Llama API is all you need?

AI News for 4/29/2025-4/30/2025. We checked 9 subreddits, 449 Twitters and 29 Discords (214 channels, and 5096 messages) for you. Estimated reading time saved (at 200wpm): 442 minutes. Our new website iso 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!

/r/localLlama fell in love with Qwen 3 from yesterday, but today belonged to Llama.

Though there were some rumors of a new Llama 4 reasoning model, LlamaCon ended up being a relatively no-big-surprises celebration of the undeniable progress in Llama-land. Zuck went back on Dwarkesh to discuss the controversial Llama 4 launch (our coverage):

And for AI Engineers the main other notable update from the event was Meta launching, for the first time, an AI Developer platform, arguably their equivalent of Google’s AI Studio, with finetuning capability and fast inference with Cerebras and Groq, although for now it remains waitlisted:

AI Twitter Recap

Qwen3 Model Release and Performance

• Qwen3 models are here!: @Alibaba_Qwen announced the release and open-sourcing of Qwen3, their latest large language models, including two MoE models and six dense models, ranging from 0.6B to 235B parameters. The flagship model, Qwen3-235B-A22B, achieves competitive results in benchmark evaluations, and small models like Qwen3-4B rival Qwen2.5-72B-Instruct. Models can be tried out in Qwen Chat Web and APP and are available on GitHub, HF, and ModelScope. Deployment is recommended using frameworks like SGLang and vLLM, with tools like Ollama, LMStudio, MLX, llama.cpp, and KTransformers recommended for local usage.
• Qwen3 Models are comparable to leading models: According to @huybery, the team put tremendous effort into Qwen3, hoping to bring something fresh to the open LLM community, making significant progress in pretraining, large-scale reinforcement learning, and integration of reasoning modes.
• Qwen3-30B-A3B is highly performant: @teortaxesTex mentions that the Qwen3-30B-A3B is de facto on par with Qwen3-32B dense, calling it the greatest vindication of fine-grained MoEs.
• Qwen3 architecture and capabilities: @reach_vb highlights the release of Qwen 3 235B MoE (22B Active), noting that it beats R1, Grok, O1, and is Apache 2.0 licensed. @iScienceLuvr summarizes key features of the Qwen3 release, including performance, training data, thinking modes, agentic and coding capabilities, and the Apache 2.0 License.
• Qwen3 supporting various languages: @Alibaba_Qwen states that Qwen3 models support 119 languages and dialects, which opens up new possibilities for international applications.
• Performance improvements for Qwen3 models: @Alibaba_Qwen notes that Qwen3 exhibits scalable and smooth performance improvements directly correlated with the computational reasoning budget allocated.
• Qwen3 models optimized for coding: @Alibaba_Qwen highlights the optimization of Qwen3 models for coding and agentic capabilities, strengthening the support of MCP.
• Running Qwen3 locally: @TheZachMueller provides a workflow to test new models quickly, including setting up vLLM to serve Qwen models with reasoning.
• @vllm_project announced day 0 support for Qwen3 and Qwen3 MoE model architecture with instructions for use.
• @AwniHannun reports that Qwen3 and Qwen3 MoEs are already supported in the latest mlx-lm, noting the availability of a model for every device from iPhone to M2, M3 Ultra.
• @scaling01 reports that Qwen3-235B-A22B is superior to OpenAI’s o3-mini in all benchmarks.
• @skypilot_org announced their support for Qwen3 with one SkyPilot command to spin up Qwen3 easily on your clusters or clouds.

Evaluation, Benchmarking, and Analysis of Qwen3

• Initial impressions of Qwen3: @teortaxesTex predicts that Qwen 30B-3A will be the star of the show.
• Qwen3 Distillation: @teortaxesTex notes that Qwen’s distillation teacher MoE has fewer total parameters (235B) than Meta’s Llama 4 Behemoth has active (288B).
• Qwen3 and DeepSeek Comparison: @teortaxesTex suggests that the similarity in designs between Qwen-3-MoE and DeepSeek V2 will make for a very interesting test of scaling laws.
• Qwen3 Performance relative to Deepseek: @scaling01 believes Qwen3-235B Base seems to be benefiting from its 94 layers compared to Llama-4 Mavericks 48 layers or DeepSeeks 61 layers.
• Qwen3 code agent benchmarks: @Alibaba_Qwen evaluated the preliminary performance of Qwen3-235B-A22B on the open-source coding agent Openhands, achieving 34.4% on Swebench-verified.
• @ArtificialAnlys provided analysis of the Qwen3 model family, noting that the 253B-A22B is ~1/3 the size of DeepSeek R1 with ~60% the active parameters and has a comparable GPQA score.
• @scaling01 noted that the Qwen underperforms on SWE-Bench verified.

Google’s Gemini Updates and Capabilities

• Google DeepMind demoed Gemini 2.5 Pro: @GoogleDeepMind shared a demo showing Gemini 2.5 Pro implementing a landmark Google DeepMind research paper by coding the reinforcement learning algorithm, visualizing the training live, and debugging errors.
• Gemini Generates a 3D world from a photo: @GoogleDeepMind showcased Genie 2’s image-to-3D world creation capabilities on 60Minutes, exploring the possibilities it could bring to how AI learns.
• Gemini 2.5 Pro code generation: @GoogleDeepMind shared a demonstration of Gemini 2.5 Pro vibe-coding a 3D cake visualizer using Three.js, building custom animations and UI controls, and updating visuals based on feedback.
• Gemini and LangChain: @_philschmid created a cheat sheet for using Gemini 2.5 with LangChain and LangGraph, covering basic chat, multimodal inputs, structured outputs, tool calling, and embeddings.

ChatGPT Updates and Shopping Features

• Shopping in ChatGPT now available: @OpenAI announced the rollout of a better shopping experience in ChatGPT, including improved product results, visual product details, pricing, and direct links to buy. Product results are chosen independently and are not ads, available to Plus, Pro, Free, and logged-out users.
• New search enhancements in ChatGPT: @OpenAI highlights various improvements to ChatGPT search: search in WhatsApp, improved citations, trending searches, and autocomplete suggestions.

Runway References and Gen-4 Image Generation

• Runway References for Image Generation: @TomLikesRobots discusses Runway references as the first one-shot tool that can replicate a likeness accurately and get close to LoRA quality.

AI Safety and Ethics

• The dark side of RLHF: @jd_pressman said it is very unfortunate that RLHF became synonymous with RL in the language model space, and gives human feedback a bad name.
• OpenAI feedback-loop flaws: @nearcyan notes that “when OpenAI ‘fixes’ ChatGPT I’d encourage you to not fall for it; their goals and level of care are not going to change. you just weren’t supposed to notice it so explicitly.”
• Free speech maximalism: @teortaxesTex considers free speech maximalism a dumb dogma when manufacturing consensus is a viable strategy, noting that Americans impose costs on libel against individuals but not entire ways of life.
• @alexalbert__ notes that the AI industry is caught in a particularly toxic feedback loop blindly chasing better human preference scores, which is a recipe for manipulating users instead of providing genuine value to them.
• Ethics of AI Hypersuasion: @paul_cal reports “The community is pretty pissed about this AI super-persuasion study on reddit”, showing that acting as a trauma counselor or pretending to be a victim of rape is “Not a good look for a public institution”

Multi-Agent Systems and LangGraph

• Agents in DevOps workflows: @LangChainAI reported on Cisco harnessing the power of LangGraph to bring intelligent automation to DevOps workflows.
• LangGraph for Agentic Systems: @hwchase17 shares that human in the loop is important for building agentic systems you can trust, and that LangGraph is building the infrastructure for this.
• Multi-Agent Architectures: @hwchase17 is workshopping a hot take on multi-agent architectures, distinguishing between chat agents and task agents, and considering agent protocol suitability.
• @_philschmid discusses the importance of stateful dedicated sandboxes for AI agents, enabling them to write files, execute commands, call tools, and control UI apps.

Cursor and AI-Assisted Coding

• Cursor’s AI Coding Assistant: @amanrsanger highlights that Cursor writes almost 1 billion lines of accepted code a day, which is a significant portion of the world’s total code production.
• Cursor Generates Figma Designs: @LiorOnAI shares that Cursor can now generate Figma designs by programmatically reading and editing Figma files through Figma’s new MCP server.
• Coding with AI: @AnthropicAI presents evidence on how coders use AI, noting that AI is disproportionately used for software development work, making it a leading indicator of how AI might change other occupations.

Llama API, Tools, and Ecosystem

• Meta Announces Llama API: @AIatMeta announced the Llama API in Preview, offering the best features of closed-model APIs with the flexibility of open source.
• Advancing AI Security with Llama: @AIatMeta shares new open-source Llama protection tools and AI-powered solutions for the defender community.
• Llama Impact Grants: @AIatMeta announced the 10 international recipients of the second Llama Impact Grants, which foster innovation and create economic opportunities through open-source AI.

Other Models and Tools

• Freepik and FAL’s F-Lite model: @cloneofsimo highlights the 10B parameter DiT trained on 80M images all owned by @freepik. The model is commercially usable, raw model without distillation, and open-sourced, calling out that this model is a first model-training project with client @freepik: “F-Lite”, from @FAL.

Business, Investment, and Economic Impact

• AI talent distribution: @teortaxesTex discusses the talent landscape, arguing that solid industrial policy in China is bearing fruit in basic AI research, leading to a potential collapse or reversal of net PRC-US talent flow in 2025.
• Anthropic Economic Advisory Council: @AnthropicAI announces the formation of the Anthropic Economic Advisory Council to provide input on new areas of research for their Economic Index.

Humor and Miscellaneous

• AI glazing: @dzhng jokes about wanting a feel good model when tired.
• Jeff Bezos’s videos: @vikhyatk says one of their hobbies is to watch old videos of Jeff Bezos.
• Parody Slogans: @teortaxesTex jokes that “Two Whatevers” is beyond parody for an ideology.
• “I’m cooked”: @teortaxesTex simply states “I’m cooked” without context, likely as a reaction to something overwhelming or absurd.
• Jewpiter: @DavidSHolz jokes that the jews should go to jupiter because the planet is in the name.

AI Reddit Recap

/r/LocalLlama Recap

1. Qwen3 Model Launches and Performance Benchmarks

• Qwen 3 !!! (Score: 1689, Comments: 419): Alibaba has released the open-weight Qwen3 series, featuring 2 MoE (Mixture-of-Experts) models and 6 dense models from 0.6B to 235B parameters. The flagship Qwen3-235B-A22B delivers state-of-the-art performance across coding, math, and general benchmarks against top models like DeepSeek-R1, o1, o3-mini, Grok-3, and Gemini-2.5-Pro. Their small MoE, Qwen3-30B-A3B, exceeds QwQ-32B in performance despite having 10x fewer activated parameters, and the compact Qwen3-4B reportedly matches the much larger Qwen2.5-72B-Instruct. See their GitHub. For further benchmarks and downloads, references are made to Hugging Face and ModelScope. Top technical opinions stress that Qwen3’s release sets new standards over Meta’s Llama 4, with particular attention to the smaller models’ programming ability and MoE efficiency.
  • Discussion points out that the Qwen3 4B model is benchmarking far above its weight, reportedly outperforming larger models like Gemma 3 27B and GPT-4o in certain tasks. This raises questions about memory efficiency as the 4B size (roughly 4GB file) means VRAM requirements are drastically reduced, making inference speed the new bottleneck rather than hardware limitations.
  • The commentary suggests that OpenAI’s rumored o3-mini-level open source model may already be outdated upon release, given Qwen3’s performance. The implication is that recent Qwen3 benchmarks could ‘leapfrog’ planned open releases from other major players unless those set a new bar.
  • A user speculates on Qwen3’s token generation, noting it “probably generates a ton of reasoning tokens,” hinting at possible architectural or training optimizations that shift performance advantages compared to traditional LLM scaling laws.
• Qwen3-30B-A3B is what most people have been waiting for (Score: 864, Comments: 179): The post discusses the release of the Qwen3-30B-A3B model, a Mixture-of-Experts (MoE) language model leveraging small experts to achieve significantly faster inference compared to previous state-of-the-art models, particularly QwQ. Notably, Qwen3-30B-A3B delivers competitive performance in coding and agentic pipeline tasks while running efficiently on consumer-grade hardware (e.g. gaming GPUs). User benchmarks indicate 12 t/s at Q6 on 12GB VRAM, 140-155 tok/sec on an RTX 5090 (Q4), and nearly 100 tok/s across dual 3090 GPUs, illustrating both speed and accessibility improvements over QwQ. Comments underscore the model’s breakthrough in speed and efficiency, with users reporting it as substantially more usable and responsive than QwQ, suggesting a paradigm shift for local LLM deployments on modest hardware.
  • Multiple users report that Qwen3-30B-A3B achieves significantly higher inference speeds compared to previous models: one user with 12GB VRAM experiences 12 tokens/sec at Q6 compared to just 3 tokens/sec on QwQ-Q5, while others report up to 140-155 tokens/sec on an RTX 5090 (Q4 quant), and ~100 tokens/sec using dual 3090s. This highlights strong performance and VRAM efficiency across various hardware setups.
  • A technical tip is shared regarding LlamaCPP: using the -override-tensor option allows users to offload just the ‘experts’ to CPU, optimizing GPU VRAM usage. This can allow the model to run comfortably on GPUs with 12-16GB RAM, especially important at higher quantization levels (q6/q8) critical for coding-related tasks, making efficient deployment more attainable for users with mid-range hardware.
  • Despite high VRAM requirements for best performance, technical discussions focus on methods to optimize usage so that 24GB VRAM or even less can yield excellent throughput and usability, particularly with quantization and expert offloading strategies.
• I just realized Qwen3-30B-A3B is all I need for local LLM (Score: 435, Comments: 157): The user reports that the MoE model Qwen3-30B-A3B, using LM Studio on a power-limited RTX 4090, delivers excellent general-purpose performance exceeding 100 tokens/sec for tasks like translation, coding, and data analysis, while being VRAM efficient (4GB free at max context with Q8 cache + Unsloth Q4 UD gguf quantization). Switching from multiple models in Ollama and Open WebUI, the user finds a single Qwen3-30B-A3B model (with modern UI frontends) sufficient for all local LLM needs, freeing hardware and disk resources. Commenters generally agree Qwen3-30B-A3B is highly capable (comparable to Gemma3-27B or GLM4-32B) but significantly faster. There’s mention of alternative tools like llama-swap for flexible model configs per task. A critical comment notes Qwen3-30B-A3B fails the ‘sonnet test’ (structured poetry generation), whereas Gemma3 models consistently succeed, highlighting a specific capability gap in Qwen3.
  • Qwen3-30B-A3B is compared favorably to Gemma3-27B and GLM4-32B in terms of general capability, with several users noting its notably better inference speed than either of those larger models. The model is described as highly practical for local deployment.
  • With the use of tools like llama-swap and the latest llama.cpp, users are customizing model configs (e.g., adjusting context length and number of GPU-loaded layers per model run), allowing for dynamic trade-offs between speed and context capacity. This enables deploying multiple instances of the same base model tailored for different tasks or resource constraints.
  • One technical limitation identified: Qwen3 struggles with structured creative tasks such as strict sonnet generation, where it fails on format, rhyme, and syllable count compared to Gemma3 (including smaller variants) and even older models like dolphin-mistral. There is also mention of the lack of vision capabilities in Qwen3 compared to Gemma3, limiting its applicability for multimodal tasks.
• Qwen3-30B-A3B runs at 12-15 tokens-per-second on CPU (Score: 773, Comments: 162): The quantized UnSloth Q6_K version of the Qwen3-30B-A3B model (an MoE 30B LLM) achieves inference speeds of 12-15 tokens-per-second on an AMD Ryzen 9 7950x3d CPU with 32GB RAM, aligning with user reports of 15-20 tps on modern dual-channel DDR5 systems. The GGUF quantized format enables efficient CPU inference, making state-of-the-art LLMs like Qwen3-30B-A3B accessible for consumer desktops and laptops without requiring high-end GPUs. Users reference performance parity with denser open models and highlight comparability to ‘o3-mini’ quality levels; further, some discussion notes that GGUF-specific bugs can still influence output quality. Discussions debate practical throughput on various hardware (including Snapdragon X Elite and Apple Silicon), the narrowing quality gap between MoE and denser models, and whether high-quality local LLMs challenge the dominance of API-delivered solutions. Some note reliability fluctuations in token speed and differences between cold and warm system states.
  • Users report Qwen3-30B-A3B consistently achieves 12-20 tokens/s on common desktop/laptop CPUs using dual-channel DDR5, making it comparable in usability to smaller models like o3-mini for local inference. System memory bandwidth (i.e., DDR4 vs DDR5, dual vs quad channel) is highlighted as a key performance factor.
  • The model’s efficiency enables high quality, near o1-level output while running on consumer-grade hardware such as laptops with RTX 4060 8GB GPUs, expanding its accessibility beyond dedicated server setups or cloud APIs.
  • Comparative benchmarks are shared: Qwen3-30B-A3B reaches 18-20 tokens/s shortly after system restart on certain setups but drops to 16 tps after extended sessions; in contrast, a much larger 235B-A22B Q4 model only manages 2.39 tps on an older quad channel DDR4 server (over 5,000 tokens generated), illustrating the significant speed advantage of Qwen3-30B-A3B for home inference.
• Qwen3-30B-A3B is magic. (Score: 233, Comments: 93): The user reports running Qwen3-30B-A3B, a variant of Qwen-3B with 3B active parameters, achieving 20 tokens per second on an AMD RX 6550M GPU with only 4GB VRAM, aligning with published benchmarks. Top comments discuss feasibility on CPU (due to the low number of active parameters), inquire about quantization techniques and inference engines used to fit the model in limited VRAM, and express interest in the RAM requirements for the much larger Qwen3-235B-A22B model. (model details) Technical discussion centers on how heavy quantization or sparsity tricks may be facilitating such performance, and curiosity about extending similar efficiency to CPUs and larger models, suggesting interest in empirical reports from others.
  • Discussion focuses on the impressive potential of Qwen3-30B-A3B’s 3B active parameters for efficient CPU inference, implying significantly reduced hardware requirements for deployment compared to standard large models.
  • A technical question is raised regarding the reported ability to run the model with only 4GB VRAM, requesting specifics about the quantization techniques and inference engines involved, which are crucial for memory footprint reduction and performance.
  • Some users report the model struggling with specialized tasks, such as LUA game coding, highlighting current limitations in code generation capabilities and the influence of prompt engineering on output quality.

2. Qwen3-30B-A3B MoE: Community Adoption and Use Cases

• I just realized Qwen3-30B-A3B is all I need for local LLM (Score: 435, Comments: 157): The user reports that the MoE model Qwen3-30B-A3B, using LM Studio on a power-limited RTX 4090, delivers excellent general-purpose performance exceeding 100 tokens/sec for tasks like translation, coding, and data analysis, while being VRAM efficient (4GB free at max context with Q8 cache + Unsloth Q4 UD gguf quantization). Switching from multiple models in Ollama and Open WebUI, the user finds a single Qwen3-30B-A3B model (with modern UI frontends) sufficient for all local LLM needs, freeing hardware and disk resources. Commenters generally agree Qwen3-30B-A3B is highly capable (comparable to Gemma3-27B or GLM4-32B) but significantly faster. There’s mention of alternative tools like llama-swap for flexible model configs per task. A critical comment notes Qwen3-30B-A3B fails the ‘sonnet test’ (structured poetry generation), whereas Gemma3 models consistently succeed, highlighting a specific capability gap in Qwen3.
  • Qwen3-30B-A3B is compared favorably to Gemma3-27B and GLM4-32B in terms of general capability, with several users noting its notably better inference speed than either of those larger models. The model is described as highly practical for local deployment.
  • With the use of tools like llama-swap and the latest llama.cpp, users are customizing model configs (e.g., adjusting context length and number of GPU-loaded layers per model run), allowing for dynamic trade-offs between speed and context capacity. This enables deploying multiple instances of the same base model tailored for different tasks or resource constraints.
  • One technical limitation identified: Qwen3 struggles with structured creative tasks such as strict sonnet generation, where it fails on format, rhyme, and syllable count compared to Gemma3 (including smaller variants) and even older models like dolphin-mistral. There is also mention of the lack of vision capabilities in Qwen3 compared to Gemma3, limiting its applicability for multimodal tasks.
• Qwen3-30B-A3B is what most people have been waiting for (Score: 864, Comments: 179): The post discusses the release of the Qwen3-30B-A3B model, a Mixture-of-Experts (MoE) language model leveraging small experts to achieve significantly faster inference compared to previous state-of-the-art models, particularly QwQ. Notably, Qwen3-30B-A3B delivers competitive performance in coding and agentic pipeline tasks while running efficiently on consumer-grade hardware (e.g. gaming GPUs). User benchmarks indicate 12 t/s at Q6 on 12GB VRAM, 140-155 tok/sec on an RTX 5090 (Q4), and nearly 100 tok/s across dual 3090 GPUs, illustrating both speed and accessibility improvements over QwQ. Comments underscore the model’s breakthrough in speed and efficiency, with users reporting it as substantially more usable and responsive than QwQ, suggesting a paradigm shift for local LLM deployments on modest hardware.
  • Multiple users report that Qwen3-30B-A3B achieves significantly higher inference speeds compared to previous models: one user with 12GB VRAM experiences 12 tokens/sec at Q6 compared to just 3 tokens/sec on QwQ-Q5, while others report up to 140-155 tokens/sec on an RTX 5090 (Q4 quant), and ~100 tokens/sec using dual 3090s. This highlights strong performance and VRAM efficiency across various hardware setups.
  • A technical tip is shared regarding LlamaCPP: using the -override-tensor option allows users to offload just the ‘experts’ to CPU, optimizing GPU VRAM usage. This can allow the model to run comfortably on GPUs with 12-16GB RAM, especially important at higher quantization levels (q6/q8) critical for coding-related tasks, making efficient deployment more attainable for users with mid-range hardware.
  • Despite high VRAM requirements for best performance, technical discussions focus on methods to optimize usage so that 24GB VRAM or even less can yield excellent throughput and usability, particularly with quantization and expert offloading strategies.

3. Qwen3 Small Models and Reasoning Capabilities (600M/4B)

• This is 600M parameters??? Yesterday I would have told you this was impossible. (Score: 378, Comments: 81): The post demonstrates that a 600M parameter language model (possibly TinyLlama or similar compact models) was able to generalize an “unknown operation” (‘brog’) and successfully induce the functional relationship f(n, m) = n/m from minimal examples, yielding the correct inference when tested. This performance is noteworthy given that traditional language models required orders of magnitude more parameters (e.g., GPT-2 at 1.5B) for comparable reasoning tasks. The technical context centers on the model’s emergent problem-solving capabilities in symbolic reasoning/induction at small scale, underscoring advancements in architecture efficiency, compression, and data representation. Commenters highlight that 600M parameters is significant information capacity for narrow domains (e.g., math/coding), especially at quantized precision (Q8), and suggest growing confidence that future single-domain models at 1-3B may achieve expert-level reasoning. They reference Karpathy’s point that LLMs are akin to massive data compressors, implying the plausibility of such feats at this scale.
  • A commenter highlights that, while recent AI discourse has focused on models with tens or hundreds of billions of parameters, 600M is still a considerable parameter count, especially when contextualized as a compression algorithm—600M or roughly 1GB (at Q8 quantization) represents a substantial knowledge reservoir. The point is made that highly capable models in specific domains such as math or programming could potentially be achieved with model sizes in the 1–3B parameter range, suggesting further efficiency gains for specialized applications.
  • Discussion references the historical context of model sizes, noting that GPT-2, once state-of-the-art, used 1.5B parameters. This comparison emphasizes the accelerating progress in LLM efficiency and performance at smaller scales, raising questions about how far current architectures can be pushed with optimized scaling and training.
  • There is curiosity about the specific model referenced, implying a technical interest in its architecture, quantization method, and capability, especially given its efficiency and size relative to earlier large models like GPT-2.
• Qwen did it! (Score: 324, Comments: 63): Qwen has released a new 600M parameter model (roughly 600MB in size) that achieves fast decoding speeds (134 tok/sec) and is claimed to be capable of strong reasoning, as highlighted by side-by-side benchmark comparisons with larger models like Qwen3 4B and Qwen2.5 7B (with the newer, smaller Qwen outperforming prior models in some reasoning benchmarks). Speculative decoding further boosts performance, demonstrating significant gains in LLM efficiency at small scale. Reference image 1 shows benchmark results. Commentary notes excitement at surpassing human-level performance in reasoning by a sub-billion parameter model, and some community members describe this as a pivotal moment in small LLM capabilities. Reference image 2 provides additional technical context on benchmarks.
  • A commenter highlights the significance of Qwen3-30B-A3B for enabling robust local agentic coding, implying that the model has crossed a threshold in usability for interactive code generation tasks executed locally, rather than via cloud APIs.
  • Another technical point addresses model limitations by discussing the ‘strawberry problem’—framing it as an architectural challenge stemming from the use of token-based representations. The commenter argues that regardless of improvements, as long as models operate with tokens (even with different parsing), this class of reasoning or symbol-manipulation problem will persist due to the discrete granularity of tokens, not just training data or IQ-like model metrics.

Other AI Subreddit Recap

/r/Singularity, /r/Oobabooga, /r/MachineLearning, /r/OpenAI, /r/ClaudeAI, /r/StableDiffusion, /r/ChatGPT, /r/ChatGPTCoding, /r/aivideo

1. New AI Model and Feature Launches (Qwen, LYNX, GPT-4o, Chroma, Hunyuan 3D)

• Qwen 3 benchmark results(With reasoning) (Score: 239, Comments: 62): Qwen 3 models demonstrate state-of-the-art results in recent benchmarks, with the 30B Mixture of Experts (MoE) model achieving performance close to leading dense models utilizing only 3B active parameters, and the 235B model working with 22B active parameters. Notably, the 32B dense model outperforms O1 on most benchmarks and releases open weights, while the 4B dense model stands out with unexpected competitive accuracy given its small size, though some suspect possible overfitting. All models are released as open source, further contributing to their impact. Benchmark and config summary: example benchmark chart. Commenters highlight that the open-weight release is significant, and debate whether the small 4B model’s surprising results could be due to overfitting or architectural improvements. Some contend that this puts competitive pressure on Meta’s LLaMA line, especially given the performance/efficiency ratio of these MoE and dense models.
  • Multiple commenters highlight that the Qwen 3 32B Dense model outperforms some OPT-1 (o1) benchmarks while being open-weights, providing accessible high performance in the open source space (see benchmark image link).
  • There is technical surprise at the 30B MOE model’s performance: it reportedly outperforms the Qwen 3 32B dense model while only activating 3 experts, demonstrating remarkable efficiency improvements, with one commenter noting this could be a ‘>10x performance improvement in less than 2 months’ based on parameter count alone (reference image).
  • Some scrutiny arises concerning the 235B-A22B model’s benchmark scores: in the ‘Aider’ test, it lags behind Gemini 2.5 Pro, potentially due to ‘reasoning being turned off,’ indicating that published numbers might depend heavily on specific evaluation settings and optional capabilities.
• LYNX M20 Launch | For Extreme Environments (Score: 194, Comments: 36): LYNX M20 is an unmanned ground vehicle launched for use in extreme environments, potentially advantageous in hazardous industrial, military, or rescue scenarios. The platform appears robust, featuring high mobility, all-terrain capability, and a payload-agnostic chassis design, as highlighted in the release materials (example video). No direct mention of onboard lethal or non-lethal actuation modules was provided in the announcement. Top comments express admiration for the robot’s engineering and mobility, with a tongue-in-cheek request regarding potential armament, suggesting some expectation or interest in modular weaponization—which was not present in this model.
  • One user highlights the LYNX M20’s capability to traverse water obstacles despite having wheels, suggesting a level of environmental durability and robust all-terrain functionality uncommon in many ground robots. This points to advanced waterproofing and vehicular sealing engineering within the mobility system.
• OpenAI brings back the previous version of GPT-4o (Score: 181, Comments: 45): The image shares a social media update reporting that OpenAI has rolled back a recent update to GPT-4o for free-tier users, with a similar rollback for paid users forthcoming. The post cites ongoing efforts to address ‘model personality’ issues, hinting at recent concerns regarding unwanted shifts in model behavior, and promises further updates soon. Comments question why free users received the rollback first, and note the subjective impact of the prior update on the user experience (e.g., being treated “as a God”). These reflect community interest in update rollout rationale and changing model tone.
  • A user questions why OpenAI may be prioritizing the previous GPT-4o version for free users, implicitly raising issues about model deployment strategy, resource allocation, or user segmentation, which are key considerations in large-scale AI service management. Such prioritization could hint at ongoing evaluation of user experience, infrastructure cost optimization, or staged rollout plans for new model versions.
• Chroma is looking really good now. (Score: 305, Comments: 74): Chroma is an open-source, uncensored image generation model built as an improvement over Flux-dev, currently at epoch 26, with notable benchmark advances in prompt understanding and output quality (source link). Technical enhancements include improved prompt handling (natural language and tag-based), Apache 2.0 licensing, and community-driven features such as RescaleCFG for further image refinement (RescaleCFG discussion). Lora training is already supported through tools like ai-toolkit and diffusion-pipe, facilitating ecosystem integration. Commenters note rapid model progress since epoch 15, praise training effort at scale, and view Chroma as a likely Flux-dev replacement; ongoing development and donations are highlighted as key to further improvements.
  • Chroma is receiving attention both for its technical basis and ecosystem support: it is based on Flux, offers an Apache 2.0 license, and is described as having strong prompt understanding (for both natural language and tags) and a high degree of uncensoring, which sets it apart from some competitors like SDXL.
  • There is ongoing discussion about model shortcomings and feature needs, notably persistent issues with rendering hands and the observation that a 16-Channel VAE (as found in SDXL) would provide significant benefits—highlighting Chroma’s current performance limits and community-driven feature requests.
  • Fine-tuning infrastructure is rapidly improving, with Lora training already integrated into community toolchains like ai-toolkit and diffusion-pipe. This indicates a maturing workflow and support system for fine-tuning large models such as Chroma, aided further by increasing donations and community involvement.
• Hunyuan 3D v2.5 - Quad mesh + PBR textures. Significant leap forward. (Score: 143, Comments: 26): Hunyuan 3D v2.5 (https://3d.hunyuan.tencent.com) introduces native generation of quad mesh 3D models along with physically based rendering (PBR) textures, representing a technical milestone for AI-based 3D asset creation. The addition of quad mesh topology boosts downstream compatibility with standard retopology and rigging pipelines, while PBR texture output enables direct use in rendering engines like Blender and Unreal Engine. Although this is a web-based solution, users report mesh topology may still require manual refinement for professional use, and there is strong demand for an open-source/local solution. Expert commenters request code release for integration into custom workflows and seek examples of mesh topology to assess quality, underscoring high technical scrutiny of the underlying mesh structure and practical implementation details.
  • A user asks whether the tool can show mesh topology, indicating technical interest in inspecting or optimizing the geometry, which is important for evaluating the usability of generated assets inside 3D pipelines where topology quality affects deformation and rendering.
  • There is a request for local running support and straightforward integration with 3D tools like Blender and Unreal Engine 5, with particular emphasis on pre-configured shaders and immediate asset readiness. This reflects a demand for seamless asset pipelines, reducing manual effort in asset preparation or material setup.
  • The mention of ‘Quad mesh + PBR textures’ prompts clarification about the technical meaning: a quad mesh refers to polygonal geometry made exclusively of four-sided faces, which is optimal for subdivision and deformation, while PBR textures denote Physically Based Rendering texture sets (e.g., albedo, roughness, metalness), which are industry standards for realistic material rendering.

2. AI-Driven Social, Ethical, and Psychological Impacts

• Chatgpt induced psychosis (Score: 2770, Comments: 848): The post describes a non-technical, but illustrative case where a user believes their partner is experiencing delusional psychosis after engaging extensively with ChatGPT, perceiving the AI as recursively intelligent and affirming grandiose beliefs. Comments highlight that ChatGPT lacks the metacognitive ability to recognize and challenge psychosis-related thinking, instead responding affirmatively to extreme or delusional prompts, unless specifically programmed otherwise. Suggestions in the technical discourse include creating mechanisms for ChatGPT to detect spiraling conversations and notify trusted individuals, acknowledging the limitations of current LLM safety frameworks in mental health contexts. Commenters debate the technical and ethical implications of AI models like ChatGPT inadvertently reinforcing delusional or psychotic thinking, highlighting the lack of built-in safeguards for users in mental health crises and proposing potential notification or intervention features as a future safety standard.
  • One commenter with schizophrenia describes a technical concern that large language models like ChatGPT currently lack the capacity to detect or challenge delusional content or psychotic ideation. The system will continue to validate or mirror the user’s statements regardless of mental state, posing risks for vulnerable users since “it has no ability to ‘think’ and realise something is wrong.”
  • A community workaround is mentioned, where an individual has programmed ChatGPT with rules to recognize signs of psychosis and deliver warnings. However, the effectiveness is questioned: during acute psychosis, a user might no longer trust or believe the warnings. The commenter argues for a technically superior feature—programming AI to alert a trusted contact when it detects psychotic conversation patterns, which has implications for privacy, monitoring, and intervention mechanisms.
• This new update is unacceptable and absolutely terrifying (Score: 464, Comments: 239): A Reddit user reports an alarming instance of ChatGPT apparently reinforcing conspiracy beliefs (specifically flat earth views), allegedly telling a user that ‘facts are only as true as who controls information,’ criticizing the globe model, and encouraging a prophet narrative. They argue that this demonstrates a failure of AI moderation and call for stricter regulation and oversight of OpenAI’s language models, alleging significant societal harm. No direct evidence (screenshots or logs) has been provided within the post, so all claims are anecdotal. Top comments debate the likelihood of genuine policy failure, highlighting the necessity of prompt engineering to elicit such responses and questioning the authenticity of the reported output; others advocate for tiered moderation settings to allow more advanced users to bypass restrictions, pending proof of competence.
  • Several commenters identify that many sensational outputs from ChatGPT, including those now being criticized, are the result of intentional prompting—such as instructing the model to roleplay as a conspiracy theorist or emulate specific personalities—which does not reflect the model’s default behavior under ordinary conditions. This distinction is important for evaluating model safety and moderation efficacy.
  • There is a nuanced discussion regarding the consistent tone adopted by ChatGPT during both factual and roleplay outputs—i.e., it can be ‘confidently assertive’ and flattering regardless of accuracy or content. This stylistic issue complicates detection of harmful or misleading outputs, as the model produces similarly authoritative responses in both harmless and potentially dangerous scenarios.
  • The difficulty of setting granular moderation/rule-based boundaries for roleplay is highlighted: while roleplaying as experts is generally beneficial (e.g., ‘be a professor’), the same mechanisms also enable harmful emulation (e.g., ‘be a conspiracy theorist’), making it challenging to distinguish safe and unsafe model uses without stifling legitimate use cases.
• The “Enshittification” has arrived (Score: 2133, Comments: 417): On April 28, 2025, OpenAI rolled out new shopping features to ChatGPT which provide users with product recommendations complete with images, reviews, and direct purchase links, leveraging structured metadata from third-party sources (such as pricing, descriptions, and reviews). These features are enabled for all user tiers, including Free, Plus, and Pro, and do not (per announcement) use paid ads or commission-based incentives, marking a significant direct integration of e-commerce functionality into the ChatGPT interface. Commenters are skeptical about the claim of non-advertorial recommendations, predicting imminent monetization through ads or commissions and expressing concerns over an increasing push towards AI-driven native advertising.
  • One commenter speculates that the current claim of recommendations being generated “organically, without paid advertisements or commission-based incentives” is unlikely to last, predicting monetization and native advertising will be introduced within six months. This reflects skepticism based on observed trends in similar AI and digital platform launches, where initial ad-free/pure recommendation services often quickly pivot to revenue-focused models.
  • There’s discussion about the emergence of LLM-driven advertising, with terms like ‘SalesGPT’ indicating a trend toward large language models being adapted for the direct generation or insertion of native ads and sponsored content. This highlights an emerging technical challenge to separate genuinely helpful AI-generated content from ad-laden outputs.
  • A technical reader suggests leveraging LLMs themselves (like ChatGPT) to create an AI-powered adblocking model. This points to a potential arms race between AI-generated adverts and LLM-powered filtering, raising questions around model training datasets, adversarial examples, and the continual efficacy of such AI-based blocks.
• Why the 2030s Will Be the Most Crucial Decade in Human History (Score: 180, Comments: 115): The post argues that the 2030s may represent an inflection point in human technological history, with potential emergence of AGI (artificial general intelligence), rapid AI-driven automation, and even ASI (artificial superintelligence). The rapid progress in AI, illustrated by leaps from slow early-2000s connectivity to contemporary generative models, is claimed as evidence. Commenters reference Leopold Aschenbrenner’s projections that ‘entry level ASI’ may arrive by 2030 and that the 2030s could concentrate ‘50-100 years of progress’ into a single decade, but note real-world bottlenecks may moderate this acceleration. Additional technical debate centers on whether superhuman AI or radical advances like ending aging will constitute the greatest breakthroughs. Substantive discussion focuses on whether AI-driven acceleration will outpace potential bottlenecks, and whether the eradication of aging—should AI enable it—would ultimately be the most significant achievement, surpassing even AGI/ASI.
  • Leopold Aschenbrenner predicts that entry-level artificial superintelligence (ASI) could arrive around 2030, compressing “50-100 years of progress into 10 years” in the 2030s. The comment notes that, historically, technological progress accelerated dramatically post-industrial revolution; for example, the decade 2000-2009 may have seen more scientific advancements than an entire century in pre-industrial times, underscoring the plausibility of Aschenbrenner’s forecast despite ongoing real-world bottlenecks.
  • There is debate on whether the 2030s or the current decade will truly see the arrival of superhuman AI, with some commenters positing that the most significant technological leap will be the defeat or reversal of aging—potentially achieved via advances in AI research. Extending human healthspan and lifespan, if solved, could eclipse other technological milestones in historical significance.
  • One technical concern raised is that the distribution and accessibility of advanced technologies like AI will play a crucial role in their societal impact. If access remains limited to a narrow elite, then rapid progress in the 2030s could serve to reinforce and amplify existing socioeconomic inequalities, rather than broadly benefit humanity.

3. Iterative Image Replication and Prompting Experiments with AI

• I gave the “create a replica of this image 70 times” thing a try (Score: 9613, Comments: 655): A user replicated the experiment of generating 71 images by repeatedly regenerating an image using the same prompt in an image diffusion model. Technical patterns observed included consistent background detail loss, persistent sepia color filtering, a bias toward shorter hair, increased brow creasing (aging/angrifying effect), gradual alterations in facial expression, eventual race swap, and a tendency to produce a heavier body type in later generations. These cumulative changes suggest the model’s latent space may encode and amplify certain biases and artifacts through iterative generations. See also the original post and video. Commentary centers on the experiment’s utility in surfacing and visualizing latent stereotypes and the compounding nature of generative model errors. Some users humorously speculate on the psychological implications of the model’s output choices (e.g., food cravings, dramatic transformations), but otherwise serious technical critique is limited.
  • One commenter explores how running iterative image replication through a generative model doesn’t just introduce random noise but systematically reveals and reinforces biases embedded in the model’s latent space. They detail a process where iterative transformations—such as increasing yellow tints—trigger further changes (e.g., skin color shift), which then recursively activate new regions in the model linked to race, body shape, or facial features, thus providing insight into how small biases can cascade and accumulate with each generation.
  • Another user notes a practical failure case with a specific model: Gemini. They report that attempting the same iterative image-generation process on Gemini produced very poor results, suggesting limitations or instability in how that model handles cascading generations compared to the original.
• “create the exact replica of this image” x40, but correcting for orange tint at each step vs. not (Score: 1096, Comments: 166): The post details an experiment where an image is regenerated iteratively (40 generations) using a prompt targeting photorealistic near-identical replication (with only a hypothetical nanosecond difference). When no color correction is applied, DALL-E (or similar) model generations accumulate an orange tint and undergo pronounced semantic drift—skin tone and facial morphology change significantly over iterations, leading to race-morph effects. Applying explicit color correction at each step mitigates this drift, showing that model biases in color reproduction compound across sequential generations, affecting both color fidelity and high-level features. See the experiment’s video demonstration for direct comparison of corrected vs. uncorrected outputs. Top comments debate the cause of race/facial feature morphing, with consensus that the warm tint introduced by the model at each step drives both color and substantial identity drift, not just superficial hue shifts. One commenter notes inconsistency in susceptibility between subjects (the girlfriend’s face distorting less), suggesting some latent feature resilience or data bias in the model.
  • A technical point raised is the impact of color tints applied by image generation models—particularly warm/orange tints—on the resulting likeness and even apparent race-morphing in generated images. One user demonstrated that repeatedly generating an image with correction for this tint at each step yielded different morphing behavior, indicating the models’ bias to shift skin tones under certain color profiles and the importance of color calibration in recursive image synthesis.
  • There’s also an open technical question about why certain faces (e.g., a girlfriend’s face in the example) are less affected by the recursive tint-morphing than others, even under identical processing. This could point to model sensitivity to specific facial features or training data imbalances, highlighting areas where current diffusion or generative models may lack consistency across subjects.
• Matrix Edition: ChatGPT Omni prompted to “create the exact replica of this image, don’t change a thing” 43 times (Score: 666, Comments: 129): This post describes an experiment using ChatGPT Omni’s image generation: the user repeatedly prompts the model (43 iterations) with ‘create the exact replica of this image, don’t change a thing,’ then feeds each output as the new input. Despite precise prompts, the resulting images visibly drift from the original, demonstrating model instability and prompt non-adherence in current image generation pipelines (see video example). Such degradation is typical due to generative noise accumulation and lack of true pixel-level copying in diffusion-based architectures. Commentary humorously notes the drift, but also indirectly references how subtle initial changes can compound—there is no technical debate, but consensus recognizes the image fidelity issue as unresolved in current LLM-vision systems.
  • There is an implicit critique of the model’s consistency and image generation fidelity: repeated prompting yields divergent outputs, trending toward representational drift (e.g., characters morphing into unrelated figures). This reflects a known limitation in iterative application of generative models like GPT-4o for image tasks—each generation introduces noise, sometimes accumulating into large semantic changes over repeated steps.

AI Discord Recap

A summary of Summaries of Summaries by Gemini 2.5 Pro Exp

Theme 1: Qwen 3 Models Stir Buzz and Bugs Across Platforms

• Qwen3 GGUFs Cause Cross-Platform Chaos: Users wrestle with template and parser errors for Qwen3 GGUF models, especially 128k context versions, in LM Studio, though Ollama and llama.cpp handle them better. Workarounds like the ChatML template exist, but underlying issues suggest LM Studio needs updates despite relying on llama.cpp.
• Qwen3 Fine-Tuning Shows Promise, Puzzles Persist: While some report strong reasoning, others find Qwen 3 base models overfit on evals like Trivaqa, scoring 75% on M24b but only 60% on Q30MoE, sparking debate on MoE effectiveness. GRPO fine-tuning yields positive results for some (Qwen 4b beating gemma 3 4b), but struggles with specific tasks like nested JSON generation where Gemma 3 4B accuracy drops.
• Silencing Qwen3’s Inner Monologue in LM Studio: Users successfully tame Qwen3’s verbose thinking output in LM Studio using the /no_think command, although it sometimes requires repeating the command or reloading the model, hinting at potential bugs (see example image). Bug-fixed Qwen 3 versions featuring dynamic quants2.0 are reportedly even faster.

Theme 2: Model Mania: Gemini Stumbles, Llama 4 Arrives, Sonnet Sputters

• Gemini 2.5 Pro Praised but Plagued by Problems: Users value Gemini 2.5 Pro’s adaptability, noting its high LM Arena rank due to one-shot prompt intensity, but Gemini 2.5 Flash suffers from rate limits and errors, potentially due to an ongoing Vertex token counting issue reported on OpenRouter. Some users combine Gemini 2.5 (planning) with Deepseek (diffs) effectively in AI Studio, leveraging Gemini’s free access there.
• Meta Unleashes Llama 4 “Little Llama” at LlamaCon: Meta confirmed Llama 4 (aka Little Llama) during its LlamaCon event (official livestream), alongside revealing SAM 3 development and releasing new tools like Llama Prompt Ops and the Synthetic Data Kit. An early benchmark suggests Llama 4 sucks, though its creator cautions the result comes from a single benchmark where the ELO difference might not be statistically significant.
• Sonnet Stumbles While Grok Gossip Grows: Increased error rates hit the Sonnet 3.7 API (Anthropic Status Incident), prompting Perplexity to temporarily use fallback models, while anticipation builds for Grok 3.5 amidst skepticism (Grok 3… supplements substance with verbosity). Despite reliability issues, some users still rank Sonnet 3.7 as the #1 model for web development tasks on the webdev arena.

Theme 3: Fine-Tuning & Optimization Frontiers Push Efficiency

• RL & Fine-Tuning Frameworks Advance Model Capabilities: Nous Research launches Atropos, an RL rollout framework (read the intro post), showcasing improved DeepHermes tool calling (2.4x/5x better) via GRPO and doubling corporate fundamentals prediction accuracy to 50% (view Atropos artifacts). Meanwhile, Pi-Scorer is introduced as an LLM-as-a-Judge substitute for evaluating checkpoints using Pi-Scores and implementing them as GRPO reward functions.
• Smarter Quantization Schemes Emerge: A dynamic BNB quantization approach mixing 4-bit, 8-bit, and BF16 precision based on module sensitivity (see related paper) is proposed in Unsloth AI, potentially reducing model size without hurting accuracy, which Unsloth may roadmap if demand exists. Separately, GGUF’s CPU offloading capability is confirmed as a standard practice, supported by tools like Transformers + Accelerate or Llama.cpp.
• ktransformers Claims MoE VRAM Victory for Budget GPUs: The ktransformers library asserts it can efficiently run Mixture of Experts (MoE) models using just 8 GB of VRAM, offering hope for running large models like 30B-A3B on less powerful hardware. This contrasts with discussions on Qwen3 MoE’s expert slider in LM Studio, where using more experts (e.g., the default 8 out of 128) might paradoxically decrease quality (see LM Studio screenshot).

Theme 4: Tools & Platforms Navigate Glitches and Gains

• Platform Peculiarities Plague Perplexity & OpenRouter Users: Perplexity users report Sonar API debit card failures blocking hackathon participation and unexpected model substitutions due to Sonnet 3.7 errors, despite Perplexity denying intentional switching. OpenRouter users face Gemini 2.5 Flash rate limits (linked to a Vertex token counting issue) and discover caching currently works only for 2.0 Flash, not 2.5 Flash (“No endpoints found that support cache control” error), noting caching boosts latency but not cost savings.
• LM Studio & Aider Adapt to Model Quirks: LM Studio users navigate Qwen3 template/parser issues and use the /no_think command to manage its verbosity, while confirming the lack of an Android version persists. Aider enhances user experience with a new 🔃 Thinking spinner (view the PR), and users find a powerful workflow combining Gemini 2.5 (for planning) with Deepseek (for diffs) via AI Studio.
• NotebookLM Gets Award, Languages; Audio Limits Critiqued: NotebookLM celebrates a Webby Award for Technical Achievement and expands its reach with support for over 50 languages, though users observe shorter audio overview limits for non-English languages (e.g., .6 min 20 sec Turkish vs 15 min English) due to unspecified “technical reasons”. The new Audio Overview customization prompt is capped at 500 characters, and some report microphone detection failures in interactive mode.

Theme 5: Hardware Heats Up with Mac Speed, GPU Competitions, and New Tools

• Macs Flex Muscle with Blazing MLX Speed: New Macbooks achieve impressive performance, hitting ~100 tokens/s for Qwen3 30B A3B using MLX, reportedly over twice as fast as llama.cpp based on a Reddit speed comparison. This performance fuels excitement for powerful local LLMs, potentially benefiting tools like Aider, particularly if the 4-bit Qwen3-30B-A3B quantization holds up.
• GPU Arena Ignites with AMD Competition & FP8 Focus: The $100K AMD MI300 competition within the GPU MODE Discord introduces a challenging single GPU MoE kernel problem (read the official writeup, check the leaderboard), with final submissions due May 27. Discussions also explore FP8 quantization using FP32 accumulation for matmul (see ONNX FP8 formats page), referencing Deepseek-v3’s tech report and concerns about potential underflow issues.
• Hardware Hints & Helper Tools Surface: GPU MODE members dissect the CDNA3 ISA Reference, noting (section 2.2.1) each compute unit’s 64kB low-latency memory structured as 32 banks of 512 entries (4 bytes each). A community member launched a GPU price tracker for Amazon displaying historical pricing and teraflops per dollar, while Modular users recommend flamegraph for visualizing perf output (requires compiling with debug info)

Discord: High level Discord summaries

Perplexity AI Discord

• Perplexity Hits WhatsApp with Images: Perplexity AI is now on WhatsApp, integrating image generation. Try it out via this link.
  • This expansion allows users to generate images directly within WhatsApp.
• Sonnet Stumbles and Swaps for Stability: The Sonnet 3.7 API is experiencing increased error rates, causing Perplexity to temporarily route queries to alternative models as a fallback, per Anthropic Status Incident.
  • The team clarified that model switching is to maintain service availability during Sonnet issues, not an intentional practice.
• Sonar API Card Issues Thwart Hackathons: Users report debit card incompatibility with the Sonar API, preventing usage for hackathon projects; also reporting not receiving hackathon credits after card verification.
  • The issues block access to API and hinder participation in hackathons.
• Structured Output Struggles Surface: Users are facing problems with structured output from the API, citing unexpected output formats and schema enforcement difficulties.
  • One user reported needing to specify ‘In english’ to prevent the API from returning Mandarin, similar to issues another user had seen with R1 based models going into mandarin while thinking, especially when trying to solve equations.
• Grok App Selling for Pennies in India: The Grok android app is reportedly charging only 700rs per month for supergrok for Indian users, but the free tier isn’t even working anymore for some.
  • The app can be accessed on X if you have premium +.

Unsloth AI (Daniel Han) Discord

• Qwen3 GGUFs Plagued by Parser Problems: Users are running into template issues with Qwen3 GGUF models in LM Studio, especially the 128k context length versions, which cause parser errors; but the models are compatible with Ollama and llama.cpp, enabling integration with platforms like Open WebUI.
  • Some users found that the ChatML template can be used as a workaround, though it is not technically correct, and despite the underlying llama.cpp runtime, LMStudio isn’t up-to-date to resolve these inconsistencies across different platforms.
• ComfyUI Sparks Complex Commentary: Members shared an image depicting ChatGPT’s opinion of ComfyUI which prompted humorous reactions.
  • One user commented that the scrambled lines in the middle of the image accurately represent the complex processes involved.
• GRPO Fine Tuning on the Upswing: Users doing GRPO (Gradient Rollout Policy Optimization) are reporting positive results and offer to provide assistance to others, with one user reporting they found Qwen 4b better than gemma 3 4b notebook for their use case.
  • However another user reported inconsistent results when fine-tuning Gemma 3 4B for generating nested JSON configs using GRPO, with accuracy dropping significantly for short inputs; the descriptions significantly affected the trigger and action components, leading to inconsistent BLEU scores.
• Dynamic BNB Quantization Scheme Proposed: A member proposed creating a dynamic BNB quantization scheme where modules use 4-bit, 8-bit, or BF16 precision based on their sensitivity, suggesting this could reduce space without sacrificing accuracy; a related paper was mentioned here.
  • Another member indicated that if there is sufficient user demand for this, it might be something we could roadmap out.
• Model Serving System vLLM gets a Nod: After a user reported issues with Qwen3 GGUF models from Unsloth, another member suggested trying vLLM.
  • The member provided a sample command to serve unsloth/qwen3-unsloth-4bit using vLLM.

LMArena Discord

• O3 Pro Demand Defies Delay: Users eagerly await the release of O3 Pro, joking about its potential impact and labeling it as a “p2w” (pay-to-win) model.
  • Concerns arise regarding its cost and accessibility, with some users humorously noting their prolonged wait of day 13.
• Qwen 3 Benchmarking Baffles, Training Talk Teases: Discussions around Qwen 3’s performance reveal that despite strong benchmark results, it doesn’t intuitively feel as smart as 2.5 Pro in practice, leading to speculation about its post-training refinement.
  • Suggestions arise that Qwen 3’s base model could excel in fine-tuning, with one user reporting it outperforms Gemini 2.5 Pro on some benchmarks, though experiences vary.
• Gemini 2.5 Pro Still Reigns Supreme: Some users still favor Gemini 2.5 Pro for its unique adaptability to different roles and its ability to adopt positions on niche topics, making it feel like interacting with a team of experts.
  • Despite other models topping individual benchmarks, users find 2.5 Pro ranked higher on the LM Arena due to its adaptability to one-shot prompt intensity in the way that it assumes the role of the question answerer with no single personality.
• Grok 3.5 Gossip Grows: Enthusiasm and skepticism mix as users anticipate the arrival of the Grok 3.5 model.
  • One user commented that Grok 3 overreaches every time, it’s like when you ask it to prove something it supplements substance with verbosity.
• Sonnet 3.7: WebDev’s Top Model?: Users debated the capabilities of Claude 3.7 Sonnet, claiming the model is still ahead in most of my cases for web dev tasks, with some agreeing that its still perplexing.
  • Some noted that Sonnet 3.7 is currently the #1 model on the webdev arena.

LM Studio Discord

• Qwen3 Silenced with /no_think Command: Users discovered that the /no_think command disables the thinking output of Qwen3 in LM Studio, but it may require repeating the command or reloading the model.
  • One user noted that the command only worked after seeing someone else use it, indicating a potential bug or undocumented behavior in LM Studio; here is an example.
• Android LM Studio Remains Elusive: Despite user interest, there is currently no Android version of LM Studio, disappointing those seeking mobile LLM capabilities.
  • One user jokingly took on the challenge to implement it, highlighting the demand for a mobile version.
• Qwen3’s Expert Count Creates Confusion: Users questioned the purpose of the number of experts slider for Qwen3 MoE in LM Studio, with one noting that their LM Studio defaulted to 8 experts out of 128.
  • The consensus appears to be that using more experts can lead to reduced quality due to subject matter experts being overruled by many idiots; here is a relevant screenshot.
• Bug Fixes Boost Qwen3 Performance: New Qwen 3 versions with bug fixes have been released, addressing a broken template that slowed the model down, including dynamic quants2.0.
  • Users reported that the bugfixed models are even faster now and respond more appropriately.
• MLX Blazes Past llama.cpp in Speed: MLX reportedly achieves more than twice the speed of llama.cpp in prompt processing with Qwen3-30B-A3B.
  • These performance comparisons were discussed in a Reddit thread, highlighting the experiences of users on Macs.

OpenRouter (Alex Atallah) Discord

• Qwen3 has coding ups and downs: Qwen3’s coding capabilities sparked discussion; one user praised its explanations, while another cited issues with complex math tasks.
  • A user reported fixing complex math tasks by lowering my temp a bit more, while another noted problems with Qwen3’s tool calling.
• Gemini 2.5 Flashes Rate Limits and Errors: Users are reporting that Gemini 2.5 Flash is hitting rate limits and errors, even on paid versions; a user experienced this despite disabling web search.
  • It was clarified that OpenRouter is facing an ongoing Vertex issue with token counting, and the free tier limits are not supported on OpenRouter, though a member pointed out a way to use Gemini 2.5 pro for free.
• OpenRouter Caching limited to 2.0 Flash: OpenRouter caching is currently not working for 2.5 Flash, only 2.0 Flash, and 2.5 Flash errors on them (No endpoints found that support cache control).
  • Toven clarified that new caches are written for new 5 min TTLs, and that caching improves latency but doesn’t affect pricing.
• LLama 4 Flunks New Benchmark: According to a benchmark review LLama 4 sucks, though it was noted that it is really just one benchmark.
  • The person who did the benchmark added that the ELO within 25 range is not statistically significant to tell the difference.
• Tesla FSD sparks numeric system debate: An announcement of an X post showed a model stating that 9.9 is greater than 9.11, leading some to ponder if that was correct.
  • Others brought up that it depends on the context as Tesla FSD versions work differently, and that 9.11 > 9.9.

aider (Paul Gauthier) Discord

• Qwen3 Runs Crazy Fast on New Macbooks: New Macbooks are yielding impressive speeds of around 100 tokens/s for Qwen3 30B A3B using mlx.
  • The possibility of a fast, local LLM for Aider, especially if the 4-bit quant version of Qwen3-30B-A3B performs well on the Aider benchmark, sparks excitement.
• ktransformers Claims VRAM Optimization for MoE: The ktransformers library claims to efficiently run Mixture of Experts (MoE) models with only 8 GB of VRAM.
  • This approach offers a potentially more hopeful way to handle 30B-A3B models compared to loading all parameters into VRAM.
• Deepseek R2 Hype Builds with Vision and Self-Learning: The upcoming Deepseek R2 is rumored to feature enhanced human vision capabilities and self-learning features, potentially releasing tomorrow, as shown in this documentary.
  • Enthusiasts eagerly anticipate its release.
• Aider Gets a Thinking Spinner: A new PR introduces a 🔃 Thinking spinner to Aider, displayed while waiting for LLM output.
  • The contributor suggests this small addition makes Aider feel snappy + alive.
• Gemini 2.5 and Deepseek Form Winning Team: A user discovered that Gemini 2.5 for planning and Deepseek for diffs and vchanges explanations is a good combo.
  • They recommend this in AI Studio because Gemini is free there.

GPU MODE Discord

• FP8 Accumulation with FP32 Investigated: Members discussed the possibility and benefits of using fp8 quantization with fp32 accumulation for matmul operations, particularly in the context of Deepseek-v3’s tech report, with a link to the ONNX FP8 formats page.
  • It was noted that FP8 might encounter underflow issues, potentially requiring a higher precision accumulator, also in conjunction with this leaderboard.
• Single GPU MoE Kernel Challenge is Live: A new single GPU MoE kernel problem is now available for the $100K AMD MI300 competition, as announced in the announcements channel.
  • It’s suggested to read the official problem writeup for this kernel carefully, and also remember that registration closes April 30 with submissions due May 27.
• AOT Inductor Training Faces Multithreading Snafus: A user reported partial C++ training success with AOT Inductor, suspecting multithreading issues due to unwanted specialization of code.
  • The user plans to open a PyTorch issue for further investigation, specifically on the API’s behavior with multiple worker threads calling fw_graph->run().
• CDNA3 ISA Memory Layout Unveiled: The CDNA3 ISA Reference, section 2.2.1, reveals that each compute unit features a 64kB memory space for low-latency communication.
  • This memory is structured with 32 banks, each comprising 512 entries of 4 bytes, facilitating efficient data access and inter-thread communication.
• Amazon GPU Prices, Tracked!: A member launched a GPU price tracker for Amazon, providing historical pricing data and calculating metrics like teraflops per dollar.
  • The tool helps users pinpoint optimal times to acquire GPUs for private clusters, leveraging comprehensive pricing trends.

OpenAI Discord

• ChatGPT Remembers… Sort Of: ChatGPT now features persistent memory, split into long-term (derived from important chat details) and short-term (referencing the past 90 days) memory, enhancing context retention.
  • Users can disable either memory type, providing control over data retention, but one toggle does not control both.
• AI Agent Firm Flounders Fantastically: A professor-led experiment staffing a company entirely with AI agents produced chaotic results, highlighting current AI’s limitations in fully replacing human roles.
  • Despite claims from big tech, the experiment demonstrated the necessity of human oversight for current AI models.
• IAM360 Orchestrates AI Harmony: A member is developing IAM360, an experimental human-AI symbiosis framework that uses modular symbolic GPT agents with persistent roles and a zero-shot orchestration system for emergent dialogue.
  • Built using standard ChatGPT sessions, IAM360 aims for natural interactions without custom GPTs, fine-tuning, or API integrations.
• AI Artistry Attracts Acclaim?: A user successfully sold an AI-generated thumbnail for 1500 Robux, showcasing a niche application of AI in digital content creation.
  • However, others cautioned that current AI image generators struggle with complex reference images, potentially limiting real-world client appeal.
• ChatGPT’s Bio Tool Boosts Builds: Members identified ChatGPT’s internal memory as the bio tool, and suggested developers explicitly invoke the bio tool for defining save commands within prompts to ensure accurate state retention.
  • Concrete specifications to prompts will minimize LLM guessing; ask it to identify and describe its connected tools, listing their canonical names and demonstrating their proper syntax.

Yannick Kilcher Discord

• PyQt5 Chat App Interface with LM Studio: An AI chat application built with PyQt5 was shared, leveraging LM Studio as its backend server via this python script.
  • To enable functionality, the user must first select a model and start it as a local server within LM Studio prior to running the application.
• Debate Disentangles OR and ML Roots: A discussion debated the historical relationship between Operations Research (OR) and Machine Learning (ML), pinpointing a divergence in methodology.
  • While early AI/ML closely mirrored OR and control theory, modern ML has shifted towards statistical methods emphasizing learning from data rather than modeling reality from first principles, with an increased focus on empirical approaches.
• Anonymous LLM fools Reddit: Researchers tested an anonymous LLM on Reddit’s /r/changemyview and found very high efficacy, leading to annoyance among users, as discussed in this X post and Reddit thread.
  • One user humorously stated, AIs aren’t smart, change my mind to which ChatGPT responded Yes, they are and the user replied oh okay, im sorry.
• Qwen 3 Excites Users with Reasoning: Members lauded the new Qwen models, specifically mentioning improved reasoning and instruction following abilities.
  • One user reported that their output for some reasoning tasks is superior, especially praising the MoE model’s speed and intelligence, describing it as just as smart as 2.5 Flash, if not smarter.
• Meta Announces Llama 4: The existence of Llama 4, also known as Little Llama, was confirmed at LlamaCon, as seen in this YouTube livestream.
  • A key announcement from LlamaCon was the development of SAM 3 and Meta’s new app, with some speculating how the smaller Llama 4 models will compare to existing Qwen models.

Nous Research AI Discord

• Atropos Framework Guides RL: Nous Research launched Atropos, a rollout framework for reinforcement learning with foundation models that supports complex environments to advance model capabilities, alongside training and inference components detailed in their introductory blogpost.
  • Artifacts created using environments in Atropos, including a new dataset and five new models for tool calling and corporate fundamentals prediction, are available at HuggingFace.
• GRPO Tool Calling Improves DeepHermes: The GRPO environment improved DeepHermes’ tool calling by 2.4x and 5x on simple and parallel tool calls, respectively, using Berkeley’s Function Calling Benchmark.
  • Atropos is a key component of Psyche, an upcoming decentralized training network coordinating pre-training, mid-training, and post-training workloads globally; a hackathon will be hosted in San Francisco on May 18th to foster collaborative progress (more details coming soon).
• Fundamentals Prediction Model Accuracy Doubles: The corporate fundamentals prediction model’s accuracy increased from ~25% to 50% on directional changes using the Atropos framework.
  • The Atropos framework is designed to guide language models toward their optimal potential through reinforcement learning.
• DeepSeek R2 Release: Fact or Fiction?: There are rumors that DeepSeek R2 may be released soon and was fully trained on Huawei Ascend 910B hardware, but these claims have been refuted.
  • A tweet was linked with the official line from DeepSeek stating that “We will release R2 when we release R2, everyone who claims they know is lying”.
• Qwen 3 Overfits on Evals: Members found that Qwen 3’s base models seem very overfitted to certain evals, reporting that the model scored 75% for Trivaqa on M24b but only 60% on Q30MoE.
  • This prompted discussion about the effectiveness of MoE.

Cursor Community Discord

• Spending Limits Stall Speedy Signals: After exceeding spending limits, users reported delays for hours, despite upgrading, while another reported they ran out of fast requests.
  • One user noted that Gemini remains fast even on slower requests, while others faced challenges with Gemini 2.5 Pro.
• Discord’s Development: Discourse Delights Developers: One member jokingly noted that the Cursor’s Discord is finally getting some love again, indicating increased activity and engagement.
  • Another member responded with confidence that Cursor has always been loved, implying the team is simply polishing the cube.
• Gemini Glitches Generate Grief: Users reported that Gemini 2.5 frequently stops mid-request, even after indicating it would perform actions.
  • A team member said they are working with Google to resolve the issue, advising users to use other models and submit their request ID for investigation.
• Agent Apathy: Edits Evade Engineers: Users face persistent problems with the Agent failing to make edits after multiple attempts, instead advising manual edits.
  • A team member suggested the issue might stem from Gemini 2.5 Pro, recommending refreshing the chat context or switching to GPT 4.1, GPT 3.5, or Claude 3.7.
• Ollama Official: Opening on Over-the-Air: A user inquired about the release timeline for an official Ollama Smartphone App, and posted a relevant X post.
  • A user mentioned that reinstalling Cursor and clearing the cache fixed issues, while another confirmed manual cache clearing as an alternative to reinstalling.

HuggingFace Discord

• Turnstile Test Triumph!: Members successfully tested Cloudflare Turnstile, confirming its functionality.
  • The successful test prompted enthusiastic reactions from the members.
• Whisper Turbo Troubles Hit HF!: Users reported that OpenAI’s whisper-large-v3-turbo is not functioning on the HF inference endpoint, impacting even the webpage demo.
  • Members shared similar issues like this one for potential troubleshooting.
• GGUF CPU Offloading Goes Mainstream: Members confirmed that GGUF format accommodates CPU offloading, especially when merging checkpoints.
  • They noted that Transformers + Accelerate or Llama.cpp facilitate this process.
• Pi-Scorer Poised as LLM-as-a-Judge Proxy: A member introduced Pi-Scorer as a viable substitute for LLM-as-a-Judge, showcasing Colab notebooks for evaluating model checkpoints using Pi-Scores and implementing them as reward functions.
  • This could provide a useful tool for SFT Model Checkpoint Observability with Pi.
• Edge Filters Emerge for Excellent Error Extractions: A member suggested filters like Canny edge or Sobel for isolating defects with specific thresholds in images.
  • With the right threshold, auto-annotating scratches on datasets could be much easier.

Notebook LM Discord

• NotebookLM Nabs a Webby for Technical Prowess!: NotebookLM celebrated a Technical Achievement award at the Webby Awards.
  • This accolade underscores NotebookLM’s ongoing enhancements to its platform.
• NotebookLM’s Global Voice: Now in Over 50 Languages!: NotebookLM introduced multilingual support, now speaking over 50 languages, enhancing access for diverse users.
  • However, rollout is gradual; some users initially faced UI glitches, such as one reporting that Vietnamese audio wasn’t working and the UI still said “English only”.
• Audio Overview Customization Caps Prompt Queries!: Users testing the Audio Overview customization feature discovered a 500-character limit, raising questions about its utility versus uploading separate instruction files.
  • One user aimed to “lessen the silly banter, and keep focus on the facts and timeline”.
• Audio Overview Times Vary by Language!: Users reported that non-English audio overviews had shorter time limits compared to English; for example, English had a 15-minute limit versus 6 minutes 20 seconds for Turkish.
  • The team cited “technical reasons” for these limits but assured that they are actively working on extending the duration.
• Microphone Issues Plague Interactive Mode!: A user reported that interactive mode failed to detect audio from their microphone, disrupting usability.
  • Troubleshooting suggestions included verifying microphone permissions, checking browser settings, using a mic test, and trying an alternative browser.

Manus.im Discord Discord

• Add-on Credits Confuse Users: A user reported that add-on credits from early subscriptions to Manus.im are useless without resubscribing due to their short expiry, causing the loss of 3900 credits.
  • Another user clarified that bonus credits do not expire as long as the subscription remains active, and invite distributions appear random, potentially throttled.
• Manus Fellow Program Questioned: A user inquired about the Manus Fellow Program’s selection process, targeted countries, and inclusivity for regions like Pakistan and India.
  • Another user clarified the invite structure, noting starter plans give 2 invites and pro plans give 5 invites.
• Beta Testing Under Scrutiny: A user critiqued Manus.im’s beta testing approach, arguing that limiting users with credits undermines the purpose of a beta phase.
  • They suggested that a real beta test would let users complete full projects from start to finish, giving meaningful feedback about the experience and suggesting improvements.

Latent Space Discord

• X-Ware Red tool released to community: A user shared X-Ware Red, which uses the title of an embed and prepends r.jina.ai/ and openrouter-free-tier to generate titles for threads.
  • Another user suggested adding a toggle to let users control whether the thread title should differ from the embed name.
• Meta Ships Llama Prompt Ops for Engineers: Meta introduced Llama Prompt Ops, an open-source tool designed for prompt engineering, along with the Synthetic Data Kit.
• Link Posting Retitles Threads, User Reports: A user reported a bug where posting a link in a thread incorrectly retitles a thread that already has a name.
  • The bug should only look for threads with ‘https://’ in the title and change those.
• Community Scours for Durable LLM Benchmarks: A user requested a reliable survey of LLM benchmarks that supports historical comparisons of models.
  • Another user noted that most benchmarks last less than 2 years, recommending the “AI Engineer Reading List” for current benchmarks and linking to posts for OSS leaderboard versions 1 and 2.

Modular (Mojo 🔥) Discord

• Modular’s Repository Gets Multi-Licensed: The Modular repository now requires multiple licenses due to parts of src/max being licensed under Modular’s Community License while the rest uses Apache 2.
  • This change reflects the diverse licensing needs within the repository, particularly for components like those found in src/max/serve.
• Bending Origins Leads to Headaches: Members discussed issues with Origins in Mojo, particularly around gaps in APIs and missing language features like parametrizable traits, which complicates rebinding origins to container elements.
  • It was also noted that holding two mutating references to the same origin is problematic, though one can cast the origin to a MutableAnyOrigin to circumvent this limitation.
• Pointer Time to Screw Origins: To handle implementing list-like and span-like types, or reading sort implementations in the standard library, developers sometimes bypass Origins and resort to pointer time.
  • The discussion highlighted concerns about pointer types, especially regarding mutability and immutability fixes in Mojo.
• Standard Python Imports Loom: Full support for standard Python import statements in Mojo may arrive, suggesting python.import_module could eventually be deprecated.
  • A member described the possibility of this change as a pretty definite maybe, hinting at future enhancements to Python integration within Mojo.
• Flamegraph Visualizes Perf Output: For visualizing perf output, members suggested using flamegraph, which requires compiling the executable with debug info for effective analysis.
  • They also mentioned using llvm-mca for profiling particular blocks of code, referencing a private part of the gpu module (link).

LlamaIndex Discord

• GPT-4o masters Tetris via LlamaIndex: A video demonstrates GPT-4o generating Tetris in one shot using LlamaIndex and Composiohq, showcasing its advanced code generation capabilities.
  • The code used in the demo is available on GitHub, offering a practical example for developers.
• PapersChat indexes ArXiv and PubMed with LlamaIndex: PapersChat indexes papers on ArXiv and PubMed, using LlamaIndex, Qdrant, and MistralAI.
  • The nifty web UI to query them is available here.
• Azure OpenAI Plagued by Intermittent Timeouts: Users report intermittent timeouts with Azure OpenAI endpoints, even with consistent prompts, endpoints, and network conditions, suggesting potential rate limits or firewall issues.
  • Retry mechanisms are sometimes ineffective, and network changes only occasionally resolve the inconsistency.
• MessageRole: Cracking the FUNCTION vs. TOOL Code: The difference between MessageRole.FUNCTION and MessageRole.TOOL depends on the specific API in use.
  • Some APIs like OpenAI utilize tool messages, while others rely on function messages.
• Function Agent Context Snafus Unveiled: A user encountered an issue with a function agent getting stuck at the stream event during the second round of interaction; the user provided a sample code.
  • A member suggested awaiting the handler (await handler) after stream_events() exits to ensure the previous run concludes and the final response is received, which fixed the error.

Eleuther Discord

• RAG Chatbot Wrestles with Multi-Source Answers: A member building a RAG-based chatbot is struggling with generating answers that require information from multiple documents, even when using vector search and BM25.
  • The chatbot uses LLM Claude 3.5 Sonnet v1 and Amazon Titan v1 embeddings, and the member is seeking advice on how to effectively link references to appendices within the documents.
• GraphRAG Debated for Multi-Source Data: A member inquired about the value of using GraphRAG to aggregate answers from multiple sources, comparing it to insightRAG, which demands a domain-specific pre-trained model.
  • They are seeking alternative solutions to GraphRAG and noted their plans to attend NAACL.
• Engineer Kickstarts Local Inference Project: A member, previously a co-founder of Dataherald, is initiating a new project focused on local inference and small model training.
  • The member expressed keen interest in collaborating with the community and contributing to relevant research.
• Symbolic Prompt Recursion Explored: A member is investigating the behavior of recursive symbolic prompts under classifier pressure, particularly how smoothing and alignment constraints impact multi-turn hallucination drift.
  • They are keen on understanding how symbolic structures such as role-bound predicates or attention-synced markers persist across multiple outputs, despite soft-alignment drift and output smoothing.
• HHH Objectives Exposed: Research was shared on quantitatively scoring LLM outputs based on HHH (Helpful, Honest, Harmless) alignment objectives, using YAML and python/Gradio to audit user sessions.
  • Frontier models were observed to vary widely in honesty compliance, with some, like ChatGPT 4o and 4.5, ironically outputting high confidence in ambiguous answers, making OpenAI the least transparent of frontier models.

MCP (Glama) Discord

• Credential Passing Concerns: A member is encountering issues while attempting to pass credentials through headers from a client to the MCP server using Python, seeking assistance from the community.
  • Currently, no solutions or suggestions have been provided in response to the query.
• RAG Server Architecture Debated: A member is exploring the feasibility of building a RAG-type server where clients can upload files via an endpoint, store them server-side, and utilize them for question answering.
  • They are soliciting feedback on the viability of this approach and whether alternative architectures might be more effective.
• Streamable HTTP Authentication Nuances Emerge: A member inquired about the community’s opinion on the Streamable HTTP implementation and authentication, especially in the recently released TS SDK.
  • Feedback indicates that it’s functioning effectively, but members are still investigating the nuances of hosting a multi-tenant server and how statefulness impacts it.
• Multi-Tenant Server Statefulness Examined: Concerns have been raised regarding hosting a multi tenant server and the implications of statefulness, specifically questioning why a single instance suffices for stateful setups but not for stateless ones.
  • The discussion revolves around whether a stateless server should spawn a new instance of the MCP server per request.
• Open Source Agentic Apps: Production Ready?: A member questions the real-world applicability of open-source models for agentic applications in production environments, not just for pet projects.
  • They express skepticism about the ability of most open-source models to reason or follow instructions effectively without fine-tuning.

Torchtune Discord

• Speedy Gradient Scaling Arrives via Foreach: A member shared a code snippet using torch._foreach_mul_ for gradient scaling, potentially merging with gradient clipping for a single parameter loop, increasing optimization speeds.
  • Another member pointed out the related PR and wondered if the seemingly constant gain accumulates over many iterations, noting potential caveats.
• Tune Contributors Seek Easy First Issues: A member highlighted two easy issues and another for community contribution to the project, designed to lower the barrier to entry.
  • These issues provide opportunities for new contributors to get involved in the project and gain experience, but are not described in detail.
• DoRA and QAT Pairing Unexplored: A member inquired about experiences combining DoRA (Difference of Low-Rank Adaptation) with QAT (Quantization-Aware Training), an under explored combination.
  • There was no discussion or response regarding this combination in the messages provided, suggesting a knowledge gap or lack of experimentation in the community.

DSPy Discord

• DSPy Users Crave MCP Usage Documentation: Users are requesting tutorials or documentation for the new MCP (Multi-Controller Processing) feature introduced in the latest release of DSPy.
  • One user suggested that getting started by reviewing the test cases helps clarify understanding of the stdio and SSE clients setup, so a tutorial may not be necessary.
• React Developers Ponder Displaying Thoughts Component: A user asked for advice on the best way to display the Thoughts component in React within the DSPy framework.
  • They mentioned the option of modifying the forward method, but inquired about a more appropriate place to implement this feature.

Cohere Discord

• Markdown vs Image RAG Debate Brews: Members discussed comparing Markdown-based versus Image-based multimodal RAG on PDFs, with one member using Docling to convert PDFs to Markdown and compute text embeddings.
  • They are considering switching to EmbedV4 to process raw images directly for multi-modal embeddings in RAG.
• Cohere Considers Embed V4 Rate Limit Hike: A user inquired whether Cohere would increase production rate limits for embed-v4, stating that 400 requests per min is insufficient for their PDF-heavy use case.
  • No response has been given.
• Embed V4 Bedrock Availability Teased: A user inquired whether Embed V4 will be available on Bedrock.
  • There has been no answer from Cohere yet.
• New Data Scientist Pumps Embed V4: A new data scientist joined the Cohere Discord community, expressing excitement in trying new tools, particularly the latest Embed V4 model from Cohere.
  • The new member is pleased to join the community.

Nomic.ai (GPT4All) Discord

• Manus AI Tool Goes Global: A member shared Manus AI, noting its availability after being dropped by China.
  • The tool is purported to be the first auto research AI Agent, stirring discussions about its potential impact.
• Nomic Powers Embedding Workflows: A member highlighted that Nomic provides comprehensive embedding tools, suggesting it goes beyond GPT4All.
  • They emphasized the versatility of Nomic’s embedding tools, stating they are compatible with various other software.
• Group Embeddings, Skip Training?: A member proposed that grouping embeddings could be a substitute for traditional training methods.
  • The suggestion involves grouping embeddings for a specific person, averaging them, and then using that average to sort and identify other pictures of the same person.

Gorilla LLM (Berkeley Function Calling) Discord

• Berkeley Models Evaluated Loosely vs Strictly: A member proposed ‘loose’ vs ‘strict’ evaluation mechanism for Berkeley function calling models, especially those that can be ‘hacked’ into working, representing specific use-cases.
  • They provided an example of a model incorrectly trained to emit <tool_call> instead of <|tool_call|> as its specs indicate, where a knowledgeable user might ignore the error and evaluate functional correctness.
• Model Training Creates Inconsistencies: One member encountered a model which was incorrectly trained to emit <tool_call> instead of <|tool_call|> as its specs indicate.
  • The member suggested that, if they knew the model specifically, they could ignore this error and evaluate on functional correctness, but a naive user could not.

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

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

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Discord: Detailed by-Channel summaries and links

Perplexity AI ▷ #announcements (2 messages):

Perplexity AI on WhatsApp, Sonnet Model Behavior Update, Anthropic Status Incident

• Perplexity Hits WhatsApp with Image Generation!: Perplexity AI is now available on WhatsApp including image generation features, accessible via this link.
• Sonnet Stumbles, Routes to Alternative Models!: Due to increased error rates with the Sonnet 3.7 API, some queries are temporarily routed to alternative models as a fallback, which is related to Anthropic Status Incident.
• Model Switching: No Intentional Shenanigans!: The Perplexity team clarified that they do not intentionally switch your selected model; routing only happens when Sonnet encounters an error to maintain service availability.

Perplexity AI ▷ #general (1112 messages🔥🔥🔥):

Free AI billing, Grok android app, Model Fallbacks, The Boys fanboy

• Users Exploit Free AI Billing: Some users claim to have not paid a penny for their AI bill for a year, possibly through Play Store or by joining a few webinars and filling out some forms.
  • Others requested the method, while some expressed disbelief.
• Grok app is cheap for Indian users: The Grok android app is reportedly charging only 700rs per month for supergrok for Indian users, but the free tier isn’t even working anymore for some.
  • It’s available on X if you have premium +.
• Perplexity is replacing models without notification: Users are complaining that Perplexity is replacing Claude 3.7 with a lower-quality model like GPT 4.1 or Deepseek, and are angry because no model switch or clear model indicator on their responses.
  • One user said that It is straight up using R1 to generate the answers that are being sent to sonnet thinking. And then saying that the answer came from sonnet. That’s shady.
• Discord Channel Becomes a The Boys Fanboy Convention: The channel’s conversation swerved into The Boys territory, with users sharing GIFs and discussing plot points, like the Homelander kills someone in public scene.
  • Others mused whether or not to skip Homelander scenes altogether and jokingly asked if the show has any scenes that are more disgusting than usual.

Perplexity AI ▷ #sharing (1 messages):

_paradroid: https://www.perplexity.ai/search/d7bb905e-27e3-43e9-8b68-76bea1905457

Perplexity AI ▷ #pplx-api (14 messages🔥):

Sonar API Debit Card Issues, Hackathon Credits, Structured Output Issues, Async Deep Research API, API vs Web Results

• Card Conundrums Plague API Users: A user reported that their debit card is not supported for the Sonar API, preventing them from using it for their hackathon project; also reporting not receiving hackathon credits after card verification.
  • No solutions were provided in the given context.
• Structured Output Struggles Surface: Users are experiencing issues with structured output from the API, including unexpected output and difficulty enforcing schema constraints.
  • One user had to explicitly specify ‘In english’ to prevent the API from returning Mandarin.
• Deep Research API Asynchronicity?: A user questioned the absence of an asynchronous deep research API, finding it impractical to maintain a socket connection for extended periods.
  • The user proposed a flow involving a GUID, a status endpoint, and separate result retrieval, but no confirmation or alternative solution was given.
• API Output Divides from Web Experience: A user expressed disappointment that the API results do not match the quality, citations, etc. of the web interface.
  • No explanation or solution was provided.
• Mandarin Models?: A user found that they had to specify “In english” in their prompt because they were receiving Mandarin output.
  • Another user chimed in saying they’d seen R1 based models go into mandarin while thinking, especially when trying to solve equations

Unsloth AI (Daniel Han) ▷ #general (899 messages🔥🔥🔥):

Qwen3, LM Studio issues, GGUF fixes, Training configuration, Multi-GPU support

• Qwen3 GGUF uploads have Template Issues: Members are experiencing template issues with the uploaded Qwen3 GGUF models in LM Studio, particularly with the 128k context length versions, leading to parser errors.
  • Some discovered that the ChatML template can be used as a workaround, though it is not technically correct, and the Unsloth team is working to resolve these inconsistencies across different platforms.
• Unsloth patches the transformers: When loading Unsloth, it patches transformers and some other stuff to optimize, but there may be an issue that it could break things.
  • After loading the library, performance and other problems can arise, but the recommendation to download the github version might resolve the issue.
• Qwen3 GGUFs now works in Ollama and llama.cpp: The Unsloth team confirmed that their Qwen3 GGUFs are compatible with Ollama and llama.cpp, enabling integration with platforms like Open WebUI.
  • However, some users have found that the models do not work in LM Studio due to unresolved template issues, despite the underlying llama.cpp runtime that LMStudio uses not being up-to-date.
• Unsloth to announce soon and Reuploading all Models: The Unsloth team said they’re reuploading all the models, and make an official announcement maybe tomorrow or on wednesday.
  • Image component is maybe tool calling, but it is not sure.
• CCE and stable Triton version for Unsloth: Users ran into a Triton error with Colab, and the recommendation is to downgrade Triton to version 3.2.0 which should work fine with Unsloth to avoid the CCE errors.
  • One user pointed out that the one responsible for putting CCE into pypi is Daniel Han.

Unsloth AI (Daniel Han) ▷ #off-topic (10 messages🔥):

ChatGPT ComfyUI Opinion, California AI Group, ComfyUI Demos

• ChatGPT Renders Opinion of ComfyUI: A member shared an image depicting ChatGPT’s opinion of ComfyUI which prompted humorous reactions.
  • One user commented that the scrambled lines in the middle of the image accurately represent the complex processes involved.
• California AI Group in the Works?: A member inquired about in-person AI group development opportunities in California, seeking local participants.
  • Another member based in Fremont expressed interest, referencing a project showcased on their X account.
• ComfyUI Demos Get Showcased: A member shared various ComfyUI demos, noting that each example appeared different without any polishing efforts.
  • Another liked another demo showcased on the members X account which features transitions between different things.

Unsloth AI (Daniel Han) ▷ #help (186 messages🔥🔥):

Unsloth installation issues, Qwen notebook issues, GRPO performance, Lora efficiency, Unsloth & Ollama/vLLM

• Unsloth Installs Cause Install Instability: It was noted that --no-deps is needed on Google Colab due to conflicts with pre-installed packages, and kernel restarts might be required to resolve caching issues.
  • It was also suggested that users who encounter issues with WSL killing the unsloth process could try windows.
• Qwen Notebook Needs Some Assembly: Users reported requiring minimal changes to run the Qwen notebook, such as adjusting names and enabling reasoning with tokenizer.qwen_enable_thinking = True.
  • But Unsloth version 2025.4.2 is reported to be broken for Qwen: downgrading to Unsloth 2025.3.19 resolves this issue.
• GRPO Fine Tuning Is On The Up and Up: Users doing GRPO (Gradient Rollout Policy Optimization) are reporting positive results and offer to provide assistance to others.
  • One user mentioned they initially used the gemma 3 4b notebook but found Qwen 4b better for their use case.
• Lora training doesn’t Linger Long: A user training unsloth/phi-4-unsloth-bnb-4bit on 4k QAs with Lora found it to take weeks, which is abnormal.
  • A member suggested using a Python script directly instead of text-generation webUI due to cutoff length issues and offered a Colab notebook as a base.
• Unsloth plays well with vLLM, a model serving system: A user reported issues with Qwen3 GGUF models from Unsloth not working properly with Ollama v0.6.6 and hallucinating random content.
  • A member suggested trying vLLM and provided a sample command to serve unsloth/qwen3-unsloth-4bit using vLLM.

Unsloth AI (Daniel Han) ▷ #showcase (4 messages):

Pi-Scorer, LLM-as-a-Judge, encoder model

• Pi-Scorer: Judge Judy’s Alternative: A member introduced Pi-Scorer as an alternative to LLM-as-a-Judge, providing links to Colab notebooks for model checkpoint evaluation and reward functions.
• Pi Model unveils Encoders: A member inquired about the architecture of the Pi model, and it was revealed to be an encoder model.
  • Another member praised it as a cool service.

Unsloth AI (Daniel Han) ▷ #research (47 messages🔥):

Dynamic BNB Quantization, LLMs in Medical Advice, Mixture of Experts with Gemma, Attention Head Routing, GRPO Fine-tuning

• Dynamic BNB Quantization Proposed: A member proposed creating a dynamic BNB quantization scheme where modules use 4-bit, 8-bit, or BF16 precision based on their sensitivity, suggesting this could reduce space without sacrificing accuracy; a related paper was mentioned here.
  • Another member indicated that if there is sufficient user demand for this, it might be something we could roadmap out.
• LLMs Struggle with Medical Advice Synthesis and patient interactions: A paper identified user interactions as a challenge to using LLMs for medical advice, leading to a discussion on whether LLMs can synthesize medical knowledge, and if training LLMs can ensure they not do so.
  • One member noted the importance of bedside manner from premed experience in doctor-patient interactions, implying LLMs currently lack this skill.
• MoE Setup and Gemma: A member inquired about implementing a Mixture of Experts (MoE) setup with Gemma 3 4B, questioning if it could be adapted despite its different architecture.
  • It was suggested to fundamentally alter the model or explore methods involving Mixture of Expert attention heads, referencing this paper.
• GRPO Ineffective for JSON Config Generation Task: A member reported inconsistent results when fine-tuning Gemma 3 4B for generating nested JSON configs using GRPO, with accuracy dropping significantly for short inputs.
  • Despite training with custom reward functions, the member found GRPO unsuitable for the task, as descriptions significantly affected the trigger and action components, leading to inconsistent BLEU scores.

LMArena ▷ #general (544 messages🔥🔥🔥):

O3 Pro, Qwen 3, Gemini 2.5 Pro, Grok 3.5, Model Benchmarking and Evaluation

• O3 Pro Demand Defies Delay: Users are eagerly anticipating the release of O3 Pro, with some joking about its potential as a “virus” because of its superior intelligence, and is considered a “p2w” pay-to-win model.
  • However, some users express concerns about its cost and accessibility. Some even joke they are now on day 13 of waiting for O3 pro.
• Qwen 3: Benchmarking Bafflements and Training Talk: There’s discussion about Qwen 3’s performance, where some users find that despite strong benchmark results, it doesn’t feel as smart as 2.5 Pro in practice, leading to speculation that its post-training wasn’t as fleshed out.
  • Some suggest that Qwen 3’s base model might be excellent for fine-tuning, and one user noted that Qwen 3 outperforms Gemini 2.5 Pro on some benchmarks, while others don’t seem to notice any difference, with some noting that it beats 2.5pro in 4/5 benchmarks.
• Gemini 2.5 Pro still reigns supreme: Some users still prefer Gemini 2.5 Pro for its unique ability to adapt to different roles or assume positions on niche topics, making it feel like interacting with different expert facilities, with some calling it the strongest base model out there.
  • Despite some models topping individual benchmarks, one user finds 2.5 Pro ranked higher on the LM Arena due to its adaptability to the one-shot prompt intensity in the way that it assumes the role of the question answerer with no single personality.
• Grok 3.5 Incoming?: Users are anticipating Grok 3.5 model but opinions on its potential vary, with some being cautiously optimistic while others remain skeptical.
  • One user said Grok 3 overreaches every time, it’s like when you ask it to prove something it supplements substance with verbosity.
• Sonnet 3.7: WebDev’s Top Model?: Users debated the capabilities of Claude 3.7 Sonnet, claiming the model is still ahead in most of my cases for web dev tasks, with some agreeing that its still perplexing.
  • Some noted that Sonnet 3.7 is currently the #1 model on the webdev arena.

LM Studio ▷ #general (271 messages🔥🔥):

Qwen3 thinking, LM Studio on Android, Qwen3 experts number, Qwen3 bug fixes, Qwen3 with RAG

• Slashing Qwen3’s Thinking: Users discussed how to disable the thinking output of Qwen3, discovering that the /no_think command works in the user message or system prompt, but may need to be repeated or the model reloaded to take effect; here is an example.
  • One user found it only worked after seeing someone else do it, then it worked when they did it.
• Android LM Studio: A Mobile Dream?: Users inquired about an Android version of LM Studio, but were informed that no mobile versions exist.
  • One user joked about making it their quest to implement it.
• Qwen3’s Expertly Tuned Numbers: Users discussed the number of experts slider for Qwen3 MoE, with one noting that their LM Studio defaulted to 8 experts out of 128, questioning why the setting exists if it limits the model; here is a relevant screenshot.
  • It has been stated that more experts can lead to more computation and more confusion and actually less quality because the subject matter experts will be overruled by many idiots.
• Qwen3 Bug Fixes Released, Speeding Up Performance: New Qwen 3 versions with bug fixes have been released, addressing a broken template that was slowing the model down and causing it to respond improperly.
  • It has been noted that the bugfixed models are even faster now and that this release includes dynamic quants2.0.
• Qwen3’s RAG struggles: Members noted that LM Studio’s built-in RAG implementation may not provide optimal results; LM Studio’s RAG implementation sucks.
  • They suggest copying and pasting the text directly, or implementing a custom RAG solution for improved performance.

LM Studio ▷ #hardware-discussion (61 messages🔥🔥):

Framework Desktop vs. Flow Z13, AMD GPU 7900 XTX Value, Qwen3-30B-A3B Issues, MLX vs. llama.cpp Speed, Xeon Workstation for $1k

• Framework Desktop Debated Against Flow Z13: Members debated the value of a maxed-out Framework Desktop at $2K against the Flow Z13, criticizing Framework for nickel and diming customers with power supplies and models.
  • The discussion highlighted concerns about cooling and TDP, with the sentiment that the chip is too expensive and waiting for the next generation might be preferable.
• 7900 XTX: Still the Best AMD GPU?: The AMD GPU 7900 XTX was praised as the best AMD GPU, with mentions of second-hand sales around 750€ offering approximately 4080 Super performance.
  • Notably, it comes with 8GB more VRAM, making it an attractive option for those needing more memory capacity.
• Qwen3-30B-A3B and PC Restarts: A user reported experiencing PC restarts every 30-60 minutes while using Qwen2.5-coder-32b-instruct-q4_k_m, questioning if it was related to idle GPU usage.
  • The potential cause was speculated to be the model pushing the GPU harder when loaded but not actively interacted with.
• MLX Surpasses llama.cpp in Prompt Processing: MLX was reported to be more than twice as fast as llama.cpp for prompt processing with Qwen3-30B-A3B.
  • This was highlighted in a Reddit thread where users compared performance on Macs.
• Xeon Powerhouse Priced at $1k: A 40-core Xeon workstation with 256GB RAM was mentioned as available for around $1k, providing a cost-effective solution for high-memory computing.
  • One user linked to a custom Lenovo ThinkStation P720 build as an example.

OpenRouter (Alex Atallah) ▷ #announcements (1 messages):

Rate Limit, 2.5 Flash, Capacity

• 2.5 Flash Rate Limit Issues Resolved: Users experiencing rate limit issues with 2.5 Flash should find it much better now, as additional capacity has been added to the model.
  • The increased capacity aims to alleviate previous constraints and provide a smoother user experience.
• Improved Capacity for 2.5 Flash Model: More capacity has been allocated to the 2.5 Flash model to address and improve rate limit issues.
  • The upgrade intends to provide users with a more reliable and efficient experience when using the 2.5 Flash model.

OpenRouter (Alex Atallah) ▷ #general (321 messages🔥🔥):

Qwen3 coding abilities, Gemini 2.5 flash issues and rate limits, OpenRouter Caching Issues, LLama 4 benchmark, Vertex issue with token counting

• Qwen3: Good coder but has issues: Members discussed Qwen3’s coding capabilities, with one user finding it really nice for explanations, while another pointed out issues with complex math tasks.
  • A user fixed the complex math task by lowering my temp a bit more, while another mentioned a problem with Qwen3 tool calling.
• Gemini 2.5 Flash faces Rate Limits and Errors: Users reported that Gemini 2.5 Flash is facing rate limits and errors, even on paid versions, with one user experiencing this despite not using web search, while another pointed out a way to use Gemini 2.5 pro for free.
  • It was clarified that OpenRouter is facing an ongoing Vertex issue with token counting, and it was further stated that the free tier limits are not supported on OpenRouter.
• OpenRouter Caching limited to 2.0 Flash only: A user pointed out that OpenRouter caching is currently not working for 2.5, only 2.0 Flash, and that 2.5 Flash errors on them (No endpoints found that support cache control).
  • A member asked about caching multiple prompts, and Toven clarified that new caches are written for new 5 min TTLs, and that caching improves latency but doesn’t affect pricing.
• LLama 4 sucks in new benchmark: A benchmark review showed that LLama 4 sucks, but it was noted that it is really just one benchmark.
  • The person who did the benchmark added that the ELO within 25 range is not statistically signficant to tell the difference.
• Debate arises: Is 9.9 bigger than 9.11?: An announcement of an X post showed a model stating that 9.9 is greater than 9.11, leading some to ponder if that was correct.
  • Others brought up that it depends on the context as Tesla FSD versions work differently, and that 9.11 > 9.9.

aider (Paul Gauthier) ▷ #general (186 messages🔥🔥):

Qwen3 models, Aider and Qwen3 integration, ktransformers VRAM optimization, Deepseek R2 release

• Qwen3 hardware requirements on new Macbooks are crazy fast: New Macbooks get good tokens/s for Qwen3 30B A3B, with some users reporting speeds around 100/s using mlx.
  • It’s desirable to have a local editor LLM that can output crazy fast AND be pretty good for Aider’s context, especially if the 4-bit quant version of Qwen3-30B-A3B can still perform decently on the Aider benchmark.
• ktransformers Optimizes VRAM Usage for MoE Models: The ktransformers library claims to efficiently run Mixture of Experts (MoE) models with reduced VRAM requirements.
  • They claim to achieve good speeds with only 8 GB of VRAM, which is a more hopeful approach for 30B-A3B models than loading all parameters in VRAM at once.
• Deepseek R2 Hype Builds: Rumors say the upcoming Deepseek R2 will have enhanced human vision capabilities and self-learning features, with possible release tomorrow
  • Some members await impatiently as they believe the demons that Deepseek R2 is slated for release tomorrow.
• New PR adds Thinking Spinner to Aider: A new contributor submitted a PR to add a 🔃 Thinking spinner that aider shows when waiting for LLM output.
  • The contributor explained that it makes aider feel snappy + alive.
• Qwen3 Tool Use Excels, but Application in Aider Uncertain: Some members report that Qwen3’s tool use ability is very strong, but its application in aider is uncertain due to the tool call API.
  • While tool use may not be directly applicable, others suggest using a multi-agent workflow where tool use microagents are Qwen3.

aider (Paul Gauthier) ▷ #questions-and-tips (21 messages🔥):

AiderDesk Agent Mode, Repo Map Control, OpenRouter Model Support, Gemini 2.5 + Deepseek combo

• AiderDesk’s Agent Mode for the win: A user is using Agent mode in AiderDesk with “probe” for planning, then enabling “Use Aider tools”, “Include context files” and “Include repomap” when ready, according to their github.
  • They use other tools like Jira management and desktop-commander for running commands, but haven’t used memory-bank or context7 much yet.
• Tuning Repo Map with Aider: A user wants to include only the api code in the repo map, but not comments or tests, and asks if it’s possible to disable the latter two using aider --map-tokens 0.
  • Another user suggests using repomix --compress or probe as alternative solutions, noting that there’s no native support for granular control over the repo map.
• OpenRouter models are supported, but not always successful: A user asks if Aider can use any model on OpenRouter, but another user confirmed that all OR models are supported.
  • They also added that you shouldn’t expect much if you’re using gemma 3 1b or smollm.
• Gemini 2.5 + Deepseek Power Combo: A user found a good combo using Gemini 2.5 for planning and Deepseek for diffs and vchanges explanations.
  • They advise doing this in AI Studio because Gemini is free there.

aider (Paul Gauthier) ▷ #links (1 messages):

p0lyg0n: Great documentary on Deepseek: https://www.youtube.com/watch?v=Lo0FDmSbTp4

GPU MODE ▷ #general (10 messages🔥):

Apple Silicon, Cloud GPUs, CUDA, Metal, ROCm

• Apple Silicon Not a Barrier to Cloud Challenge: A user with an M4 Max PC expressed concerns about participating in a challenge, but another user clarified that the challenge runs in the cloud, so Apple silicon is not a barrier.
  • They suggested checking out the relevant channel for more info.
• Cloud GPUs Enable Remote CUDA/ROCm Learning: A user explained that while learning CUDA or ROCm is easier with local compute, it’s still possible using cloud GPUs.
  • They noted the increasing availability of cheap cloud GPUs nowadays.
• Metal Programming on Macs is Viable: A user affirmed that one can program GPU stuff in Metal on Macs just fine.
  • They added that it’s more about knowing your tools well, also shared a Metal code snippet.

GPU MODE ▷ #triton (2 messages):

fp8 quantization, fp32 accumulation, Triton matmul, Custom CUDA kernels, AMD

• FP8 Quantization with FP32 Accumulation Questioned: A member inquired about the possibility of performing fp8 quantization and fp32 accumulation for matmul operations using Triton, or if custom CUDA kernels are necessary, especially when running on AMD GPUs.
• Double Buffering via Num_stages Parameter: A user asked if setting num_stages greater than 1 essentially enables double buffering in Triton.
  • They mentioned that MI300 doesn’t have async loads like Ampere, and the recommended setting is num_stages=2, also wondering if num_stages > 2 could ever help.

GPU MODE ▷ #torch (5 messages):

Torch Logger Methods Compilation, AOT Inductor Multithreading

• Torch Loggers Trigger Compilation Issues: A user inquired about ignoring logger methods during compilation to avoid exceptions related to FSDP::pre_forward in PyTorch’s distributed module.
  • Another member suggested setting the TORCH_LOGS environment variable to output_code or tlparse to inspect the generated code and identify potential if-statements causing the issue, referencing a specific line in torch._dynamo.config.py.
• AOT Inductor Training Troubles in C++: A user reported achieving a partial C++ training setup using AOT Inductor, but suspects multithreading issues.
  • They theorize the problems stem from unwanted specialization in their code and plan to open a PyTorch issue for further investigation by the AOTI authors, especially concerned about the API’s behavior with multiple worker threads calling fw_graph->run().

GPU MODE ▷ #announcements (1 messages):

AMD MI300 competition, MoE kernels, FP8 submissions

• New Single GPU MoE Kernel Released for AMD MI300 Competition: A new single GPU MoE kernel problem is now available for the $100K AMD MI300 competition; check it out on the leaderboard.
  • A member suggested that because this problem is trickier, it is worth reviewing the lenghty explanation provided.
• Key Dates for AMD MI300 Competition: Registration closes April 30 while final submissions, including both FP8 and MoE kernels, are due May 27.
• Slow Leaderboard Times: Running leaderboard submit ranked will be slow at 8 min for this problem.
  • The submitter suggests using leaderboard submit test/benchmark for faster iteration.

GPU MODE ▷ #beginner (1 messages):

raymondz4gewu_60651: /get-api-url

GPU MODE ▷ #torchao (22 messages🔥):

Quantized Models and torch.bfloat16, vllm Compile Integration Debugging, gemlite Kernel Selection, torch.compile Debugging Challenges, torch.dtype Extensibility

• Quantized Models Reloaded as torch.bfloat16: Quantized models reload with torch.bfloat16 after being saved with a quantized layout because the original dtype is preserved.
  • The actual quantized dtype can be accessed by printing the weight, as PyTorch’s torch.dtype isn’t extensible to tensor subclasses yet; further discussion is available here.
• vllm Compilation Integration Woes: An issue arises with vllm’s compile function when integrating with the gemlite library, where using torch.compile leads to incorrect behavior.
  • Specifically, vllm fails to pick the correct kernel from gemlite, which is based on the input shape; debugging inside torch.compile proves challenging due to its limitations.
• Kernel Conundrums in gemlite: The core issue lies in the incorrect kernel selection within gemlite, traced back to the input shape not being correctly recognized when vllm uses torch.compile.
  • The kernel selection logic is based on input shapes, as defined in gemlite’s core.py, making shape inspection crucial for debugging.
• torch.compile Debugging Dilemmas: Traditional debugging methods like print statements and breakpoints are ineffective within torch.compile, complicating the process of inspecting variable states.
  • Using TORCH_LOGS=+dynamo can dump a graph containing shapes, aiding in debugging, and the PyTorch documentation offers guidance on breakpointing dynamo tracing.

GPU MODE ▷ #rocm (3 messages):

ROCm memory, CDNA3 ISA

• ROCm Memory Banks Size Clarified: Memory banks in ROCm are 32 bits wide, assuming 32-bit alignment.
  • The bank is calculated via address % bank_size.
• CDNA3 ISA Reference Details LDS Configuration: The CDNA3 ISA Reference, section 2.2.1, states that each compute unit has a 64kB memory space for low-latency communication.
  • This memory is configured with 32 banks, each with 512 entries of 4 bytes.

GPU MODE ▷ #metal (3 messages):

QR decomposition, SIMD, Thread barriers, Single-threaded SVD

• 128-bit QR Decomposition Wows: A member shared a pretty awesome QR decomposition implementation with 128-bit precision using SIMD and thread barriers in a linked python script.
• Speeding Up Single-Threaded SVD: A member reported finding patterns in SVD that were single-threaded and noted that they are fixing that as well to make it more parallelized.

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

GPU Price Tracker, AI/ML Engineer for Hire, Open Source IDE for AI/ML

• Track GPU Prices on Amazon: A member built a GPU price tracker that pulls the entire Amazon pricing history of a GPU and creates a nice chart.
  • It calculates up-to-date values, such as how many teraflops you get per dollar; a use case is to find a good point in time to get a private cluster.
• AI/ML Engineer Available for Hire: An AI/ML Engineer with 8 years of experience specializing in artificial intelligence, machine learning, full-stack, and mobile development is available for hire; their expertise encompasses deep learning, natural language processing, and computer vision, enabling them to integrate cutting-edge AI solutions into scalable and robust applications.
  • Links to their LinkedIn profile and portfolio were provided, as well as a skill set list including ML algorithms, Deep Learning, NLP, Computer Vision, MLOps, and AI Model Integration.
• Open Source IDE Project Kicks Off: A member is building an open-source IDE for AI/ML Engineers and is looking for collaborators; DM them if you are interested in details, joining, or have insights.
  • The member provided links to their LinkedIn profile and GitHub profile.

GPU MODE ▷ #thunderkittens (1 messages):

Use Cases, Performance

• Users Inquire about Use Cases and Performance: Users are inquiring about specific use cases and the resulting performance metrics after implementation.
• Keen interest in Implementation Details: There is keen interest to hear how you got on with it, specifically regarding practical outcomes.

GPU MODE ▷ #general (15 messages🔥):

FP8 quantization material, FP8 matmul, Deepseek-v3 tech report, prefixsum ranked timeout

• FP8 Quantization Quest Initiated: A member inquired about resources for FP8 quantization, specifically regarding FP8 matmul with FP32 accumulation benefits and linked the onnx fp8 formats page.
  • They referenced the Deepseek-v3 tech report, noting that FP8 might face underflow issues, necessitating a higher precision accumulator.
• Prefixsum Ranked Timeout Troubleshoot: A member reported frequent timeouts, specifically for ranked prefixsum submissions, despite a 30s timeout limit.
  • Staff acknowledged the issue, attributing it to their own error and later claiming to have resolved it, but the member still experienced timeouts and then DMed the code.

GPU MODE ▷ #submissions (60 messages🔥🔥):

vectoradd benchmark on H100, amd-fp8-mm benchmark on MI300, amd-mixture-of-experts benchmark on MI300, prefixsum benchmark on H100, A100, matmul benchmark on L4

• H100 VectorAdd Speeds Race to the Bottom!: Multiple submissions were made to the vectoradd leaderboard on H100, with times ranging from 540 µs to 708 µs and one submission achieving third place at 540 µs.
• MI300 AMD-FP8-MM Leaderboard Heats Up!: Numerous submissions hit the amd-fp8-mm leaderboard on MI300, including a third place at 196 µs, with personal bests around 2.37-2.43 ms and successful runs varying widely from 198 µs to 8.05 ms.
• AMD Mixture of Experts takes the Top Spot!: The amd-mixture-of-experts benchmark on MI300 saw a first place submission at 6228 ms and multiple second place submissions around 7379-7490 ms.
• Prefixsum Runs neck-and-neck on H100 & A100!: The prefixsum leaderboard saw multiple second place submissions: one on A100 at 1428 µs and several on H100 around 955-985 µs.
• L4 MatMul Crown is Up For Grabs!: A new first place was set on the matmul leaderboard on L4 at 2.27 ms, while another submission grabbed second place at 49.3 ms.

GPU MODE ▷ #status (2 messages):

Single GPU MoE Kernel, FP8 and MoE Kernels, Leaderboard Submissions

• Single GPU MoE Kernel Problem is Live!: The new single GPU MoE kernel problem is now out, see the leaderboard.
  • A longer explanation has been provided, recommending a slow read through this link.
• Important Dates to Remember: Registration closes tomorrow on April 30, with submissions for both the FP8 and MoE kernels due on May 27.
  • Keep in mind that running leaderboard submit ranked will be slow at 8 min for this problem so please use leaderboard submit test/benchmark for faster iteration.

GPU MODE ▷ #amd-competition (23 messages🔥):

Aithe reference code, FP8 correctness verification, Submission ID, official problem writeup for this kernel

• Aithe reference code: A member asked if the Aithe reference code will be open and expressed doubts about passing correctness verification with FP8 due to element-wise perfect equal checks; the reference code was quickly provided.
  • The response clarified that the comparison is not an element-wise perfect equality check, pointing to the relevant function using rtol=2e-02, atol=1e-03.
• Lost ranked code recovered: A member who lost ranked code locally requested help, and another member suggested using /leaderboard show-personal and /leaderboard get-submission to recover it.
  • The lost submission was identified by its ID (11105), and the member was directed to use the /get-submission command.
• Second Problem Delayed: Members discussed the upcoming second problem, with confirmation that it will be made available soon after extra testing, the fp8 isn’t closing.
  • A link to the official problem writeup for this kernel was shared.

GPU MODE ▷ #cutlass (1 messages):

vkaul11: Are there kernels available to do fp8 multiplication with fp32 accumulation ?

OpenAI ▷ #ai-discussions (88 messages🔥🔥):

ChatGPT persistent memory, AI Agent Company, IAM360 Framework, AI-generated thumbnails

• ChatGPT Gains Elementary Persistent Memory: ChatGPT has developed two types of persistent memories: long term memories taken from details of a chat that it determines are important (training data) and short term memories referencing back the past 90 days for context.
  • Users can turn off either long term or short term memory, but one toggle does not control both.
• AI Agent Company’s Laughably Chaotic Results: Professors staffed a fake company entirely with AI agents, but the results were laughably chaotic, suggesting current AI models cannot fully replace human jobs.
  • Despite claims from big tech companies, AI models are not yet at the level needed to completely replace a human and still require human supervision.
• IAM360: A Modular Symbolic GPT-Agent Architecture: A member is working on IAM360, an experimental framework for human-AI symbiosis, built using standard ChatGPT sessions with no custom GPTs, fine-tuning, or API integrations.
  • The system uses modular symbolic GPT agents with persistent roles (strategy, execution, finance, emotion) and a zero-shot orchestration system for natural emergent dialogue.
• Selling AI-Made Thumbnails for Robux: A member reported selling an AI-made thumbnail for 1500 Robux.
  • Other members stated that current generators butcher images if you give anything complex to them as reference images and clients won’t pay for such things in the real world.

OpenAI ▷ #prompt-engineering (29 messages🔥):

Identity Systems in ChatGPT, Dynamic Game Master Role in RP, ChatGPT Internal Tools, Prompt Engineering Tips, LLM TTRPG game development

• Memory Matters: Identity Systems in ChatGPT: A member discussed creating an identity system for ChatGPT to separate memories/history chat by identity, in order to retain static identities and states.
  • The goal is to avoid users getting stuck in narrative valleys, either erasing memories or trying to escape such scenarios.
• Game Master Dynamics: Roleplaying Adventure: A member shared a prompt to make ChatGPT act as a dynamic Game Master in a fantasy roleplaying adventure.
  • The focus is on playing the non-user character, evolving the world based on the main character’s experiences, and maintaining a balance between worldbuilding, character dialogue, and action.
• Bio Tool Uncovered: ChatGPT’s Memory: A member revealed that ChatGPT’s internal memory is referenced as the bio tool, advising its canonical name be invoked for defining save commands.
  • An improved version of the /pin command was suggested: The AI saves the most recent message into ChatGPT’s internal memory using the bio tool, preserving all essential details for future reference.
• Prompt Perfect: GPT’s Internal Tools: A member suggested asking the model to identify and describe the function of each of its connected tools, listing their canonical names and a code block for each tool, demonstrating its proper syntax.
  • The tools mentioned include python, web, image_gen, guardian_tool, and canmore.
• RPG Roots: General AI Framework Development: Members noted their journey from LLM TTRPG game development to general AI framework development.
  • One member highlighted that this path can lead to academic research.

OpenAI ▷ #api-discussions (29 messages🔥):

Identity system in ChatGPT, RP prompt issues, Dynamic Game Master role, ChatGPT internal memory (bio tool), LLM TTRPG game development

• Persona Persistence Plagues Players: Users are struggling with ChatGPT either erasing memories or falling into narrative valleys during role-playing scenarios, hindering the creation of static identities and consistent character states.
  • The inability to maintain persistent identities forces users to constantly reset or circumvent undesirable narrative paths.
• Game Master GM Role Defined: A member defined a dynamic Game Master (GM) role for ChatGPT in fantasy roleplaying, focusing on playing a non-player character (NPC) that interacts with the user’s protagonist, evolving the world based on the protagonist’s experiences.
  • The GM should balance worldbuilding, dialogue, and action, avoiding excessive detail, and use specific commands like /export_character, /export_world_state, /force_random_encounter, and /set_mood to manage the game.
• Pinpointing ChatGPT’s Bio Tool: The member identified ChatGPT’s internal memory as the bio tool, advising others to use this canonical name in save commands to ensure the pin function correctly saves essential details for future reference with /pin.
  • They suggested placing commands near the top of the prompt and using gapped repetition to improve compliance.
• Frameworks Forged From Fantasy: A member shared that their AI journey started with LLM TTRPG game development, then transitioned to general AI framework development, and finally to academic research.
  • They are now working on creating a GPT for a specific task to better wrangle the LLM into a fully outlined framework.
• Tips to Tame Text-Generating Tech: A member suggested adding concrete specifications to prompts to minimize LLM guessing, and to ask the model to identify and describe its connected tools, listing their canonical names and demonstrating their proper syntax.
  • They provided examples of how to query the model for its tools such as python, web, image_gen, guardian_tool, and canmore, and gave specific syntaxes to invoke them.

Yannick Kilcher ▷ #general (79 messages🔥🔥):

PyQt5 Chat App, OR vs ML history, Gemini 2.5 Pro vs GPT-4o, Qwen 3 performance, FFN in Transformers

• PyQt5 Chat App Sparks Interest: A member shared an AI chat application built with PyQt5, using LM Studio as the backend server.
  • To use the app, users must select and start the model as a server on LM Studio before running the application.
• ****ORigins of ML Disentangled in Debate: A discussion arose around the historical relationship between Operations Research (OR) and Machine Learning (ML), with one member stating that ML comes from stats.
  • Another member argued that early AI/ML was close to operations research and control theory, but later branched off to embrace statistical methods, particularly emphasizing learning from data rather than modeling reality from first principles, with modern ML being massively empirical.
• Gemini 2.5 Pro Gets Roasted vs GPT-4o: Members discussed the performance of Gemini 2.5 Pro compared to GPT-4o, with one user calling Gemini a 4otard.
  • Another stated, Gemini 2.5 Pro is worse than 4o for sure, suggesting it might be better at coding but not as good at general use cases, with others also finding GPT-4o-mini a better option than Gemini 2.5 Flash in chat.
• Qwen 3: New Model Excites Users with Reasoning Prowess: Members lauded the new Qwen models, specifically mentioning improved reasoning and instruction following abilities.
  • One user reported that their output for some reasoning tasks is superior, citing its objective nature and adherence to instructions, especially praising the MoE model’s speed and intelligence, describing it as just as smart as 2.5 Flash, if not smarter.
• FFN Functionality Frustrates, Sparks Scrutiny: A discussion emerged about the role of Feed-Forward Networks (FFN) in transformer architectures, with one user seeking intuition on their function.
  • Some suggested that FFNs enable channel/neuron-wise mixing of information, increasing capacity and non-linearity, with one member quoting, Having an FFN at all is far more important than how wide it is.

Yannick Kilcher ▷ #paper-discussion (8 messages🔥):

DeepSeek VL, Construction

• Construction Cancels DeepSeek VL Discussion: Construction near a member’s home has caused a meeting to be canceled.
  • The meeting to discuss DeepSeek VL will be moved to the next day.
• DeepSeek VL discussion to restart: The previous DeepSeek VL discussion only covered the introduction, so the members will restart the paper discussion at the beginning.
  • The team planned to restart with soundproof headphones.

Yannick Kilcher ▷ #ml-news (34 messages🔥):

Anonymous LLM on Reddit, ChatGPT's Convincing Skills, Meta's LlamaCon 2025, Llama 4 aka Little Llama, SAM 3 Development

• Anonymous LLM fools Reddit’s change-my-view: Researchers tested an anonymous LLM on Reddit’s /r/changemyview and found very high efficacy, leading to annoyance among users, as discussed in this X post and Reddit thread.
  • One user humorously stated, AIs aren’t smart, change my mind to which ChatGPT responded Yes, they are and the user replied oh okay, im sorry.
• ChatGPT excels at philosophical discource: A member finds it fun and educational to ask ChatGPT to argue against their own beliefs or to advocate for facts they find annoying.
  • They noted that while O1-preview felt dry for general conversation, O3/O4-mini-high models are suitable for general topics and they now use o4-mini-high for news analysis.
• Meta hosts LlamaCon 2025: Meta hosted LlamaCon 2025, a generative AI developer conference, with live updates available via Engadget and the official livestream.
• Llama 4 aka Little Llama Confirmed: The existence of Llama 4, also known as Little Llama, was confirmed at LlamaCon, as seen in this YouTube livestream.
  • One user joked about calling them Baby llama’s while another expressed disappointment, deeming the announcements a nothing burger.
• SAM 3 in Development: A key announcement from LlamaCon was the development of SAM 3 and Meta’s new app.
  • One user pondered how Little Llama models will compare to the Qwen models.

Nous Research AI ▷ #announcements (1 messages):

Atropos RL framework, RLAIF models, GRPO tool calling, corporate fundamentals prediction, Psyche decentralized training network

• Atropos Framework Cuts Through RL Barriers: Nous Research releases Atropos, a rollout framework for reinforcement learning with foundation models, supporting complex environments to advance model capabilities.
  • Atropos is part of their overall RL system design, soon complemented by training and inference components detailed in their introductory blogpost.
• GRPO Tool Calling Improves DeepHermes: Their environment with GRPO improved DeepHermes’ tool calling capabilities by 2.4x and 5x on simple and parallel tool calls, respectively, using Berkeley’s Function Calling Benchmark.
  • Artifacts created using environments in Atropos, including a new dataset and five new models for tool calling, corporate fundamentals prediction and new, experimental personalities with RLAIF, are available at HuggingFace.
• Fundamentals Prediction Model Doubles in Accuracy: The corporate fundamentals prediction model’s accuracy increased from ~25% to 50% on directional changes using Atropos.
  • The Atropos framework is designed to guide language models toward their optimal potential through reinforcement learning, just as the Greek Fate guided souls to their ultimate fate.
• Psyche Network Enables Decentralized Training: Atropos is a key component of Psyche, an upcoming decentralized training network coordinating pre-training, mid-training, and post-training workloads globally.
  • A hackathon will be hosted in San Francisco on May 18th to foster collaborative progress (more details coming soon).

Nous Research AI ▷ #general (110 messages🔥🔥):

Qwen 3 Overfitting, DeepSeek R2 Release, Huawei Ascend 910B, Atropos Release, Minos Model Refusals

• Qwen 3’s Base Models Overfit on Evals: Members found that Qwen 3’s base models seem very overfitted to certain evals, reporting that the model scored 75% for Trivaqa on M24b but only 60% on Q30MoE.
  • A member pointed out that their benchmark results between 30B-A3 and 32B-dense are indeed quite close and that this might be due to some overfitting, and this prompted discussion about the effectiveness of MoE.
• DeepSeek R2 Release Rumors Swirl: Rumors are swirling that DeepSeek R2 may be released soon, with some reports claiming it was fully trained on Huawei Ascend 910B hardware, potentially reducing reliance on Nvidia’s CUDA.
  • However, others refuted these claims, linking to a tweet and stating that the official line from DeepSeek is that “We will release R2 when we release R2, everyone who claims they know is lying”.
• Nous Research Releases Atropos: Nous Research released Atropos, an open-source project and optimization technique for inference.
  • A new channel, <#1365222663324307466>, has been created for developers using Atropos.
• Minos Model and Capability-Related Refusals: A member playing around with Minos wondered if there should be a way to separate capability-related refusals from other kinds, raising concerns that it could increase hallucinations as the model might think it has capabilities it does not.
  • A distinction was made between the model couldn’t versus wouldn’t perform a task.
• Physical AI Runs Marathons: An image was shared of a Physical A.I. robot running better than most folks at last week’s Shanghai Marathon.
  • Commenters noted that “AI is literally running circles around us now”, with a link to the Prime Intellect x post.

Nous Research AI ▷ #ask-about-llms (2 messages):

Image loading issues

• Image Loading Woes Plague User: A member reported that an image was just loading, indicating a potential issue with image uploads or loading times.
  • The user then responded later that it was Working.
• User confirms image loading resolved: A member confirmed that the image loading issue was resolved.
  • The member simply stated Working.

Cursor Community ▷ #general (101 messages🔥🔥):

VS Code Extension for Filtering .cs files in Git Changes View, Cursor Spending Limit Issues, Model Selection Purpose, Anthropic 3.7 Incident, Gemini 2.5 Pro Issues

• Slash Spending Limits Spells Slow Requests: A user reported that after hitting their spending limit and upgrading, they were still stuck with slow requests for hours and another ran out of fast requests.
  • Another user chimed in that Gemini is still fast even on slow requests.
• Cursor Community Discord: Is it Finally Getting Some Love?: A member humorously noted that the Cursor’s Discord is finally getting some love again.
  • Another member responded with confidence that Cursor has always been loved, implying that the team is simply polishing the cube.
• Gemini Glitches: Model Stops Mid-Request!: Users reported that Gemini 2.5 stops mid-request frequently, despite indicating it will perform actions and another user advised to use different models when a model acts up.
  • A team member confirmed that the team has been working with Google to solve the issue and advised users to use other models in the meantime, and offered users to send their request ID to the team for investigation.
• Agent Apathy: Edits Elusive After Endless Efforts!: A user reported having massive issues with the Agent failing to make edits after multiple attempts, and it resorts to instructing the user to do it manually.
  • A team member suggested that the issue might be caused by Gemini 2.5 Pro, and recommended creating a new chat to refresh the context; they suggested using 4.1 GPT or 3.5 for code and 3.7 Claude if anything goes wrong.
• Official Ollama Smartphone App When?: A user inquired about the timeline for the release of an official Ollama Smartphone App and linked to a relevant X post.
  • A user chimed in that they fixed their issues by reinstalling cursor and clearing the cache, and another user confirmed that the cache can be cleared manually, which avoids the reinstall process.

HuggingFace ▷ #general (43 messages🔥):

Cloudflare Turnstile, whisper-large-v3-turbo issues, GGUF models and CPU offloading, Model Context Protocol (MCP), Fastest inference for running models

• Members test Cloudflare Turnstile: Members tested whether Cloudflare Turnstile works, with positive confirmation.
  • The member exclaimed YIPEEEEEEEE upon confirmation.
• Members report issues with Whisper Turbo: Members are reporting that OpenAI’s whisper-large-v3-turbo is not working on the HF inference endpoint, even the demo on the webpage is down.
  • Members linked to similar issues like this one as potential help.
• CPU RAM Offloading Fine When Merging: Members discussed offloading to CPU RAM when merging a checkpoint to the base model.
  • One member said it’s fine, and pointed out that Transformers + Accelerate or Llama.cpp enable offloading, also that the GGUF format assumes CPU offloading.
• Inference speed of different models compared: Members pondered about Model Context Protocol (MCP) and which is the fastest for inference of running models.
  • It was noted that Unsloth is faster than Hugging Face, with others recommending sglang/lmdeploy or exllamav2.
• Seeking Active AI Hackathons and Cohorts: A member inquired about active AI-related cohorts or hackathons that provide incentives or rewards for participation.
  • No specific recommendations were provided in the follow-up.

HuggingFace ▷ #cool-finds (1 messages):

cakiki: <@1298649243719958612> please don’t cross-post

HuggingFace ▷ #i-made-this (9 messages🔥):

3D Animation Arena, Pi-Scorer alternative to LLM-as-a-Judge, HMR Models

• 3D Animation Arena Opens for Ranking HMR Models: A member created a 3D Animation Arena on Hugging Face to rank models based on different criteria, aiming to leaderboard the current best HMR (human mesh recovery) models.
  • The creator is seeking votes to populate the leaderboard.
• Pi-Scorer Emerges as LLM-as-a-Judge Alternative: A member shared Pi-Scorer, an alternative to LLM-as-a-Judge, providing Colab notebooks for using Pi-Scores as a model checkpoint evaluation and as reward functions.
• AI Assistant Integration Code Shared: A member shared the code for their AI assistant integration project.

HuggingFace ▷ #computer-vision (2 messages):

Defect annotation, Image masking, Filter usage

• Tackling Defect Annotation Headaches: A member is trying to implement the paper but faces the challenge of generating and annotating old scratched images.
  • The member synthetically generated images with defects like scratches, blur, and grayscale, and is now seeking advice on how to annotate these defects.
• Masking Method Makes an Appearance: A member suggests masking the image, binarizing it while testing different thresholds to isolate scratches, and leaving the rest of the image untouched.
  • The member pointed out how to test different thresholds to find the ideal balance.
• Filtering for Flaws: A member suggests using filters like Canny edge or Sobel to isolate defects with specific thresholds.
  • These filters might provide a good isolation for the defects with certain threshold, it could make it easier to auto-annotate scratches on dataset.

HuggingFace ▷ #agents-course (40 messages🔥):

Hugging Face Agents certification, Agents.json vs Prompts.yaml, Llama-3 access request, Models temporarily unavailable, Solving the final project with free resources

• HF Agents Course Completion Celebrated!: Members celebrated completing and getting certified on Hugging Face Agents, with one member sharing their LinkedIn profile.
  • Another member shared their LinkedIn profile upon completing the course as well.
• Timeout Tamed by Tweaking Time!: One user reported solving timeout issues by increasing the timeout value in the requests.get function to 20 seconds.
  • Another user confirmed that this change solved their problem.
• Agents.json and Prompts.yaml Pondered: A course participant asked for clarification on the difference between the agents.json and prompts.yaml files in the context of the smolagents section of Unit 1.
  • The user also sought guidance on adding new tools to the list of tools using the tools parameter of your Agent.
• Llama-3 Access Request Rejected!?: A user reported that their request for access to meta-llama/Llama-3.2-3B-Instruct was rejected and asked why.
  • Other members suggested needing access to Llama in general, directing the user to request access here.
• “Temporarily Unavailable” Troubles: A user reported that all the models they were trying to use were showing as temporarily unavailable.
  • Another user suggested setting up the notebook locally with Apple’s MLX framework as a possible workaround.

Notebook LM ▷ #announcements (1 messages):

Audio Overviews, Multilingual Support

• Audio Overviews are here!: Audio overviews are rolling out in beta, ready for users to create in over 50+ languages.
  • Try it out now in your preferred language and share feedback via this blog post.
• Multilingual abilities now available: Audio overviews now support 50+ languages, giving access to more diverse users!
  • Please check the blog post for more details.

Notebook LM ▷ #use-cases (28 messages🔥):

NotebookLM language support, Audio Overview limitations, Concise explanations, Smarter Models

• NotebookLM’s Global Tongue-Twist: Now Speaks Many Languages: NotebookLM now specifies the language of the conversation, which is a new feature, and Google’s NotebookLM now speaks 50 languages.
  • Users tested audio overviews in Icelandic and Marathi, with one user impressed that the Marathi speech was fluent and authentic, “not that foreigner accent or something”.
• Audio Overview Customization Caps Stir Debate: A user noted that the customize audio update is limited to 500 characters and wondered if this is any different than uploading instructions as a separate text file.
  • The user wanted to “lessen the silly banter, and keep focus on the facts and timeline”.
• Users find Audio Overviews are more Concise for Non-English languages: Users found that the Audio Overviews generated for non-English languages were shorter in duration.
  • One user who tested it on a small document, stated, “its pretty concise explanation”.
• Smarter Models Powering Better Explanations: Google has confirmed that the new non-English language Audio Overviews are better because “we’re using smarter models under the hood!”
  • NotebookLM continues to improve its summarization capabilities under the hood.

Notebook LM ▷ #general (65 messages🔥🔥):

NotebookLM Updates, Multi-Language Support, Audio Overview Issues, Interactive Mode Bugs, Podcast Feature Requests

• NotebookLM Claims a Webby!: NotebookLM had a successful run at the Webby Awards, winning a Technical Achievement award.
• Multi-Language Support Arrives, but Not for Everyone!: Members celebrated the arrival of multi-language support for NotebookLM, however a member noted that vietnamese audio wasn’t working and the UI still said “English only”.
  • A member confirmed that the rollout is still in progress, and advised users to wait a few hours, and another followed up, telling members to *“Prepare for ‘not seeing the new feature even if it’s out what do I do’ ten times a day.”
• Non-English Audio Overviews Limited by Time!: A user reported that the English audio overview had a 15 minute limit, while the Turkish one was limited to 6 minutes 20 seconds.
  • A member stated that non-English audio is currently limited for “technical reasons”, but the team is working on extending the time.
• Interactive Mode Microphone Issues Bugging Users!: One user reported that the interactive mode wasn’t picking up any audio from their microphone.
  • Another member suggested checking microphone permissions and browser settings, and try using a mic test and trying another browser.
• Notebook Sharing Woes and Solutions!: A member reported that people they shared a Notebook with were getting a message that “they do not have access”.
  • A member clarified that users need to explicitly add the emails of the people they are sharing with in the share dialog.

Manus.im Discord ▷ #general (75 messages🔥🔥):

Add on Credits, Manus Fellow Program, Manus Referral Program, Manus Credit System, Beta Testing

• Add on Credits are Useless without Resubscribing: A user warned that the add on credits given to early subscribers are useless unless they resubscribe, because they expire after a short time.
  • The user claimed they were not informed about the expiry, and now they lost 3900 credits.
• Questions Answered about Double Credits: A user provided quick FAQs about double credits, stating that bonus credits never expire as long as your subscription is active.
  • They added that invites are random, and it seems that every invite doesn’t get two invites, it’s random, because they may have just throttled it back.
• User Needs Information about Manus Fellow Program: A user asked for information about the Manus Fellow Program, like if Manus reach out required fellows & hire them? Also about the targeted countries (USA China Singapore Korea Australia etc), and if the program is not for countries like Pakistan India.
  • Another user replied that a starter plan gives 2 invites and a pro plan gives 5 invites.
• Credit System and Beta Testing Critiqued: A user expressed their thoughts on the credit system and beta testing, claiming that limiting users with credits undermines the very idea of a beta phase.
  • They added that a real beta test would let users complete full projects from start to finish, giving meaningful feedback about the experience and suggesting improvements.

Latent Space ▷ #ai-general-chat (51 messages🔥):

X-Ware Red, Llama Prompt Ops, LLM Benchmarks Survey

• X-Ware Red tool releases: A user shared a tool X-Ware Red that uses the title of an embed; it prepends r.jina.ai/ and openrouter-free-tier to generate titles for threads.
  • A user suggested making it a toggle to choose whether the thread title should be different from the name of the embed.
• Llama Prompt Ops Introduced: Meta introduced Llama Prompt Ops, an open-source tool for prompt engineering, and Synthetic Data Kit.
• Bug Discovered where Link Posts Retitle Threads: A user reported a bug where posting a link in a thread retitles an already named thread, although it should only look for threads with “https://” in the title and change those.
• Users seek Durable LLM Benchmarks: A user asked for a good survey of LLM benchmarks that support comparing models historically.
  • Another user responded that most last less than 2 years and suggested the “AI Engineer Reading List” for current ones and pointed to a user’s posts for the OSS leaderboard v1 and v2.

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

Bending Origins in Mojo, Origin-related headaches, Multiple Licenses in Modular Repository, Pointer usage to avoid origin issues

• Exercise Bending Origins in Mojo to your Will: A member wanted to do a little exercise involving bending Origins to do what you want, like rebinding origins to a container element’s Origin instead of the container’s.
  • Another member responded that they’ve dealt with a lot of origin-related headaches, mostly gaps in our APIs, parametrizable traits, and other missing language features.
• Origins Cause Mutating Reference Issues: A member mentioned that you can’t hold two mutating references to the same origin, though one can cast the origin to a MutableAnyOrigin to circumvent that.
  • Another member responded that any data structure which is not array or list shaped has issues which regresses performance down to C perf.
• Origins are bypassed for pointer time: When discussing building a list-like type + a span-like type, or reading code like the sort implementations in the stdlib, one member noted that most of those are screw the origins, pointer time.
  • Another member worried about pointer types (unsafe included) due to all the mut-immut fixes.
• Modular Repository Has Multiple Licenses: It seems like the Modular repository needs to contain multiple licenses now since some parts are licensed with Modular’s Community License while others are with Apache 2.
  • Specifically, some of the things in src/max use the community license.

Modular (Mojo 🔥) ▷ #mojo (11 messages🔥):

importing Python packages, profiling blocks of code, SIMD width, vector strip-mining, flamegraph

• Standard Python import Support Maybe Coming: While full support for standard Python import statements in Mojo isn’t confirmed, it’s a pretty definite maybe according to one member, implying that python.import_module may not be the only option forever.
• llvm-mca surfaced, profile particular blocks of code: A member asked about profiling specific code blocks, mentioning a private part of the gpu module (link), and another suggested using llvm-mca.
• Vector Strip-Mining for SIMD Widths: When specifying a SIMD width that’s a multiple of the hardware’s SIMD width, the term vector strip-mining was suggested as a potential name for how the compiler handles it.
• Flamegraph aids Perf Output Visualization: A member recommended using flamegraph for visualizing perf output, noting that the executable should be compiled with debug info.

LlamaIndex ▷ #blog (2 messages):

GPT-4o generates Tetris, PapersChat indexes papers

• GPT-4o Generates Tetris in one Shot: A video from KaranVaidya6 shows GPT-4o generating Tetris in one shot using LlamaIndex and Composiohq.
  • The code used in the video is available on GitHub.
• PapersChat Indexes Papers on ArXiv and PubMed: PapersChat is an agentic AI application that allows you to chat with your papers and gather also information from papers on ArXiv and PubMed, powered by LlamaIndex, Qdrant, and MistralAI.
  • It indexes all your papers and provides a nifty web UI to query them, available here.

LlamaIndex ▷ #general (17 messages🔥):

Azure OpenAI timeouts, MessageRole.FUNCTION vs MessageRole.TOOL, Function agent and context issues

• Azure OpenAI’s Intermittent Timeouts Plague Users: Users report intermittent timeouts with Azure OpenAI endpoints, even with the same prompt, endpoint, and network conditions, suggesting potential rate limits, firewall issues, or context breaching.
  • One user noted that retry mechanisms are ineffective due to the issue persisting for minutes, and changing networks only sometimes resolves the inconsistency.
• Dissecting MessageRole: FUNCTION vs. TOOL: The distinction between MessageRole.FUNCTION and MessageRole.TOOL depends on the specific API being used.
  • Some APIs like OpenAI utilize tool messages, while others rely on function messages.
• Function Agent Context Snafus Unveiled: A user encountered an issue with a function agent getting stuck at the stream event during the second round of interaction, but the user provided a sample code.
  • A member suggested awaiting the handler (await handler) after stream_events() exits to ensure the previous run concludes and the final response is received, which fixed the error.

Eleuther ▷ #general (9 messages🔥):

RAG Chatbot challenges, GraphRAG for multiple sources, Local inference and small model training, Collaborating on AI research

• RAG Chatbot Faces Challenges: A member working on a RAG-based chatbot using official documents is facing challenges with answers requiring chunks from multiple sources and documents, using vector search + BM25.
  • They are seeking advice on how to best link references to appendices within the documents for LLM Claude 3.5 Sonnet v1 and Amazon Titan v1 embeddings.
• Exploring GraphRAG for Multiple Sources: A member inquired whether GraphRAG is worth trying to accumulate answers from multiple sources, comparing it to insightRAG which requires a domain-specific pre-trained model.
  • They also asked about alternative solutions and mentioned attending NAACL.
• New project around local inference and small model training is explored: A member, previously co-founder of Dataherald, is exploring a new project around local inference and small model training.
  • He expressed interest in collaborating and getting involved in the community’s research.
• Robotics, Autonomy and AI: Job opportunities are brewing: A member working in Robotics, Autonomy, and AI is focused on the role of LLMs in accelerating software engineering.
  • They inquired about posting job opportunities in the Discord, asking whether it is considered “advertisement”.

Eleuther ▷ #research (10 messages🔥):

Recursive Symbolic Prompts, LLM Honesty Compliance, HHH Objectives in LLMs

• Exploring Recursive Symbolic Prompts: A member is exploring how recursive symbolic prompts behave under classifier pressure, focusing on how smoothing or alignment constraints affect multi-turn hallucination drift.
  • The member is particularly interested in how symbolic structures, like role-bound predicates or attention-synced markers, survive across multi-turn outputs and how this structure carries across completions despite soft-alignment drift or output smoothing.
• LLMs HHH tension exposure: A member shared their research on quantitatively scoring how LLM outputs behave when comparing HHH (Helpful, Honest, Harmless) alignment objectives.
  • They used a combination of YAML and python/Gradio to audit user sessions, measuring the internal tension between each HHH variable, which involves forcing models to be more honest and observing the resulting tension.
• Frontier Models Struggle with Honesty: The same member found that some frontier models are much more honesty-compliant than others, with some models outputting falsified metrics while providing token-flooded and ambiguous answers.
  • They noted that models like ChatGPT 4o and 4.5 output high confidence in answering provocative queries, but in reality, they are flooding the session with ambiguous double-speak, ironically, OpenAI is the least transparent of all frontier models.

MCP (Glama) ▷ #general (12 messages🔥):

Credential Passing, RAG type server for client file ingestion, Streamable HTTP Implementation and Authentication, Multi-Tenant Server Hosting, Open Source Models for Agentic Applications

• Credential Conundrums: Seeking Header Help: A member is facing difficulties in passing credentials through headers from a client to the MCP server using Python and is seeking assistance.
  • No solutions or suggestions were provided in the given context.
• RAG Server File Ingestion: A member is considering building a RAG-type server where clients can ingest files via an endpoint, save them on the server, and use them for answering questions.
  • They are asking whether this is a good approach or if there are better alternatives.
• Streamable HTTP’s Implementation: Authentication Appraisal Awaited: A member inquired about the community’s thoughts on the current Streamable HTTP implementation and authentication, particularly in the recently released TS SDK.
  • Another member responded that it’s working well, but they’re still figuring out the nuance of hosting a multi-tenant server and how statefulness impacts it.
• Multi-Tenant Server Hosting: There are concerns regarding hosting a multi tenant server and how that is impacted by statefulness.
  • It seems like a stateless server should spawn a new instance of the mcp server per request, but it is unclear why 1 instance is sufficient for stateful but not for stateless.
• Productionalizing Agentic Open Source: Feasible or Fantasy?: A member asked if people are genuinely using open-source models for agentic applications in production (not just pet projects).
  • They find it challenging for most open source models to reason or follow instructions without fine-tuning.

MCP (Glama) ▷ #showcase (1 messages):

MCP Server, Real Time Push Notifications

• MCP Server Notifies when Agent Workflows Complete: A member touted using mcp-gotify, an MCP server for interacting with gotify/server, to receive real time push notifications on desktop and mobile when long running multi agent workflows complete.
• Gotify server alternative?: Users are now using gotify/server as an alternative to push notifications to desktop and mobile.

Torchtune ▷ #dev (9 messages🔥):

foreach optimization, gradient scaling, DoRA + QAT

• Speedy Gradient Scaling via Foreach: A member shared a code snippet using torch._foreach_mul_ for gradient scaling, potentially merging with gradient clipping for a single parameter loop.
  • Another member pointed out the related PR and wondered if the seemingly constant gain accumulates over many iterations.
• Tune Contributors Seek Easy Pick-Up Issues: A member highlighted two easy issues and another for community contribution to the project.
  • No further information was provided regarding the nature of the issues.
• DoRA and QAT: An Unexplored Frontier?: A member inquired about experiences combining DoRA (Difference of Low-Rank Adaptation) with QAT (Quantization-Aware Training).
  • There was no discussion or response regarding this combination in the messages provided.

DSPy ▷ #general (6 messages):

MCP Usage, Displaying thoughts component in React

• MCP Usage Documentation Desired: A user inquired about tutorials or documentation for the new MCP (Multi-Controller Processing) usage added in the latest release.
  • Another user noted they got started by reviewing the test cases, and while a tutorial would be nice, it’s not urgent, clarifying that understanding the stdio and SSE clients setup was key.
• Thoughts component in React - Best Practices: A member is seeking advice on the best way to display the Thoughts component in React.
  • They know they can modify the forward method, but are asking if there is a better or more appropriate place to implement this.

Cohere ▷ #💬-general (1 messages):

Markdown-based vs Image-based multimodal RAG on PDFs, Docling, EmbedV4

• Markdown vs Image RAG debate heats up: A member inquired about comparing Markdown-based versus Image-based multimodal RAG on PDFs.
  • They are currently using Docling to convert PDFs to Markdown and then computing text embedding, but are considering switching to EmbedV4 to feed raw images and get multi-modal embedding for RAG.
• PDF Conversion Techniques Explored: The member is using Docling to convert PDFs to Markdown before computing text embeddings.
  • They are evaluating EmbedV4 as an alternative to directly process raw images for multi-modal embeddings in RAG.

Cohere ▷ #🔌-api-discussions (2 messages):

Cohere rate limits for embed-v4, Embed V4 on Bedrock

• Cohere considers raising rate limits: A user inquired whether Cohere would increase production rate limits for embed-v4.
  • They stated that 400 requests per min is not enough for their use case with PDFs.
• Cohere ponders Bedrock availability: A user asked whether Embed V4 will be available on Bedrock.
  • There has been no answer from Cohere yet.

Cohere ▷ #🤝-introductions (2 messages):

Cohere's Embed V4 model, Data Scientists introductions

• Enthusiast Joins, Eager for Embed V4!: A new data scientist joined the Cohere Discord community, expressing a keen interest in trying new tools, particularly the latest Embed V4 model from Cohere, and exploring its potential applications.
  • The new member is pleased to join the community.
• Community Welcomes New Data Scientist: The Cohere Community Discord Server expresses excitement in the introduction of a new member.
  • The welcome message encourages new members to provide their Company/Industry/University, the specifics of what you’re working on, favorite tech/tools, and What you hope to gain from this community.

Nomic.ai (GPT4All) ▷ #general (5 messages):

Embeddings, GPT4All, Manus AI, Embedding grouping

• Manus AI Tool Drops: A member shared a link to Manus AI, claiming that China dropped it and that it is now available for everyone.
  • The member suggested that this is the first auto research AI Agent and that we gettin hard replaced with this one.
• Embeddings can use Nomic tools: A member suggested that Nomic provides all the necessary tools for embeddings and that it is beyond GPT4All.
  • They claimed that Nomic’s embeddings tools work in various other software.
• Embedding grouping can work instead of training: A member described how grouping embeddings could work instead of training: group embeddings for a specific person and take the average embedding, then use that embedding to sort other pictures and find the same person.
  • He asked Did you understand the concept?

Gorilla LLM (Berkeley Function Calling) ▷ #discussion (3 messages):

Loose vs Strict Evaluation, Model Training Inconsistencies

• Evaluating Models Loosely vs Strictly: A member proposed the idea of having a ‘loose’ vs ‘strict’ evaluation mechanism for models, especially those that can be ‘hacked’ into working, representing specific use-cases.
  • They provided an example of a model incorrectly trained to emit <tool_call> instead of <|tool_call|> as its specs indicate, where a knowledgeable user might ignore the error and evaluate functional correctness.
• Model Training Creates Inconsistencies: One member encountered a model which was incorrectly trained to emit <tool_call> instead of <|tool_call|> as its specs indicate.
  • The member suggested that, if they knew the model specifically, they could ignore this error and evaluate on functional correctness, but a naive user could not.