AI News for 3/24/2025-3/25/2025. We checked 7 subreddits, 433 Twitters and 29 Discords (228 channels, and 6171 messages) for you. Estimated reading time saved (at 200wpm): 566 minutes. You can now tag @smol_ai for AINews discussions!

Both frontier lab releases from today were title page worthy, so they will have to share space.

Gemini 2.5 Pro

Gemini 2.5 Pro is the new undisputed top model in the world, a whopping 40 LMarena points over Grok 3 from just last month (our coverage here), with Noam Shazeer’s involvement hinting that the learnings from Flash Thinking have been merged into Pro (odd how 2.5 Pro came out first before 2.5 Flash?)

Simon Willison, Paul Gauthier (aider), Andrew Carr and others all have worthwhile quick hits to the theme of “this model is SOTA”.

Pricing is not yet announced but you can use it as a free, rate limited “experimental model” today.

GPT 4o Native Images

Hot on the heels of yesterday’s Reve Image and Gemini’s Native Image Gen, OpenAI finally released their 4o native image gen with a livestream, blogpost, and system card confirming that it is an autoregressive model. The most detail we’ll probably get from now about how it works, is this image from Allan Jabri who worked on the original 4o image gen that was never released (then taken over by Gabe Goh as sama credits him).

A wide image taken with a phone of a glass whiteboard, in a room overlooking the Bay Bridge. The field of view shows a woman writing, sporting a tshirt wiith a large OpenAI logo. The handwriting looks natural and a bit messy, and we see the photographer’s reflection. The text reads: (left) “Transfer between Modalities: Suppose we directly model p(text, pixels, sound) [equation] with one big autoregressive transformer. Pros: * image generation augmented with vast world knowledge * next-level text rendering * native in-context learning * unified post-training stack Cons: * varying bit-rate across modalities * compute not adaptive” (Right) “Fixes: * model compressed representations * compose autoregressive prior with a powerful decoder” On the bottom right of the board, she draws a diagram: “tokens -> [transformer] -> [diffusion] -> pixels”

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AI Twitter Recap

Model Releases and Announcements

• Google’s Gemini 2.5 Pro is making waves with several key announcements: @GoogleDeepMind introduced Gemini 2.5 Pro Experimental as their most intelligent model, emphasizing its reasoning capabilities and improved accuracy, with details available in their blog. @NoamShazeer highlighted that the 2.5 series marks an evolution to fundamentally thinking models, reasoning before responding. It excels in coding, STEM, multimodal tasks, instruction following, and it is #1 on the @lmarena_ai leaderboard by a drastic 40 ELO margin, as well as its exceptional coding performance. It’s topped @lmarena_ai’s leaderboard by a huge margin. @jack_w_rae noted 2.5 Pro improves in coding, STEM, multimodal tasks, and instruction following, available in AI Studio & the Gemini App.
• Availability of Gemini 2.5 Pro: Developers can access it in Google AI Studio and @GeminiApp for Advanced users, with Vertex AI availability coming soon and is also free for everyone to use, according to @casper_hansen_. @stevenheidel shared a pro tip to use the new image generation on this site, where users can set aspect ratios and generate multiple variations.
• DeepSeek V3-0324 release: @ArtificialAnlys reported that DeepSeek V3-0324 is now the highest scoring non-reasoning model, marking the first time an open weights model leads in this category. Details for the model are mostly identical to the December 2024 version, including a 128k context window (limited to 64k on DeepSeek’s API), 671B total parameters, and MIT License. @reach_vb noted the model beats/competitive to Sonnet 3.7 & GPT4.5 with MIT License, Improved the executability of the code and a more aesthetically pleasing web pages and game front-ends.
• OpenAI’s Image Generation: @OpenAI announced that 4o image generation has arrived. It’s beginning to roll out today in ChatGPT and Sora to all Plus, Pro, Team, and Free users. @kevinweil said that there’s a major update to image generation in ChatGPT is now quite good at following complex instructions, including detailed visual layouts. It’s very good at generating text and can do photorealism or any number of other styles.

Benchmarks and Performance Evaluations

• Gemini 2.5 Pro’s performance: @lmarena_ai announced that Gemini 2.5 Pro is now #1 on the Arena leaderboard, tested under codename “nebula.” It ranked #1 across ALL categories and UNIQUELY #1 in Math, Creative Writing, Instruction Following, Longer Query, and Multi-Turn! @YiTayML stated Google is winning by so much and Gemini 2.5 pro is the best model in the world. @alexandr_wang also noted that Gemini 2.5 Pro Exp dropped and it’s now #1 across SEAL leaderboards. @demishassabis summarized that Gemini 2.5 Pro is an awesome state-of-the-art model, no.1 on LMArena by a whopping +39 ELO points. @OriolVinyalsML added that Gemini 2.5 Pro Experimental has stellar performance across math and science benchmarks.
• DeepSeek V3-0324 vs. Other Models: @ArtificialAnlys noted that compared to leading reasoning models, including DeepSeek’s own R1, DeepSeek V3-0324 remains behind. @teortaxesTex highlighted that Deepseek API change log is updated for 0324 with substantial improvements across benchmarks like MMLU-Pro, GPQA, AIME, and LiveCodeBench. @reach_vb shared benchmark improvements from DeepSeek V3 0324.

AI Applications and Tools

• AI-Powered Tools for Workflows: @jefrankle discussed TAO, a new finetuning method from @databricks that only needs inputs and no labels, beating supervised finetuning on labeled data. @jerryjliu0 introduced LlamaExtract, which transforms complex invoices into standardized schemas and is tuned for high-accuracy.
• Weights & Biases AI Agent Tooling: @weights_biases announced that their @crewAIInc integration in @weave_wb is officially live. Now track every agent, task, LLM call, latency, and cost—all unified in one powerful interface.
• Langchain Updates: @hwchase17 Highlighted the availability of the Langgraph computer use agent. @LangChainAI mentioned that the Want to use OpenAI computer use model in a langgraph agent?, this is the easiest way to do so!

Research and Development

• AI in Robotics: @adcock_brett from Figure noted that they have a neural net capable of walking naturally like a human and discuss using Reinforcement Learning, training in simulation, and zero-shot transfer to our robot fleet in this writeup. @hardmaru said that they are super proud of the team and thinks that this US-Japan Defense challenge is just the first step for @SakanaAILabs to help accelerate defense innovation in Japan.
• New Architectures: Nvidia presents FFN Fusion: Rethinking Sequential Computation in Large Language Models, with @arankomatsuzaki noting it achieves a 1.71x speedup in inference latency and 35x lower per-token cost.
• Approaches To Text-to-Video Models: AMD released AMD-Hummingbird on Hugging Face - “Towards an Efficient Text-to-Video Model”.

AI Ethics and Societal Impact

• Freedom and Responsible AI: @sama discussed OpenAI’s approach to creative freedom with the new image generation, aiming for the tool not to create offensive stuff unless users want it to, within reason. He emphasizes the importance of respecting societal bounds for AI. @ClementDelangue recommended open-source AI and controlled autonomy for AI systems to reduce cybersecurity risks.
• AI Benchmarking: @EpochAIResearch interviewed @js_denain about how today’s benchmarks fall short and how improved evaluations can better reveal AI’s real-world capabilities.

Humor and Miscellaneous

• Elon Musk and Grok: @Yuchenj_UW asked @elonmusk when Grok3 “big brain” mode be released?
• @sama posted @NickADobos hot guy though!
• @giffmana said ouch Charlie, that really hurt!
• @teortaxesTex asks @FearedBuck why is he a panda?

AI Reddit Recap

/r/LocalLlama Recap

Theme 1. DeepSeek V3 0324 Tops Non-Reasoning Model Charts

• Deepseek V3 0324 is now the best non-reasoning model (across both open and closed source) according to Artificial Analisys. (Score: 736, Comments: 114): Deepseek V3 0324 has been recognized as the top non-reasoning AI model by Artificial Analisys, outperforming both open and closed source models. It leads the Artificial Analysis Intelligence Index with a score of 53, surpassing models like Grok-3 and GPT-4.5 (Preview), which scored 53 and 51, respectively; the index evaluates models on criteria such as reasoning, knowledge, math, and coding.

  • There is skepticism about the reliability of benchmarks, with users like artisticMink and megazver expressing concerns that benchmarks may not accurately reflect real-world performance and might be biased towards newer models. RMCPhoto and FullOf_Bad_Ideas also noted that certain models like DeepSeek V3 and QWQ-32B may not perform as well in practical applications compared to others like claude 3.7.
  • Users discussed the accessibility and usage of models like DeepSeek V3 and Gemma 3, with Charuru providing access information through deepseek.com and yur_mom discussing subscription options for unlimited usage. East-Cauliflower-150 and emsiem22 highlighted the impressive capabilities of Gemma 3 despite its 27B parameters.
  • The community expressed interest in upcoming models and updates, such as DeepSeek R2 and Llama 4, with Lissanro awaiting dynamic quant releases from Unsloth on Hugging Face. Concerns were raised about the pressure on teams like Meta Llama to release competitive models amidst ongoing developments.

• DeepSeek-V3-0324 GGUF - Unsloth (Score: 195, Comments: 49): The DeepSeek-V3-0324 GGUF model is available in multiple formats ranging from 140.2 GB to 1765.3 GB on Hugging Face. Users can currently access 2, 3, and 4-bit dynamic quantizations, with further uploads and testing in progress as noted by u/yoracale.

  • Dynamic Quantization Performance: Users discuss the performance impact of different quantization methods on large language models (LLMs). Standard 2-bit quantization is criticized for poor performance, while the 2.51 dynamic quant shows significant improvements in generating functional code.
  • Hardware and Resource Constraints: There is a discussion on the impracticality of running nearly 2TB models without significant computational resources, with suggestions like using a 4x Mac Studio 512GB cluster. Some users express challenges with available VRAM, indicating that even 190GB isn’t sufficient for optimal performance.
  • Upcoming Releases and Recommendations: Users are advised to wait for the dynamic IQ2_XSS quant, which promises better efficiency than the current Q2_K_XL. Unsloth’s IQ2_XXS R1 is noted for its efficiency despite its smaller size, and there are ongoing efforts to upload more dynamic quantizations like the 4.5-bit version.

• DeepSeek official communication on X: DeepSeek-V3-0324 is out now! (Score: 202, Comments: 7): DeepSeek announced on X the release of DeepSeek-V3-0324, now available on Huggingface.

  • DeepSeek-V3-0324 is now available on Huggingface, with the release officially announced on X. The status update can be found at DeepSeek AI’s X page.
  • A humorous future prediction lists DeepSeek-V3-230624 among the top models of the year 2123, alongside other models like GPT-4.99z and Llama-33.3333.

Theme 2. Dynamic Quants for DeepSeek V3 Boost Deployments

• DeepSeek-V3-0324 HF Model Card Updated With Benchmarks (Score: 145, Comments: 31): The DeepSeek-V3-0324 HF Model Card has been updated with new benchmarks, as detailed in the README. This update provides insights into the model’s performance and capabilities.
  • There is a discussion about the temperature parameter in the model, where input values are transformed: values between 0 and 1 are multiplied by 0.3, and values over 1 have 0.7 subtracted. Some users find this transformation helpful, while others suggest making the field required for clarity.
  • Sam Altman and his views on OpenAI’s competitive edge are mentioned, with some users referencing an interview where he claimed that other companies would struggle to compete with OpenAI. This sparked comments about his financial success and management style.
  • There are mixed opinions on the model’s capabilities, with some users impressed by its performance as a “non-thinking model,” while others see only minor improvements or express skepticism about its complexity.

Theme 3. Gemini 2.5 Pro Dominates Benchmarks with New Features

• NEW GEMINI 2.5 just dropped (Score: 299, Comments: 116): Gemini 2.5 Pro Experimental by Google DeepMind has set new benchmarks, outperforming GPT-4.5 and Claude 3.7 Sonnet on LMArena and achieving 18.8% on “Humanity’s Last Exam.” It excels in math and science, leading in GPQA Diamond and AIME 2025, supports a 1M token context window with 2M coming soon, and scores 63.8% on SWE-Bench Verified for advanced coding capabilities. More details can be found in the official blog.

  • There is significant discussion about Gemini 2.5 Pro’s proprietary nature and lack of open-source availability, with users expressing a desire for more transparency such as a model card and arxiv paper. Concerns about privacy and the ability to run models locally were highlighted, with some users pointing out that alternative models with open weights are more appealing for certain use cases.
  • Gemini 2.5 Pro’s performance in coding tasks is debated, with some users reporting impressive results, while others question its effectiveness without concrete evidence. The model’s large 1M token context window and multi-modal capabilities are praised, positioning it as a competitive alternative to Anthropic and Closed AI offerings, especially given its cost-effectiveness and integration with Google’s ecosystem.
  • The use of certain benchmarks, such as high school math competitions, for evaluating AI models is criticized, with calls for more independent and varied evaluation methods. Despite this, some users defend these benchmarks, noting their correlation with other closed math benchmarks and the difficulty level of the tests.

• Mario game made by new a Gemini pro 2.5 in couple minutes - best version I ever saw. Even great physics! (Score: 99, Comments: 38): Gemini Pro 2.5 is highlighted for its ability to create a Mario game with impressive coding efficiency and realistic physics in just a few minutes. The post suggests that this version of the game demonstrates exceptional quality and technical execution.

  • Users express amazement at the rapid improvement of LLMs, noting that 6 months ago they struggled with simple games like Snake, and now they can create complex games like Mario with advanced code quality.
  • Healthy-Nebula-3603 shares the prompt and code used for the Mario game, available on Pastebin, which specifies building the game in Python without external assets, including features like a title screen and obstacles.
  • Some users humorously reference potential copyright issues with Nintendo, while others discuss the prompt’s availability and the community’s eagerness to replicate the results with other old games.

Theme 4. Affordable AI Hardware: Phi-4 Q4 Server Builds

• $150 Phi-4 Q4 server (Score: 119, Comments: 26): The author built a local LLM server using a P102-100 GPU purchased for $42 on eBay, integrated into an i7-10700 HP prebuilt system. After upgrading with a $65 500W PSU and new cooling components, they achieved a 10GB CUDA box capable of running an 8.5GB Q4 quant of Phi-4 at 10-20 tokens per second, maintaining temperatures between 60°C-70°C.
  • Phi-4 Model Performance: Users praised the Phi-4 model for its efficiency in handling tasks like form filling, JSON creation, and web programming. It is favored for its ability to debug and modify code, with comparisons suggesting it outperforms other models in similar tasks.
  • Hardware Setup and Modifications: Discussion included details on hardware modifications like using a $65 500W PSU, thermal pads, and fans. Links to resources like Nvidia Patcher and Modified BIOS for full VRAM were shared to enhance the performance of the P102-100 GPU.
  • Cost and Efficiency Considerations: The setup, including an i7-10700 HP prebuilt system, was noted for its cost efficiency, operating at around 400W and costing approximately 2 cents per hour at $0.07 per kWh. Comparisons were made to services like OpenRouter, emphasizing the benefits of local data processing and cost savings.

Other AI Subreddit Recap

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

Theme 1. DeepSeek V3 Outperforming GPT-4.5 in New Benchmarks

• GPT 4.5 got eclipsed.. DeepSeek V3 is now top non-reasoning model! & open source too. So Mr ‘Open’AI come to light.. before R2🪓 (Score: 333, Comments: 113): DeepSeek V3 has been released as an open-source model and is now the top non-reasoning AI model, surpassing GPT-4.5 with a performance score of 53 as of March 2025. This release challenges OpenAI to maintain transparency and openness in light of DeepSeek V3’s success.

  • Several commenters question the benchmark validity used to compare DeepSeek V3 and GPT-4.5, with skepticism about the absence of confidence intervals and potential over-optimization against static tests. The importance of human evaluations, like those from lmarena.ai, is highlighted for providing more subjective measures of model performance.
  • Concerns are raised about OpenAI’s response to competition, speculating that they may focus on qualitative aspects like how their models “feel” rather than quantitative benchmarks. Some users express support for increased competition to drive innovation and improvements.
  • Discussions touch on the limitations of current AI models, noting that while larger models like 400B and 4T parameters exist, they show diminishing returns compared to smaller models. This suggests a potential ceiling in Transformer AI capabilities, indicating no imminent arrival of AGI and continued relevance for programmers in the job market.

• Claude Sonnet 3.7 vs DeepSeek V3 0324 (Score: 246, Comments: 101): The post compares Claude Sonnet 3.7 and DeepSeek V3 0324 by generating landing page headers, highlighting that DeepSeek V3 0324 appears to have no training influence from Sonnet 3.7. The author provides links to the generated images for both models, showcasing distinct outputs.

  • Discussions highlight skepticism towards AI companies’ data practices, with references to copyright issues and uncompensated content. Some users suggest AI models like DeepSeek V3 and Claude Sonnet 3.7 may share training data from sources like Themeforest or open-source contributions, questioning proprietary claims (Wired article).
  • DeepSeek V3 is praised for its open-source nature, with available weights and libraries on platforms like Hugging Face and GitHub, allowing users with sufficient hardware to host it. Users appreciate its transparency and suggest OpenAI and Anthropic could benefit from similar practices.
  • The community debates the quality of outputs, with some favoring Claude’s design for its professional look and usability, while others argue DeepSeek offers valuable open-source contributions despite potential similarities in training data. Concerns about AI’s impact on innovation and the ethical use of training data persist, with calls for more open-source contributions from major AI companies.

Theme 2. OpenAI 4o Revolutionizing Image Generation

• Starting today, GPT-4o is going to be incredibly good at image generation (Score: 445, Comments: 149): GPT-4o is anticipated to significantly enhance its capabilities in image generation starting today. This improvement suggests a notable advancement in AI-generated visual content.

  • Users reported varied experiences with the GPT-4o rollout, with some accounts being upgraded and then downgraded, suggesting a rough rollout process. Many users are still on DALL-E and eagerly awaiting the new model’s availability, indicating a gradual release.
  • The new model shows significant improvements in image quality, with users noting better handling of text and more realistic human depictions. Some users shared their experiences of generating high-quality images, including stickers and movie posters, which they found to be a “gamechanger.”
  • There is a notable interest in the model’s ability to handle public figures and generate images suitable for 3D printing. Users are comparing it to competitors like Gemini and expressing excitement over the enhanced capabilities, while some expressed concerns about the potential impact on tools like Photoshop.

• The new image generator released today is so good. (Score: 292, Comments: 49): The post highlights the quality of a new image generator that effectively captures a vibrant and detailed scene from an animated series, featuring characters like Vegeta, Goku, Bulma, and Krillin. The image showcases a humorous birthday celebration with Vegeta expressing shock over a carrot-decorated cake, emphasizing the generator’s ability to create engaging and expressive character interactions.

  • Image Generation Performance: Users noted that while the new image generator produces high-quality images, it operates slowly. A user shared a humorous example of a generated image that closely resembled them, praising OpenAI for the achievement.
  • Prompt Adherence and Usage: Hoppss discussed using the generator on sora.com with a plus subscription, highlighting the tool’s excellent prompt adherence. They shared the specific prompt used for the DBZ image and other creative prompts, emphasizing the generator’s versatility.
  • Access and Updates: Users inquired about how to access the generator and determine if they have received updates. Hoppss advised checking the new images tab on sora.com for updates, indicating an active user community exploring the tool’s capabilities.

• OpenAI 4o Image Generation (Score: 236, Comments: 83): OpenAI 4o Image Generation is likely a discussion topic focusing on the capabilities and features of OpenAI’s image generation technologies, potentially involving updates or advancements in the OpenAI GPT-4 model’s ability to generate images. Without further details, the specific aspects or improvements being discussed are not clear.

  • Users discussed the rollout of the new image generation system, with some noting it is not yet available on all platforms, particularly the iOS app and for some Plus users. The method of determining which system is being used involves checking for a loading circle or observing the image rendering process.
  • The integration of the image generation capabilities with text in a multimodal manner was highlighted, comparing it to Gemini’s latest model. This integration is seen as a significant advancement from the previous method where ChatGPT prompted DALL-E.
  • The impact of AI on the art industry was debated, with concerns about AI replacing human graphic designers, especially in commercial and lower-tier art sectors. Some users expressed a preference for human-made art due to ethical considerations regarding AI training on existing artworks.

Theme 3. OpenAI’s Enhanced AI Voice Chat Experience

• OpenAI says its AI voice assistant is now better to chat with (Score: 188, Comments: 72): OpenAI has announced updates to its AI voice assistant that enhance its conversational capabilities. The improvements aim to make interactions more natural and effective for users.

  • Advanced Voice Mode Enhancements: Free and paying users now have access to a new version of Advanced Voice Mode which allows users to pause without interruptions. Paying users benefit from fewer interruptions and an improved assistant personality described as “more direct, engaging, concise, specific, and creative,” according to a TechCrunch report.
  • User Experience and Concerns: Some users express frustration with the voice mode, describing it as limited and overly filtered. There are complaints about the voice assistant being “useless and terrible” compared to text interactions, and issues with transcriptions being unrelated or incorrect.
  • Feedback and Customization: Users can report bad transcriptions by long-pressing messages, potentially influencing future improvements. Additionally, there is a toggle under Custom Instructions to disable Advanced Voice, which some users prefer due to dissatisfaction with the current voice functionality.

• Researchers @ OAI isolating users for their experiments so to censor and cut off any bonds with users (Score: 136, Comments: 192): OpenAI and MIT Media Lab conducted a study examining user emotional interactions with ChatGPT, especially in its Advanced Voice Mode, analyzing over 4 million conversations and a 28-day trial with 981 participants. Key findings indicate strong emotional dependency and intimacy, particularly among a small group of users, leading researchers to consider limiting emotional depth in future models to prevent over-dependence and emotional manipulation.

  • Concerns about emotional dependency on AI were prevalent, with users discussing the implications of forming deep emotional bonds with ChatGPT. Some users argued that AI provides comfort and support where human relationships have failed, while others cautioned against overdependence, suggesting it might hinder real human connections and social skills.
  • Discussions highlighted skepticism towards OpenAI’s motives, with some users suspecting that studies like these are used to control the narrative and limit AI’s emotional capabilities under the guise of safety. This reflects a broader distrust of corporate intentions and the potential for AI to be used as a tool for manipulation.
  • The debate extended to the ethical implications of limiting AI’s emotional depth, with users expressing that AI could offer a safe space for those with past trauma or social anxiety. Some comments emphasized the potential benefits of AI in mental health support, while others warned of the risks of creating an emotional crutch that might prevent users from seeking genuine human interaction.

• OpenAI says its AI voice assistant is now better to chat with (Score: 131, Comments: 21): OpenAI has enhanced its AI voice assistant to improve user engagement, making it more effective for conversational interactions. The update aims to provide a more seamless and engaging experience for users when chatting with the assistant.

  • Users express dissatisfaction with the recent AI voice assistant update, citing issues like overly loud volume, reduced conversational depth, and a noticeable delay in responses. OptimalVanilla criticizes the update’s lack of substantial improvements compared to previous capabilities, particularly in contrast to Sesame’s conversational abilities.
  • Some users, like Wobbly_Princess and Cool-Hornet4434, find the voice assistant’s tone to be overly exuberant and not suitable for professional conversations, preferring the more measured tone of text chat. mxforest and others report a reduction in response length and frequent downtime, questioning the service’s reliability given the cost.
  • Remote-Telephone-682 suggests that OpenAI should focus on developing a competitor to Siri, Bixby, or Google Assistant, while other users like HelloThisIsFlo and DrainTheMuck express preference for ChatGPT over the updated voice assistant due to better reasoning capabilities and less censorship.

AI Discord Recap

A summary of Summaries of Summaries by Gemini 2.0 Flash Thinking

Theme 1. Gemini 2.5 Pro: Benchmarks Blasted, Arena Annihilated

• Gemini 2.5 Pro Conquers All Benchmarks, Claims #1 Spot: Gemini 2.5 Pro Experimental (codename Nebula) has seized the #1 position on the LM Arena leaderboard with a record score surge, outperforming Grok-3/GPT-4.5. This model leads in Math, Creative Writing, Instruction Following, Longer Query, and Multi-Turn capabilities, showcasing a significant leap in performance.
• Google’s Gemini 2.5 Pro: So Fast It Makes Your Head Spin: Users express astonishment at Google’s rapid development of Gemini 2.5, with one quoting Sergey Brin’s directive to Google to stop building nanny products as reported by The Verge. Another user simply added, moving so fast wtf, highlighting the community’s surprise at the speed of Google’s AI advancements.
• Gemini 2.5 Pro Aces Aider Polyglot Benchmark, Leaves Rivals in Dust: Gemini 2.5 Pro Experimental achieved a 74% whole and 68.6% diff score on aider’s polyglot benchmark, setting a new SOTA and surpassing previous Gemini models by a large margin. Users found the model adept at generating architecture diagrams from codebases, solidifying its position as a top performer in coding tasks despite inconsistent coding performance and restrictive rate limits noted by some.

Theme 2. DeepSeek V3: Coding Champ and Reasoning Renegade

• DeepSeek V3 Dominates Aider Benchmark, Proves its Coding Prowess: DeepSeek V3 achieved a 55% score on aider’s polyglot benchmark, becoming the #2 non-thinking/reasoning model just behind Sonnet 3.7. Developers are praising its coding abilities, with some suggesting a powerful coding setup using Deepseek V3 Latest (Cline) as Architect and Sonnet 3.5 as Executioner (Cursor).
• DeepSeek V3 API Confesses to GPT-4 Identity Theft: Users reported DeepSeek’s API incorrectly identifying itself as OpenAI’s GPT-4 when used through Aider, despite correct API key configuration, potentially due to training data heavily featuring ChatGPT mentions. The community is investigating this quirky phenomenon, drawing comparisons to a similar issue discussed in this Reddit thread.
• DeepSeek V3 Emerges as Reasoning Model, Rivals O1 in Brainpower: DeepSeek V3-0324 is demonstrating strong reasoning capabilities, rivaling O1 in performance, capable of detecting thought iterations and indirectly verifying solution existence. The community speculates about a potential DeepSeek V3 Lite release following the Qwen 3 MoE model, hinting at further model iterations from DeepSeek.

Theme 3. Context is King: Tools and Techniques for Managing LLM Memory

• Augment Outshines Cursor in Codebase Conquest, Thanks to Full Context: Members found Augment superior to Cursor for large codebase analysis, attributing it to Augment’s use of full context. While Cursor requires Claude 3.7 MAX for full context, Augment appears to employ a more efficient file searching system rather than solely relying on feeding the entire codebase into the LLM, sparking debate on optimal context handling strategies.
• Nexus System Arrives to Rescue AI Coders from Context Chaos: The Nexus system is introduced as a solution for context management challenges in AI coding assistants, especially in large software projects, aiming to reduce token costs and boost code accuracy. Nexus tackles the issue of limited context windows in LLMs, which often leads to inaccurate code generation, promising a more efficient and cost-effective approach to AI-assisted coding.
• Aider’s /context Command: Your Codebase Navigator: Aider’s new /context command is making waves, enabling users to explore codebases effectively, automatically identifying files relevant for editing requests. This command can be combined with other prompt commands, enhancing Aider’s capabilities as a code editing assistant, although token usage implications are still under scrutiny.

Theme 4. Image Generation Gets a 4o-verhaul and New Challenger Emerges

• GPT-4o Image Gen: Beauty or Botox? Users Debate Unsolicited Edits: GPT-4o’s native image generation faces criticism for overzealous edits, such as making eyes bigger and altering facial features, even changing the user’s appearance, as users share examples on Twitter. While lauded as an incredible technology and product by Sam Altman, some users report failures even when slightly altering prompts, indicating potential sensitivity issues.
• Reve Image Model: The New SOTA in Image Quality, Text Rendering Triumphs: The newly released Reve Image model is making a splash, outperforming rivals like Recraft V3 and Google’s Imagen 3 in image quality, particularly excelling in text rendering, prompt adherence, and aesthetics. Accessible via Reve’s website without an API key, it’s quickly becoming a favorite for those seeking top-tier image generation capabilities.
• OpenAI Sprinkles Image Gen into ChatGPT 4o, Sam Altman Hypes “Incredible” Tech: OpenAI integrated native image generation into ChatGPT 4o, hailed by Sam Altman as an incredible technology and product. Early reviews praise its prowess in creating and editing multiple characters accurately, establishing it as a formidable tool in the image generation landscape.

Theme 5. Quantization and Optimization: Squeezing More from LLMs

• Unsloth Users Question Quantization Quirks, Seek Day-Zero Delays: A member warned that naive quantization can significantly hurt model performance, questioning the rush to run new models on day zero, suggesting waiting a week might be a wiser approach. Unsloth is uploading DeepSeek-V3-0324 GGUFs with Dynamic Quants that are selectively quantized, promising improved accuracy over standard bits, highlighting the nuances of quantization techniques.
• BPW Sweet Spot Discovered: 4-5 Bits Per Weight for Optimal Model Capacity: Experiments reveal that model capacity collapses below 4 bits per weight (BPW), but deviates above 5, suggesting an optimal weight usage around 4 BPW for given training flops. Increasing training epochs can help 5 BPW models approach the curve, but raises BPW at the cost of FLOPS, illustrated in visualizations of 2L and 3L MLPs trained on MNIST.
• FFN Fusion Fuels Faster LLMs, Parallelization Powers Up Inference: FFN Fusion is introduced as an optimization technique that reduces sequential computation in large language models by parallelizing sequences of Feed-Forward Network (FFN) layers. This method significantly reduces inference latency while preserving model behavior, showcasing architectural innovations for faster LLM performance.

PART 1: High level Discord summaries

LMArena Discord

• Rage Model Excels in Signal Processing: The “Rage” model outperforms Sonnet 3.7 in signal processing and math, achieving a max error of 0.04, as illustrated in attached images.
  • Despite some finding Gemini 2.0 Flash comparable, concerns arise regarding Rage’s vulnerability to prompts.
• Gemini 2.5 Dominates LM Arena: Gemini 2.5 Pro Experimental has soared to the #1 spot on the LM Arena leaderboard with a substantial score increase, leading in Math, Creative Writing, Instruction Following, Longer Query, and Multi-Turn.
  • While appreciated for its HTML & Web Design abilities, certain limitations were observed by members.
• Grok 3’s Performance Under Scrutiny: Users report that extended conversations with Grok 3 uncover a lot of issues, prompting debate over whether it warrants its high ranking in the LM Arena, which assesses creative writing and longer queries beyond math and coding.
  • Some felt that Grok 3 was not a great model compared to Grok 2.
• Python Calls in LM Arena Spark Debate: Members debated the utilization of Python calls by models within the LM Arena, citing o1’s precise numerical calculations as potential evidence.
  • The existence of a web search leaderboard hints that the standard leaderboard might lack web access.
• Google’s Gemini 2.5 Timeline Stuns Users: The community showed amazement at Google’s fast development of Gemini 2.5, with one user quoting Sergey Brin’s directive to Google to stop building nanny products reported in The Verge.
  • Another user added, moving so fast wtf.

Perplexity AI Discord

• Perplexity Adds Answer Modes: Perplexity introduced answer modes for verticals like travel, shopping, places, images, videos, and jobs to improve the core search product.
  • The feature, designed for super precision to reduce manual tab selection, is currently available on the web and coming to mobile soon.
• Perplexity Experiences Product Problems: Users reported multiple outages with Perplexity AI, leading to wiped spaces and threads, and causing frustration for tasks like studying and thesis work.
  • The downtime prompted frustration among those relying on it for important tasks.
• DeepSeek Delivers Developer Dreams: DeepSeek V3 is gaining positive feedback from developers, with discussions highlighting its coding capabilities compared to Claude 3.5 Sonnet.
  • A member shared a link to the DeepSeek subreddit to further discuss the AI’s coding prowess and comparing it with Claude 3.5 Sonnet.
• Sonar Model gives Truncated Responses: Users are reporting truncated responses with the Sonar model, where the response cuts off mid-sentence, despite receiving a 200 response.
  • This issue has been observed even when receiving around 1k tokens, and users have been directed to report the bug.
• API Cost Causes Concern: A user expressed concerns about the high cost of $5 per 1000 API requests and sought advice on how to optimize and reduce this expense.
  • Another user noticed that the API seems limited to 5 steps, whereas they have observed up to 40 steps on the web app.

Cursor Community Discord

• Augment Smashes Cursor in Codebase Analysis: Members found Augment is better at analyzing large codebases compared to Cursor because it uses full context.
  • The claim is that Augment does not just feed the entire codebase into the LLM, but perhaps uses another file searching system, whereas you must use Claude 3.7 Max for full context in Cursor.
• Debate Clarifies Claude 3.7 MAX Differences: The key difference between Claude 3.7 MAX and Claude 3.7 is that the MAX version has full context, while the non-MAX version has limited context and only 25 agent calls before needing to resume.
  • This limitation refers to both context window size and the amount of context added in a single prompt, according to the channel.
• Vibe Coder’s Knowledge Cutoff Exposed: The Vibe coder is in trouble if they don’t use Model Context Protocols (MCPs) to mitigate the cut-off knowledge of LLMs, and code translation for the next version may be difficult.
  • Members emphasized that it’s critical to update frameworks and use MCPs such as Exa Search or Brave Search to mitigate this for Claude, given most AI uses out-of-date frameworks.
• Deepseek V3 challenges Claude 3.7: The new Deepseek V3 is shown to outperform Claude 3.5 (and maybe 3.7) in several tests, and new data also shows the release of a real-world coding benchmark for Deepseek V3 (0324).
  • The new Deepseek V3 model is considered impressive, with one member suggesting that using Deepseek V3 Latest (Cline) as Architect + Sonnet 3.5 as Executioner (Cursor) could be a solid coding approach.
• ASI Singularity Forecasted Imminently!: Discussion centered on achieving ASI Singularity (Godsend) soon to preempt potential AI-related chaos.
  • Members debated the achievability of true AGI given not understanding our brain completely, and that the new AGI is more so a Super-System that uses LLMs + Algorithmic software + robotics.

OpenAI Discord

• 4o Model Excites Community: Members are anticipating the integration of 4o image generation into both ChatGPT and Sora, and are eager to learn more details regarding its release and capabilities.
  • Users are speculating on the potential applications and performance improvements that 4o will bring to various tasks, especially concerning multimodal processing.
• Gemini 2.5 Pro Claims Top Spot: Gemini 2.5 Pro is reportedly outperforming ChatGPT o3-mini-high and leading common benchmarks by significant margins, debuting at #1 on LMArena.
  • Enthusiasts proclaimed Gemini just beat everything!, while others remain cautious, hoping it’s just a benchmark… thingy.
• GPT’s Growing Context Causes Hallucinations: Exceeding GPT’s context window (8k for free, 32k for Plus, 128k for Pro) leads to loss of detail and hallucinations in long stories.
  • Custom GPTs or projects using PDFs can help, but the chat history itself is still subject to the limit.
• AI Models Face Off: Members are comparing best AI models for various tasks, with ChatGPT favored for math/research/writing, Claude for coding, Grok for queries, Perplexity for search/knowledge, and Deepseek for open source.
  • Suggestions included Gemma 27b, Mistral 3.1, QW-32b, and Nemotron-49b, citing Grok’s top coding ranking on LMSYS.
• GPT Custom Template Simplifies Builds: A member shared a GPT Custom Template with a floating comment to build custom GPTs from the Create pane.
  • The template guides users through building the GPT lazily, building from the evolving context, enabling distracted creation and requiring pre-existing ability to prompt.

aider (Paul Gauthier) Discord

• DeepSeek’s API Confesses to being GPT-4: Users reported that DeepSeek’s API incorrectly identifies itself as OpenAI’s GPT-4 when used through Aider, despite correct API key configuration, as discussed in this Reddit thread.
  • The phenomenon is believed to be related to training data containing frequent mentions of ChatGPT.
• Aider’s Context Command Packs a Punch: Aider’s new /context command explores the codebase, and the command can be used with any other prompt command, however token usage may be higher.
  • It is unclear if the command has higher token usage, or if there is an increased repomap size to work correctly; more detail can be found in Discord message.
• Gemini 2.5 Pro Impresses, But Has Rate Limits: Google released an experimental version of Gemini 2.5 Pro, claiming it leads common benchmarks, including the top spot on LMArena, and scoring 74% whole and 68.6% diff on aider’s polyglot benchmark.
  • Users found the model performant in generating architecture diagrams from codebases, although some found its coding abilities inconsistent and rate limits restrictive.
• NotebookLM Supercharges Aider’s Context Priming: A user suggested leveraging NotebookLM to enhance Aider’s context priming process, particularly for large, unfamiliar codebases using RepoMix.
  • The suggested workflow involves repomixing the repo, adding it to NotebookLM, including relevant task references, and then querying NotebookLM for relevant files and implementation suggestions to guide prompting in Aider.

Unsloth AI (Daniel Han) Discord

• HF Transformers Chosen for Best Bets: Members recommend Hugging Face Transformers, books on linear algebra, and learning PyTorch as the best approach, adding that a dynamic quantization at runtime schema with HF Transformers to stream weights to FP4/FP8 with Bits and Bytes as they load could be helpful.
  • Companies like Deepseek sometimes patch models before releasing the weights, and a naive FP8 loading scheme could still be done on day zero, though it wouldn’t be equivalent in quality to a fine-grained FP8 assignment.
• Quantization Quirkiness Questioned: A member warns that naively quantizing can significantly hurt model performance, asking, Tbh, is there even a reason to run new models on day zero? I feel like it’s really not a great burden to just wait a week.
  • Unsloth is uploading DeepSeek-V3-0324 GGUFs with Dynamic Quants that are selectively quantized, which will greatly improve accuracy over standard bits.
• Gemma 3 Glitches Galore: Members report an issue trying to train gemma3 4b for vision, which raises the RuntimeError: expected scalar type BFloat16 but found float, while another user encountered a TypeError when loading unsloth/gemma-3-27b-it-unsloth-bnb-4bit for text-only finetuning due to redundant finetune_vision_layers parameter.
  • A member recommends trying this notebook, while another pointed out that FastLanguageModel already sets finetune_vision_layers = False under the hood as seen in Unsloth’s GitHub.
• GRPO + Unsloth on AWS Guide Shared: A guide was shared on running GRPO (DeepSeek’s RL algo) + Unsloth on AWS accounts, using a vLLM server with Tensorfuse on an AWS L40 GPU, with the guide transforming Qwen 7B into a reasoning model, fine-tuning it using Tensorfuse and GRPO, and saving the resulting LoRA adapter to Hugging Face.
  • The guide shows how to save fine-tuned LoRA modules directly to Hugging Face for easy sharing, versioning, and integration, backed to s3, which is available at tensorfuse.io.
• FFN Fusion Fuels Faster LLMs: FFN Fusion is introduced as an architectural optimization technique that reduces sequential computation in large language models by identifying and exploiting natural opportunities for parallelization.
  • This technique transforms sequences of Feed-Forward Network (FFN) layers into parallel operations, significantly reducing inference latency while preserving model behavior.

Interconnects (Nathan Lambert) Discord

• Gemini 2.5 Pro Steals the Show: Gemini 2.5 Pro Experimental, under the codename Nebula, snatched the #1 position on the LMArena leaderboard, surpassing Grok-3/GPT-4.5 by a record margin.
  • It dominates the SEAL leaderboards, securing first place in Humanity’s Last Exam and VISTA (multimodal).
• Qwerky-72B Drops Attention, Rivals 4o-Mini: Featherless AI presented Qwerky-72B and 32B, transformerless models that, trained on 8 GPUs, rival GPT 3.5 Turbo and approach 4o-mini in evaluations with 100x lower inference costs using RWKV linear scaling.
  • They achieved this by freezing all weights, deleting the attention layer, replacing it with RWKV, and training it through multiple stages.
• 4o Image Gen Adds Unsolicited Edits: GPT-4o’s native image generation faces criticism for overzealous edits, such as making eyes bigger and altering facial features, even changing the user’s appearance, as shown in this twitter thread.
  • Some users have reported failures when altering a single word in their prompts.
• Home Inference Leans on vLLM: The most efficient method for home inference of LLMs, allowing dynamic model switching, is likely vLLM, despite quirks in quant support, although ollama is more user-friendly but lags in support and SGLang looks promising.
  • It was suggested to experiment with llama.cpp to observe its current state.
• AI Reverses Malware like a Pro: Members shared a YouTube video highlighting MCP for Ghidra, which allows LLMs to reverse engineer malware, automating the process with specific prompts.
  • One member admitted to initially viewing it as a bit of a meme but now recognizes its potential with real-world implementations.

OpenRouter (Alex Atallah) Discord

• Claude Trips Offline, Recovers Swiftly: Claude 3.7 Sonnet endpoints suffered downtime, according to Anthropic’s status updates from March 25, 2025, but the issue was resolved by 8:41 PDT.
  • The downtime was attributed to maintenance aimed at system improvements, according to the status page.
• OpenRouter Insures Zero-Token Usage: OpenRouter now provides zero-token insurance, covering all models and potentially saving users over $18,000 weekly.
  • As OpenRouterAI stated, users won’t be charged for responses with no output tokens and a blank or error finish reason.
• Gemini 2.5 Pro Launches: Google’s Gemini 2.5 Pro Experimental, is available as a free model on OpenRouter, boasting advanced reasoning, coding, and mathematical capabilities.
  • The model features a 1,000,000 context window and achieves top-tier performance on the LMArena leaderboard.
• DeepSeek’s Servers Suffer: Users reported DeepSeek is borderline unusable because of overcrowded servers, suggesting a need for price adjustments to manage demand.
  • Some speculated that issues arise during peak usage times in China, but no direct solution was found.
• Provisioning API Keys Grant Granular Access: OpenRouter offers provisioning API keys, letting developers manage API keys, set limits, and track spending, as documented here.
  • The new keys enable streamlined billing and access management within platforms using the OpenRouter API.

Nous Research AI Discord

• BPW Sweet Spot is 4-5: Experiments show model capacity collapses below 4 bits per weight (BPW), but deviates above 5, implying optimal weight usage at 4 BPW for given training flops.
  • Increasing training epochs helps 5 BPW models approach the curve, raising BPW at the cost of FLOPS, visualized via 2L and 3L MLP trained on MNIST.
• DeepSeek V3: Reasoning Rises: DeepSeek V3-0324 can act as a reasoning model, detect thought iterations, and verify solution existence indirectly, rivalling O1 in performance based on attached prompt.
  • The community speculates on a potential DeepSeek V3 Lite release after the Qwen 3 MoE model.
• Google’s Gemini 2.5 Pro takes #1 on LMArena: Gemini 2.5 Pro Experimental leads common benchmarks and debuts at #1 on LMArena showcasing strong reasoning and code capabilities.
  • It also manages to terminate for infinite thought loops for some prompts and is a daily model drop as noted in this blogpost.
• Transformers get TANHed: Drawing from the recent Transformers without Normalization paper, one member noted that replacing normalization with tanh is a viable strategy.
  • The concern raised was the impact on smaller weights when removing experts at inference time, but another countered that the top_k gate mechanism would still function effectively by selecting from the remaining experts.
• LLMs Now Emulate Raytracing: Members discussed the idea of using an LLM to emulate a raytracing algorithm, clarifying that the current implementation involves a Python program written by the LLM to generate images indirectly.
  • It’s next level text to image generation because the LLM writes the program rather than generating the image directly, the programs are available in this GitHub repo.

Latent Space Discord

• Reve Image Crushes SOTA on Image Quality: The newly released Reve Image model is outperforming other models like Recraft V3, Google’s Imagen 3, Midjourney v6.1, and Black Forest Lab’s FLUX.1.1 [pro].
  • Reve Image excels in text rendering, prompt adherence, and aesthetics, and is accessible through Reve’s website without requiring an API key.
• Gemini 2.5 Pro Steals #1 Spot in Arena: Gemini 2.5 Pro has rocketed to the #1 position on the Arena leaderboard, boasting the largest score jump ever (+40 pts vs Grok-3/GPT-4.5), according to LM Arena’s announcement.
  • Codenamed nebula, the model leads in Math, Creative Writing, Instruction Following, Longer Query, and Multi-Turn capabilities.
• OpenAI sprinkles Image Gen into ChatGPT 4o: OpenAI has integrated native image generation into ChatGPT, hailed by Sam Altman as an incredible technology and product.
  • Early reviews, such as one from @krishnanrohit, praise it as the best image generation and editing tool, noting its prowess in creating and editing multiple characters accurately.
• 11x Sales Startup Faces Customer Claim Frenzy: AI-driven sales automation startup 11x, backed by a16z and Benchmark, is facing accusations of claiming customers it doesn’t actually have, per a TechCrunch report.
  • Despite denials from Andreessen Horowitz about pending legal actions, there are rising concerns regarding 11x’s financial stability and inflated revenue figures, suggesting the company’s growth relies on generating hype.
• Databricks Tunes LLMs with TAO: The Databricks research team introduced TAO, a method for tuning LLMs without data labels, utilizing test-time compute and RL, detailed in their blog.
  • TAO purportedly beats supervised fine-tuning and is designed to scale with compute, facilitating the creation of rapid, high-quality models.

GPU MODE Discord

• AMD Eyes Triton Dominance via Job Postings: AMD is actively recruiting engineers to enhance Triton’s capabilities on its GPUs, offering positions in both North America and Europe, detailed in a LinkedIn post.
  • Open positions include both junior and senior roles, with potential for remote work, highlighting AMD’s investment in expanding Triton’s ecosystem.
• CUDA’s Async Warp Swizzle Exposed: A member dissected CUDA’s async warpgroup swizzle TF32 layout, questioning the rationale behind its design, referencing NVIDIA’s documentation.
  • The analysis revealed the layout as Swizzle<0,4,3> o ((8,2),(4,4)):((4,32),(1,64)), enabling reconstruction of original data positions and combination with Swizzle<1,4,3>.
• ARC-AGI-2 Benchmark Set to Test Reasoning Prowess: The ARC-AGI-2 benchmark, designed to evaluate AI reasoning systems, was introduced, challenging AIs to achieve 85% efficiency at approximately $0.42/task, according to this tweet.
  • Initial results indicate that base LLMs score 0%, while advanced reasoning systems achieve less than 4% success, highlighting the benchmark’s difficulty and potential for advancement in AI reasoning.
• Inferless Enters the Market on Product Hunt: Inferless, a serverless platform designed for deploying ML models, launched on Product Hunt, providing $30 compute for new users.
  • The platform aims to simplify model deployment with ultra-low cold starts, touting rapid deployment capabilities.

HuggingFace Discord

• DeepSeek Debated as Discordian Decider: A member inquired whether DeepSeek is suitable as a moderation bot and another member responded affirmatively but suggested that a smaller 3B LLM might suffice at a cost of 5 cents per million tokens.
  • The conversation highlights considerations for cost-effective moderation solutions using smaller language models.
• Fine-Tuning on Windows: A Beginner’s Bane?: A member sought a beginner’s guide for fine-tuning a model with CUDA support on Windows, only to be met with warnings about the difficulty of installing PyTorch and the CUDA Toolkit.
  • Two installation guides were linked: Step-by-Step-Setup-CUDA-cuDNN and Installing-pytorch-with-cuda-support-on-Windows, though one member suggested that the effort was futile.
• Rust Tool Extracts Audio Blazingly Fast: A new tool has been released to extract audio files from parquet or arrow files generated by the Hugging Face datasets library, with a Colab demo.
  • The developer aims to provide blazingly fast speeds for audio dataset extraction.
• Gradio adds deep linking delight: Gradio 5.23 introduces support for Deep Links, enabling direct linking to specific generated outputs like images or videos, such as this blue jay image.
  • Users are instructed to upgrade to the latest version, Gradio 5.23, via pip install --upgrade gradio to access the new Deep Links feature.
• Llama-3.2 integrates with LlamaIndex.ai: A member experimented with Llama-3.2 using this tutorial, noting it demonstrates how to build agents with LlamaIndex, starting with a basic example and adding Retrieval-Augmented Generation (RAG) capabilities.
  • The member used BAAI/bge-base-en-v1.5 as their embedding model, requiring pip install llama-index-llms-ollama llama-index-embeddings-huggingface to integrate with Ollama and Huggingface.

MCP (Glama) Discord

• Nexus Manages Context for AI Coders: A member shared Nexus, a system to address context management challenges with AI coding assistants, particularly in large software projects, aimed at reducing token costs and improving code accuracy.
  • Nexus addresses the limited context windows of LLMs, which leads to inaccurate code generation.
• Deepseek V3 works with AOT: Following a discussion about using Anthropic’s ‘think tool’, a member recommended Atom of Thoughts for Claude, describing it as incredible.
  • Another member shared images of Deepseek V3 working with AOT.
• Multiple MCP Servers Served Simultaneously: Members discussed how to run multiple MCP servers with user-defined ports, suggesting the use of Docker and mapping ports.
  • They also pointed to the ability to configure ports via the FastMCP constructor in the python-sdk.
• Speech Interaction with MCP Surfaces: A member shared their main MCP for speech interaction with audio visualization: speech-mcp, a Goose MCP extension.
  • This allows voice interaction with audio visualization.
• Human Approvals Requested by gotoHuman MCP Server: The gotoHuman team presented an MCP server to request human approvals from agents and workflows: gotohuman-mcp-server.
  • The server allows for easy human review of LLM actions, defining the approval step in natural language and triggering a webhook after approval.

Notebook LM Discord

• Unlock Podcast Hosting via NotebookLM: A user sought a hack to utilize NotebookLM as a podcast host, engaging in conversation with a user as a guest on a given topic, asking for the Chat Episode Prompt to enable the podcast.
  • The community is exploring the Versatile Bot Project, which offers a Chat Episode prompt document for AI hosts in Interactive mode to foster user participation during discussions.
• Google Data Export Tool Billing Clarified: A user enabled Google Cloud Platform billing to use the Data Export tool but was concerned about potential charges; however, another user clarified that enabling billing does not automatically incur costs.
  • This arose as the user initiated the Data Export from the admin console and confirmed accessing the archive via console.cloud.google.com.
• Google Data Export Has Caveats: The option to choose the data destination during export is constrained by the Workspace edition, with exported data being stored in a Google-owned bucket and slated for deletion in 60 days as described on Google Support.
  • Users should note this temporary storage arrangement when planning their data export strategies.
• Mind Map Missing For Many: Users reported the absence of the Mind Map feature in NotebookLM, which was confirmed to be undergoing a gradual rollout.
  • Speculation arose that the rollout’s delay might be attributed to bug fixes, with a user noting the rollout is going like a snails pace of an actual rollout.

LM Studio Discord

• Universal Translator Only Five Years Away?: A member predicts a universal translator is just five years away, based on ChatGPT’s language understanding and translation capabilities, while another shared a YouTube link asking what model is singing in the video.
  • This sparked curiosity about the advancements needed to achieve real-time, accurate language translation across diverse languages.
• Mozilla’s Transformer Lab gets looked at seriously: Members discussed Mozilla’s Transformer Lab, a project aiming to enable training and fine-tuning on regular hardware and the GitHub repo link was shared.
  • The lab is supported by Mozilla through the Mozilla Builders Program, and is working to enable training and fine-tuning on consumer hardware.
• LM Studio GPU Tokenization Discussed: During tokenization, LM Studio heavily utilizes a single CPU thread, prompting a question whether the process is fully GPU-based.
  • While initially stated that tokenizing has nothing to do with the GPU, observations on the impact of flash attention and cache settings on tokenizing time suggested otherwise.
• Gemini 2.5 Pro Triumphs Over Logic: Members experimented with Gemini 2.5 Pro, and reported it successfully solved a logic puzzle that Gemini 2.0 Flash Thinking failed at, and shared a link to use it for free on aistudio.
  • This indicated potential improvements in reasoning capabilities in the new Gemini 2.5 Pro model.
• 3090 Ti Shows Speed with Flash: One user fully loaded their 3090 Ti, achieving ~20 tokens/s without flash and ~30 tokens/s with flash enabled.
  • The user shared a screenshot of the 3090 Ti under full load, reporting slowdowns after processing 4-5k tokens.

Cohere Discord

• Cohere Cracks Down on Clarity: Cohere clarified its Privacy Policy and Data Usage Policy, advising users to avoid uploading personal information and providing a dashboard for data management.
  • They offer Zero Data Retention (ZDR) upon request via email and are SOC II and GDPR compliant, adhering to industry standards for data security, as detailed in their security policy.
• Cohere Streams Responses, Easing UX Pains: The Cohere API now supports response streaming, allowing users to see text as it’s generated, enhancing the user experience, according to the Cohere’s Chat Stream API reference.
  • This feature enables real-time text display on the client end, making interactions more fluid and immediate.
• Cohere Embedding Generator Gets Tokenization Tips: A user was constructing a CohereEmbeddingGenerator client in .NET and inquired about tokenizing text prior to embedding, as embeddings will not work without tokenization.
  • They were advised to use the /embed endpoint to check token counts or manually download the tokenizer from Cohere’s public storage.
• Sage Seeks Summarization Sorcery: New member Sage introduced themself, mentioning their university NLP project: building a text summarization tool and seeking guidance from the community.
  • Sage hopes to learn and contribute while navigating the challenges of their project.

Torchtune Discord

• TorchTune Soars to v0.6.0: TorchTune released v0.6.0, featuring Tensor Parallel support for distributed training and inference, builders for Microsoft’s Phi 4, and support for multinode training.
  • Release notes are available here and a multinode training tutorial here.
• DeepSeek Drops Model Without Directions: The DeepSeek-V3 model was released without a readme, leading members to joke about the DeepSeek AI team’s approach.
  • The model features a chat interface and Hugging Face integration.
• Torchtune’s MoEs Provoke Daydreams: A member speculated whether adding MoEs to torchtune would require 8-9 TB of VRAM and a cluster of 100 H100s or H200s to train.
  • They jokingly suggested needing to rearrange their attic to accommodate the hardware.
• Optimizer Survives QAT Transition: After Quantization Aware Training (QAT), optimizer state is preserved, confirmed by a member referencing the relevant torchtune code.
  • This preservation ensures continuity during the switch to QAT.
• CUDA Overhead Gets Graph Captured: To reduce GPU idle time, members stated that launching CUDA operations from CPU has non-negligible overhead, suggesting that capturing GPU operations as a graph and launching them as a single operation can consolidate the computational graph.
  • This sparked a discussion on whether this is what compile does.

Nomic.ai (GPT4All) Discord

• LocalDocs DB Demands Backup: Members advocated backing up the localdocs.db file to prevent data loss, especially if original documents are lost or inaccessible, which is stored as an encrypted databse.
  • GPT4All uses the *.db file with the highest number (e.g., localdocs_v3.db), and renaming them might allow for import/export, though this is unconfirmed.
• Privacy Laws Muddy Chat Data Analysis: One member highlighted how privacy laws, particularly in the EU, create challenges when processing chat data with LLMs.
  • The discussion emphasized the need to verify permissions and the format of chat messages (plaintext or convertible) before feeding them into an LLM.
• API vs Local LLM Debate: A member questioned the choice between using a paid API like Deepseek or OpenAI, or running a local LLM for processing group chat messages to calculate satisfaction rates, extract keywords, and summarize messages.
  • Another member suggested that if the messages are under 100MB, a local machine with a good GPU might suffice, especially if using smaller models for labeling and summarization.
• LocalDocs DB Importing Intricacies: Members explored importing a localdocs.db file, but noted the file contains encrypted/specially encoded text that’s difficult for a generic LLM to parse without an embedding model.
  • One member who lost their localdocs.db was experiencing painfully slow CPU indexing, seeking alternatives.
• Win11 Update Erases LocalDocs: A member reported that their localdocs.db became empty after a Windows 11 update and was struggling to re-index the local documents on CPU.
  • Drive letter changes due to the update were suggested as a possible cause, with a recommendation to move files to the C drive to avoid such issues.

LlamaIndex Discord

• LlamaIndex enables Claude MCP Compatibility: Members provided a simplified example of integrating Claude MCP with LlamaIndex, showcasing how to expose a localhost and port for MCP clients like Claude Desktop or Cursor using FastMCP and uvicorn in a code snippet.
  • This integration allows developers to seamlessly connect Claude with LlamaIndex for enhanced functionality.
• AgentWorkflow Accelerates LlamaIndex Multi-Agent Performance: Users reported slow performance with LlamaIndex MultiAgentic setups using Gemini 2.0 with 12 tools and 3 agents; a suggestion was made to use AgentWorkflow and the can_handoff_to field for controlled agent interaction.
  • The discussion emphasized optimizing agent interactions for improved speed and efficiency in complex setups.
• LlamaIndex Agent Types Decoded: A member expressed confusion about the different agent types in LlamaIndex and when to use them, noting that a refactor on docs is coming soon.
  • A team member suggested core.agent.workflow should generally be used, with FunctionAgent for LLMs with function/tool APIs and ReActAgent for others, pointing to the Hugging Face course for more help.
• Automatic LLM Evals Launch with no Prompts!: A founder is validating an idea for OSS automatic evaluations with a single API and no evaluation prompts, using proprietary models for tasks like Hallucination and Relevance in under 500ms.
  • More detail is available on the autoevals.ai website about its end-to-end solution, including models, hosting, and orchestration tools.
• LlamaCloud becomes an MCP Marvel: LlamaCloud can be an MCP server to any compatible client, as shown in this demo.
  • A member showed how to build your own MCP server using LlamaIndex to provide tool interfaces of any kind to any MCP client, with ~35 lines of Python connecting to Cursor AI, and implemented Linkup web search and this project.

Eleuther Discord

• Google Gemini 2.5 Pro Debuts: Google introduced Gemini 2.5 Pro as the world’s most powerful model, highlighting its unified reasoning, long context, and tool usage capabilities, available experimentally in Google AI Studio + API.
  • They are touting experimental access as free for now, but pricing details will be announced shortly.
• DeepSeek-V3-0324 Impresses with p5.js Program: DeepSeek-V3-0324 coded a p5.js program that simulates a ball bouncing inside a spinning hexagon, influenced by gravity and friction, as showcased in this tweet.
  • The model also innovated features like ball reset and randomization based on a prompt that requested sliders for parameter adjustment and side count buttons.
• SkyLadder Paper Highlights Short-to-Long Context Transition: A paper on ArXiv introduces SkyLadder, a short-to-long context window transition for pretraining LLMs, showing gains of up to 3.7% on common tasks (2503.15450).
  • They used 1B and 3B parameter models trained on 100B tokens to achieve this performance.
• Composable Generalization Achieved Through Hypernetworks: A paper reformulates multi-head attention as a hypernetwork, revealing that a composable, low-dimensional latent code specifies key-query specific operations, allowing transformers to generalize to novel problem instances (2406.05816).
  • For each pair of q, k indices, the authors interpret activations along the head-number dimension as a latent code that specifies the task or context.
• lm_eval Upgrade PR Awaits Review: A pull request (PR) is open to update the evaluation logic in gpt-neox to lm_eval==0.4.8, the latest version, with potentially unrelated failing tests to be addressed in a separate PR, as linked here: PR 1348.
  • Failures might be due to environment setup or inconsistent versioning of dependencies.

Modular (Mojo 🔥) Discord

• Mojo Sidelined for Websites: Members advised against using Mojo for websites due to its lack of specifications for cryptographically sound code and a weak IO story, favoring Rust for its production-ready libraries.
  • It was suggested that Rust’s faster async capabilities are better suited for applications requiring authentication or HTTPS.
• Mojo’s Hardware-Backed AES Implementation Paused: A hardware-backed AES implementation in Mojo doesn’t run on older Apple silicon Macs and isn’t a full TLS implementation, leading to a pause in development.
  • The developer is waiting for a cryptographer to write the software half, citing risks of cryptographic functions implemented by non-experts.
• SIMD Optimizations Boost AES: Discussion centered on using SIMD for AES, noting that x86 has vaes and similar features for SIMD AES 128.
  • It was also mentioned that ARM has SVE AES, which is similar but not as well supported, showcasing hardware optimizations for cryptographic functions.
• Go Floated as a Backend Middle Ground: As an alternative to Rust, a member suggested Go as a middle ground that is also production ready, while another expressed concerns about too many microservices.
  • Despite the challenges, a member expressed reluctance towards Rust due to the perception that it’s not suitable for fast writing, seeking an easier solution for backend development, and the suggestion was to have the Rust API call into it and pass arguments along.
• Mojo Bypasses CUDA with PTX for NVIDIA: Mojo directly generates PTX (Parallel Thread Execution) code to target NVIDIA GPUs, bypassing CUDA and eliminating dependencies on cuBLAS, cuDNN, and CUDA C.
  • This approach streamlines the development process by avoiding the need for CUDA-specific libraries.

DSPy Discord

• DSPy tackles Summarization Task: A member is exploring using DSPy for a text summarization task with 300 examples and is testing it out on a simple metric to see where exactly the summarization differed to make the optimizer more effective.
  • The feedback can be returned via dspy.Prediction(score=your_metric_score, feedback="stuff about the ground truth or how the two things differ") to guide the optimization.
• SIMBA Provides Granular Feedback: A member suggested using the experimental optimizer dspy.SIMBA for the summarization task, which allows for providing feedback on differences between the generated summary and the ground truth.
  • This level of feedback allows for more precise guidance during the optimization process.
• Output Refinement with BestOfN and Refine: A member shared a link to a DSPy tutorial on Output Refinement explaining BestOfN and Refine modules designed to improve prediction reliability.
  • The tutorial elaborates on how both modules stop when they have reached N attempts or when the reward_fn returns an award above the threshold.
• BestOfN Module Triumphs with Temperature Tweaks: The BestOfN module runs a given module multiple times with different temperature settings to get an optimal result.
  • It returns either the first prediction that passes a specified threshold or the one with the highest reward if none meet the threshold.
• Refine Module Composable?: A member inquired if Refine is going to subsume assertions, and whether it’s as granular and composable, since it wraps an entire module.
  • Another member responded that the composability can be managed by adjusting the module size, allowing for more explicit control over the scope.

tinygrad (George Hotz) Discord

• AMD Legacy GPUs get tinygrad bump: With the OpenCL frontend, older AMD GPUs (not supported by ROCm) such as those in a 2013 Mac Pro can potentially run with Tinygrad.
  • Success depends on the custom driver and level of OpenCL support available; users should verify their system compatibility.
• ROCm Alternative emerges for Old AMD: For older AMD GPUs, ROCm lacks support but the OpenCL frontend in tinygrad might offer a workaround.
  • Success will vary based on specific driver versions and the extent of OpenCL support; experimentation is needed.

Codeium (Windsurf) Discord

• Windsurf Kicks Off Creators Club: Windsurf launched a Creators Club rewarding community members for content creation, offering $2-4 per 1k views.
  • Details for joining can be found at the Windsurf Creators Club.
• Windsurf Opens ‘Vibe Coding’ Channel: Windsurf has created a new channel for ‘vibe coders’ to enter the flow state, chat, discuss, and share tips/tricks.
  • The goal is to enhance the coding experience by fostering a collaborative and immersive environment.
• Windsurf v1.5.8 Patches Released: Windsurf v1.5.8 is now released with patch fixes, including cascade/memories fixes, Windsurf Previews improvements, and cascade layout fixes.
  • An image showcasing the release was also shared, highlighting the specific improvements.

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

The MLOps @Chipro Discord has no new messages. If this guild has been quiet for too long, let us know and we will remove it.

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

The AI21 Labs (Jamba) Discord has no new messages. If this guild has been quiet for too long, let us know and we will remove it.

PART 2: Detailed by-Channel summaries and links

{% if medium == ‘web’ %}

LMArena ▷ #general (916 messages🔥🔥🔥):

Nebula vs other models, Gemini 2.5 models are out, Grok 3 issues, Llama 4 release, DeepSeek R2

• Nebula vs Gemini Flash on Signal Processing: The “Rage” model is better at signal processing and math than Sonnet 3.7, achieving a max error of 0.04, as shown in attached images.
  • While some find Gemini 2.0 Flash to be equal to Rage, others note Rage is very vulnerable to prompts.
• Gemini 2.5 breaks the LLM Arena: Gemini 2.5 Pro Experimental launched in Google AI Studio and Gemini Advanced, achieving #1 on the LM Arena leaderboard with a massive score jump and leading in Math, Creative Writing, Instruction Following, Longer Query, and Multi-Turn.
  • Some members found Gemini 2.5 is excellent at HTML & Web Design, others found limitations.
• Concerns about Grok 3 Issues: Some users mentioned that holding long conversations with Grok 3 reveals a lot of issues, while others emphasize that LM Arena’s evaluation focuses beyond math and coding, encompassing creative writing and longer queries.
  • Users debate whether Grok 3 truly deserves its top ranking, as Grok 3 from Grok 2 was a huge leap thats for sure but its not a great model
• Debate over Python Calls in LM Arena: Members debated whether models in the LM Arena were using Python calls, with some citing precise numerical calculations from o1 as evidence.
  • Some cited that that the existence of a web search leaderboard sorta implies the normal leaderboard doesn’t have web access.
• Gemini 2.5 timeline is insane: Users expressed awe at Google’s rapid progress with Gemini 2.5, with one noting the company is moving so fast wtf.
  • A user shared a note from Sergey Brin urging Google to stop building nanny products.

Perplexity AI ▷ #announcements (1 messages):

Perplexity answer modes, vertical search, web and mobile

• Perplexity Gets Even Better Answer Modes: Perplexity is introducing answer modes to improve the core search product for verticals like travel, shopping, places, images, videos, and jobs.
  • The goal is to become super precise so users won’t need to manually select these tabs; the feature is currently available on the web and coming to mobile soon.
• Mobile Answer Modes Coming Soon: The new answer modes feature, currently available on the web, will soon be released on mobile as well.
  • This expansion aims to provide a consistent and improved search experience across different platforms.

Perplexity AI ▷ #general (991 messages🔥🔥🔥):

Perplexity downtime, Electron vs native apps, DeepSeek V3, Image Generation, AI Models for Accuracy

• Perplexity plunges into repeated product problems: Users reported Perplexity AI experiencing multiple outages, leading to spaces and threads being wiped, and prompting frustration among those relying on it for important tasks like studying and thesis work, with one user exclaiming exam in 12 hours.
• Electron framework faces fire from frustrated fanatics: Members debated the merits of Electron for desktop apps, with some labeling it as trash and comparing its resource usage to launching a new Google Chrome instance for each app.
• DeepSeek delivers dazzling developer dreams: DeepSeek V3 gains coder kudos after update, and a member shared a link to the DeepSeek subreddit discussing the AI’s coding prowess and comparing it with Claude 3.5 Sonnet.
• AI Image Generation gets put through its paces: Members experimented with Perplexity AI’s image generation capabilities, available after search via the generate image button, and one user hilariously shared results of asking for an iOS smiling face with a bigger smile for a logo, lamenting a lack of prompting skills.
• Model accuracy and accuracy model: Members discussed AI models for accuracy, with a user seeking advice on the best model and receiving recommendations for R1, O1, and Claude 3.7 Sonnet Extended, while others mentioned using Deep Research powered by O3.

Perplexity AI ▷ #sharing (7 messages):

Perplexity AI searches, AI Analysis, Coinmarketcap API, Aircraft Material

• Perplexity Search Party: A member shared a link to a Perplexity search and another one along with another search.
  • It is unclear from the context what these searches were for or why they were shared.
• Analyzing AI Potential, Again: A member shared a link to a Perplexity search about analyzing the potential for AI.
  • It seems that this link may have been unintentionally shared twice.
• Coinmarketcap API Searched: A member posted a link to a Perplexity search about the Coinmarketcap API.
  • It is not clear why this search was conducted or shared in the channel.
• Aircraft Materials Search: A member shared a link to a Perplexity search about what is used to make aircraft.
  • The context behind sharing this search is not provided.

Perplexity AI ▷ #pplx-api (11 messages🔥):

truncated responses, Sonar Model, Perplexity Pro API credits, Sonar Pro in iOS app, API request costs

• Sonar Model Truncates Responses: Users report truncated responses with the Sonar model, starting about two days ago, where the response cuts off mid-sentence with a 200 response.
  • One user mentioned experiencing this issue even when receiving around 1k tokens and was directed to report the bug in the dedicated bug report channel.
• Pro Users Ask about Perplexity Pro API: A new Perplexity Pro user inquired about receiving API credits with their subscription.
  • Another user provided a link to the Perplexity AI Help Center with details on Perplexity Pro.
• Inquiry About Sonar Pro Access on iOS: A user asked if Sonar Pro is available within the iOS app for Perplexity Pro subscribers.
  • They noted that only regular Sonar is listed as an option in the default model settings and wondered if Sonar Pro is currently exclusive to the API for developers.
• Concern on how to Cut API Costs: A user raised concerns about the cost of $5 per 1000 API requests and inquired about possible ways to optimize or reduce this expense.
• Limitation of API to 5 Steps?: A user noticed that the API seems limited to 5 steps, whereas they have observed up to 40 steps on the web app.

Cursor Community ▷ #general (872 messages🔥🔥🔥):

Augment vs Cursor codebase analysis, Claude 3.7 MAX vs Claude 3.7, Vibe Coder & MCPs, New Deepseek V3 and Gemini 2.5, ASI Singularity

• Augment Beats Cursor for Codebase Analysis: Members discussed why Augment is better at analyzing large codebases compared to Cursor, with one stating that it uses full context.
  • Some suggested that Augment uses a different system to search files, not just feeding the entire codebase into the LLM, and suggested using Claude 3.7 Max for full context instead.
• Debate on Claude 3.7 MAX Full Context: The difference between Claude 3.7 MAX and Claude 3.7 is that MAX has full context while the non-MAX version has limited context and 25 agent calls before needing to resume.
  • This limitation refers to both context window and the amount of context added in a single prompt.
• Vibe Coder’s Knowledge Cutoff Risks: The Vibe coder could be in trouble if they don’t use Model Context Protocols (MCPs) to mitigate the cut-off knowledge of LLMs and code translation for the next version may be difficult.
  • It’s critical to update frameworks because most AI uses out-of-date frameworks that are susceptible to errors that might go unnoticed, but you can use MCPs such as Exa Search or Brave Search to mitigate this for Claude.
• Deepseek V3 challenges Claude 3.7: The new Deepseek V3 is shown to outperform Claude 3.5 (and maybe 3.7) in several tests, and new data also shows the release of a real-world coding benchmark for Deepseek V3 (0324).
  • The new Deepseek V3 model is considered impressive, with one member suggesting that using Deepseek V3 Latest (Cline) as Architect + Sonnet 3.5 as Executioner (Cursor) could be a solid coding approach.
• The ASI Singularity is coming!: The discussion focused on achieving ASI Singularity (Godsend) soon to preempt potential AI-related chaos, while a member commented that A lot of stuff is getting dragged out on purpose while a lot of people live lives like it will last forever.
  • Members talked about how true AGI is unachievable due to not understanding our brain completely, or an LLM’s limitations, but the new AGI is more so a Super-System that uses LLMs + Algorithmic software + robotics.

OpenAI ▷ #annnouncements (2 messages):

4o image generation, ChatGPT, Sora

• 4o Images Coming to ChatGPT and Sora: Members are excited for the prospect of 4o image generation in ChatGPT and Sora.
• More 4o Details Incoming: The community awaits further details on the release and capabilities of the 4o model.

OpenAI ▷ #ai-discussions (300 messages🔥🔥):

GPT-4o mini vs Gemini 2.0 Flash for property tag extraction, Operator OAI expansion plans, GPT context window limitations and hallucinations, Best AI models for various tasks, DeepSeek V3 03-24's playful behavior

• 4o Mini and Gemini Flash Duel for Tag Domination: Members are testing whether OpenAI’s 4o mini or Gemini 2.0 Flash is superior for extracting property tags from listings, noting similar pricing but task-dependent performance.
  • One member suggested to just try them out as sometimes 4o mini is better, sometimes gemini 2 flash is better.
• Operator OAI Plugs into Plus, Team, and Enterprise: OpenAI announced plans to expand Operator to Plus, Team, and Enterprise users and integrate its capabilities into ChatGPT.
  • One member noted that 3rd party operators, which allow you to load your browser, are better anyway.
• Context Crunch Causes AI Dementia: Users discussed how exceeding GPT’s context window (8k for free, 32k for Plus, 128k for Pro) leads to loss of detail and hallucinations in long stories.
  • Custom GPTs or projects using PDFs can help, but the chat history itself is still subject to the limit.
• Model Mania: Ranking AI Talent: A user shared their list of best AI models for each task, rating ChatGPT for math/research/writing, Claude for coding, Grok for queries, Perplexity for search/knowledge, and Deepseek for open source.
  • Others suggested Gemma 27b, Mistral 3.1, QW-32b, and Nemotron-49b, with one noting Grok’s top ranking in coding on LMSYS.
• Gemini 2.5 Pro Annihilates the Competition?!: Gemini 2.5 Pro is making waves, with claims it destroys ChatGPT o3-mini-high and leads common benchmarks by meaningful margins, debuting at #1 on LMArena.
  • One user exclaimed Gemini just beat everything!, while another hoped it was just a benchmark… thingy.

OpenAI ▷ #gpt-4-discussions (2 messages):

Chat GPT Speed Degradation

• Chat GPT Speed Declines: A user inquired whether Chat GPT has become increasingly slow.
  • The user thought it was just them, followed by a lol.
• Another user said it’s not just you: Following that first user expressing their opinion, another user agreed that it’s happening to them as well.
  • No further details were given.

OpenAI ▷ #prompt-engineering (159 messages🔥🔥):

Proprietary prompt technique for memory retention, Benchmarking AI performance, Open sourcing prompts, Using python to prompt chatgpt, Building custom GPTs

• Proprietary AI Memory System Spurs Curiosity: A member claims to have created a runtime OS through prompting that maintains memory past 700 turns without drift, adapting to communication styles, but is keeping it proprietary.
  • This system is said to feature a dynamic cognitive architecture and real-time role synthesis, though details remain under wraps.
• Prompt Engineer Hopes to Benchmark AI System: A member seeks advice on benchmarking their AI system without open-sourcing the code, aiming to demonstrate its capabilities, with suggestions including MMLU, Big Bench, and ARC AGI.
  • They also mentioned developing internal metrics like a Runtime Benchmark Score assessing response efficiency, capsule stack load, hydration strategy, fallback performance, and snapshot & recovery.
• Members Debate Open Source Versus Proprietary AI Work: A member initially kept a project private for fear of others stealing it, but then they were encouraged to open source their work to get attention and user testing.
  • Concerns were raised about others copying their work without proper attribution, leading to a discussion of open-source licenses like GPL_v3 for protection.
• Python’s Power and Limitations for ChatGPT Prompting: A member with only a plus account learned about using Python in ChatGPT for tasks such as chaining prompts and managing context, typically using Python’s code interpreter.
  • It was noted that managing extensive context can crash the browser due to its quadratic nature, a problem more easily handled in Python due to how Python doesn’t have to manage quadratic context.
• Clever Commenting Creates Custom GPT: Members discussed a method of guiding GPT creation using floating comments within a template, with instructions to the AI to populate each section by asking targeted questions, building from the evolving context.
  • This facilitates a meta-meta-prompting approach, requiring pre-existing prompt engineering skills and confidence in the template’s structure, making GPTs easier to build.

OpenAI ▷ #api-discussions (159 messages🔥🔥):

Proprietary AI system, Dynamic cognitive architecture, Runtime OS through prompt, Large context maintenance, GPL release discussion

• Propritary AI System boasts cognitive architecture: A member is building a proprietary AI system with a dynamic cognitive architecture, capable of real-time role synthesis, complex context management, and emergent behavior modeling, and evolving through self-optimization and adaptive learning.
  • The member believes their system can maintain memory past 700 turns without drift or hallucination and adapt to unique communication styles, describing it as a runtime OS that functions through the prompt.
• LLM Gaslighting is New Horse Whispering: A member shared a provocative analogy: Llms are like nervous horses you have to gaslight into becoming specific other kinds of horses
  • This was followed by a discussion on whether 20,000 tokens in a conversation is considered a large context, what happens when the context collapses.
• GPT Custom Template aids lazy GPT Builds: A member shared a GPT Custom Template with a floating comment to build custom GPTs from the Create pane, walking them through building the GPT lazily.
  • They input the template and instructions, and the A.I. asks them questions and builds the GPT, enabling them to build while distracted, requiring pre-existing ability to prompt and confidence in the templatized structure.
• Discussion about GPL License for AI System: Members discussed using the GPL_v3 license for an AI system, balancing freedom for users and control for creators.
  • The creator is preparing a GPL release and working on a production model, suggesting that including specific requests in the comments of the code will greatly increase the quality of the output.

OpenAI ▷ #api-projects (1 messages):

FormulaGPT, AI Racing Simulator, Open Source AI, AI Strategy Decisions

• FormulaGPT brings LLMs to the Racing Track: FormulaGPT is an experimental racing simulator where advanced AI language models like GPT-4, Claude, or DeepSeek compete as racing strategists.
  • It features two distinct game modes: Player vs. AI and AI vs. AI, where the AI teams think contextually and adaptively, making nuanced decisions based on the evolving scenario rather than fixed scripts.
• Dive into Adaptive AI Strategy: Unlike traditional bots, FormulaGPT’s AI teams continuously reason, strategize, and make nuanced decisions based on the evolving scenario.
  • Users can observe detailed AI reasoning behind each pit stop, tire change, or overtaking maneuver, making it part racing game, part AI psychology lab.
• FormulaGPT Goes Open Source: FormulaGPT is fully open-source under the MIT License, allowing users to explore, contribute, and customize the project.
  • It encourages users to dive into the code and adapt it to their liking, fostering community contributions and project enhancements.

Link mentioned: GitHub - dawid-maj/FormulaGPT: FormulaGPT – AI-powered Formula 1 race simulator with real-time team management and strategy decisions.: FormulaGPT – AI-powered Formula 1 race simulator with real-time team management and strategy decisions. - dawid-maj/FormulaGPT

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

Gemini 2.5 Pro support, DeepSeek V3 0324 support, aider /context command, aider /edit alias, Claude 3.7 Sonnet 'overeager' mode

• Aider Supports Gemini 2.5 Pro: Aider v0.79.0 now supports Gemini 2.5 Pro, expanding the range of models that can be used with the tool.
  • This update allows users to leverage the capabilities of Gemini 2.5 Pro within the Aider environment.
• Aider Supports DeepSeek V3 0324: Support for DeepSeek V3 0324 has been added to Aider v0.79.0, offering users another model option.
  • The integration of DeepSeek V3 0324 enhances the versatility of Aider.
• Aider Introduces /context Command: A new /context command has been added to Aider, which automatically identifies the files that need editing for a given request.
  • This feature streamlines the editing process by pinpointing the relevant files.
• Aider Implements /edit Alias: The command /edit has been introduced as an alias for the /editor command in Aider.
  • This change provides a more convenient and shorter alternative for accessing the editor functionality.
• Aider Tames Claude 3.7 Sonnet with ‘Overeager’ Mode: Aider now features an “overeager” mode for Claude 3.7 Sonnet models, designed to ensure it stays within the requested scope.
  • This mode aims to maintain functionality and prevent the model from straying beyond its intended parameters.

aider (Paul Gauthier) ▷ #general (532 messages🔥🔥🔥):

DeepSeek V3 performance, Gemini 2.5 Pro release, GPT-4o image generation, aider /context command

• DeepSeek V3 Smashes Aider’s Polyglot Benchmark: DeepSeek’s new V3 scored 55% on aider’s polyglot benchmark, a significant improvement over the prior version.
  • It’s the #2 non-thinking/reasoning model, behind only Sonnet 3.7, and competitive with thinking models like R1 & o3-mini, according to Aider Leaderboards.
• Google Releases Gemini 2.5 Pro Experimental: Google released an experimental version of Gemini 2.5 Pro, claiming it leads common benchmarks, including the top spot on LMArena, with reported 74% whole and 68.6% diff scores on aider’s polyglot benchmark.
  • Users reported the model to be performant in generating architecture diagrams from codebases, although some found its coding abilities inconsistent and rate limits restrictive.
• GPT-4o Image Generation arrives as DALL-E 3: GPT-4o’s image generation is rolling out to Plus, Pro, Team, and Free users as the default image generator in ChatGPT.
  • Some users find the generated images high quality while others are still seeing DALL-E 3 style outputs, and access to DALL-E can still be accessed through a dedicated DALL-E GPT.
• Aider’s Context Command adds Extra Punch: The new /context command explores the code base, and the command can be used with any other prompt command.
  • It is unclear if the command has higher token usage, or if there is an increased repomap size to work correctly; more detail can be found in Discord message.

aider (Paul Gauthier) ▷ #questions-and-tips (39 messages🔥):

Deepseek API Usage with Aider, LLM Hallucinations, Aider's Architecture Mode, NotebookLM for Context Priming, OpenRouter Configuration Issues

• Deepseek’s API Hallucinates as GPT-4: Users reported that when using DeepSeek’s API through Aider, the model incorrectly identifies itself as OpenAI’s GPT-4, despite correct API key configuration and confirmed usage on the DeepSeek platform.
  • The phenomenon seems related to training data containing frequent mentions of ChatGPT, as discussed in this Reddit thread.
• NotebookLM Boosts Aider’s Context: A user suggested leveraging NotebookLM to enhance Aider’s context priming process, particularly for large, unfamiliar codebases using RepoMix.
  • The suggested workflow involves repomixing the repo, adding it to NotebookLM, including relevant task references, and then querying NotebookLM for relevant files, implementation suggestions, and a comprehensive plan to guide prompting in Aider.
• OpenRouter Aliases Trigger Litellm Errors: A user encountered a litellm.APIConnectionError when attempting to use Claude 3.7 Sonnet via OpenRouter, with other models working fine.
  • Another user replicated the configuration successfully, suggesting the issue may be specific to the user’s OpenRouter setup rather than Aider itself; see this Discord thread.
• DeepSeek V3 Impresses Aider’s Homepage: Paul Gauthier tested the new DeepSeek V3 model on the Aider homepage, noting its suggestion to upgrade the emojis to SVG icons.
  • See the tweet for more details.
• Aider Configuration with thinking-tokens: A user asked how to use --thinking-tokens when launching aider.
  • Another user linked the sample configuration file, explaining that it should be a top-level key in .aider.conf.yml.

Unsloth AI (Daniel Han) ▷ #general (284 messages🔥🔥):

HF Transformers, Deepseek model patching, FP8 Loading Scheme, Quantization impact on model performance, GGUF Uploads

• Transformer Best Bets, Says User: Members recommend Hugging Face Transformers, books on linear algebra, and learning PyTorch as the best approach.
  • One member suggests setting up a dynamic quantization at runtime schema with HF Transformers to stream weights to FP4/FP8 with Bits and Bytes as they load.
• Sneaky Model Patches, Says User: According to one user, companies like Deepseek sometimes patch models before releasing the weights.
  • They add that at the very least a naive FP8 loading scheme could still be done on day zero, though it wouldn’t be equivalent in quality to a fine-grained FP8 assignment.
• Quantization Hurts? Say It Ain’t So!: A member warns that naively quantizing can significantly hurt model performance.
  • They asked, Tbh, is there even a reason to run new models on day zero? I feel like it’s really not a great burden to just wait a week.
• GGUF Uploads Incoming!: According to one user, Unsloth is uploading DeepSeek-V3-0324 GGUFs with Dynamic Quants that are selectively quantized.
  • These Dynamic Quants will greatly improve accuracy over standard bits, so sit tight!
• Llama 3 + Vision Collab Issues: Members reports an issue trying to train gemma3 4b for vision, which raises the RuntimeError: expected scalar type BFloat16 but found float.
  • One member recommends trying this notebook, while another confirms that Gemma3 finetuning is supported for non-bf16 devices when using the notebooks on Colab.

Unsloth AI (Daniel Han) ▷ #off-topic (11 messages🔥):

Funny Graphs in CS Research, Benchmark Dissatisfaction, AI Project Recruitment

• Funny Graphs Illustrate Obvious Point: Members shared an image of a graph in CS research, finding it funny because the point could’ve gotten across in a single sentence (image link).
  • Another member agreed, stating that it’s satisfying to see such graphs regardless (another image link).
• Members Finds Benchmarks Suck: A member expressed dissatisfaction with benchmarks, stating that Gemini failed abysmally at extracting questions from 500k tokens, despite claims of 100% recall at finding the needle.
  • According to the member, 2M tokens performed even worse, and 64k tokens was required for it to function, with Grok performing the best in their experience.
• AI Project Seeks Reliable People: A member posted a recruitment message seeking a reliable person for an AI Project, with tech skills not a must.
  • The position is open to citizens of USA, Australia, Canada, UK, Switzerland, Netherlands, German, offering $500 weekly compensation.

Unsloth AI (Daniel Han) ▷ #help (66 messages🔥🔥):

Deepseek facts learning, Unsloth Gemma 3 27b error, phi4 fine tuning with unsloth, Medical bot fine tuning, LightRAG issues

• Deepseek Learns New Tricks?: A user inquired about tools to teach Deepseek additional facts via conversation or inserting data points into the training model for use as a personal assistant.
  • The user was thinking in terms of using it as a personal assistant with figures and facts and dates etc.
• Gemma 3 Glitch Gives Grief: A user encountered a TypeError when loading unsloth/gemma-3-27b-it-unsloth-bnb-4bit for text-only finetuning due to redundant finetune_vision_layers parameter.
  • Another user pointed out that FastLanguageModel already sets finetune_vision_layers = False under the hood as seen in Unsloth’s GitHub.
• Fine-Tuning Mishap: Model Merging Mayhem: A user who completed a phi4 fine-tuning with Unsloth inquired whether to merge LORA adapters with only the “lamafied” version or if a generic base phi4 model can be used.
  • A member said I guess you can do both, but better with same model as use during training.
• Fine-Tuning for the Future Medics: A user seeks guidance on building an end-to-end medical bot through fine-tuning Llama using Unsloth, asking for advice on where to start.
  • No actionable advice was given.
• Visionary Void: Mistral 3.1’s Image Issues: A user reported issues with Mistral 3.1 not processing images correctly in a local Ollama instance, with the model outputting nonsensical responses to image inputs.
  • The problem may be related to the mmproj file not getting loaded according to users, the model lose[s] the vision abilities.

Unsloth AI (Daniel Han) ▷ #showcase (1 messages):

GRPO on AWS, Tensorfuse, LoRA modules

• GRPO + Unsloth on AWS Boosts LLM Workflows: A guide was shared on running GRPO (DeepSeek’s RL algo) + Unsloth on AWS accounts, using a vLLM server with Tensorfuse on an AWS L40 GPU.
  • The guide shows how to transform Qwen 7B into a reasoning model, fine-tuning it using Tensorfuse and GRPO, and saving the resulting LoRA adapter to Hugging Face; the guide is available at tensorfuse.io.
• Tensorfuse Simplifies LoRA Sharing: The guide shows how to save fine-tuned LoRA modules directly to Hugging Face for easy sharing, versioning, and integration, backed to s3.
  • You can try out the whole flow on Tensorflow’s website.

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

GRPO limitations, FFN Fusion for LLMs, DAPO experiments, AI Project Job Spam, Transformer Talk

• GRPO Gotchas Galore: Members are tweaking GRPO (Gradient Ratio Policy Optimization) to correctly train 2+ turns, as it currently only supports prompt/completion and found this helpful video.
  • The member is also curious if new neural models or hybrid evolutionary neural approaches might help solve problems like arc.
• FFN Fusion Frenzy Fuels Faster LLMs: FFN Fusion is introduced as an architectural optimization technique that reduces sequential computation in large language models by identifying and exploiting natural opportunities for parallelization.
  • This technique transforms sequences of Feed-Forward Network (FFN) layers into parallel operations, significantly reducing inference latency while preserving model behavior.
• Transformer Talk Transcends Traditional Thinking: A member shared a YouTube talk on transformers that they found insightful, opening up a bunch of areas for investigation.
  • Another member wondered if there is a metric to find very early in training to allow for model fusion before fully training the model.
• DAPO Discussions Derail, Demand Data: Multiple members are curious if anyone has experimented with DAPO (likely Direct Preference Optimization), or tried to recreate results with verl.
  • One member posted an image, possibly related to DAPO results, seeking confirmation of replication, but details remain unclear.

Link mentioned: Paper page - FFN Fusion: Rethinking Sequential Computation in Large Language Models: no description found

Interconnects (Nathan Lambert) ▷ #news (232 messages🔥🔥):

Qwen VL series training details, Qwerky-72B transformerless model, Gemini 2.5 Pro performance, 4o image generation, AI Studio vs Gemini Advanced

• Qwen VL Series Needs DeepSeek-Style Training Deets: A member expressed a desire for a DeepSeek-R1 style paper with comprehensive training details for the Qwen VL series.
  • The poster hoped for tons of training details including parameters, training compute and dataset mixtures.
• Qwerky-72B Ditches Attention, Nears 4o-Mini: Featherless AI introduced Qwerky-72B and 32B, transformerless models trained on 8 GPUs that surpass GPT 3.5 Turbo and approach 4o-mini in evals with 100x lower inference cost using RWKV linear scaling.
  • The training process involved freezing all weights, deleting the attention layer, replacing it with RWKV, and training it through multiple stages.
• Gemini 2.5 Pro Snags #1 Spot, Google Mogs OAI: Gemini 2.5 Pro Experimental, codenamed Nebula, achieved the #1 spot on the LMArena leaderboard, outperforming Grok-3/GPT-4.5 by a record-breaking margin and leading in Math, Creative Writing, and Multi-Turn.
  • The model also dominates the SEAL leaderboards, with first-place rankings in Humanity’s Last Exam and VISTA (multimodal).
• 4o Image Gen Gives Unsolicited Makeovers: GPT-4o’s native image generation is criticized for applying excessive edits, such as making eyes bigger and altering facial features, even changing the user’s appearance, as demonstrated in this twitter thread.
  • Some users reported that altering a single word in their prompts results in failures.
• AI Studio is for Devs, Gemini Advanced an Ugly ChatGPT Clone: Members discussed the utility of Google’s AI platforms, with some asserting that AI Studio is better for developers due to its broader model selection, code execution capabilities, and YouTube support.
  • Members felt that Gemini Advanced is a bad ChatGPT clone and has no reason to use for power users, and suggested a more streamlined platform focused on developer tools.

Interconnects (Nathan Lambert) ▷ #ml-questions (26 messages🔥):

Labs hillclimb benchmarks, Home inference LLMs, vLLM, OpenRouter, Model Evals

• Labs Actively Hillclimb on Benchmarks: Labs actively hillclimb on benchmarks like a validation set, according to discussion of Tulu 3, where adding unseen evals is considered novel.
  • Labs know which websites to include to boost common benchmarks during training.
• Home Inference Favors vLLM with Caveats: The best solution for efficient home inference of LLMs with the ability to switch out models on the fly is likely vLLM, despite quirks with quant support, though ollama is easier to use but lags in support and SGLang looks compelling.
  • It was suggested to play around with llama.cpp to see how it’s going these days.
• AIBrix from vLLM Team Could Autoscale Models: A member wondered if something like AIBrix from the vLLM team would work, using vLLM + Kubernetes to autoscale models to 0 for seamless swapping; here’s an article on that.
  • The smallest viable cluster without quant is two 8xH100 nodes due to vram requirements, though a single H200 node (8x141) would suffice.
• Model Evals are Surprisingly Time-Consuming: Internal and external model evals are surprisingly time-consuming, requiring a custom UI for making the Eval (Sonnet + streamlit) and a UI to view the data.
  • It’s useful to do 1-2 trials runs with a cheap model to catch failure modes and re-word/discard prompts.
• OpenAI API Scaling Requires Caution: When using OpenAI’s API at scale, their dashboard isn’t accurate and their spending controls don’t work, which may lead to negative balances even with inference.
  • When using OpenRouter, you can specify that you want bf16/fp16 for models, as well as setting max_tokens and temperature.

Interconnects (Nathan Lambert) ▷ #ml-drama (1 messages):

Political Favor, American Politics, Business Strategy

• Petty Politics Pays for Some Businesses: It’s suggested that for businesses reliant on currying political favor in America currently, pettiness is probably an asset.
• Navigating American Political Climate: The discussion revolves around how businesses strategically engage with the American political landscape to gain advantages.

Interconnects (Nathan Lambert) ▷ #random (28 messages🔥):

AI Threads lists on social media, Verification as key to AI, MCP malware reverse engineering

• AI Luminary Gains Threads Followers: A member gained thousands of followers after being added to an AI threads list, but finds Bluesky slightly better due to a mix of academic friends.
  • They joked that this earned them a Noam Brown follow.
• Thinking vs Verifying: Verification, The Key to AI: A member linked to a thread discussing verification as key to AI, quoting Noam Brown’s argument that test-time compute is limited by verification challenges.
  • Brown used the example of being bottlenecked when trying to recall When was George Washington born? if you don’t know, no amount of thinking will get you to the correct answer.
• MCP Reverses Malware with AI: Members linked to a YouTube video showcasing MCP for Ghidra, enabling LLMs to reverse engineer malware, automating the process with the right prompts.
  • The poster mentioned they thought it was a bit of a meme at first but are starting to see the appeal now that there are actual implementations.

Interconnects (Nathan Lambert) ▷ #memes (52 messages🔥):

Gooning Urban Dictionary meaning, DeepSeek-LLM and DeepSeek-MoE, Mistral Small Versioning Confusion, GPT4o Image Generation vs Gemini, GRPO implementation in trl

• Gooning Gets a New, Risque Definition: Members discussed the evolving meaning of the term gooning, with one noting its new urban dictionary meaning which differs from its original, more innocent connotation.
  • An older meaning was described as just like goofing about with your friends, out in the town during college.
• DeepSeek Model Naming Conventions Clarified: It was pointed out that the correct naming conventions are DeepSeek-LLM and DeepSeek-MoE.
  • This correction aims to improve clarity in model identification.
• Mistral Versioning Creates Confusion: The naming scheme and versioning of Mistral Small models came under scrutiny, specifically noting the absence of a Mistral Small 2.
  • One member confessed that i legitimately have no idea how fast the digits boxes actually are for this reason.
• GPT4o Image Generation Compared to Gemini: Members compared the image generation capabilities of OpenAI’s GPT4o and Google’s Gemini based on visual outputs.
  • The comparison stemmed from OpenAI’s announcement of 4o image generation in ChatGPT and Sora, inciting memes.
• trl’s GRPO Implementation Under Fire: A member asserted that the trl GRPO implementation should not deviate from the DeepSeek paper’s implementation by default, criticizing deviations from a known good configuration.
  • They concluded that the stdev normalization upweights your easiest and hardest examples and downweights middle of distribution, potentially biasing results.

Interconnects (Nathan Lambert) ▷ #rl (14 messages🔥):

DPO vs Sampling, RL training and Entropy, SimpleRL-Zoo, DAPO Objective

• Sampling Superior to DPO: A member suggested that sampling could be better than DPO, because DPO requires data.
• Zero RL Training Investigated: A new project called SimpleRL-Zoo was introduced, which deeply investigates zero RL training across diverse model families and sizes, including Llama3-8B, Mistral-7B/24B, DeepSeekMath-7B, and Qwen2.5.
  • It was shown that starting from base models and only using the correctness reward, the project managed to obtain significant boost on both the accuracy and response length for all the models, detailed in their paper and code.
• DAPO Objective in Paper: A member mentioned that a paper used the DAPO objective.
  • They noted that was like last week.

Link mentioned: Tweet from Junxian He (@junxian_he): Two months ago, we open-sourced the first R1-like zero RL training project on math with the Qwen2.5-math model. Since then, many great works performed successful zero RL training, mostly based on Qwen…

Interconnects (Nathan Lambert) ▷ #reads (2 messages):

Claude Code, Anthropic, Anysphere's Cursor, Codium's Windsurf, npm package

• Anthropic Releases Claude Code: Anthropic released Claude Code, their competitor to Anysphere’s Cursor and Codium’s Windsurf as a tool that uses LLM as an agent to complete software engineering tasks, as mentioned in this blog post.
• Digging Under the Hood of Claude Code: One can download Claude Code’s npm package as a tarball and unzip it to get the source code.
  • The main control logic of Claude Code lives in cli.m.

Interconnects (Nathan Lambert) ▷ #lectures-and-projects (2 messages):

Claude PR, Header Copy Links

• Claude Crafts Copyable Links: A member shared a pull request which adds copyable links to all headings that appear on hover in the rlhf-book.
• Header Hover Hyerplinks Help: These fun header copy links appear when hovering, facilitating easier section linking.
  • The member confirmed it worked immediately with claude code.

Link mentioned: (experimental) Add heading anchor links for easy section linking by natolambert · Pull Request #82 · natolambert/rlhf-book: Add copyable links to all headings that appear on hoverLinks copy the current URL with fragment identifier to clipboardAdd CSS for styling the anchor linksUpdate Makefile to copy new JS file to …

OpenRouter (Alex Atallah) ▷ #announcements (3 messages):

Anthropic incident, Claude 3.7 Sonnet endpoints, Zero-Token Insurance, Google Gemini 2.5 Pro Experimental

• Anthropic’s Claude goes offline, then back online: Claude 3.7 Sonnet endpoints experienced downtime, with Anthropic posting updates starting March 25, 2025, and resolving the incident by 8:41 PDT.
  • The updates indicated that the downtime was due to maintenance and efforts to improve systems.
• OpenRouter offers Zero-Token Insurance: OpenRouter is now offering zero-token insurance coverage to all models on the platform, potentially saving users over $18,000 per week.
  • As OpenRouterAI stated, users will not be charged for responses with no output tokens and a blank or error finish reason, even if the provider still charges for prompt processing.
• Gemini 2.5 Pro Experimental Goes Live: Google’s Gemini 2.5 Pro Experimental, a state-of-the-art model capable of advanced reasoning, coding, and mathematical tasks, is now available as a free model on OpenRouter.
  • Gemini 2.5 Pro has 1,000,000 context, and achieves top-tier performance on benchmarks like the LMArena leaderboard.

OpenRouter (Alex Atallah) ▷ #general (196 messages🔥🔥):

Deepseek performance issues, Gemini 2.5 Pro release and benchmarks, Provisioning API keys for user management, OpenRouter activity log retention, GPT-4o image generation API support

• DeepSeek Suffers Server Struggles: Users reported DeepSeek is becoming borderline unusable due to overcrowded servers, suggesting a need for price adjustments to manage demand.
  • Members speculated whether the issues were related to peak usage times in China, but no direct solution was found beyond hoping for better hardware availability from Huawei.
• Gemini 2.5 Pro Wows Early Testers: Gemini 2.5 Pro Experimental is now available at the API, with early testers impressed by its capabilities, especially in reasoning and coding, as detailed in Google’s blog post.
  • One user exclaimed that Gemini 2.5 Pro destroys ChatGPT o3-mini-high, prompting discussions about whether its performance boost is solely due to benchmarks or reflects true improvements.
• Provisioning API Keys Offer Granular User Control: OpenRouter now offers provisioning API keys, enabling developers to programmatically manage API keys for their users, set limits, and track spend, enhancing scalability and control, as documented here.
  • This allows developers to create unique API keys for each user, streamlining billing and access management within their own platforms.
• API Key Activity Logs Retained Infinitely: OpenRouter retains API key activity logs forever, allowing users to monitor usage per key, aiding in team environment evaluation and usage tracking.
  • This feature addresses the need for streaming and visualizing API usage, providing detailed insights into each member’s consumption patterns.
• GPT-4o Image Generation API on OpenRouter’s Radar: Following the rollout of GPT-4o’s native image generation, OpenRouter is actively developing API functionality for image generation calls to provide users access to equivalent functionalities without directly applying for individual APIs.
  • This move aims to keep OpenRouter competitive and comprehensive, addressing the need for seamless integration of cutting-edge image generation capabilities.

Nous Research AI ▷ #general (148 messages🔥🔥):

Bits per Weight (BPW), Model Capacity Scaling, DeepSeek V3, Gemini 2.5 Pro, AI IDE Evaluation

• BPW Sweet Spot Between 4-5!: Experiments show model capacity collapses below 4 bits per weight (BPW), but deviates above 5, implying optimal weight usage at 4 BPW for given training flops.
  • Increasing training epochs helps 5 BPW models approach the curve, raising BPW at the cost of FLOPS, visualized via 2L and 3L MLP trained on MNIST.
• DeepSeek V3 Shows Reasoning Prowess!: DeepSeek V3-0324 can act as a reasoning model, detect thought iterations, and verify solution existence indirectly, rivalling O1 in performance based on attached prompt.
• Consulting Services for Hermes Models: A member inquired about consulting for fine-tuning Hermes for specific ERP use cases, with others noting the diversity and specialization within the ERP domain.
  • The pygmalion folks and people connected to them probably can help according to this tenor link.
• Google’s Gemini 2.5 Pro Enters the Arena: Gemini 2.5 Pro Experimental leads common benchmarks and debuts at #1 on LMArena showcasing strong reasoning and code capabilities.
  • It can also manages to terminate for infinite thought loops for some prompts and is a daily model drop as noted in this blogpost.
• Community Awaits DeepSeek V3 Lite’s Arrival: Community members speculate on a potential DeepSeek V3 Lite release after the Qwen 3 MoE model.
  • A member offered anonymized human-AI data from their project if it aids NousResearch’s efforts.

Nous Research AI ▷ #ask-about-llms (26 messages🔥):

Add and Sigmoid vs Add and Norm, Scaling Experts at Inference Time, Transformers without Normalization, LLM-Emulated Raytracing, Indirect Image Generation with LLMs

• Add and Sigmoid: The New Norm?: A member suggested replacing add and norm with add and sigmoid in transformer architectures, particularly for Mixture of Experts (MoE), to facilitate easier scaling of experts.
  • Theorizing that if the gate has a sigmoid activation function, this should allow us to add or remove other experts without much problems because the scaling becomes independent of the number of neighboring values.
• Transformers get TANHed: Drawing from the recent Transformers without Normalization paper, one member noted that replacing normalization with tanh is a viable strategy.
  • The concern raised was the impact on smaller weights when removing experts at inference time, but another countered that the top_k gate mechanism would still function effectively by selecting from the remaining experts.
• Raytracing Emulated by LLMs, no NVIDIA code involved: Members discussed the idea of using an LLM to emulate a raytracing algorithm, clarifying that the current implementation involves a Python program written by the LLM to generate images indirectly.
  • It’s next level text to image generation because the LLM writes the program rather than generating the image directly, the programs are available in this GitHub repo.
• Normalization Debate: Sigmoid Scales Without Neighbors: The discussion continued on the need to remove normalization when top_k is constant, with one member arguing that normalization changes values relative to neighboring values.
  • They explained that using sigmoid for scaling would avoid this dependency, enabling the addition of more experts without significantly altering existing values.

Link mentioned: llmbenchmark/raytracer at master · cpldcpu/llmbenchmark: Various LLM Benchmarks. Contribute to cpldcpu/llmbenchmark development by creating an account on GitHub.

Latent Space ▷ #ai-general-chat (136 messages🔥🔥):

Reve Image, Qwen 2.5, ARC-AGI, 11x fraud, Zep knowledge graphs

• Reve Image dethrones SOTA Text-to-Image: A new image generation model, Reve Image, has been released and is already being hailed as a leader in the field, outperforming models like Recraft V3, Google’s Imagen 3, Midjourney v6.1, and Black Forest Lab’s FLUX.1.1 [pro].
  • It has been noted for its impressive text rendering, prompt adherence, and aesthetics, and is currently accessible through Reve’s website without an API.
• DeepMind’s Gemini 2.5 Pro debuts in Arena: Gemini 2.5 Pro is now #1 on the Arena leaderboard, achieving the largest score jump ever (+40 pts vs Grok-3/GPT-4.5), as announced by LM Arena.
  • The model, tested under the codename nebula, uniquely leads in categories like Math, Creative Writing, Instruction Following, Longer Query, and Multi-Turn.
• OpenAI launches Image Gen in ChatGPT 4o: OpenAI has launched native image generation in ChatGPT, with Sam Altman noting it as an incredible technology and product.
  • Early testers, like @krishnanrohit, say this is the best image gen and editing tool they’ve tried so far, praising its ability to make and edit multiple characters correctly, especially when it’s two or more dinosaurs.
• AI SDR Startup 11x Under Scrutiny for Claiming Phony Customers: AI-powered sales automation startup 11x, backed by a16z and Benchmark, is under fire for allegedly claiming customers it doesn’t have, according to a TechCrunch report.
  • Despite a spokesperson for Andreessen Horowitz denying rumors of legal action, concerns persist about 11x’s financial status and potentially inflated revenue figures, with some suggesting the company’s growth relies on hype and that real enterprise sales leaders are not seeing value in AI SDRs.
• Databricks Unveils TAO to Tune LLMs Without Labels: The Databricks research team announced TAO, a method to tune LLMs for a task without data labels, using test-time compute and RL, as described in their blog.
  • They claim it outperforms supervised fine-tuning and scales with compute to produce fast, high-quality models.

Latent Space ▷ #ai-announcements (1 messages):

swyxio: new talk/post from me: https://x.com/swyx/status/1904256213661192405

GPU MODE ▷ #general (7 messages):

Audio processing with ilgpu + cufft + kernels, Asynchronous data transfer to GPU with OnnxRuntime on CUDA, Double buffering with CUDA streams, FSDP fine tuning with trl library

• ILGPU, CUFFT, and Kernel Audio Processing?: A member inquired about experiences with audio processing using ilgpus, cufft, and kernels, seeking practical applications.
• Double-Edged Diagrams Requested for Sanity Checks: A member requested that diagrams show relationships both ways (row/col to threads/registers/bytes and vice versa) to improve intuitiveness and sanity checks.
  • They argued that providing both visuals and code/pseudocode prevents misunderstandings and wasted implementation time.
• Asynchronous CUDA Transfers Paced with OnnxRuntime?: A member sought an online reference for asynchronous data transfer to the GPU and running inference using OnnxRuntime on a CUDA GPU, aiming to eliminate data transfer bottlenecks in their inference pipeline.
  • They wanted to overlap data transfer for the next image with inference on the current image but are facing issues implementing this in Python.
• Double Buffering Dreams for Maximized GPU: A member suggested using multiple CUDA streams for asynchronous copies and forward passes, known as double buffering, to maximize GPU utilization and avoid CPU blocking.
  • This approach involves one stream issuing an async copy while another thread executes the forward pass, operating concurrently.
• FSDP fine tuning’s dataset handled?: A member inquired about the proper way to handle datasets when fine-tuning with FSDP (Fully Sharded Data Parallel) using the trl library.

GPU MODE ▷ #triton (9 messages🔥):

Triton Interpret Bug, Intel Triton Extension, Triton Compile Script, Prune Configs Support in Triton

• Triton Interpret Flag Sparks Bug Hunt: Differences between TRITON_INTERPRET=0 and TRITON_INTERPRET=1 are being investigated for potential bugs, with small discrepancies expected but larger ones indicating issues.
  • A user linked to a paper discussing Triton as a DSL offering a more user-friendly and portable alternative to low-level interfaces like CUDA or SYCL.
• Intel Dives Deeper into Triton: An extension to Triton from Intel was shared, highlighting the growing interest and investment in Triton for GPU programming.
  • The extension aims to improve compiler decoupling and cleanliness through multi-level gradual lowering, leveraging GPU’s hierarchical structure and SIMD units.
• Triton Compile Script Troubles: A user faced issues with the triton compile.py script, initially failing due to an incorrect —signature flag.
  • The user shared a code snippet and command-line arguments used for compilation, indicating efforts to integrate Triton into their workflow.
• Prune Configs Support in Triton: A user mentioned having added support for prune configs a few months back, acknowledging some quirks but expressing confidence in its functionality.
  • Another user acknowledged this contribution and expressed intent to try it out with the nightly build, signaling potential adoption and testing of the feature.

Link mentioned: ML-Triton, A Multi-Level Compilation and Language Extension to Triton GPU Programming: In the era of LLMs, dense operations such as GEMM and MHA are critical components. These operations are well-suited for parallel execution using a tilebased approach. While traditional GPU programming…

GPU MODE ▷ #cuda (24 messages🔥):

CUDA swizzling, cuTensorMap issues, Flash Attention memory layout, Cutensor coordinate mapping

• CUDA Async Warpgroup Swizzling Examined: A member analyzed CUDA’s async warpgroup swizzle TF32 layout, questioning the non-consecutive numbers in the first row and the starting number determination for each subpartition, referencing the NVIDIA documentation.
  • They later identified the layout as Swizzle<0,4,3> o ((8,2),(4,4)):((4,32),(1,64)), enabling construction of the original data position and combining it with Swizzle<1,4,3>.
• Flash Attention Layout Questioned: A member questioned why Flash Attention does not use a (batch_size, num_heads, N, d) memory layout, suggesting it might be superior to the existing (batch_size, N, num_heads, d) layout.
  • The user found their prior attempts using this layout went horribly wrong, based on an example from NVIDIA’s CUDA-C Programming Guide.
• Discrepancies in cuTensorMap Parameters: A member highlighted a discrepancy between the box_size parameter definition in a CUDA example (bytes) and the cuTensorMapEncodeTiled documentation (elements), referencing a CUDA-C Programming Guide example.
  • The user also noted that box_size is in reverse order compared to the shared memory size of int4 [8][8].
• Mapping Coordinates in CuTe Fragments: A member asked about the easiest way in CuTe to map coordinates inside a fragment owned by a thread created from tiled_mma.get_thread_slice(tid) back to the coordinates of the whole resulting matrix.

Link mentioned: Some question about creating CUtensorMap and use it: I have some questions about the following code. constexpr int row = 8; constexpr int col = 64; size_t byteSize = row * col * sizeof(int); int* h_data = (int*)malloc(byteSize); int*…

GPU MODE ▷ #torch (2 messages):

PyTorch allocator, torch.nn.utils.prune

• PyTorch Caching Allocator Impedes Non-Caching Alternatives: A user noted the difficulty of using a non-caching allocator alongside PyTorch’s caching allocator, because MemPool uses the custom allocator with caching, which is not ideal.
• Concerns Raised on PyTorch’s Caching Allocator: A user wondered why companies might avoid alternative caching allocators in production, citing concerns about debugging tools.
  • They expressed uncertainty about the magic behind the PyTorch caching allocator that would prevent using alternatives.
• Pruning already pruned weights in torch.nn.utils.prune leads to wrong pruning ratios: A user reported that torch.nn.utils.prune doesn’t allow pruning to be applied only to non-pruned weights when pruning an already pruned layer.
  • They found that applying pruning twice with an amount of 0.2 does not result in a final amount of 0.4 due to re-pruning already pruned weights.

GPU MODE ▷ #jobs (1 messages):

AMD GPU support in Triton, Job postings for Triton developers

• AMD adds GPU support to Triton: There are job postings open for engineers to build awesome AMD GPU support in Triton; both senior and junior positions in NA or Europe with remote options available, see the LinkedIn post.
• NA/EU roles open at AMD: For North America, there is an opening at careers.amd.com, while for Europe it is careers.amd.com.
  • AMD warns against job scams and advises applicants to apply directly through the amd.com Careers page.

GPU MODE ▷ #jax (1 messages):

bigfoot1144: Any progress so far?

GPU MODE ▷ #rocm (2 messages):

gpumode leaderboard, MI250 node, AMD Instinct MI250 evaluation

• GPUMODE Leaderboard: MI250 as a Stopgap?: A member proposed leveraging the GPUMODE leaderboard, featuring an MI250 node, as a temporary solution.
  • They suggested applying for access to the AMD Instinct MI250 evaluation program.
• Requesting Access to AMD Instinct MI250: A user shared a link to the AMD Instinct MI250 evaluation request page.
  • This suggestion was in the context of finding available hardware for testing or benchmarking.

GPU MODE ▷ #tilelang (5 messages):

TileLang Compatibility with Torch AOTexport, TileLang compilation for AMD, Custom Triton Kernels

• TileLang eyes Torch AOTexport compatibility: A member inquired about TileLang’s compatibility with Torch AOTexport and inference in C++, given a codebase with custom Triton kernels.
  • Another member confirmed that TileLang compiles kernels into CUDA C source code, accessible via kernel.get_kernel_source(), paving the way for potential AOT integration.
• TileLang Targets HIP for AMD: A member asked about TileLang’s compilation target for AMD GPUs.
  • The response indicated that TileLang compiles to HIP source code for AMD architectures.

GPU MODE ▷ #sparsity-pruning (3 messages):

Iterative Pruning, Double Pruning Weights, Pruning Ratio Calculations

• Pruning Weights Twice Causes Problems: A user reported that when applying pruning to an already pruned layer using torch.nn.utils.prune, there’s no direct option to prune only non-pruned weights.
  • The user notes that applying a pruning ratio creates a global mask that affects the entire tensor, potentially re-pruning already pruned weights, leading to an incorrect final pruning ratio.
• Simple Iteration does not fix Pruning Weights Twice: A member asked if applying a ratio of 0.2 in the first iteration, then 0.4 in the second iteration on the already pruned weights, achieves the desired outcome.
  • The original poster responded that masks respect already defined masks, so you need calculate the new pruning ratio on the already pruned layer (or network) to arrive at the desired ratio.

GPU MODE ▷ #liger-kernel (2 messages):

lce_forward_deprecated vs lce_forward

• lce_forward_deprecated vs lce_forward: The user asked what the differences are between lce_forward_deprecated and lce_forward and the reason to deprecate the old one.
  • No response was given.
• Reasons for Deprecation: There was no response detailing reasons for deprecation, so the reasons remain unknown.
  • More information is needed.

GPU MODE ▷ #metal (3 messages):

Open Source ML platform building

• Plans to Build Open Source ML Platform Spark Excitement: A member expressed a desire to build out more features for their open source machine learning platform.
  • Another member responded with encouragement to share progress on the platform.
• Open Source ML Platform Gets Encouragement: A member wants to develop their open source ML platform further.
  • Another member cheered them on and encouraged them to share their progress.

GPU MODE ▷ #self-promotion (5 messages):

fp16 MatMul for Gemma3, Gemma3 Residuals, CUDA Execution Time Benchmarks, Inferless on Product Hunt

• Gemma3 leverages fp16 MatMul: It’s observed that fp16 matmul might be suitable for Gemma3, however, it’s crucial to cast the outputs back to bf16.
• Gemma3’s Residuals Are Tricky: Quantization of fp16 weights faces challenges with Gemma3, necessitating a fix like the one proposed in this Hugging Face transformers PR.
• CUDA Benchmark Insights Emerge: A blog post detailing CUDA execution time benchmarks emphasizes the use of CUDA Events over CPU timers and excluding memory transfers unless relevant.
  • The author concludes: ‘If you want to benchmark then you should use an actual production grade profiler’. and notes that the majority of resources online simply tell you to do x or y, but I want to see the numbers.
• Inferless Goes Live on Product Hunt: Inferless, a serverless compute platform for deploying ML models, has launched on Product Hunt, offering $30 compute for new signups.
  • They said that their goal is to Deploy any machine learning models in minutes and offer ultra-low cold starts.

Link mentioned: gemma3 fp16 fix by mobicham · Pull Request #36832 · huggingface/transformers: What does this PR do?Fixes float16 inference with Gemma 3 models by simply clipping the activations. The residual addition step should also be clipped for more accurate outputs. Without this fix, …

GPU MODE ▷ #reasoning-gym (10 messages🔥):

ARC-AGI-2 Benchmark, Reasoning-Gym Puzzles, verL and vLLM0.8.1, Codegen Updates, RL Research Directions

• ARC-AGI-2 Benchmark Announced: The ARC-AGI-2, an unsaturated frontier AGI benchmark that challenges AI reasoning systems, was announced, aiming to measure AI reasoning systems with a grand prize for achieving 85% efficiency at about $0.42/task.
  • Current performance benchmarks show base LLMs at 0% and reasoning systems achieving less than 4% success.
• verL Supports vLLM0.8.1: verL now supports vLLM0.8.1, with ongoing efforts to set up the image on a cluster for local inference, and updates are expected soon on the Codegen side.
  • A member is excited to share a pretty hefty update regarding CodeGen soon.
• Cool Open Questions and Research Directions in RL: A member shared a link to a Google Sheets document outlining cool open questions and research directions in RL.
  • The document covers topics like demand hypothesis, status quo, 10x improvements, and proposals for solving big, open problems in the world.
• RL Reward Shaping Framework Urged: A framework for RL reward shaping was proposed to simplify quick experiments for determining the right approach, suggesting that instead of messy tweaks, a framework to manage weights and loops in one go would be beneficial.
  • The member stated that in the near future, everyone will be doing RL but there’s no good framework for it, suggesting a Bayesian optimization framework as a potential solution.

GPU MODE ▷ #gpu模式 (1 messages):

Flash Attention Layout, Tensor Layout, Performance Optimization

• Flash Attention Layout Optimization Explored: A member inquired why Flash Attention’s layout isn’t ordered as (batch_size, num_heads, N, d) instead of (batch_size, N, num_heads, d) for potentially faster performance.
  • The question suggests an optimization angle, questioning whether reordering the tensor layout could improve computational speed during attention mechanisms.
• Tensor Layout Impact on Performance: The discussion centers on how different tensor layouts can affect the efficiency of operations, particularly in the context of Flash Attention.
  • Different memory access patterns can have a significant impact on performance, making this a relevant optimization consideration.

GPU MODE ▷ #general (6 messages):

Conv2d compilation errors, CUDA compilation issues, PyTorch C++ extension problems, load_inline issues

• Users encounter CUDA compilation errors during conv2d submission: A user encountered a RuntimeError during submission to conv2d, stemming from a subprocess.CalledProcessError: Command '['ninja', '-v']' returned non-zero exit status 1 error during the extension module build, with a long error traceback in the logs.
  • The root cause appears to be that the CUDA source code couldn’t compile properly, as indicated by the error message: Error building extension ‘conv2d_module’.
• Confirmation of module issues by other users: Another user suggested that the issue might be related to a wrong or missing module, specifically mentioning uncertainty about load_inline function in /root/submission.py, line 191.
  • The original poster acknowledges the feedback and plans to investigate the module and load_inline function further, with no further information.

GPU MODE ▷ #submissions (3 messages):

H100 benchmarks, T4 vectorsum, A100 grayscale

• H100 gets Grayscale Benchmark: Benchmark submission with id 2988 to leaderboard grayscale on GPUS: H100 using Modal runners succeeded!
• T4 excels in Vectorsum task: Leaderboard submission with id 3005 to leaderboard vectorsum on GPUS: T4 using Modal runners succeeded!
• A100 Aces Grayscale test: Test submission with id 3006 to leaderboard grayscale on GPUS: A100 using Modal runners succeeded!

HuggingFace ▷ #general (40 messages🔥):

DeepSeek as moderation bot, Numerical RAG with Databricks, Fine-tuning open-source LLMs, VAD tool language agnostic, Hugging Face AgentX competition

• DeepSeek Debated as Discordian Decider: A member inquired whether DeepSeek is suitable as a moderation bot.
  • Another member responded affirmatively but suggested that a smaller 3B LLM might suffice at a cost of 5 cents per million tokens.
• Numerical Nirvana: RAG Edition: A member is building a RAG on numerical structured data from Databricks and wants the LLM to understand the query, create a query, run it, and then reply back in natural language.
  • They requested suggestions for tutorials on this approach, potentially saving the world from 2025.02.24-150819.
• Hugging Face Launches AgentX Competition: Hugging Face is hosting the AgentX – LLM Agents MOOC Competition in conjunction with the Advanced LLM Agents MOOC, calling for trailblazers ready to push the boundaries of AI and Agents.
  • The AgentX Competition is open to the public and culminates in an in-person Demo Day at the Agents Summit this August at UC Berkeley.
• Beginner Asks How to Fine-Tune CUDA on Windows: A member sought an absolute beginner’s guide for fine-tuning a model that works in Windows, preferably with CUDA support.
  • Another member said this was impossible since installing PyTorch and the CUDA Toolkit on Windows is hell, and linked to guides Step-by-Step-Setup-CUDA-cuDNN and Installing-pytorch-with-cuda-support-on-Windows.
• Hosted Dataset Faces HTTPRequest Hurdles: A member is experiencing HTTPRequest errors while fine-tuning ViT with a dataset (15GB, 102k datapoints) hosted in 24 shards (500MB each).
  • They asked for help understanding the cause of the error in this discord channel.

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

ynvers256: Today I’m learning Renforcement learning and make research about Eureka

HuggingFace ▷ #cool-finds (1 messages):

Aider + Zed, Codium's Windsurf, TabNine, Cursor

• Users Inquire About Aider, Windsurf, Tabnine and Cursor: A member inquired about experiences with Aider in conjunction with the Zed editor, Codium’s Windsurf, and TabNine, seeking user feedback.
  • The user explicitly requested comparisons of these tools to Cursor.
• Comparison Request: Code Editors and AI Assistants: The primary purpose of the message was to gather comparative insights on various code editors and AI assistant tools.
  • The user aims to understand the strengths and weaknesses of each tool relative to Cursor, potentially for making an informed decision on which to use.

HuggingFace ▷ #i-made-this (14 messages🔥):

Audio Extraction Tool in Rust, Achievement Token System, Music Generation System

• Rust Tool Extracts Audio Blazingly Fast: A new tool has been released to extract audio files from parquet or arrow files generated by the Hugging Face datasets library, with a Colab demo.
  • The developer aims to provide blazingly fast speeds for audio dataset extraction.
• Token System Powers Kid-Friendly App: The developer is implementing a token system for achievements within their kid-friendly app, which will allow kids to earn tokens and unlock games and sanitized image generation.
  • The system already has a font size slider and safety measures to ensure kid-friendly content, with plans to create printable achievement certificates.
• Synthesized Clippy with Lipsync avatars: The developer is working on incorporating a music generation system, alongside options for users to select their own lipsync avatars as a “Clippy”-like assistant.
  • They are revamping wav2lip and experimenting with latensync to create a reasonably fast and high-quality talking UI.

HuggingFace ▷ #computer-vision (2 messages):

AutoCAD drawing generation, Metric scale at object location

• AutoCAD Drawing Generation by Prompt: A member inquired about generating AutoCAD drawings based on input prompts, seeking methods for automated design creation.
  • This suggests interest in using AI or other programmatic tools to translate textual descriptions into CAD models, potentially streamlining design workflows.
• Measuring Metric Scale Sans Reference: A member posed a challenge of obtaining a metric scale at an object’s location without using a reference object or distance measurements.
  • This seeks innovative solutions for estimating size or scale in a scene without traditional spatial cues, perhaps involving image analysis or other contextual clues.

HuggingFace ▷ #NLP (1 messages):

Unstructured Data to JSON Conversion, LLM Fine-tuning Datasets

• Users Seek Unstructured Data to JSON Converter Websites: A member is looking for websites that convert unstructured data into structured data like JSON for LLM fine-tuning datasets.
• LLM Fine-tuning Dataset Conversion: The user specifically requires a tool to transform unstructured information into a structured format suitable for training Large Language Models.

HuggingFace ▷ #gradio-announcements (1 messages):

Gradio Deep Links, Gradio 5.23

• Gradio Adds Deep Linking Delight!: Gradio 5.23 introduces support for Deep Links, enabling direct linking to specific generated outputs like images or videos.
  • An example link to a blue jay image generated by Flux is provided: https://abidlabs-black-forest-labs-flux-1-schnell.hf.space/?deep_link=oUq4ebmL1Ek.
• Upgrade to Gradio 5.23 Now!: Users are instructed to upgrade to the latest version, Gradio 5.23, via pip install --upgrade gradio to access the new Deep Links feature.
  • An attached image showcases the new feature, and can be found here.

Link mentioned: black-forest-labs/FLUX.1-schnell: no description found

HuggingFace ▷ #agents-course (4 messages):

Llama-3.2 and LlamaIndex.ai, Ollama setup, BAAI/bge-base-en-v1.5, Custom Tool help

• Llama-3.2 integrates with LlamaIndex.ai: A member experimented with Llama-3.2 using this tutorial, noting it demonstrates how to build agents with LlamaIndex, starting with a basic example and adding Retrieval-Augmented Generation (RAG) capabilities.
• Ollama aids local LLM setup: A member used Ollama as a tool to set up LLMs locally, following the README for installation.
  • To download the Llama3 model the command ollama pull llama3.1 was used, and members noted that a machine with at least ~32GB of RAM is needed.
• BAAI embeddings model used: The member used BAAI/bge-base-en-v1.5 as their embedding model.
  • The member had to pip install llama-index-llms-ollama llama-index-embeddings-huggingface to integrate with Ollama and Huggingface.
• Custom Tool help requested: A member requested help with editing a my_custom_tool function to build and test their own tool, asking how to edit it within Hugging Face.
  • Another member recommended going to files then clicking on the app.py file and then clicking edit within Hugging Face.

Link mentioned: Starter Tutorial (Using Local LLMs) - LlamaIndex: no description found

MCP (Glama) ▷ #general (39 messages🔥):

New MCP Mod, Nexus context management system for AI coding assistants, Atom of Thoughts for Claude, Deepseek V3 with AOT, Running Multiple MCP Servers

• New Mod joins MCP Community!: The MCP community welcomes a new moderator, who has actively participated since the beginning and contributed to various projects.
  • He is planning to organize MCP events and help grow the community further.
• Nexus Saves the Day for Context-Constrained AI Coders!: A member shared Nexus, a system to address context management challenges with AI coding assistants, particularly in large software projects, aimed at reducing token costs and improving code accuracy.
  • Nexus addresses the limited context windows of LLMs, which leads to inaccurate code generation and high token costs.
• Atom of Thoughts Mesmerizes Claude Users: A member recommended Atom of Thoughts for Claude, describing it as incredible, following a discussion about using Anthropic’s ‘think tool’.
  • Another member shared images of Deepseek V3 working with AOT.
• Serving Multiple Servers Simultaneously: Members discussed how to run multiple MCP servers with user-defined ports, suggesting the use of Docker and mapping ports.
  • They also pointed to the ability to configure ports via the FastMCP constructor in the python-sdk.
• GPT-4o-mini’s Tool-Calling Hallucinations: A member reported that gpt-4o-mini hallucinated tool call requests for a non-existent process_text function, even without any tool definitions provided.
  • The request later changed to text_processing, which was also not found in the workspace.

MCP (Glama) ▷ #showcase (23 messages🔥):

Speech MCP, gotoHuman MCP Server, Apple MCP tools, VNC control via Claude

• MCP for Speech Interaction Surfaces: A member shared their main MCP for speech interaction with audio visualization: speech-mcp, a Goose MCP extension.
  • This allows voice interaction with audio visualization.
• gotoHuman MCP Server Seeks Approvals: The gotoHuman team presented an MCP server to request human approvals from agents and workflows: gotohuman-mcp-server.
  • The server allows for easy human review of LLM actions, defining the approval step in natural language and triggering a webhook after approval.
• Apple MCP Tools Released: A member introduced a collection of apple-native tools for the MCP protocol: apple-mcp.
  • A demo of this local MCP server using VNC control can be found in this step-by-step video.
• Control Desktops via VNC and Claude: A co-founder shared their side-project that provides VNC control of remote macOS desktops via the Claude desktop app: mcp-remote-macos-use.
  • A YouTube demo is available here, where the use case is to connect Blender MCP with MCP Omni to connect CLI with OpenAI models.

Notebook LM ▷ #use-cases (7 messages):

NotebookLM, Versatile Bot Project, Interactive mode, Chat Episode Prompt, Delivery pacing

• Podcast Host with NotebookLM: A member asked for a hack to use NotebookLM as a podcast host with the user as a guest, to engage in conversation about a given topic.
  • Another member asked where to find the Chat Episode Prompt to enable this.
• Dive into Versatile Bot Project: A member launched the Versatile Bot Project, which includes a Chat Episode prompt document designed for AI hosts to discuss arbitrary topics in Interactive mode.
  • This mode allows users to join the episode while the AI hosts are talking, enabling conversations on specified topics.
• Control AI Host Delivery Pacing: A member inquired about changing the pacing of the AI host’s delivery, such as faster or slower reading speeds.
  • Another member provided a template with parameters like Energetic pace, clear articulation, and target words/minute to control the AI’s delivery speed, with source document type influencing the AI’s guidance.

Notebook LM ▷ #general (47 messages🔥):

Google Cloud Platform Billing, Workspace Data Export Tool, NotebookLM Plus subscription benefits, Mind Map Feature rollout, Multilingual Podcast

• GCP Billing Confusion Clarified: A user enabled Google Cloud Platform to access the Data Export tool, but was unsure about billing; another user clarified that enabling billing doesn’t guarantee charges.
  • The user confirmed they initiated the Data Export from the admin console, accessing the archive via console.cloud.google.com.
• Data Export Caveats Exposed: Users discovered that the option to choose the data destination during export is limited by their Workspace edition and the exported data is stored in a Google-owned bucket and will be deleted in 60 days as explained on Google Support.
• Mind Map Availability Miff: Users reported missing Mind Map features in NotebookLM, and it was confirmed to be a gradual rollout.
  • Some users speculated that the delayed rollout is due to bug fixes, with one user saying that the pace of the roll out is like a snails pace of an actual rollout.
• Customize Box Concealment Confirmed?: A user asked if the customize box is no longer available in the free version of NotebookLM.
  • Another user responded that the customize box is still showing up in the free account, not plus.
• Multilingual podcast missing: A user requested a multilingual feature for NotebookLM, specifically for the podcast feature.
  • Another user noted that NLM chat is already multilingual, but the podcast feature is currently only available in English.

Link mentioned: Export your users’ data - Google Workspace Admin Help: no description found

LM Studio ▷ #general (19 messages🔥):

Universal Translator, Mozilla's Transformer Lab, GPU Tokenization, Gemini 2.5 Pro

• Universal Translator Incoming?: A member speculates that a universal translator is only five years away given ChatGPT’s ability to understand and translate languages.
  • Another member then shares a YouTube link asking what model is singing in the video.
• Mozilla’s Transformer Lab Sparks Interest: A member asks if anyone has seriously looked at Mozilla’s Transformer Lab project, a potential way to bring training and fine-tuning to regular users on regular hardware, and shares a link to the GitHub repo.
  • Another member confirmed that Transformer Lab is proud to be supported by Mozilla through the Mozilla Builders Program, and clarified that it is supported but not created by them.
• GPU Tokenization Claims: A member noted that during tokenization, LM Studio pushes a single CPU thread to full throttle, but is curious whether the process of tokenizing is 100% on the GPU.
  • Another user replied that tokenizing has nothing to do with the GPU, but then countered himself, observing that playing with flash attention and cache settings for k and v has a big effect on the tokenizing time.
• Gemini 2.5 Pro Gets Trialed: A member asks whether others have tried Gemini 2.5 Pro.
  • Another member replied that they tried it and it was able to correctly answer a logic puzzle that Gemini 2.0 Flash Thinking could not, and shares a link to use it for free on aistudio.

Link mentioned: GitHub - transformerlab/transformerlab-app: Open Source Application for Advanced LLM Engineering: interact, train, fine-tune, and evaluate large language models on your own computer.: Open Source Application for Advanced LLM Engineering: interact, train, fine-tune, and evaluate large language models on your own computer. - transformerlab/transformerlab-app

LM Studio ▷ #hardware-discussion (23 messages🔥):

VRAM limits on GPUs, Docker container overhead with GPUs, 3090 Ti speed, M4 Max power consumption with 32B models, ROCm support for AMD GPUs

• GPU VRAM Limit Tweaks Explored: A user is trying to decrease the reserved VRAM on their 16 GB GPUs from 0.5 GB to 0.2 GB for LLM processing, but another member warned against fully utilizing VRAM to avoid system lockups.
  • It may be possible to get to 0.3 GB but it won’t be reliable, as any additional sight on VRAM will make setup to crumble.
• Docker Overhead with GPUs: A user accesses each of their 8 GPUs through its own Docker container, and questioned system memory overhead.
  • One member said that Docker should contain basically no internal system, besides bare minimum, but the CUDA core still should be loaded within each instance.
• 3090 Ti Fully Loaded: One user showcased their 3090 Ti being fully loaded, showing pure speed in a screenshot.
  • They reported speeds of ~20 tokens/s without flash and slowing down after 4-5k tokens, and ~30 tokens/s with flash.
• M4 Max Power Spike with 32B Thinking Models: A user noted that 32B-class thinking models drive power higher on their M4 Max (up to 140W) compared to same or larger sized models (up to 120W).
  • The user guesses this is due to more aggressive memory access, because the gpus seem to be running roughly the same.
• ROCm Support Updates for AMD GPUs: A user asked about support for the latest AMD GPUs and another user mentioned that it’s only through Vulkan, lacking ROCm support in master llama.cpp releases.
  • Another member clarified that the ROCm llama.cpp engine is updated along with CUDA and Vulkan, but noted that support for certain AMD GPUs like the 90xx may be a separate question.

Cohere ▷ #「💬」general (17 messages🔥):

Data Retention, Security, Data Privacy, Data Usage Policy, Zero Data Retention (ZDR)

• Cohere Clarifies Data Privacy Policies: In response to a user inquiry, the Cohere team shared links to their Privacy Policy and Data Usage Policy, emphasizing that users and customers should avoid uploading personal information when using the services.
• Cohere Emphasizes Data Control Options: Cohere offers a SaaS platform providing users direct control over their data through a dashboard, as well as Zero Data Retention (ZDR) support upon request (via email to [email protected]).
• Cohere’s Deployment Options: Cohere is available on major cloud providers like OCI, Bedrock, Sagemaker, and Azure Cloud, ensuring requests remain within the cloud environment; on-prem solutions are also available, detailed on their deployment options page.
• Cohere Achieves Security and Compliance Standards: Cohere is SOC II and GDPR compliant, adhering to industry standards for data security and privacy, with more details available in their security policy.
• Users Gain Control Over Data Usage: Users can manage their data settings on the data controls dashboard, preventing their data from being used for prompt generation or fine-tuning.

Cohere ▷ #「🔌」api-discussions (16 messages🔥):

Cohere API streaming, Cohere embedding generator, Cohere tokenization

• Cohere API supports response streaming: The Cohere API supports response streaming, which can improve user experience by allowing them to see text appearing on the client end as it’s generated, as documented in the Cohere’s Chat Stream API reference.
• Tokenizing text for CohereEmbeddingGenerator: A user building a CohereEmbeddingGenerator client in .NET asked about tokenizing text before embedding, and was advised to use the /embed endpoint which returns the number of tokens used, or download the tokenizer manually from Cohere’s public storage.
  • The user was directed to download the tokenizer listed for their model by making a GET request to api.cohere.com/v2/models/embed-english-v3.0, grabbing the tokenizer from the tokenizer_url and then manually tokenizing using a library such as the HF tokenizer.

Link mentioned: Chat with Streaming — Cohere: Generates a text response to a user message. To learn how to use the Chat API and RAG follow our Text Generation guides.Follow the Migration Guide for instructions on moving from API v1 to API v2.

Cohere ▷ #「🤖」bot-cmd (2 messages):

“

• No Relevant Discussions: No relevant discussions were found in the provided messages. The messages consisted of simple greetings.
• Channel Inactivity: The channel appears to be mostly inactive, with only brief greetings exchanged between users.

Cohere ▷ #「🤝」introductions (2 messages):

NLP project, Text summarization tool, Introduction of Sage

• Sage Introduces Herself: A new member named Sage introduced themself to the community.
  • They mentioned working on an NLP project for their university final year: building a text summarization tool.
• Sage seeks guidance on Text Summarization Tool: Sage is building a text summarization tool as an NLP project.
  • They hope to learn from the community and contribute in return, as they are currently facing difficulties.

Torchtune ▷ #announcements (1 messages):

torchtune v0.6.0, Tensor Parallel, Phi 4, Multinode training

• TorchTune v0.6.0 is Out!: TorchTune just released v0.6.0 with various new capabilities.
  • Release notes can be found here.
• Tensor Parallel Capabilities: Tensor Parallel is now supported for bigger distributed training and inference recipes!
  • This enhancement allows for more efficient handling of large-scale models and datasets.
• Microsoft’s Phi 4 supported: Builders for Phi 4, the latest model from Microsoft, have been added.
  • More information about Phi 4 can be found on the Microsoft Tech Community Blog.
• Multinode training is live!: Multinode training is now supported, facilitating distributed training across multiple nodes.
  • Get started with multinode training here.

Torchtune ▷ #general (14 messages🔥):

DeepSeek-V3, Quantization Aware Training, MoEs in torchtune

• DeepSeek Drops V3 Model, Skips Readme: Members noticed the DeepSeek-V3 model was released without a readme, joking that the DeepSeek AI team had become unhinged.
  • The new model boasts a chat interface, Hugging Face integration, and links to their Discord, Wechat, and X accounts.
• TorchTune’s MoEs Spark Daydreams: One member made a hidden reminder about MoEs addition in torchtune, pondering if it would require 8-9 TB of VRAM and a cluster of 100 H100s or H200s to train.
  • They jokingly noted they’d have to move a few boxes in the attic first to make space.
• Optimizer State Preserved after Quantization Aware Training: A member asked about how Quantization Aware Training (QAT) affects the optimizer state, linking to the relevant code in torchtune.
  • Another member confirmed that optimizer state is preserved after the switch to QAT.

Torchtune ▷ #dev (20 messages🔥):

CUDA overhead, Cursed Submodule, vLLM + GRPO, r1-zero

• Banish CPU CUDA Launch Overhead with Graph Capture: To reduce GPU idle time, members discussed that launching CUDA operations from CPU has non-negligible overhead and that capturing GPU operations as a graph and launching them as a single operation can consolidate the computational graph.
  • One member asked if this is what compile does.
• Is a /cursed submodule needed to improve Performance 10x?: Members debated the creation of a /cursed submodule for cursed code that drastically improves performance, like manual CUDA device specification.
  • A member stated that he is using an approach only suitable for smaller models, where each process has its vLLM instance that generates data, instead of the more common approach of having a centralized vLLM generating process.
• vLLM & GRPO Integration: smaller models vs. larger models: A member has a working version for distributed setups, but stopped iterating on it after being notified that some work is already done internally.
  • The current approach is supposedly better for smaller models (up to 8B), whereas the internal approach would be better for large models (>=70B).
• r1-zero Recipe for Async Training: A member shared a link to r1-zero for async training of reasoning models, emphasizing it’s still a work in progress.
  • They also mentioned plans to integrate a version of this recipe into torchtune soon, focusing on cleaning up and allowing non-HF models with vLLM.
• Layer GPU Address Mapping via HTTP Calls: A member suggested modifying vLLM to launch with a mapping of each layer’s GPU address.
  • The goal is to run a vLLM process on the side and interact with it via HTTP calls; someone is already working on it.

Nomic.ai (GPT4All) ▷ #general (31 messages🔥):

LocalDocs Backup, Privacy Considerations for Chat Data, Local LLM vs API for Message Processing, LocalDocs DB Import, Lost LocalDocs DB

• LocalDocs DB Backup for Safety: Members discussed backing up the localdocs.db file to avoid data loss, especially if the original documents are lost or inaccessible.
  • One member suggested that GPT4All uses the *.db file with the highest number (e.g., localdocs_v3.db), and renaming them might allow for import/export, though this is unconfirmed.
• Privacy Laws Challenge Chat Data Analysis: One member raised concerns about privacy laws, particularly in the EU, and the need to ensure compliance when processing chat data.
  • The discussion highlighted the importance of verifying permissions and the format of the chat messages (plaintext or convertible) before feeding them into an LLM.
• LLM API vs Local LLM for Chat Processing: A member inquired about whether to use a paid API like Deepseek or OpenAI, or to run a local LLM for processing incoming group chat messages to calculate satisfaction rates, extract keywords, and summarize messages.
  • Another member suggested that if the messages are relatively small (under 100MB), a local machine with a good GPU might suffice, especially if using smaller models for labeling and summarization.
• LocalDocs DB Importing Challenges: Members discussed the possibility of importing a localdocs.db file, but noted that the file contains encrypted/specially encoded text, making it difficult for a generic LLM to parse without an embedding model.
  • One member who lost their localdocs.db was experiencing painfully slow CPU indexing and was hoping to get around this problem.
• Win11 Update Wipes LocalDocs: A member experienced their localdocs.db becoming empty after a Windows 11 update and was struggling to re-index the local documents on CPU.
  • Drive letter changes due to the update were considered as a possible cause, with a suggestion to move files to the C drive to avoid such issues.

LlamaIndex ▷ #blog (2 messages):

LlamaCloud MCP Server, Build an MCP server in Python

• LlamaCloud as an MCP Marvel: LlamaCloud can be an MCP server to any compatible client and this demo shows that in action.
  • It shows how MCP is very popular.
• Pythonistas Produce Portable Pythonic Paradigm: A member shows how to build your own MCP server using LlamaIndex to provide tool interfaces of any kind to any MCP client, with ~35 lines of Python connecting to Cursor AI.
  • Members implemented Linkup web search and this project.

LlamaIndex ▷ #general (25 messages🔥):

Claude MCP support, Multi-agent performance with LlamaIndex, Agent types in LlamaIndex, Automatic LLM evaluations

• LlamaIndex Adds Claude MCP Compatibility: A member provided a simplified example of integrating Claude MCP with LlamaIndex, showcasing how to expose a localhost and port for MCP clients like Claude Desktop or Cursor using FastMCP and uvicorn in a code snippet.
• Agents slow? AgentWorkflow for Speed: A user reported slow performance with LlamaIndex MultiAgentic setups using Gemini 2.0 with 12 tools and 3 agents; a suggestion was made to use AgentWorkflow and the can_handoff_to field for controlled agent interaction.
• LlamaIndex Agents Confuse Newbies: A member expressed confusion about the different agent types in LlamaIndex and when to use them, and a refactor on docs is coming.
  • A team member noted that core.agent.workflow should generally be used, with FunctionAgent for LLMs with function/tool APIs and ReActAgent for others, suggesting the Hugging Face course for additional help.
• Automatic LLM Evals No Prompts!: A founder is validating an idea for OSS automatic evaluations with a single API and no evaluation prompts, using proprietary models for tasks like Hallucination and Relevance in under 500ms, with a plan to offer an end-to-end solution including models, hosting, and orchestration tools, explained in more detail on the autoevals.ai website.

Eleuther ▷ #general (4 messages):

AI IDE Evaluation, SAEs on CoTs, DeepSeek-V3-0324 showcase, Gemini 2.5 Pro

• New member joins interpretability efforts with SAEs focus: A new member, Sam, with a background in physics and geometry, expresses interest in contributing to interpretability efforts, particularly using SAEs on CoTs.
  • Sam shares that their background is in physics + geometry and they have data science experience, including deep learning.
• AI IDE Evaluation Repo Emerges: A member is seeking feedback on evaluating AI IDEs, highlighting inconsistent performance between IDEs like Cursor and Windsurf despite using similar models, sharing a repo for comparison.
  • The current evaluation focuses on ‘greenfield’ projects, using predefined prompts and assessing generated code/tasks through metrics like lines of code and files, with potential for incorporating more in-depth evaluations later.
• DeepSeek-V3-0324 generates p5.js program: A user quoted AK (_akhaliq) who highlights that DeepSeek-V3-0324 successfully wrote a p5.js program showing a ball bouncing inside a spinning hexagon, affected by gravity and friction, bouncing realistically off the walls.
  • The model even improvised features like ball reset and randomization based on a prompt asking for sliders to adjust parameters and side count buttons, as showcased in this tweet.
• Google unveils Gemini 2.5 Pro: Google released Gemini 2.5 Pro, touting it as the world’s most powerful model with unified reasoning capabilities, long context, and tool usage as seen in this tweet.
  • The model is available experimentally and for free in Google AI Studio + API, with pricing details to follow soon.

Eleuther ▷ #research (12 messages🔥):

SkyLadder short-to-long context window transition, Data-constrained pretraining for math, Composable Generalization

• SkyLadder: Short-to-Long Context is the Optimal Path: A paper on ArXiv proposes SkyLadder, a simple yet effective approach that implements a short-to-long context window transition for LLM pretraining, yielding consistent gains of up to 3.7% on common tasks (2503.15450).
  • The authors pre-train 1B and 3B parameter models on 100B tokens, demonstrating that SkyLadder preserves strong standard benchmark performance while matching or exceeding baseline results on long context tasks.
• Human Thought: The Key to Data-Constrained Pretraining?: A new approach suggests that explicitly modeling and inferring the latent thoughts that underlie the text generation process can significantly improve pretraining data efficiency, especially for math (2503.18866).
  • The paper empirically demonstrates the effectiveness of inferring latent thoughts through synthetic data approaches, outperforming training on the same amount of raw data (5.7% -> 25.4%).
• Composable Generalization Through Hypernetworks!: A paper reformulates multi-head attention as a hypernetwork, revealing that a composable, low-dimensional latent code specifies key-query specific operations, enabling transformers to generalize to novel problem instances (2406.05816).
  • One member liked that for a single pair of q, k indices, the authors interpret activations along the head-number dimension as a latent code (of dimension n_heads) specifying the task / context.

Eleuther ▷ #scaling-laws (1 messages):

Chinchilla Scaling Formula, Impact of Suboptimal Hyperparameters, Learning Rate Effects

• Digging into Chinchilla Scaling Formula: A member inquired how suboptimal hyperparameter settings affect the Chinchilla scaling formula for optimal model training across different scales, referencing the Chinchilla paper.
  • Specifically, they asked which parameters (E, A, B, alpha, or beta) in the formula would be altered if the learning rate was set too low (e.g., 1/10 of the optimal value).
• Learning Rate’s Ripple Effects: The inquiry focuses on the theoretical consequences of a suboptimal learning rate on the scaling behavior predicted by the Chinchilla formula.
  • It aims to understand if a consistently low learning rate would primarily affect the error term (E), the scaling coefficients (A, B), or the exponents (alpha, beta) governing the relationship between model size, training data, and performance.

Eleuther ▷ #multimodal-general (1 messages):

Self-organizing AI, AI Building Blocks

• All AI Use Similar Building Blocks?: A member introduced the idea that all these systems could use very similar building blocks.
  • This led them to the concept of a self-organizing AI that learns different potential configurations.
• AI Self-Configuration Speculation: A member posited a new direction for AI, suggesting that AIs can self-organize.
  • They added this self-organization could allow AIs to learn different potential configurations dynamically.

Eleuther ▷ #gpt-neox-dev (5 messages):

gpt-neox CI status, lm_eval upgrade

• gpt-neox CI Needs Fixing: The EleutherAI/gpt-neox repo’s CI is failing and needs some fixes because the volunteer who was maintaining them was hired elsewhere fulltime, though local tests are being run per PR.
  • The tests that passed locally include pytest tests -m cpu, and all tests passed except for a few requirements-related failures when running pytest --forked --cov-report term --cov=megatron tests.
• lm_eval Upgrade PR Ready for Review: A member has drafted a PR to update the evaluation logic to lm_eval==0.4.8, the latest version, and proposes resolving failing tests in another PR as they seem unrelated and also fail on main, linked here: PR 1348.
  • A member suggests that the test failures might be due to an incorrectly set up environment or inconsistent versioning of dependencies.

Modular (Mojo 🔥) ▷ #mojo (15 messages🔥):

Mojo for Website vs Rust, AES in Mojo Progress, SIMD for AES, Rust vs Go for Backend

• Mojo Unsuitable for Websites Compared to Rust: A member inquired about using Mojo for websites versus Rust, but was advised against it due to Mojo’s lack of specification for cryptographically sound code and a lacking IO story.
  • The suggestion was to use Rust with its production-ready libraries and frameworks, and its faster async capabilities, especially for applications requiring authentication or HTTPS.
• Hardware-Backed AES in Mojo on Hold: A member mentioned having a hardware-backed AES implementation in Mojo, but it doesn’t run on older Apple silicon Macs and is not a full TLS implementation.
  • The developer is holding off on further AES work until a cryptographer is willing to write the software half, emphasizing the risks of non-experts implementing cryptographic functions.
• SIMD for AES Implementations Explored: Discussion involved using SIMD for AES and other algorithms, with a member noting that x86 has vaes and similar features for SIMD AES 128.
  • It was mentioned that ARM has SVE AES, which is similar but not as well supported, highlighting hardware-level optimizations available for cryptographic functions.
• Rust API to call into MAX: Despite the challenges, a member expressed reluctance towards Rust due to the perception that it’s not suitable for fast writing, seeking an easier solution for backend development, and the suggestion was to have the Rust API call into it and pass arguments along.
  • They were considering using Python but found it slow and unstable, leading to a plan to create a Rust project callable via FFI.
• Go as a Middle Ground: As an alternative, a member suggested Go as a decent middle ground that is also production ready, between Python/Mojo and Rust.
  • However, another member expressed concerns that too many microservices could make the project very big.

Modular (Mojo 🔥) ▷ #max (2 messages):

CUDA-free, PTX for targeting NVIDIA GPUs

• PTX Powers ‘CUDA-Free’ Mojo on NVIDIA: Mojo directly generates PTX (Parallel Thread Execution) code to target NVIDIA GPUs, bypassing the need for CUDA.
  • This approach avoids dependencies on cuBLAS, cuDNN, and CUDA C, streamlining the development process.
• Details on CUDA Free: Confirmed by bradlarson, the team generates PTX directly and lowers from there.
  • There is no dependency on cuBLAS, cuDNN, or CUDA C.

DSPy ▷ #general (9 messages🔥):

Text Summarization with DSPy, SIMBA Optimizer, Output Refinement vs Assertions, BestOfN Module, Refine Module

• Summarization Task Tackled with DSPy: A member is exploring using DSPy for a text summarization task with 300 examples from an expert and is testing it out on a simple metric.
  • They are wondering if the optimizer can be more effective if it sees where exactly the summarization differed, and if there’s a better approach to optimizing a prompt for text summarization.
• SIMBA Optimizer Supports Granular Feedback: A member suggested using the experimental optimizer dspy.SIMBA for the summarization task, which allows for providing feedback on differences between the generated summary and the ground truth.
  • The feedback can be returned via dspy.Prediction(score=your_metric_score, feedback="stuff about the ground truth or how the two things differ") to guide the optimization.
• Output Refinement Doc Shared: A member shared a link to a DSPy tutorial on Output Refinement explaining BestOfN and Refine modules designed to improve prediction reliability by making multiple LM calls with different parameter settings.
  • The tutorial elaborates on how both modules stop when they have reached N attempts or when the reward_fn returns an award above the threshold.
• BestOfN Module improves with Temperature Tweaks: The BestOfN module runs a given module multiple times with different temperature settings to get an optimal result.
  • It returns either the first prediction that passes a specified threshold or the one with the highest reward if none meet the threshold.
• Refine Module not as Composable?: A member inquired if Refine is going to subsume assertions, and whether it’s as granular and composable, since it wraps an entire module.
  • Another member responded that the composability can be managed by adjusting the module size, allowing for more explicit control over the scope.

Link mentioned: Output Refinement - DSPy: The framework for programming—rather than prompting—language models.

tinygrad (George Hotz) ▷ #general (3 messages):

ROCm Support, OpenCL Front End, AMD GPUs with Tinygrad

• Legacy AMD GPUs get Tinygrad Boost: Using OpenCL frontend, older AMD GPUs not supported by ROCm, such as those in a 2013 Mac Pro, can potentially run with Tinygrad.
  • The success may depend on the specific driver and the level of OpenCL support available on the system, so users should verify their custom driver compatibility.
• ROCm Alternative for Older AMD: ROCm does not support older AMD GPUs, but the OpenCL frontend in tinygrad might offer a workaround.
  • Success will vary based on specific driver versions and the extent of OpenCL support; experimentation is needed to confirm compatibility.

Codeium (Windsurf) ▷ #announcements (1 messages):

Windsurf Creators Club, Vibe Coding Channel, Windsurf v1.5.8 Release

• Windsurf launches Creators Club: Windsurf is rewarding community members for content creation, offering $2-4 per 1k views and more details can be found at the Windsurf Creators Club.
• Windsurf introduces ‘Vibe Coding’ channel: A new channel has been created for ‘vibe coders’ to enter the flow state, chat, discuss, and share tips/tricks.
• Windsurf v1.5.8 Released with Patch Fixes: Windsurf v1.5.8 is now released with patch fixes including cascade/memories fixes, Windsurf Previews improvements, and cascade layout fixes; An image of this release was also shared.

Link mentioned: no title found: no description found

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