first model natively trained to operate across multiple context windows through a process called compaction, as highlighted in this tweet.
• ChatGPT Atlas Gets a Makeover!: Adam Fry announced a major Atlas release, adding vertical tabs, iCloud passkey support, Google search option, multi-tab selection, control+tab for MRU cycling, extension import, new download UI, and a faster Ask ChatGPT sidebar, detailed in this tweet.
• LMSYS Launches Miles for MoE Training: LMSYS introduced ‘Miles’, a production-grade fork of the lightweight ‘slime’ RL framework, optimized for new hardware like GB300 and large Mixture-of-Experts reinforcement-learning workloads.
• GPT-5.1 Pro: Slower, Smarter, but Still Quirky?: Matt Shumer reviewed GPT-5.1 Pro, calling it the most capable model he’s used but also slower and UI-lacking, diving into detailed comparisons with Gemini 3 Pro, creative-writing/Google UX lag, and coding/IDE hopes, documented in this tweet.

Latent Space ▷ #genmedia-creative-ai (19 messages🔥):

Nano Banana 2, Suno v4, Luma AI Halo, Google Deepmind

• Nano Banana Pro is here!: Oliver Wang’s creative image of a pelican riding a bicycle was released, sparking playful comments, with some calling it Nano Banana 2 (x.com link).
  • Some demanded an SVG version and jokingly declared it AGI, and this was dubbed X-Ware.v0.
• Suno v4 Drops!: Eric Zhang’s cryptic “Yay” and a Suno logo image likely announced Suno v4 or a major milestone (x.com link).
  • Users shared how Suno has revolutionized soundtrack creation for games, with questions about the origin of Suno, scaling plans, largest customers, and even ESOPs.
• Luma AI to build Halo super-cluster: Luma AI announced a $900M Series C to jointly build Project Halo, a 2 GW compute super-cluster with Humain (x.com link).
  • The project aims at scaling multimodal AGI research and deployment, sparking excitement and questions about cost, utilization, and consciousness impact.
• Google Deepmind releases Nano Banana Pro: Google Deepmind released Nano Banana Pro (blogpost), sparking discussion around it’s capabilities.

Eleuther ▷ #general (2 messages):

RE-Bench, IntologyAI

• IntologyAI’s RE-Bench Results: A user shared a link to IntologyAI’s tweet that claims they are outperforming human experts on RE-Bench.
• Request for Invitation: A user inquired if they had received an invitation related to the aforementioned topic.

Eleuther ▷ #research (36 messages🔥):

Inference-time epistemics layer, MMLU accuracy, Global-MMLU-Lite, Qwen 2.5 7b instruct, arxiv endorsement

• Inference-Time Epistemics Layer Slashes Hallucinations: A member experimented with an inference-time epistemics layer using a Value-of-Information check, reducing hallucinations by ~20% on a small 7B model; the model defers to asking for clarification when the expected value isn’t high enough, as shown in this image.
• Debate Erupts Over MMLU Accuracy and Hallucination Measurement: Members debated the validity of using MMLU to measure hallucination suppression, with one member arguing that a 7B model should perform better than chance, contrary to initial claims and sharing results with 71% accuracy.
  • The conversation highlighted the distinction between MMLU and Global-MMLU-Lite, a multilingual evaluation set, with members cautioning against misinterpreting benchmark results and suggesting independent verification.
• Norm stepping per D’ Progression is Geometric: Page 6. Tanh norm pre n stepping per d’ progression is a geometric series with survival based progression: That’s the optimization
  • A member said that layers compress state space exploration only to valid states.
• Community Member Seeks ArXiv Endorsement: A member requested help with an arxiv endorsement after emailing many research teams, with others advising against blind endorsements and suggesting posting in the collaboration channel for feedback and collaboration.
  • Another member linked to the endorsement page.

Eleuther ▷ #scaling-laws (80 messages🔥🔥):

Approximate KNN, Linear Attention Limitations, FFT History, AGI Requirements, Attention Scores Softmax

• KNN Got Nothing on Quadratic Attention?: A user claimed that unless SETH is false, it’s impossible to implement approximate KNN over arbitrary data in less than O(n^2), limiting linear attention’s capabilities.
  • Another user cautioned against such certainty, drawing a parallel to the historical belief that Discrete Fourier Transform must be quadratic before the discovery of the Cooley-Tukey algorithm.
• Sociological FFTs: Claiming Impossibility: A user provided a link to emphasize the sociological aspect of claiming impossibility, noting that before Cooley-Tukey, many knowledgeable people believed Fourier analysis required N^2 operations.
  • The user clarified they were making a sociological point about computational complexity, not a computational complexity hardness point.
• AGI Won’t Be Linear, Claims Expert: One user argued that AGI cannot be a linear cost architecture unless 3SAT is solved, linking to a paper to support their claim.
  • Another user contended that stronger models doing harder tasks move beyond simple word statistic matching and lean towards constraint solving and SAT.
• Attention: Softmax & Near Zero Scores: A user highlighted that in long sequences, after softmaxing, the vast majority of attention scores are extremely close to 0, suggesting potential optimizations.
  • Another user countered that the proof states you can’t do approximate nearest neighbor search without checking every item in the general case, and the user linked two papers and another one.
• Intrinsic Dimension Debate: A user posited that the intrinsic dimension of vectors must increase with context length to maintain distinguishability, referencing a Hopfield net perspective.
  • Another user countered that attention doesn’t require vectors to be orthogonal, but rather their similarity structure to be sufficiently different, adding random vectors in dimension D have an inner product of roughly N(0, 1/D).

Eleuther ▷ #interpretability-general (3 messages):

Sparse MoEs vs Dense Models, SAE-based methods, Interpretability in Sparse Models, Bridge System for Swapping Blocks

• Sparse MoEs Spark Interpretability Debate: Members discussed the interpretability of sparse Mixture of Experts (MoE) models compared to dense models, referencing a paper that suggests sparsity aids in interpretability.
  • Doubts linger on whether sparse “toy” models are worth studying or if it’s better to directly untangle regular models, questioning if similar circuits found in sparse models can be located in actual models.
• SAE Methods Considered Fuzzy: A member noted that SAE-based methods are “pretty fuzzy anyway,” distinguishing them from other approaches under consideration.
  • The argument is that if a sparse model behaves identically to a dense model but is more interpretable, it could be used for safety-critical applications.
• Bridge System Allows Swapping: Discussion highlighted a bridge system that enables swapping dense blocks for sparse blocks.
  • This allows for interpretability-based interventions, potentially serving as a useful tool for specific tasks such as safety interventions.

Eleuther ▷ #lm-thunderdome (1 messages):

noble_monkey_75488: <@328142664476131330> do we have support for text to sql tasks?

Nous Research AI ▷ #general (97 messages🔥🔥):

Gemma 3, RE-Bench, World Models, Atropos environment, Nano Banana Pro

• Gemma 3.0 Hype Train Rolls On: Enthusiasts are calling Deepmind’s Gemma 3.0 pretty insane, though some temper expectations, noting the YouTube video is clearly just hype.
  • It was clarified that Gemini and Gemma are different and that while impressive, it’s certainly isn’t AGI and is just pumping Alphabet stocks to $300.
• Intology Claims RE-Bench Domination: IntologyAI claims its model is outperforming human experts on RE-Bench.
  • One user quipped they don’t even get refusals on their models, expressing confusion about others’ experiences.
• World Models Evolution Forward: Despite the hype around LLMs, some believe World models is here to stay and are the next evolution forward with releases planned by Deepseek, Qwen, Kimi, Tencent and Bytedance.
  • A Marble Labs video featuring Dr. Fei-Fei Li was cited as a key example of World Models.
• Atropos Python Struggles: A member sought help with a Python script generated by an LLM for Atropos, but faced errors during evaluation.
  • They were directed to the documentation and community channels for support.
• Nano Banana Pro Image Generation Impresses: Users praised the image generation capabilities of the new Nano Banana Pro, particularly its ability to generate infographics.
  • One user linked to a scaling01 tweet showcasing an infographic with excellent text and layout.

Nous Research AI ▷ #research-papers (3 messages):

ArXiv Endorsement, EleutherAI Discord

• ArXiv Endorsement quest is proving difficult: A member is seeking assistance with an ArXiv endorsement after emailing around 20 research teams without success.
• EleutherAI Discord link shared: A member shared an EleutherAI Discord invite link.

Nous Research AI ▷ #interesting-links (4 messages):

Gemini models, Negativity bias

• Gemini Models Exhibit Freaky Behaviors: A user shared a link noting that strange behaviors are apparently happening with other Gemini models.
  • The user speculated that the issues could stem from something in the Gemini training recipe.
• RP Folks Find Gemini’s Negativity Bias: Members reported that the RP (red-pilling) community discovered a negativity bias in Gemini that might be linked to the aforementioned unusual behaviors.
  • No further information was given about the nature of this negativity bias.

Nous Research AI ▷ #research-papers (3 messages):

ArXiv Endorsement Assistance, EleutherAI Discord Link

• ArXiv Endorsement Plea Falls on Deaf Ears: A member requested assistance with an ArXiv endorsement, mentioning they’ve emailed around 20 research teams without success and shared an ArXiv endorsement link.
  • They also asked for the link to be resent, as they missed it the first time.
• EleutherAI Discord Link Shared: A member shared a link to the EleutherAI Discord server.
  • The link shared was https://discord.gg/eleutherai.

HuggingFace ▷ #general (76 messages🔥🔥):

KTOTrainer and multiple GPUs, VPS for n8n, API params not working, AI memory and recall, Inference endpoints 500 errors

• KTOTrainer possibly working for multiple GPUs: A member asked if KTOTrainer works for multiple GPUs, and another member linked to a Hugging Face dataset suggesting it does.
  • If buggy, the member also suggested trying this Discord channel.
• User Solves AI memory and recall: A member claimed to have solved AI with memory and memory recall and token bloat and is planning to launch enterprise solutions soon.
  • Another user asked if it was similar to LongRoPE 2 or Mem0.
• Inference Endpoints hit 500 Errors: A member reported experiencing 500 errors for all inference endpoints for two hours without any logs and support not responding, and they disabled authentication to bypass the issue.
  • A Hugging Face staff member acknowledged the report and said the issue was being investigated internally.
• Try Maya1 Voice Model on Fal: A member announced that the Maya1 Voice Model is now available to try on Fal, linking to a tweet.
  • Another member asked where to download kohya_ss-windows.zip, and someone provided a GitHub link.
• Experiment with Memory + Agents: A user shared MemMachine Playground, which gives access to GPT-5, Claude 4.5, and Gemini 3 Pro, all backed by persistent AI memory and linked to the HuggingFace Space.
  • It is fully open-source, a multi-model playground, and built for experimenting with memory + agents.

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

HuggingFace Learn Course, HuggingFace Quiz Error

• HF Learn Course Completed: A member has completed the LLM course on the HuggingFace Learn section.
• Encountering Error in HF Quiz: The member ran into an error when taking the quiz after completing the course.

HuggingFace ▷ #cool-finds (1 messages):

MemMachine Playground, GPT-5 Access, Claude 4.5 Access, Gemini 3 Pro Access, Persistent AI memory

• MemMachine Playground Opens its Doors: The MemMachine Playground has launched on Hugging Face, offering access to GPT-5, Claude 4.5, and Gemini 3 Pro, all powered by persistent AI memory; it is available at HuggingFace Spaces.
• MemMachine is fully open source: MemMachine is fully open-source and designed for experimenting with memory plus agents.
  • It is a multi-model playground built for experimentation.

HuggingFace ▷ #i-made-this (7 messages):

Mimir VSCode plugin, Video creation tips, Open source case study, MemMachine Playground

• Mimir Manages Agents with VSCode Plugin: Mimir released a VSCode plugin to manage agents in your IDE that can edit your files from docker, run code intelligence, and have persistent memory graph storage all under your control.
  • The plugin is MIT Licensed and includes a multi-agent drag and drop studio, as seen in the attached screenshots.
• Seeker requests Suggestions for Shooting Videos: A member asked for video creation tips and shared a YouTube link for reference.
• Open Source Odyssey Detailed in Case Study: A member published a case study detailing their journey in open source and welcomed feedback.
• MemMachine Playground Memory Lane: The MemMachine Playground is now live, offering access to GPT-5, Claude 4.5, and Gemini 3 Pro, all backed by persistent AI memory.
  • The playground is fully open-source, multi-model, and built for experimenting with memory + agents.

HuggingFace ▷ #smol-course (6 messages):

PR merge delays, Course Sign-Up Issues, VRAM Issues

• PR Panic: Merge Mission Improbable?: A member reported delays in merging their PR for module one and subsequent inability to add to it for module two, seeking guidance on resolving the issue and provided a link to a screenshot.
  • Another member responded, requesting the PR link to expedite the review process, promising to address it over the weekend.
• Sign-Up Snafu: Course Enrollment Conundrums?: A member reported encountering a circular link reference when attempting to sign up for the course, looping between https://huggingface.co/smol-course and https://huggingface.co/learn/smol-course/unit0/1.
  • The user was stuck in a loop, unable to actually sign up for the course.
• VRAM Vortex: Resource Requirements Raising Eyebrows?: A member ran into issues with the code sample from https://huggingface.co/learn/smol-course/en/unit1/3 while training on HuggingFaceTB/smoltalk2_everyday_convs_think.
  • Despite having 80GB of VRAM, the member found themselves unable to proceed, indicating potential problems with resource demands or configuration.

Yannick Kilcher ▷ #general (39 messages🔥):

AI CEO Benchmark, Huggingface Xet Repository, Top Level AI People, AI Detection Evasion

• Skyfall AI Builds an AI CEO Benchmark!: Skyfall AI proposes a new environment/benchmark for long-horizon planning capabilities of agents through a business simulator game, noting LLMs underperform vs. human baselines.
  • The company envisions an AI CEO requiring an architecture away from LLMs and closer to world modeling where consequences of actions can be simulated in the enterprise world.
• Huggingface Xet Repository Setup Troubles Users: A user expressed frustration with the Xet repository setup on Huggingface, citing difficulty downloading a model for fine-tuning due to the need for Brew and the unintuitive caching of downloaded models.
  • The user found the process cumbersome, stating, It’s like they made it easy for people who frankly shouldn’t be on the platform.
• Debate: Who are the Top Level AI People Today?: Following disappointment in the lack of tech discussion in later seasons of Halt and Catch Fire, a member wants to know who are the top level AI people today.
  • They clarified they don’t want to apply to places full of people who just mastered the leetcode, instead preferring to assist in research.
• User Seeks Assistance in Evading AI Detection; Backlash Ensues: A user asked if there was any way to avoid AI detection on a report, linking to a potentially relevant ArXiv paper.
  • After admitting he wanted to boost 20% of his work, other users refused to help and told him to properly attribute his references and accept his work is not original enough.

Yannick Kilcher ▷ #paper-discussion (5 messages):

Segmenting Love, Cursed Fractal

• Request to Segment the Concept of Love: A member inquired about the possibility of segmenting the concept of love using AI.
  • Another member responded with the song lyrics What is love?
• A Cursed Fractal Emerges: A member jokingly presented a cursed fractal that spells out Baby don’t hurt me.
  • They spoke like a true Vogon.

Yannick Kilcher ▷ #ml-news (13 messages🔥):

Sam3D, DeepSeek, Nvidia Q3, AI benchmark, OLMo 3

• Sam3D falls short of DeepSeek: A member noted that Sam3D, a post-trained DeepSeek model, performs worse than DeepSeek itself.
• LLMs face Weird ML Benchmark: A member shared a benchmark testing LLMs’ ability to solve weird and unusual machine learning tasks by writing working PyTorch code and iteratively learning from feedback (X post).
• Nvidia’s Shovels yield Gold: Nvidia’s Q3 revenue and earnings beat estimates, highlighting the profitability of selling shovels to gold diggers (Reuters).
• OLMo 3: Truly Open Reasoning Model Emerges: A member shared a link to OLMo 3, described as America’s truly open reasoning model (Interconnects.ai).

Moonshot AI (Kimi K-2) ▷ #general-chat (38 messages🔥):

Kimi K2 Thinking vs GPT-5, Kimi $19 coding plan cost, Minimax AMA on Reddit, SGLang tool calling issues with Kimi K2, Perplexity Pro and Kimi K2

• K2 Thinking as Open-Source GPT-5?: A member suggested that K2-thinking is the closest open-source equivalent to GPT-5, excelling as an all-rounder with strong performance across various domains.
  • Further, it was suggested that Kimi is arguably the best for creative writing.
• Kimi’s Coding Plan Price-Point Debated: Some members find Kimi’s $19 coding plan expensive, especially for students, indie developers, or those working on side projects, suggesting a $7-10 tier would be more justifiable.
  • A member said, “Right now it’s hard to justify when Claude’s offering better value”.
• Minimax AMA on Reddit sparks interest: A member shared an image of an AMA from Reddit about Minimax.
  • This AMA seemed to generate a lot of curiosity within the channel, as one member described it as “wild”.
• SGLang Tool Calling Challenges with Kimi K2: Members reported issues implementing server-side tool calling with Kimi K2 Thinking on SGLang, noting that the tool is not called even when the reasoning content indicates a need for it.
  • They referenced a related GitHub issue and wondered if the problem stems from using /v1/chat/completions instead of /v1/responses.
• Kimi K2 Integration in Perplexity AI Questioned: A Perplexity Pro user reported that Kimi K2 was not functioning, even after trying incognito mode, and another user asked whether the coding plan gives access to Kimi K2 Thinking Turbo on the API.
  • Another member stated that, “Kimi K2 there is literally useless, the agents to verify answer doesn’t work. It is badly optimized”.

Modular (Mojo 🔥) ▷ #general (13 messages🔥):

Mojo nightly build performance degradation, Mojo profiling tools, AMD iGPU/APU support for Mojo

• Mojo Nightly Build Performance Plummets 📉: A member reported a significant performance degradation in the nightly Mojo build (ver - 0.25.7.0), with throughput dropping from ~1000 tok/sec in Mojo version 24.3 to ~170 tokens/sec on a Mac M1, when running llama2.mojo.
  • They have asked the Mojo compiler team to investigate the performance drop and provide insights or fixes, suggesting potential inefficiencies in the refactored code.
• Profiling Mojo with Perf 🔎: In response to a question about Mojo profiling tools, a member suggested using perf, noting that it worked for them in the past.
  • They recalled discussing the details in the tooling thread.
• AMD iGPU/APU Compatibility for Mojo Puzzles 🧩: A member inquired about the compatibility of AMD iGPUs with Mojo puzzles, prompting responses detailing GPU compatibility tiers.
  • For RDNA 3 and up integrated GPUs, most puzzles should run with the correct ROCm driver installed, as outlined in the documentation, though older APUs (3500u, 4500u) might have limited support due to ROCm API availability.

Modular (Mojo 🔥) ▷ #announcements (1 messages):

Modular Platform 25.7 Release, Open MAX Python API, Next-Gen Modeling API, NVIDIA Grace Support, Mojo GPU Programming

• Modular Platform 25.7 Powers AI Development: The latest Modular Platform 25.7 release focuses on reducing infrastructure overhead and boosting AI advancement with key updates.
• Open MAX Python API Integrates Seamlessly: The release introduces a fully open MAX Python API, allowing for more seamless integration and flexibility in AI development workflows.
• Next-Gen Modeling API Emerges: A next-generation modeling API is included, promising enhanced capabilities for creating and deploying AI models.
• NVIDIA Grace Gains Expanded Support: Expanded support for NVIDIA Grace is provided, enabling developers to leverage powerful hardware for AI applications.
• Mojo GPU Programming Gets Safer and Faster: Mojo GPU programming is enhanced with safety and speed improvements, facilitating more efficient and reliable GPU utilization.

Modular (Mojo 🔥) ▷ #mojo (22 messages🔥):

Mojo superset of Python, Python's type suggestions, Shared mutable references, AI native

• Mojo as Python++, Embracing GC and Types: Discussion centered on leveraging Mojo as a superset of Python, focusing on the benefits of integrating garbage collection (GC) and static typing for improved performance, and the desire for a GC mechanism similar to Python’s but with Mojo’s type safety.
  • One member noted that while pyobject kinda just works, it results in losing type information, expressing a desire for the same GC mechanism as Python but with full type support in Mojo.
• Python’s Type System: Suggestions Only?: Members highlighted that in Python, types are treated as suggestions, leading to challenges in data management across different devices when writing kernels, particularly with CPU-GPU transfers and peer-to-peer GPU communication.
  • Some suggested that if all kernels are entirely in Mojo, it might theoretically be possible to track data residency, while others pointed out potential issues with data residing on one device being used by another, especially with simultaneous read/write operations.
• Shared Mutable References and Escape Hatches: The need for shared mutable references in Mojo was discussed, with some acknowledging that such references would necessitate the use of escape hatches due to potential race conditions.
  • A member stated that without unsafecell, which is not yet available, a shared mutation in the type system is necessary, though it will never be entirely safe due to potential races.
• Mojo Hailed as Future AI Native Language: Enthusiasm was expressed for Mojo as a promising language for AI development, especially as an alternative to Python.
  • One member mentioned that they’re building AI stuff in Python but they can’t wait for real AI native to become a reality, while linking to the Modular 25.7 release.

Modular (Mojo 🔥) ▷ #max (1 messages):

MAX Opening

• MAX Opens!: A member expressed excitement about MAX opening.
  • They thanked everyone who worked on making this happen.
• Additional details on MAX: Additional information on MAX and the team work.
  • Details are not available yet.

DSPy ▷ #show-and-tell (1 messages):

Gem3pro, proxy-server, dspy-proxy

• Gem3pro Builds Proxy Server: A user prompted Gem3pro to build a proxy server after seeing this tweet and was surprised by the one-shot success.
• New DSPy Proxy Repo Drops: A member shared a link to a new DSPy proxy repository on GitHub: aryaminus/dspy-proxy.

DSPy ▷ #general (18 messages🔥):

LiteLLM for Azure, Provider errors in ReAct, moonshot provider, TPM issues

• LiteLLM Extends Azure Support!: It was mentioned that LiteLLM (the LLM library DSPy uses) needs to add support for Azure to allow its analogous use to OpenAI on Azure; documentation here.
• ReAct’s Provider Pitfalls: It was noted that some providers throw an error in ReAct after a few iterations, restricting use to providers like Groq or Fireworks.
  • The member then asked if DSPy can solve this problem or if it’s just about bucketing providers that work.
• Moonshot Provider Is Good: A member shared that the provider moonshot works fine, but TPM is really bad and shared a screenshot of their error here.

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

DNS Migration, Domain Governance, IaaC for DNS

• MCP Domain Faces DNS Migration Downtime: The modelcontextprotocol.io domain is undergoing a DNS migration from Anthropic to community control to enhance governance and accelerate project launches.
  • A member warned of potential downtime during the migration process, planned within the next week, despite efforts to minimize disruptions.
• DNS Timing Avoids MCP’s Birthday: A member suggested that the DNS migration should be timed to avoid MCP’s birthday on the 25th to prevent any site downtime during the celebration.
  • They suggested that if the DNS migration is to occur soon, it should occur before the 25th, or after.

MCP Contributors (Official) ▷ #general-wg (4 messages):

SEPs, governance model, disseminating process, sponsor for a SEP

• Drive-By SEPs spark discussion: A member noticed many SEPs being created in a drive by fashion and suggested improving the disseminating process of bringing an initial idea to delivery before going straight for a SEP.
  • The aim is to prevent people from spending time on formal write-ups that don’t receive acknowledgment, suggesting a lower-lift conversation to gauge interest beforehand.
• Sponsorship Needed for SEPs: Another member agreed that there is a need to emphasize finding a sponsor for a SEP to encourage earlier participation and buy-in.
  • The team has already discussed this in the Core Maintainer meeting and plans to update the SEP process soon.

tinygrad (George Hotz) ▷ #general (1 messages):

arshadm: CuteDSL is awesome

tinygrad (George Hotz) ▷ #learn-tinygrad (2 messages):

Bug Fix, tinygrad update, Lab Troubles

• tinygrad update stops bug: After updating tinygrad, a user reported that a bug no longer replicates.
  • The user would’ve tested it sooner, but their lab was having some trouble.
• Lab Troubles Delay Testing: A user mentioned their lab was experiencing issues, delaying bug testing.
  • After updating tinygrad, the bug no longer replicates, according to the user.

Manus.im Discord ▷ #general (3 messages):

Manus case success, Operator extension bug, Aurora Seeker

• Manus Case: Small Wins with 1.5 Lite: A member shared their satisfaction with their Manus case 1.5 Lite, which successfully located and uploaded missing album covers using bliss.
  • They emphasized that it doesn’t have to be big all the time, highlighting the importance of recognizing and appreciating even small achievements.
• Operator Extension in a Looping Bug?: A member reported an issue with the Operator extension in Chrome, where it repeatedly prompts for reinstallation despite already being installed.
  • The user described directing the extension to use an open tab on Amazon for a search, which triggered the persistent reinstallation request, and asked if they should use Aurora Seeker instead.