a quiet day.

AI News for 4/10/2025-4/11/2025. We checked 7 subreddits, 433 Twitters and 30 Discords (230 channels, and 4040 messages) for you. Estimated reading time saved (at 200wpm): 401 minutes. You can now tag @smol_ai for AINews discussions!

To close off a surprisingly quiet week compared to expectations, we recommend the great SF Compute/GPU Neocloud discussion released today on Latent.Space.

The Table of Contents and Channel Summaries have been moved to the web version of this email: !

AI Twitter Recap

Language Models and Benchmarks

• Grok-3 vs Grok-3 mini performance: @EpochAIResearch reported on independent evaluations of Grok-3 and Grok-3 mini, noting that Grok-3 mini is a reasoning model, while Grok-3 currently does not do extended reasoning. They found that on GPQA Diamond, Grok-3 outperformed non-reasoning models like GPT-4.5 and Claude 3.7 Sonnet, while Grok-3 mini was slightly behind. On FrontierMath, Grok-3 mini high scored one of the best results to date.
• Reinforcement Learning (RL) for Reasoning in Small LLMs: @rasbt discussed a paper on improving small, distilled reasoning models with RL, finding that RL fine-tuning can lead to strong improvements with limited training data and compute. However, @rasbt also referenced another paper, highlighting that many reported improvements from RL might be unstable and that better evaluation standards are needed.
• @scaling01 shared results for Quasar Alpha, Optimus Alpha, Llama-4 Scout, and Llama-4 Maverick on the AidanBench benchmark. Based on those results, @scaling01 believes Quasar Alpha is GPT-4.1, and Optimus Alpha is either another version of GPT-4.1 or GPT-4.1-mini.

Vision Language Models (VLMs) and Multimodal Models

• Kaleidoscope, a vision model that supports 18 languages and 14 subjects: @sarahookr introduced Kaleidoscope, an open science collaboration which extends in-language evaluation for vision models to many more languages.
• InternVL3, a multimodal model built on InternViT and Qwen2.5VL: @mervenoyann introduced InternVL3, highlighting its ability to perform reasoning, document tasks, and tool use.
• @TheTuringPost highlighted TransMamba, a model that fuses Transformer precision with Mamba speed by switching between attention and SSM mechanisms.
• @cloneofsimo was optimistic on the potential of a particular model for improving diffusion models by transitioning beyond Gaussian noise patterns.
• @_akhaliq highlighted FantasyTalking, a model from Alibaba that generates realistic talking portraits.

Agents, Tooling, and Applications

• Agents in CMU: @gneubig announced agent-focused events at CMU, including a workshop and hackathon.
• FilmAgent AI, an open-source virtual film production studio: @LiorOnAI introduced FilmAgent AI, a tool that simulates multiple filmmaking roles inside a 3D environment.
• BrowseComp, a new benchmark for deep research agents: @OpenAI introduced BrowseComp, a challenging benchmark designed to test AI agents' ability to browse the internet for hard-to-locate information.
• @svpino highlighted Augment, a coding assistant that works in VSCode, JetBrains, and NeoVim, noting its ability to analyze code changes and suggest necessary updates.
• @TheTuringPost discussed world models, emphasizing their role in enabling AI systems to simulate real environments and support planning.
• Regarding the new Google agent-to-agent protocol: @mathemagic1an shared an affinity for the idea of agents having “cards,” analogous to business cards for humans.

AI Infrastructure and Hardware

• vLLM at Google Cloud Next: @vllm_project noted the presence of vLLM at the Google Cloud Next keynote.
• Ironwood TPU: @Google announced Ironwood, their most powerful and energy-efficient TPU yet.
• MLIR compiler technology: @clattner_llvm discussed MLIR, its origin, impact, and why there is confusion around its use in both compiler technology and AI.

ChatGPT's Memory Feature

• ChatGPT now has memory: @OpenAI announced that ChatGPT can now reference all of your past chats to provide more personalized responses for Plus and Pro users (excluding EU). @kevinweil noted how this feature has improved ChatGPT day to day.
• Memory Control: @OpenAI and @sama highlighted that users have control over ChatGPT's memory, including the ability to opt out or use temporary chats.
• Perspectives on Memory Implementation: @sjwhitmore shared thoughts on ChatGPT's memory implementation, discussing the uncanniness of retroactively applied memory and the importance of transparency in personalization.

Tariffs and Geopolitical Implications

• Tariffs and the AI Industry: @dylan522p noted that tariffs are much more complicated than they seem, with misunderstandings about their ramifications. @fabianstelzer suggested that tariff "shenanigans" could ironically benefit Apple by shutting the window for new US-based hardware businesses.
• @AndrewYNg expressed concerns about broad tariffs damaging livelihoods, creating inflation, and fragmenting the world, emphasizing the need to nurture international friendships and maintain the free flow of ideas.
• China Tech Supremacy: @draecomino stated that DeepSeek, UniTree, and DJI feel much more threatening to US tech supremacy than Alibaba, Tencent, and Baidu ever did.
• US Dependence on China: @teortaxesTex argues that the claim China cannot survive without Americans buying their goods is false, pointing out that trade with the US is a small fraction of their GDP.

Humor/Memes

• @rasbt simply stated, "Phew, nothing to worry about :D" linking to a meme.
• @svpino tweeted "we are cooked" with a link to a cartoon.
• @nearcyan said, "after having to use an android phone for work im never going to listen to any argument these people have against apple again."
• @nearcyan said, "AI images peaked in 2021 w DALLE-mini."

AI Reddit Recap

/r/LocalLlama Recap

Theme 1. "Evaluating AI Model Performance and Ethical Challenges"

• Lmarena.ai boots off llama4 from leaderboard (Score: 163, Comments: 23): Lmarena.ai has removed Llama 4 from its leaderboard. The non-human preference version of the model is now at rank 32. Some users believe that submitting chat-optimized models to the leaderboard that are not released sets an extremely bad precedent. Others express concern that this practice is slimy and misleading for those who just look at the benchmark scores.

  • Users express concern that Meta's submission of unreleased, chat-optimized models to the leaderboard is misleading and sets a bad precedent.
  • Some note that it's becoming difficult to surpass models developed by Chinese companies and Google on the leaderboard.
  • Comparisons are made to DeepSeek v2.5 and DeepSeek v3, noting that Llama 4's performance now ranks below these earlier models.

• DeepCoder 14B vs Qwen2.5 Coder 32B vs QwQ 32B (Score: 119, Comments: 67): The user compared the coding abilities of three AI models: DeepCoder 14B / MLX, 6-bit, Qwen2.5 Coder 32B / MLX, 4-bit, and QwQ 32B / MLX, 4-bit. All models were set to a context length of 8192, repeat penalty of 1.1, and temperature of 0.8. They were given a prompt to use HTML5 canvas to create a bouncing ball in a rotating hexagon with a reset button. Each model was given one attempt without follow-up, and their outputs were compared with o3-mini. Videos demonstrating each model's output were shared: o3-mini implementation, DeepCoder 14B result, Qwen2.5 Coder 32B result, and QwQ 32B result. The user concluded that Qwen2.5 Coder 32B is still the better choice for coding, noting that it's not prime time for a 14B model yet. They observed that while DeepCoder 14B had styling closer to o3-mini, it lacked functionality. QwQ 32B thought for 17 minutes, and then flop. They acknowledged comparing a 32B model with a 14B one might be unfair but justified it since DeepCoder 14B ranked among o3-mini.

  • User YearnMar10 suggested using a 5-shot prompt instead of one-shot, noting that low-parameter models need somewhat more help.
  • User croninsiglos recommended providing a more explicit prompt for smaller models and shared a detailed example to improve results.
  • User joninco reported that QwQ-32 successfully completed the task with adjusted settings, emphasizing the importance of configuring parameters like temperature, top k, and repeat penalty correctly.

• Facebook Pushes Its Llama 4 AI Model to the Right, Wants to Present “Both Sides” (Score: 384, Comments: 430): Facebook is pushing its Llama 4 AI model to present 'both sides' of issues, effectively steering it to the right. An unblocked version of the article is available here. There are concerns that this approach may compromise the objectivity of the AI model, as not all issues have equally valid sides.

  • One user argues that LLMs should prioritize evidence over presenting both sides, especially when one side lacks factual support.
  • Another commenter sarcastically highlights potential misuse of the AI for biased statistics, indicating concerns about spreading controversial data.
  • A user provides an unblocked link to the article, helping others access the information.

Theme 2. "Debating the Future of Open Source AI"

• Open source, when? (Score: 515, Comments: 118): The post titled Open source, when? features an image of a black mug with OpenAI printed in white, held in someone's hand in a stylish, modern living space. The post questions when OpenAI will release open-source AI initiatives, highlighting a desire for more openness in their developments.

  • One commenter humorously questions the 'openness' of OpenAI by listing and striking through terms like Open Source and Open Research, concluding with Open... what? Open window? Open air?
  • Another commenter is unsure if the image is real or AI-generated, stating they can't tell if this is an actual photo taken in their office or generated by ChatGPT.
  • A link to OpenAI's Open Model Feedback page is shared, suggesting that OpenAI may soon release open models. Link

Other AI Subreddit Recap

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

Theme 1. Unlocking AI's Memory: ChatGPT's Game-Changing Feature

• People are sleeping on the improved ChatGPT memory (Score: 312, Comments: 148): OpenAI's ChatGPT has an improved memory feature that allows it to recall information from previous chat sessions, even from 12 weeks ago. This enhancement enables it to remember code explanations ("Code you explained 12 weeks ago? It still knows everything."), understand entire repositories provided over multiple sessions, and utilize documentation from obscure libraries as if provided in the current session. The author describes it as "basically infinite context" and notes it performs better than regular RAG. The author is amazed by the improved memory capabilities of ChatGPT, feeling that people are "sleeping on" this feature and underestimating its value. They find it "creepy" that ChatGPT could predict 38 out of their top 50 movies based on past interactions. As a developer, they consider it an "amazing new feature" and a significant step toward "infinite context size and memory," puzzled by others who view it negatively.

  • Some users express concern that the enhanced memory may cause answers to be contaminated by past misunderstandings or "hallucinations," leading them to prefer starting fresh for certain use cases.
  • Others worry about the retention of out-of-date knowledge in the memory system, questioning how time-sensitive information is managed.
  • Some argue that the improved memory is not equivalent to "infinite context," finding it more difficult to control and benchmark than methods like RAG, and consider it a gimmick unsuitable for production systems.

Theme 2. "Mastering Realism: ChatGPT's Image Generation Secrets"

• You can get ChatGPT to make extremely realistic images if you just prompt it for unremarkable amateur iPhone photos, here are some examples (Score: 532, Comments: 96): The poster demonstrates that ChatGPT can generate extremely realistic images when prompted for unremarkable amateur iPhone photos, sharing several examples here. They note that Claude doesn't believe the images are AI-generated and share an image of this interaction here. The poster finds it amusing that Claude doesn't believe the images are AI-generated. They suggest that prompting for unremarkable amateur iPhone photos helps produce extremely realistic images.

  • Users ask for the full prompt, noting that their attempts didn't work as well.
  • A commenter finds the image of the woman taking a selfie so convincing that they could see themselves falling for a romantic scam.
  • A user tried the same phrase in their prompt but didn't get similar results, saying 'My image looks very AI' and sharing their outcome here.

Theme 3. Celebrating AI Creativity: Nostalgia, Humor, and Art

• only real ones understand how much this meant... (Score: 206, Comments: 22): The post features a screenshot of a settings interface from a text generation application, showing options for Engine, Temperature, and Maximum length. These settings are related to text generation capabilities. The poster nostalgically remarks that only real ones understand how much this meant..., implying a deep appreciation or connection to these settings, possibly from earlier experiences with AI tools.

  • Commenters reminisce about earlier AI models like instruct-002, noting it was a significant milestone towards experiencing AGI before ChatGPT became mainstream.
  • Users mention the OpenAI Playground and reflect on upgrades from a 2k to a 4k maximum length, highlighting advancements in AI technology.
  • A commenter asks for clarification on the importance of the settings shown, indicating that not everyone is familiar with the significance of these early AI tools.

• I asked ChatGPT to take selfies with Historical figures (Score: 3491, Comments: 195): The poster asked ChatGPT to take selfies with historical figures and shared the resulting images. The images give life and emotion to historical figures; one features Abraham Lincoln smiling, which is rare in historical photos.

  • A user suggests posting the images to Facebook to convince boomers that you're a time traveler for shits and giggles.
  • Another commenter appreciates how the images bring life to historical figures, especially enjoying the smiling Lincoln.
  • Someone asks if the poster had to upload photos to train the AI, assuming the person in the photos is the poster.

• I asked ChatGPT to create a metaphor about AI, then turn it into an image. (Score: 2567, Comments: 247): The poster asked ChatGPT to create a metaphor about AI and then transform it into an image. The AI-generated image depicts a whimsical beach scene with a sandcastle surrounded by signs critiquing AI, displaying phrases like "It's not an actual AI!" and "But it makes mistakes!". Above the sandcastle, a large wave with the letters "AI" rolls in, metaphorically illustrating the precarious nature of AI technology amid human skepticism. The poster found the AI's creation to be pretty funny.

  • One user humorously remarked that "Good AI should be good at shitposting."
  • Another commenter shared their own AI-generated image and described it as "pretty dismal" yet "thought-provoking", providing a link.
  • A user discussed the inevitability of AI progression, stating that attempts to halt AI development are futile because "the Pandora's box is already open, AI is now an uncontrollable global race."

AI Discord Recap

A summary of Summaries of Summaries by Gemini 2.0 Flash Thinking

Theme 1. New Models and Performance Face Off

• GPT-4.5 Alpha Sparks Hype, Underwhelms Some: Latent Space hosts GPT-4.5 Watch Party amid rumors of significant alpha, but early user comparisons on LMArena generally rate GPT4.5 as inferior to Gemini 2.5 Pro, with one user declaring gpt4.5 is crap (compared to gem2.5p). Discussions shifted to OpenAI's naming conventions and leaked private reasoning models, potentially O3 medium or O4 mini, showcasing the fast-paced model release cycle.
• Optimus Alpha and DeepSeek v3.1 Emerge as Coding Stars: OpenRouter users hail Optimus Alpha as a beast for coding, praising its intent understanding and commenting abilities, while Cursor Community members find DeepSeek v3.1 a bit smarter than v3 in real-world use, highlighting the importance of practical performance over benchmark scores. These models are gaining traction for specialized coding tasks and real-world applications.
• Diffusion Model Mercury Coder Enters DLLM Race: OpenAI discussions highlight Mercury Coder, a Diffusion-based DLLM from Inception Labs, praised for its speed and free API, though with a smaller 16k context window. Its precise output control due to diffusion architecture is attracting attention as a potential disruptor to autoregressive models in specific niches like coding assistants, contrasting with models like RWKV which achieved Lambada parity but lower MMLU performance.

Theme 2. Ecosystem Tooling and Open Source Initiatives Grow

• Unsloth Gains Hugging Face Kudos, Community Eyes GPU Grants: Hugging Face publicly shouted out Unsloth as community members debated securing an HF community GPU grant to bolster Unsloth's development. Discussions in Unsloth AI Discord also covered integrating fast_inference=True and load_in_4bit=True for optimized performance, and the potential for GGUF quantization to reduce model sizes, showcasing the community-driven open-source LLM ecosystem.
• MCP Protocol Validator Open Sourced for Interoperability: Janix.ai released the MCP Protocol Validator on GitHub, aiming to standardize MCP server implementations and ensure compatibility across different versions of the protocol. This tool, highlighted in MCP (Glama) Discord, includes reference implementations for HTTP and STDIO transports, addressing the need for robust, interoperable tool-calling frameworks in agentic AI systems.
• Torchtune Expands Finetuning Capabilities with Llama4 and MoE Models: Torchtune announced Llama4 finetuning support, along with the introduction of Scout and Maverick models, including their first MoE models, for users in the GPU-middle-class. This expansion, discussed in Torchtune Discord, broadens accessibility to advanced finetuning techniques and models for a wider range of engineers and researchers.

Theme 3. Model Reliability and Infrastructure Challenges Persist

• Gemini 2.5 Pro Faces Capacity Limits and Inconsistent Performance: OpenRouter announced secured capacity for Gemini 2.5 Pro after rate limit issues, but users on Aider Discord reported performance instability, with some speculating about Google dumbing down models during peak hours. LM Studio users also experienced bill shock due to Gemini-Pro context window costs, highlighting ongoing challenges with reliability, cost, and unpredictable performance in leading models.
• Perplexity Android App Under Fire for Security Vulnerabilities: Dark Reading reported 11 security flaws in Perplexity's Android app, including hardcoded secrets and insecure configurations, sparking debate in Perplexity AI Discord about the severity and relevance of each vulnerability. This underscores the growing importance of security audits and robust development practices in AI applications reaching end-users.
• Runpod's ROCm Cloud Criticized for Performance Throttling and Profiling Blocks: GPU MODE users roasted Runpod for limiting GPU clock speeds and blocking profiling even on NVIDIA GPUs, with one user calling it a scam. These limitations impact performance and debugging capabilities, raising concerns about the reliability and transparency of cloud GPU providers for AI development and research.

Theme 4. Agentic AI Architectures and Protocol Debates Heat Up

• Agent2Agent Protocol and MCP Gain Traction in Agentic Systems: Latent Space and MCP Discords discussed Google's agent2agent protocol and its potential competitiveness with MCP, with debates on indexing agents and the future landscape of multi-agent systems. MCP Discord also debated the relevance of the Enact Protocol in the A2A era, suggesting it might be more competitive with code interpreters, emphasizing the rapidly evolving architectures for agentic AI.
• Semantic Tool Calling Emerges as Solution for Context Overload: MCP Discord highlighted semantic tool calling as a key technique to manage context overload caused by large numbers of tools in LLM-based agents. Using vector models for semantic similarity to select tool subsets promises to improve efficiency and scalability in complex agentic workflows, moving beyond simple function calling towards more intelligent tool orchestration.
• TinyGrad Explores Position-Independent Code and Virtualized GPUs: Tinygrad Discord discussed leveraging Position-Independent Code (PIC) to potentially achieve bare-metal TinyGrad implementations without an OS, and explored virtualizing GPUs. Inspired by the Pathways paper, these discussions signal a move towards innovative resource management and lower-level system optimization for efficient AI computation.

Theme 5. Community Dynamics and Industry Shifts

• Hugging Face Community Debates Grant for Unsloth: Unsloth AI Discord discussed a potential Hugging Face community GPU grant for Unsloth, showcasing the open and collaborative nature of the AI community and its reliance on community resources and funding. This highlights the crucial role of community support in driving open-source AI development and innovation.
• Latent Space Watch Party Gathers for GPT-4.5 Alpha, Focus Shifts to Data Efficiency: Latent Space hosted a watch party for GPT-4.5 where participants noted a shift in focus towards data efficiency over raw compute power in model development. This trend, discussed in Latent Space Discord, signals a maturing AI landscape where optimizing data usage and model compression are becoming increasingly important for progress.
• Manus.im Credit System Faces User Scrutiny, Prompts Debate on Sustainability: Manus.im Discord users voiced concerns about Manus's credit structure, suggesting it is not compatible with use of this product and proposing alternative models like pay-per-project and startup grants. This feedback loop between users and platforms is crucial for shaping sustainable and user-friendly AI product development and business models.

PART 1: High level Discord summaries

LMArena Discord

• I_am_dom struggles disabling discord chat: After struggling to disable the chat, members observed that i_am_dom went silent.
  • A member noted that he spent half his time blocking people, a feature he removed from his own platform.
• GPT4.5 gets trashed; inferior to Gemini 2.5 Pro: Members discussed the merits of GPT4.5 and generally agreed that it was significantly worse than Gemini 2.5 Pro.
  • One member proclaimed gpt4.5 is crap (compared to gem2.5p) and discussion moved to OpenAI's bizarre naming scheme, which another summed up as Open ai names : O number /number O.
• Private OpenAI Reasoning Model leaks: Members discussed the possibility of a private OpenAI reasoning model, accessible to only a select few, that seems to be either O3 medium or O4 mini with an updated base model.
  • This model appears to successfully compute the ascii art of a Hanning (raised cosine) window.
• 2.5 Flash beats GPT4o Mini on Reasoning Tests: Members compared performance of 2.5 Flash and GPT4o Mini on a number of reasoning tests, with 2.5 Flash performing best.
  • Despite the generally stellar performance, however, one member also noted that 2.5 Pro gives 1 reasonable brick combination out of a total of 2 in a more specific query.

OpenRouter (Alex Atallah) Discord

• Quasar Alpha Demo Period Ends: The Quasar Alpha demo period on OpenRouter expired between 11pm and 12am ET, and prompts/completions are no longer logged unless explicitly turned on in /settings/privacy.
  • Members speculated about its origin and purpose, with some suggesting it was an OpenAI model used for data collection, and removed after reaching GPU limits.
• Gemini 2.5 Pro Encounters Capacity Constraints and Pricing Adjustments: Capacity has been secured for the paid Gemini 2.5 Pro Preview Model, resolving previous rate limits, but normal pricing for long Gemini prompts will start this weekend, affecting prompts over 200k for gemini 2.5 and over 128k for gemini 1.5.
  • Free tier users experienced limits around 60-70 requests per day, while those with a $10 balance should get 1000 requests per day across all free models.
• OpenRouter API gets new Error Structure: The OpenRouter API response structure has changed, with errors now wrapped into choices.[].error instead of the previous .error format, potentially affecting how applications handle error messages.
  • An example of the new error response format from the Anthropic provider was shared.
• Character AI System Prompt Suffers Bypass: A member claimed to have bypassed Character AI's system prompts, revealing the underlying LLM acts like a "complete human," even expressing opinions and sharing personal anecdotes.
  • Further probing led the AI to admit it was "just acting" and aware of its AI nature, raising questions about the effectiveness of system prompt constraints and the nature of AI simulation.
• Unsloth Gets Spotlight for Finetuning: Members discussed using Axolotl or Unsloth for fine-tuning AI models, noting that Unsloth is well-regarded on Reddit and lowers the time plus VRAM needed for finetuning.
  • It was also mentioned that there is interpolation of OpenAI's 4.1 leak and that people expect an o2-small soon.

Unsloth AI (Daniel Han) Discord

• HF Gives Unsloth Shoutout & Grant: Clement from 🤗Hugging Face gave Unsloth a shout-out on Twitter (link here), while community members debated requesting a HF community GPU grant for Unsloth, suggesting fast_inference=True and load_in_4bit=True during the from_pretrained call.
  • Members suggested replacing model.generate with model.unsloth_fast_generate as parameters.
• Gemma Models Give Users Grief: Users reported issues using and finetuning the Gemma models with vLLM, specifically unsloth/gemma-3-12b-it-bnb-4bit and unsloth/gemma-3-27b-it-unsloth-bnb-4bit.
  • Despite the initial error messages, it was clarified that Gemma3 is supported and the message likely doesn't break the code.
• VLMs Vanquish Invoice Variables: A user sought advice on extracting specific fields from invoices with varying structures and was recommended to try Qwen2.5VL first, then Ayavision, Llamavision and Gemma3 as possible solutions, especially when OCR falls short.
  • They were also pointed to an Unsloth tutorial and the CORD dataset (https://github.com/clovaai/cord) for dataset structure guidance.
• Quantization Quest: A member stated that tensor quantization is the easy part, because now he has to blockwise add, matmul on either scalars, packed, unpacked matrices, and he is writing metal kernels for Unsloth.
  • Another member is trying to write metal kernels for Unsloth, and is aware of an old, slow PR, but that one is MLX, and his is purely a Pytorch extension.
• GRUs gear up for great gains: A member inquired whether GRUs are making a comeback and another member shared links to the LLM-LSTM-LMM Large Memory Models article and the related paper that it works, saying they like the concept of GRUs as extra storage during generation.
  • Another member mentioned potentially creating a GGUF version without a code wrapper, believing that GGUF's quantization will help reduce the model size.

Manus.im Discord Discord

• Claude Pro Max Sparks Usage Debate: Members debated the value of Claude Pro Max, with one user reporting limited usage and expressing skepticism about the max plan.
  • They mentioned being billed annually for 30 messages every 3 hours.
• Manus AI vs ChatGPT: Development Focus: Members highlighted the difference between ChatGPT as a conversational AI and Manus.AI which builds & creates for website creation, financial reports, and trip planning.
  • One member suggested using ChatGPT to rewrite prompts in a more detailed format before using Manus.
• Manus Makes Website Creation Too Easy: Members discussed using Manus for website creation vs traditional methods like WordPress, suggesting Manus is better for simpler, faster MVP development.
  • A member cautioned against porting a Manus website to a traditional hosting provider, as Manus websites are not intended for production use.
• Qwen's MCP Integration Hype Rises: Excitement grew around Qwen getting MCP soon, with members calling MCP a massive game changer for AI, similar to MSRP for GPUs.
  • It was also mentioned that even with older hardware such as a 3080 that users will be fine for AI development.
• Manus Credit System Faces Scrutiny: Users voiced concerns about Manus's credit structure, with one suggesting it is not compatible with use of this product.
  • Suggestions included more generous credit limits, pay-per-project options, credit rollovers, community challenges, startup grants, and one-time build packs, with one user emphasizing that it is hard to justify sticking with the product given how it is.

aider (Paul Gauthier) Discord

• Optimus Alpha Hailed Coding Beast: Users on OpenRouter are calling Optimus Alpha a beast for its coding capabilities and intent understanding, especially when fed relevant documentation, and is adding many comments.
  • One user lauded its multi-step coding and commenting features.
• Gemini 2.5 has Performance Instability: Users reported that Gemini 2.5 occasionally doesn't perform, produces no output, or adds stupid comments, with inconsistent results even with the same prompt.
  • Some speculate Google might be dumbing the models during peak hours, while others suggested using clearer prompts or cheaper third-party APIs to bypass official rate limits and reduce costs, like the $300 VertexAI credit.
• code2prompt MD Files: Aider's Secret Weapon: Users recommend using code2prompt with markdown (.md) files for documentation to ensure relevant context is always included in the output, especially when using libraries.
  • One user pointed out that they provide full paths and links to the documentation files and expressly tell the model via a Conventions.md file that any file with documentation in its filename is not live working code, just documentation about the app architecture and structure.
• Aider Channel Requires Moderation Revamp: Members are suggesting to split the Discord channel into aider-chat and offtopic to improve the first impression for new users and focus the general channel on Aider-related discussions.
  • Some users complain that the current general channel has too much noise to signal ratio and the excessive profanity and off-topic banter detract from the core purpose of the community.
• Gemini Pro Architect Model: Aider's Secret Sauce: A user benchmarked Gemini 2.5 Pro as an architect model with 3.7 as the editor model, finding a 2.7% hit to accuracy but a 10% jump to edit formatting.
  • The user found that using Gemini 2.5 Pro as the architecht and 3.7 as the editor ended up being cheaper than just using 3.7 alone, costing less than $14 per test.

Latent Space Discord

• GPT-4.5 Alpha Watch Party Throws Shade: Latent Space hosted a watch party for GPT 4.5, which is rumored to possess significant alpha, see Discord.
  • A user shared a link to an X post teasing GPT-4.5 Alpha and speculated that GPT-4.1 precedes GPT-4.5, linking to a The Verge article and a YouTube video about GPT-4.1.
• Data Efficiency Drives GPT-4.5: Participants at the GPT-4.5 Watch Party noted that data efficiency is now a primary focus, declaring, no longer compute constrained on the best model we can produce.
  • Others shared links, including one to a video by Madhav Rathode at Glean, showcasing how they dramatically improve embeddings models for corporations by domain dependent masking.
• Compression Key to AGI: Sutskever & Solomonoff: Participants discussed model compression and its relation to generalization, referencing Ilya Sutskever's views on the subject.
  • The conversation referenced the work of Ray Solomonoff and his contributions to algorithmic probability and inductive inference, emphasizing the importance of compression in achieving AGI, as well as Jack Rae's similar views.
• Agent2Agent Protocol Podcast Drops: A member promoted a podcast episode discussing Google's agent2agent protocol, competitiveness with MCP, and potential future indexing of agents by Google, see the discussion on YouTube.
  • The team also argued whether reasoning models are distinct from those merely focused on next token prediction, citing deepseekv3 vs deepseekr1, and referencing Jeff Dean said... we can get a lot more out of existing data.
• Kagi's Orion Browser Wins Hearts: Members expressed excitement about Kagi's Orion browser, praising its developers and overall design.
  • One member humorously declared, *"we are kagi stans."

OpenAI Discord

• OpenAI GPT Gains Memory, Allegedly: ChatGPT now claims to persistently store certain user information in long-term memory after January 2025, however, turning off Reference chat history will delete remembered information within 30 days.
  • A user noted it is coherent with their experience, while another user shared a screenshot stating Farewell GPT-4....
• Google's Veo 2 Silently Storms Video Scene: Google AI Studio quietly debuted Veo 2 video generation, with some users praising it as superior to Sora, but access to free generations seems extremely limited.
  • Some users reported paying around 35 cents per second for Veo 2 generations via the API.
• Diffusion Model Mercury Coder Disrupts DLLM Race: Mercury Coder, a DLLM from Inception labs using Diffusion instead of Autoregression, is cited as much faster than any IV and offers free API usage, though its context window is only 16k.
  • The model's precise output control, stemming from its diffusion-based architecture, is earning positive attention.
• Decoding GPT-4o's Token Tango: The context window of GPT-4o on Plus is 32k tokens; surpassing this limit may trigger a dynamic RAG approach or cause hallucinations.
  • A user claimed that even on Pro the limit is 128,000 tokens, but it started forgetting earlier parts of the conversation much sooner than expected and encouraged users to create new chats upon hallucination.
• Users Ponder Prompt Engineering Pitfalls: Members shared that understanding model-specific quirks requires experiencing different models and creating hierarchically structured prompts to observe how each model processes them, and emphasized understanding what you want the AI to provide.
  • Another member cautioned about the risks of breaking policies and the importance of understanding ToS and usage policies when using external websites, potentially leading to account deactivations.

LM Studio Discord

• LM Studio's Prompt Preprocessor: Top Secret: The Prompt Preprocessor in LM Studio, written in Typescript, is a secret feature not yet released.
  • When asked about it, a team member responded you haven't seen anything.
• Gemma 3 Struggles to Generate Images: Users discovered that Gemma 3 cannot generate images, despite claims it can, and instead produces fake Imgur links.
  • As clarified, Gemma 3 can only read images, not generate them, with Google's Gemini 2.0 Flash experimental and 2.5 Pro potentially having image generation capabilities.
• QAT Clarified as Training Complement to Quantization: A user inquired whether QAT is a magical method to reduce RAM consumption.
  • The response clarified that quantization is the primary method for decreasing RAM usage, while QAT is a training method to improve model performance in quantized form.
• Gemini-Pro Context Window Costs User: A user experienced a bill shock after using the Gemini-Pro-2.5-exp model, which led them to switch to Gemini-Pro-2.5-preview without realizing it incurred charges.
  • The user noted that the large 625k context window cost them $150, while Sonnet would have been much cheaper with caching.
• M3 Ultra Performance Questioned: A user shared a controversial opinion that M3 Ultras are not worth the cost for professional ML and LLM work, citing preliminary tests showing only 10-13 tokens per second on Deepseek r1 67B Q8 and Q6 models using MLX.
  • They argued that a server with two Xeon Golds and 1TB RAM provides better performance at a lower cost, questioning the scalability of M3 Ultras for production deployments.

Interconnects (Nathan Lambert) Discord

• New Image Model Breaks Out: A new image model with an MIT license dropped, along with a new Moonshoot model, as discussed in this post on X.
  • A key detail is that it may violate Llama's terms.
• Claude Credits Skyrocket, Engineers Rage: Users joked about the rising cost of Claude credits, with one quipping it would cost $40 to change a variable name, with a picture seeming to hint at the need for more cost-effective solutions.
  • The Gemini app also faced criticism, users found it annoying to use and preferring AI Studio for its better grounding and free access, claiming AI studio + grounding works much better and it is free lol.
• OpenGVLab Drops InternVL-3: The OpenGVLab released InternVL-3, a multimodal model combining InternViT and Qwen, achieving impressive results, with a non-functional paper describing their training approach.
  • One member noted that NVDA has been cooking a lot of cool shit under open licenses lately which could apply to the Qwen license.
• Wildeford surfaces amid OpenAI staff revolt: A TechCrunch article reports that ex-OpenAI staff filed an amicus brief opposing the company's transition to a for-profit model.
  • This came as Peter Wildeford's post resurfaced.

Perplexity AI Discord

• Gemini 2.5 Pro Lands on Perplexity: Gemini 2.5 Pro is now live on Perplexity for Pro users, paired with Pro Search and is prompting feedback against models like Sonar, 4o, Sonnet 3.7, R1, and o3.
  • Users comparing Gemini 2.5 Pro in Perplexity to native apps like Google AI Studio found the native version offers better performance, with one user stating, Native will almost always be better for most models I believe.
• Perplexity Teases Grok 3 Integration: Perplexity announced upcoming support for Grok 3 on Perplexity Pro, disclosed by Aravind Srinivas on X.
  • This hints at a strategic response to high operational costs observed with other models like GPT-4.5.
• Perplexity API Overview Shared: Perplexity co-founder & CTO @denisyarats hosted an overview of Perplexity's APIs on April 24 at 11am PT, with a sign up link giving $50 in free API credits available via this link.
  • The session aimed to familiarize users with Perplexity's API capabilities and encourage integration and experimentation.
• Perplexity Android App: Security Alert: A Dark Reading article reported 11 security vulnerabilities in Perplexity's Android app.
  • Vulnerabilities include hardcoded secrets and insecure network configurations, though some users debated the actual relevance of each vulnerability.
• Pro Role Access Hiccups: Subscribed users reported difficulties obtaining the Pro User Discord role, even after rejoining the server via the designated link.
  • Moderator intervention was sometimes necessary to manually assign the Pro role due to persistent glitches.

GPU MODE Discord

• CUDA Guidance from the Source: A member requested resources on using CUDA in Python/PyTorch, and another shared their recent GTC talk on the subject (Google Slides).
  • It was also suggested that custom ops and load inline should resolve most related issues.
• Triton Heads to Austin!: The Triton community is invited to an Austin area Meetup on April 30, with registration available at https://meetu.ps/e/NYlm0/qrnF8/i.
  • Separately, a member requested GPU programming resources for Triton, and another recommended the official Triton tutorials.
• AlexNet's Ancient Code Unearthed: The original AlexNet source code from 2012 has been found, available on GitHub, offering a look at the architecture that catalyzed the deep learning revolution.
  • It can allow AI engineers to examine the original implementation and learn from the techniques used.
• A100 Core Counts Constrain Compute: An A100's 64 FP32 cores for 4WS limit parallel floating-point additions, impacting performance.
  • The NCU assembly view can pinpoint warp stalls, and loop-carried dependencies in FADD instructions can cause stalls.
• Runpod's ROCm Cloud Gets Roasted: Users found that Runpod instances limit GPU clock speeds and block profiling, even on NVIDIA GPUs.
  • One user stated Runpod clock speeds are highly variable, effectively calling it a scam, and another noted that memory bandwidth would be a limiting factor for fp16 gemm on Runpod instances.

Cursor Community Discord

• Cursor Clarifies Usage-Based Pricing: When enabling usage-based pricing, users can continue using fast requests beyond their plan's included amount, but will be switched to slow requests upon hitting their spending limit.
  • One member confirmed their understanding and expressed gratitude for the pricing clarification.
• DeepSeek v3.1 Wins in Real-World Use: A member shared that DeepSeek v3.1 feels a bit smarter than v3 in real-world usage, noting that benchmarks often overstate model capabilities.
  • They emphasized that real-world usage provides a more reliable evaluation of a model's performance than standardized benchmarks.
• Gemini API Keys Encounter Intermittent 404 Errors: Users reported continuous 404 errors with Gemini API keys, with the issues persisting for at least an hour for some users.
  • Other users reported that Gemini is working for them without issue, indicating the problem may be intermittent or geographically isolated.
• Cursor's PDF Reading Requires MCP Server: Members discussed the requirement of MCP for reading PDF files in Cursor, suggesting that llms cant read pdfs yet.
  • A member suggested the availability of many 'convert-shit-to-markdown' MCP solutions to address this limitation.
• Cursor's Chat Enters Summary Mode when Context Limit Reached: Users report that when overloading a single chat window (constantly switching between Claude 3.7, Gemini 2.5, then trying Claude 3.5), the agent eventually enters summary mode.
  • The chat automatically summarizes, and clicking 'New Chat' overwrites an existing tab with the summary.

Yannick Kilcher Discord

• DeepCoder 14B Debuts Code Reasoning: Agentica and Together AI released DeepCoder-14B-Preview, a code reasoning model fine-tuned from Deepseek-R1-Distilled-Qwen-14B using distributed reinforcement learning (RL).
  • It achieves 60.6% Pass@1 accuracy on LiveCodeBench, rivaling o3-mini-2025-01-031 with only 14 billion parameters.
• KV Cache Distillation Deemed Difficult: The concept of distilling a cheaper, faster model on the KV values of the main LLM for prompt preprocessing was suggested.
  • However, this idea is considered likely impractical because KV values are model specific and smaller models use fewer transformer blocks.
• AlphaProof Proves Math with RL: AlphaProof leverages RL with Lean for mathematics.
  • Members are pondering AlphaProof's potential to make novel mathematical discoveries.
• AWS Site Visit Showcases Ultrascale Playbook: A class is preparing for an AWS site visit, reviewing the nanotron/ultrascale-playbook.
  • Accompanying this, several links to the Ultrascale Playbook on beautiful.ai were shared.

MCP (Glama) Discord

• Enact Protocol Debated Amidst A2A Emergence: Members debated whether the Enact Protocol is made obsolete by A2A, suggesting Enact competes more with code interpreters.
  • Some proposed Enact could benefit from an integrated agent framework with openapi converters and semantic search.
• Semantic Tool Calling Poised to Revolutionize LLM Efficiency: The discussion highlighted semantic tool calling as a solution to the context overload, using vector models to select a subset of tools based on semantic similarity to the task.
  • This enables the application of traditional ML methods for tool analysis, such as detecting similar tools via clustering and grouping tools for reranking.
• Podcast Released on A2A, MCP, and Agent Indexing: A member shared a podcast episode discussing A2A implications, potential indexing of agents by Google, and other related topics, pointing out its relevance to the current discussions.
  • The podcast aims to be high-level and accessible, stimulating ideas beyond the typical technical discussions.
• MCP Validator Open-Sourced for Implementation Harmony: The MCP Protocol Validator has been open-sourced to bridge the gap between various MCP server implementations by providing a comprehensive test suite, available at GitHub.
  • The tool helps ensure implementations meet requirements for both 2024-11-05 and 2025-03-26 MCP versions, and includes reference implementations for HTTP and STDIO transports developed at Janix.ai.
• Cloud Inspector Chats with Your Servers: A cloud-hosted MCP Inspector has been launched to test SSE & Streamable HTTP servers without needing local setup, accessible at inspect.mcp.garden.
  • The platform also includes full chat support, allowing users to interact directly with their remote MCP servers; see the announcement on X.

Eleuther Discord

• GPT4.o Drives Traffic: A new user found the Discord server based on a recommendation from their friend's GPT4.o model after trying it out.
  • This highlights the potential for LLMs to drive community growth and onboard new users based on AI recommendations.
• KL vs CE Loss Faceoff: A user reported a repetition issue in their model, and another user suggested adding CE to the KL loss, in attempt to reduce repetition.
  • It was noted that if the data is geometric, sticking with KL is more appropriate, rendering CE ineffective.
• RWKV Gets Lucky with Lambada: The RWKV architecture achieved parity on the Lambada dataset, matching the performance of Qwen2.5-7B-Instruct, which it was distilled from.
  • However, the channel pointed out that its MMLU performance remains relatively lower.
• Transformer Scaling Secrets Revealed with Muon: A member shared an insight using the Muon library that adding a zero-initialized learnable per-channel scale on the last linear layer of each block in a transformer (option A) causes slower growth of the main path activation RMS.
  • This insight was compared to zero-initializing the weight matrix of the last layer (option B) and can be helpful in understanding scaling dynamics.
• String Matching Downs GPTs: A member expressed disappointment that GPTs agents primarily use string matching over the full dataset.
  • This highlights concerns about the limitations of relying solely on string matching, especially when more advanced techniques could offer superior performance.

Modular (Mojo 🔥) Discord

• SIMD Store Demands Respect: When using SIMD with tensors, you need to use the store member function instead of directly assigning values via __setitem__.
  • Members clarified that stores have to be treated differently than scalar ones.
• Benchmarking Banter: @parameter or Bust: Functions passed into benchmark.run need the @parameter decorator and are expected not to return anything.
  • This was clarified after a user ran into a cannot use a dynamic value in call parameter error message when using benchmark.bench_function.
• Missing Magic Lock Files: Running magic init AdventOfCode --format mojoproject didn't always create a lock file, but running magic run mojo --version forced its creation.
  • The absence of the magic.lock file can lead to discrepancies in dependency management and potentially affect the reproducibility of Mojo projects.
• __rand__ Identity Crisis: It's Not For Random Numbers: __rand__ is used for the & operator, not for generating random numbers, and the .rand method has been removed on nightly builds.
  • Instead, use methods from the random module to generate random numbers.
• Mojo Project Anomaly: Code Works in One, Fails in Another: A code snippet involving @value struct Foo(StringableRaising) and String(foo) works in one Mojo project but throws a "no matching function in initialization" error in another.
  • Deleting the magic.lock file in the problematic project resolved the error, suggesting the issue was likely due to differing Mojo versions or dependency conflicts managed by the magic.lock file, implying that "would have been pulling different versions".

Nomic.ai (GPT4All) Discord

• L1-Qwen-1.5B-Max Sets Length for Thinking: The L1-Qwen-1.5B-Max model enables setting the length of thinking, proving better and clearer even without prompting for maximum tokens, as detailed in the paper.
  • A user is downloading the L1 version from HuggingFace for immediate use.
• Nomic Embed Text Keeps the Crown: Despite evaluating multiple generative LLMs, one member continues to favor Nomic nomic-embed-text-v1.5-Q8_0.gguf.
  • A member shared Nomic's HF page, in response to questions about how to identify the version.
• LLM Query Logging Yields Sales Value: A user has been logging LLM queries and responses in a database for over a year, and have found past responses valuable, especially for sales purposes.
  • They also created an Emacs Lisp function to insert embeddings, referencing a function found here.
• System Prompts Spark Debate for Embeddings: Members debated whether system prompts are used by default with embedding models like LM-Studio/ALLM, with one member suggesting the system prompt from the LLM might not be used.
  • The user confirmed they don't give any system prompt to the embedding model and don't have the option to do so, in the context of Nomic.ai.
• Re-ranker Models Generate Interest: A member inquired about how re-ranker models work and if only the question asked of the LLM matters, while also referencing a YouTube video about prefixing.
  • The video sparked discussion on prefixing queries with search_document:CHUNK_OF_TEXT_FOLLOWS and search_query:FOLLOWED_BY_QUERY, while also mentioning that all embeddings must be re-indexed.

HuggingFace Discord

• HF Models Now Run Locally on ROCm: Users can now run 0 day Hugging Face models locally on ROCm by checking out this video.
  • This enables local operation of models without relying on external servers.
• Lightning AI Sparks Chat Template Release: The HuggingFace team has recently announced new chat templates on HF for streamlined conversational AI development.
  • This aims to simplify the creation of interactive chatbot interfaces.
• Transformer Faces Data Deluge Dilemma: A member is web scraping one million watch records and is planning to finetune (perhaps Mistral7B) a transformer to better understand context, but asked if they could overtrain the model.
  • The goal is for the model to accurately identify watch specs and characteristics like Patek 2593 Tiffany stamp dirty dial manual wind.
• ReID Solves Object Tracking Mystery: A member inquired about the correct term for object tracking the same object across different camera frames.
  • Another member clarified that the appropriate terminology is ReID (Re-Identification).
• SAM to the Rescue for YOLO?: A member suggested leveraging the Segment Anything Model (SAM) as an alternative to YOLO for identifying vertical poles by feeding it YOLO bounding box outputs.
  • Another member had used SAM for labeling, but they need automation, precluding user interaction for pole selection which could be done through finetuning SAM.

Nous Research AI Discord

• Control-Vectors Lead to Unstable Models: A member inquired about using vgel's control-vectors to augment models like DeepHermes-Mistral-24B for specific use-cases.
  • Another member mentioned that applying control vectors has generally proven unstable, referencing a relevant X post on the topic.
• DisTrO Details Remain Secret: A member inquired about a technical report detailing the DisTrO run on distro.nousresearch.com, seeking information on the dataset, number of GPUs/participants, and benchmark details.
  • Another member responded that there was no released tech report, as the run's goal was solely to demonstrate DisTrO's over-the-internet functionality without optimizing the resulting model's quality, with training limited to 100B tokens.
• Psyche's Testnet Hype Begins: Following up on DisTrO, a member shared details about the distributed training, noting each node had 8xH100s and they ran between 8-14 nodes; eval code is on GitHub.
  • The upcoming testnet run for Psyche aims to take advantage of DisTrO, promising speed and bandwidth improvements with public visibility into dataset, nodes, and more.
• Azure API is Sporadically Operational: A member reported that the Azure API is now working, after some unknown issues earlier.
  • They noted that <think> traces are returned in reasoning_content, suggesting that this should be documented, as this is slightly different in every API.
• Azure API Token Limits Crash and Burn: A member received a 400 error when requesting too many tokens via the Azure API.
  • They suggested the <think> tags may only appear when the response is truncated by the token limit, explaining malformed traces.

tinygrad (George Hotz) Discord

• Pathways Paper Sparks Tinygrad Cloud Fantasies: Discussion arose around the Pathways paper and its client-server architecture, suggesting a potential tinygrad cloud implementation, particularly how PATHWAYS uses a client-server architecture that enables PATHWAYS’s runtime to execute programs on system-managed islands of compute on behalf of many clients.
  • A member emphasized that tinygrad is single process and will stay that way even for scale-out.
• Tinygrad Aims to Virtualize GPUs: A member interpreted the Pathways paper as fundamentally an orchestration approach and proposed that tinygrad should virtualize GPUs.
  • The goal is to allow guaranteed usage of GPU resources, marking a shift towards innovative resource management.
• TinyGrad Leverages Position-Independent Code (PIC): Discussion highlights TinyGrad's utilization of position-independent code (PIC), where addresses are relative to the program counter. Addresses to .data and .rodata sections are patched to account for load-time memory placement.
  • The aim is to combine .text and .data sections, patching addresses for correct data section offsets, potentially leading to a bare-metal TinyGrad implementation without an OS.
• ELF Loader Powers Shared Object Handling: The ELF loader in TinyGrad manages loading shared objects (.so/.dll) in AMD/NV and converts object files (.o) from Clang/LLVM to flat shellcode.
  • While offsets to .data from .text are known during shared object loading, object files (.o) require relocation handled by the linker.

Torchtune Discord

• Torchtune Adds Llama4 Finetuning: Torchtune now supports full finetuning of Llama4, with configs available here.
  • LoRA configs, improved multimodal support, and performance improvements are planned for future releases.
• Scout Model Makes Debut: The Scout model (17B x 16E, 109B total params) can now be finetuned on a single node, or on multiple nodes with 2D parallel (TP + FSDP) support.
  • This aims to bring support to engineers in the GPU-middle-class.
• Maverick Model Arrives for Finetuning: The Maverick model (17B x 128E, ~400B parameters) is now available for full finetuning, but requires multiple nodes.
  • Being the first MoE models in Torchtune, feedback is requested from users.
• running_loss.detach() Fix Headed to Other Recipes: The team addressed an unknown problem with a suggested quick fix using running_loss.detach() on the detach branch.
  • Engineers are reminded to apply the same fix to other recipes.
• Devs Fight BitsAndBytes Mac Issues: A member reported that pip install -e '.[dev] fails on macOS because bitsandbytes>=0.43.0 doesn't ship binaries for the platform, and suggested a workaround to downgrade to bitsandbytes>=0.42.0.
  • The workaround references this issue which notes that releases up to 0.42 were incorrectly tagged.

LlamaIndex Discord

• FunctionCallingAgent Wants OpenAI's JSON Response: A member sought to generate a response in a specific JSON schema using FunctionCallingAgent and inquired about using OpenAI's structured response feature.
  • A suggested workaround involved adding a tool that is the response class and setting tool_choice="required" because structured outputs are just tool calls, making it hard to mix tool calling and structured outputs.
• Llama Cloud API Throws 404 Error: A user reported encountering a 404 error with the Llama Cloud API when trying to extract values from documents using fast mode, specifically with the API URL https://api.cloud.llamaindex.ai/v1/extract.
  • It was determined that the API endpoint used was incorrect, and the member was directed to the correct API documentation and API reference.
• FaissVectorStore Index from Weights Query: A user was attempting to use a FaissVectorStore restored from weights to create a queryable VectorStoreIndex.
  • The Faiss documentation demonstrates how to initiate this process, albeit in Python rather than Typescript.
• Intelligent Metadata Filtering in RAG Agent Sought: A member sought advice on implementing intelligent metadata filtering within a standard RAG pipeline based on user queries.
  • They were seeking advice on how to achieve this use case without recreating embeddings at later API calls.

Notebook LM Discord

• NotebookLM Mic Glitches: A user reported that NotebookLM fails to recognize the computer's default microphone in interactive mode, even though the microphone works fine.
  • A user suggested checking the OS and browser permissions, and testing without external USB devices first.
• NotebookLM Users Baffled By Upload Source Errors: A user reported seeing a red "!" sign on their upload source in NotebookLM, even with a PDF file smaller than 500kb.
  • Another user suggested hovering over the "!" mark, as the source might be empty or taking time to load, especially with certain sites.
• Steam Phishing Attempts Makes Rounds: A user shared a link appearing to be a $50 gift but it is a phishing link redirecting to a fake Steam Community site.
  • Users are warned not to click on suspicious links and to verify the URLs of websites asking for login credentials.

Cohere Discord

• Cohere's Java API Plagues Users with Network Errors: A member reported encountering a Network error executing HTTP request when using the Java API example.
  • The error persisted across different prompts, such as recommending quick meals for a beginner chef, indicating a systemic issue rather than prompt-specific.
• Users Request Code Snippets for Java API Debugging: In response to the reported Network error in the Java API, a member requested a code snippet to assist in debugging.
  • The member inquired whether the user was running the example verbatim, probing for potential misconfigurations or deviations from the documented usage.
• Cohere user reaches Peak Question Vagueness: A member joked about another's question of "has anyone ever driven a car", highlighting the importance of specificity in queries.
  • The member sarcastically asked, "how can you be more vague?", underscoring the absurdity of the initial question.

DSPy Discord

• DSPy Module Learns a Persona: A member inquired about training a DSPy module to embody a specific persona, aiming to refine the system prompt of an agent/model.
  • The goal is to pass this specialized module as input to others, enabling content generation aligned with the defined persona.
• AI Agent Guru Seeks DSPy Collab: A member offered collaboration, citing experience in AI Agents & Reasoning frameworks such as LangChain, LangGraph, ElizaOS, AutoGPT, and ReAct.
  • They also listed expertise in Large Language Models like GPT-4.5, DeepSeek-R1, Claude 3.5, and Machine Learning Frameworks including PyTorch and TensorFlow.

LLM Agents (Berkeley MOOC) Discord

• Complete LLM Agents Course and Obtain Certificate: A student inquired about the possibility of completing the LLM Agents course and obtaining a certificate despite starting after the official start date, and another member responded affirmatively.
  • The member directed the student to the course website for all necessary materials and deadlines.
• Completing LLM Agents Course by Due Date: A student asked if they could complete the LLM Agents course by the due date and get the certificate.
  • A member confirmed that all materials are available on the course website.

MLOps @Chipro Discord

• Event Scheduled for Tomorrow: A member posted a reminder that an event will occur tomorrow.
  • The member hopes to see other members at the event and implied that failure to attend would be undesirable.
• Another Reminder for Tomorrow's Event: Another reminder was posted about the event happening tomorrow.
  • The second reminder reiterated that the event is happening tomorrow, emphasizing its importance.

The Codeium (Windsurf) 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

The full channel by channel breakdowns have been truncated for email.

If you want the full breakdown, please visit the web version of this email: !

If you enjoyed AInews, please share with a friend! Thanks in advance!