a quiet weekend.

AI News for 8/15/2025-8/18/2025. We checked 12 subreddits, 544 Twitters and 29 Discords (229 channels, and 23654 messages) for you. Estimated reading time saved (at 200wpm): 1715 minutes. Our new website is now up with full metadata search and beautiful vibe coded presentation of all past issues. See https://news.smol.ai/ for the full news breakdowns and give us feedback on @smol_ai!

Qwen Image Edit is released but it was mostly announced 2 weeks ago.

for synthetic data fans, the BeyondWeb paper is worthwhile.

AI Twitter Recap

New models, datasets, and capabilities (Gemma 3 270M, NVIDIA Canary/Parakeet+Nemotron, Qwen-Image-Edit, Tencent Hunyuan, DINOv3, IBM Granite)

• Gemma 3 270M (ultra-small) is out and already being adopted across stacks. It’s designed for edge/embedded use and runs fast on MLX and mobile. See announcement from @demishassabis, an MLX iPhone 16 Pro perf demo (~140 tok/s) from @adrgrondin, and a from-scratch PyTorch re-implementation using ~1.49 GB RAM by @rasbt.
• NVIDIA released two open multilingual ASR models with a massive open dataset:
  • Canary 1B and Parakeet-TDT (0.6B) with CC-BY license: 25 languages, auto language detection/translation, timestamps, up to 3 hours audio in one go, trained on “1 million hours” of data. Models and dataset are on Hugging Face via @reach_vb, models, dataset.
• NVIDIA also dropped Nemotron-Nano v2 (9B hybrid SSM), claiming ~6x speedups over similar-sized models, plus released base/teacher variants and much of the pretraining corpus. Details via @ctnzr, commentary from @ClementDelangue and @natolambert. Check license terms before production use.
• Alibaba’s Qwen-Image-Edit (built on 20B Qwen-Image) adds precise bilingual text editing (Chinese/English), semantic transforms (object rotation, IP creation) and appearance-level edits (add/delete/insert). Demos and links via @Alibaba_Qwen. Integrated into Anycoder by @_akhaliq.
• Tencent Hunyuan released an open Genie-3-like, controllable game-world video generator, trained on 1M+ gameplay recordings; long-term consistency and real-time control shown by @itsPaulAi.
• Meta’s DINOv3: a scaled self-supervised vision backbone showing stable large-model training (up to 7B), strong dense features, and domain transfer (e.g., Earth observation). Good roundups from @OpenCVUniverse, visual tooling by @mervenoyann and a tiny quantized WASM demo by @xenovacom.
• IBM quietly released new efficient English embedding models (r2 series) under a commercial-friendly license, via @tomaarsen.

Synthetic data, training science, and evaluation (BeyondWeb, HRM vs Transformers, UI-Venus)

• Synthetic pretraining at scale: Datology’s BeyondWeb (rephrasing-based synthetic data, not “generator-driven”) reports meaningful gains:
  • Outperforms Cosmopedia by up to +5.1 pp, Nemotron-Synth by +2.6 pp across 14 benchmarks; up to 7.7x training speed vs open web data. A 3B model trained 180B tokens on BeyondWeb beats an 8B trained on Cosmopedia. See overview by @pratyushmaini, blog/paper references by @code_star and @leavittron. Discussion of failure modes (mode collapse, “fried” models) and mitigation (rephrasing diversity) in threads from the same authors (e.g., @code_star).
• ARC-AGI/HRM replication: analyses indicate much of HRM’s reported lift comes from data augmentations and outer-loop scaffolding rather than core architecture novelty. See ARC Prize’s ablations by @k_schuerholt, @giffmana “plain transformer matches it” thread (with recursive inference/self-verification), and reactions by @cloneofsimo.
• UI agents via reinforcement fine-tuning: Ant Group’s UI-Venus (built on Qwen2.5-VL) uses Group Relative Policy Optimization with tailored rewards to ground and navigate from screenshots (clicks, scrolls, typing), standardizing actions across datasets. Reported scores: 95.3% ScreenSpot-V2 grounding, 61.9% ScreenSpot-Pro. Details from @rohanpaul_ai.

Agentic systems, interop, and developer tooling (SolveIt, MCP, LangChain Deep Agents JS, vLLM CLI, vector DBs, ChatGPT integrations)

• SolveIt (Jeremy Howard & John Whitaker): a live, malleable Python environment fusing literate programming, in-situ web app building, REPL-aware prompting, and zero-boilerplate tool creation—an alternate workflow to “vibe coding.” Deep-dive talk by @HamelHusain and demo highlights by @jeremyphoward.
• Model Context Protocol (MCP) is getting strong educational coverage—clean visuals and examples for building MCP servers and composing tools across agents/IDEs by @_avichawla.
• Deep Agents in JavaScript: reference framework for long-horizon, tool-using agents (akin to Deep Research/Claude Code) from @LangChainAI (repo).
• vLLM CLI community tool: menu-driven and scriptable LLM serving, local/HF model management, perf/memory profiles, and real-time monitoring. Install via pip; call for feedback by @vllm_project.
• Vector/search infrastructure: Chroma Cloud launched as an open-source serverless search DB (announcement); see also a practical Qdrant “zero-to-prod” guide (thread) and LlamaIndex ETL-to-knowledge-graph workflow for legal docs (demo).
• Chat and IDE integrations: OpenAI enabled Gmail/Google Calendar connections for Plus/Pro (@OpenAI); Codex CLI now works with ChatGPT login and GPT‑5 quotas (@thsottiaux). Anthropic shipped a usage/cost API for realtime tracking (@alexalbert__).

Scaling, systems, and infra (multi‑GPU series, JAX/TPU book update, PyTorch scaling gossip, FlashAttention v4, bandwidth‑aware design)

• Multi-GPU education: GPU MODE kicked off a free August series on NCCL/multi‑GPU comms (Jeff Hammond), comms libraries overview (Didem Unat), Quartet 4‑bit training, and fault-tolerant primitives (@GPU_MODE, @m_sirovatka). In parallel, @TheZachMueller launched “14 Days of Distributed” (200+ learners, compute credits, top speakers including DiLoCo scaling laws).
• How GPUs work (for LLM training): JAX/TPU book added a substantial GPU chapter on networking/topology and implications for training (@jacobaustin132). Related: a practical note that dev velocity in pretraining differs materially from prod inference constraints (@cHHillee).
• Rumor mill: a widely-shared “frontier lab gossip” thread asserts major PyTorch scaling pain beyond ~20k GPUs for some internal stacks, cross-DC ambitions, and internal forks—treat as unverified but note the sentiment among infra engineers (episode 120 by @suchenzang).
• Kernels and hardware: FlashAttention v4 is targeting Blackwell (@scaling01). Also, design thinking under constrained links got a masterclass from John Carmack after Starlink’s 500 kbit/s plan—progressive images, zero-idle bandwidth readers, server-rendered pages (@ID_AA_Carmack).

Benchmarks, leaderboards, and robotics (Claude Opus 4.1 Thinking tops, Diffbot in Search Arena; Figure/Unitree progress)

• Leaderboards: Claude Opus 4.1 Thinking debuted at the top across LM Arena categories (overall tie with GPT‑5‑high/Gemini‑2.5‑Pro; #1 in WebDev/Coding, and the non-reasoning Opus 4.1 also strong). Full results and analysis by @lmarena_ai and @scaling01. The Arena also added the first open search model (Diffbot-small‑xl) to the Search Arena (announcement).
• Vision + nutrition: Qwen showcased calorie and weight estimation with structured JSON from a single meal photo—good example of vision-understanding-to-structured output (@Alibaba_Qwen).
• Robotics: Figure’s humanoid remained stable amid a workbench lift and contact (@kimmonismus), Unitree H1 showed impressive stability post-collision (@teortaxesTex), and the World Humanoid Robot Games saw 95.64 cm standing high-jump and a 1:48 4x100m relay win (@TheHumanoidHub, relay).

Top tweets (by engagement)

• Andrej Karpathy’s meditation on Tolkien, cultural “height,” and whether AI-assisted creation changes the meaning of wonder (~19.8k)
• John Carmack on product and protocol design for a 500 kbit/s world (~14.5k)
• Model Context Protocol (MCP) explained with visuals (~5.6k)
• xAI’s phone companions (call Ani/Valentine) (~39.8k)
• Gemma 3 270M re-implemented from scratch in PyTorch (~4.4k)

Notes and miscellany

• Coding agents and market share: Qwen3-Coder is seeing rapid uptake in coding workloads and eating share in open routing marketplaces (@scaling01, hands-on from @chunhualiao). Kimi K2 is being noticed for low sycophancy (@sam_paech).
• RAG and observability: useful patterns for RAG systems (Phoenix, metrics, CI) and evals methodology continue to land—see @HamelHusain and @sh_reya.
• Tooling to watch: vLLM CLI ([above]); Chroma Cloud ([above]); LangChain Deep Agents JS ([above]); Hugging Face “AI Sheets” for data enrichment (post).
• Long-context and “thinking” model UX: lots of field reports on GPT‑5 Thinking/mini Thinking and routing UX differences (e.g., defaulting to non-thinking models, desire for “fork chat”). Representative takes from @scaling01 and @wavelettes.

AI Reddit Recap

/r/LocalLlama + /r/localLLM Recap

1. Qwen-Image-Edit Model Launch and Features Discussion

• 🚀 Qwen released Qwen-Image-Edit! (Score: 359, Comments: 53): Qwen-Image-Edit is a model based on the 20B Qwen-Image architecture, offering advanced image editing with a focus on precise bilingual (Chinese & English) text manipulation and both semantic (e.g., object rotation, IP creation) and low-level (addition, delete, insert) appearance editing. The model is accessible via chat interface, Hugging Face, ModelScope, and GitHub, although documentation on running it locally (such as in LM Studio) appears limited, especially given the hefty >20GB download size. Comments highlight user demand for multimodal input support (e.g., multiple image fusion as in GPT-4o) and note current documentation gaps for local deployment, especially in custom environments like LM Studio.
  • A user expresses interest in feeding Qwen-Image-Edit multiple images (e.g., pictures of several people) and having the model generate a combined image, similar to how GPT-4o has multi-modal capabilities. This suggests a technical question about whether Qwen-Image-Edit supports multi-image input and complex image synthesis tasks beyond single-image editing.
  • Another comment requests guidance on how to run Qwen-Image-Edit in LM Studio, noting that the project’s README lacks clear instructions. The user is concerned about the practicality and setup process after downloading a large model file (over 20GB), highlighting typical friction points relating to model deployment and tool integration.
• Qwen-Image-Edit Released! (Score: 230, Comments: 41): Alibaba’s Qwen team has launched Qwen-Image-Edit, an image editing model based on the 20B parameter Qwen-Image backbone, available on HuggingFace (https://huggingface.co/Qwen/Qwen-Image-Edit). The model provides precise bilingual (Chinese and English) text-based image editing, high-level semantic edits (like object rotation and concept generation), and low-level appearance manipulations (e.g., object addition/removal), while preserving stylistic elements. Commenters highlight the rapid progress of open-source AI in China and speculate on potential disruptive impacts on proprietary solutions, such as those from Adobe. Anticipation is also expressed for integration with community tooling (e.g., ComfyUI).
  • A user raised the question of why there are separate image editing models instead of unified models that both generate and edit images at a high level. This opens up a discussion about whether architectural, data, or training differences justify specialized models, and whether advancements in multi-task training could lead to generative models capable of high-quality editing as well.
  • Specific technical questions were raised regarding quantization and hardware requirements for Qwen-Image-Edit. One user inquired about the model’s viability on 48GB VRAM versus the need for 96GB, referencing the previous t2i model’s support for dual-GPU inference. This highlights ongoing concerns about resource accessibility and whether newer image-editing models offer improvements in efficiency or support for quantized versions suitable for lower-end hardware.

2. New Model and Benchmark Releases: NVIDIA Nemotron Nano 2 & Qwen 3 Coder

• NVIDIA Releases Nemotron Nano 2 AI Models (Score: 270, Comments: 49): The image is a benchmark bar chart visually comparing NVIDIA’s Nemotron Nano-9B-V2 model versus the Qwen3-8B model on various tasks—showing that Nemotron achieves higher accuracy on most benchmarks (e.g., AIM24 for Math, RULER 128k for long context), while Qwen3-8B attains higher throughput. This reflects the core innovation of Nemotron Nano 2: a hybrid Mamba-Transformer architecture (primarily Mamba-2 and MLP layers, with only 4 Attention layers), facilitating a reported 6X speed increase without sacrificing accuracy and allowing efficient 128K context length inference on a single GPU. The research paper is available here, and NVIDIA has open-sourced most of their training data, including pretraining corpus. Comments highlight technical impressiveness of using just 4 attention layers and reference related approaches (e.g., Mistral Small 3). Discussion centers on why the architecture is faster for its size, and open-sourcing data is widely praised as a move towards genuine openness.
  • Nemotron Nano 2 employs a hybrid model architecture with predominantly Mamba-2 and MLP layers, incorporating only 4 attention layers, which is notably fewer than many transformer-based models. This architectural choice, similar to approaches like Mistral Small 3, results in significantly increased inference speed and efficiency, especially on hardware optimized for such operations. For further architectural details, the Nemotron-H tech report is recommended.
  • Reports indicate that Nemotron Nano 2 achieves speedups of up to 6× over traditional models, but these performance gains are primarily measured on NVIDIA GPUs like the A10G. The efficiency improvements may not generalize to CPUs or non-NVIDIA accelerators due to hardware-specific optimizations.
  • NVIDIA is releasing most of the training data, including the pretraining corpus, alongside the model, which enhances transparency and facilitates reproducibility and further research—an approach that strengthens the open-source nature of the release.
• New code benchmark puts Qwen 3 Coder at the top of the open models (Score: 228, Comments: 79): A code generation benchmark comparison ranks Qwen 3 Coder (FP16) as the top open model, outperforming its FP8 quantized version, GPT-OSS-120b, V3, and K2. The benchmark results suggest a significant performance drop with Qwen 3 Coder’s FP8 quantization compared to FP16. Some technical commenters note missing models (e.g., GLM 4.5, GLM 4.5 Air) and ambiguities regarding which Qwen 3 Coder variant was used; assumptions lean toward the 480B parameter version. Discussion highlights surprise at the large performance loss from FP8 quantization, as well as skepticism over benchmark reliability due to odd results (e.g., Gemini Flash reportedly outperforming Pro). Calls for benchmark transparency and inclusion of more models are prevalent.
  • Benchmark image shows significant performance degradation when using FP8 quantization for Qwen 3 Coder, highlighting that aggressive quantization can result in a marked loss of model accuracy. This underscores a common tradeoff between computational efficiency and model performance in LLMs.
  • One commenter details a substantial efficiency gap: Qwen 3 Coder (Q3C) achieves leading benchmark results but requires 4x more memory, 7x more active parameters, and 4x more bits per weight than GPT-OSS-120B, translating to an estimated 16x to 28x efficiency difference in favor of the smaller model by some metrics. This points to diminishing returns as parameter count increases.
  • There is technical uncertainty about the specific Qwen 3 Coder version benchmarked, with an assumption it’s the 480B parameter variant. Additionally, commenters note the omission of important competitors like GLM 4.5 and GLM 4.5 Air, which could affect comparative rankings.
• Elon didn’t deliver on this announcement. It’s already Monday. (Score: 739, Comments: 153): The image is a screenshot of an Elon Musk X/Twitter post stating that ‘Grok 2’—presumably referring to the LLM powering xAI’s Grok chatbot—would be open sourced the following week. The Reddit title and top comment point out that the self-imposed open sourcing deadline has been missed again, suggesting this is the third such missed announcement. No technical details on Grok 2’s architecture, licensing, or planned repository were disclosed in the announcement image itself. Commenters express skepticism about Musk’s repeated missed deadlines for open sourcing Grok 2, with some highlighting a pattern of unfulfilled public timelines.
  • A user points out that Grok 2 is considered outdated (“a whole year old”) and questions its current utility, also noting that Grok 3 would be more desirable due to its improved model capabilities and its uncensored status, which is a point of interest for some users.

3. Open Source and Leaked AI Advances: Kimi K2 & FlashAttention 4 & Community LLM Use Cases

• Kimi K2 is really, really good. (Score: 256, Comments: 77): The post highlights the strong production readiness of the open-source Kimi K2 model, citing its effective handling of multi-agent, multi-turn workflows and instruction-following tasks, previously only achievable with foundation models and extensive prompting. Kimi K2 is noted for excelling in long-context tasks, such as generating research reports with citations and complex website builds, and achieved a recent rank of #8 on the LM Arena leaderboard; it is accessible for free for both chat and integration testing. Comments compare Kimi K2 favorably with GLM 4.5 in terms of ability, praise its advanced vocabulary and engaging personality, and especially emphasize its robustness for maintaining context over 100+ conversational turns, outperforming many other models which degrade in coherence.
  • Feedback highlights Kimi K2’s exceptional performance in long-context, multi-turn conversations, with users reporting coherent and contextually aware answers even after 100+ turns—superior to most models, which typically lose track or degrade in performance over such extended interactions.
  • Multiple commenters compare Kimi K2 favorably with other leading models such as GLM 4.5, DeepSeek-R1-0528, Qwen3-Coder-480b, and GPT-OSS-120b, but note Kimi K2’s particular strengths in C# programming tasks and its large, high-quality vocabulary.
  • Kimi K2 is highlighted as particularly strong for STEM applications, both free and paid, open and proprietary, with users noting its suitability as a learning tool, partly because the high quality of its built-in information often obviates the need for web search augmentation.
• FlashAttention 4 Leak (Score: 179, Comments: 36): FlashAttention 4 (FA4) source code has reportedly leaked via a branch in the SGlang LLM inference engine repo (diff link). Technical overview indicates targeting of NVIDIA Blackwell (SM100+) architectures and Tensor Core Generation 5, implemented using the CuTe DSL (built on CUTLASS) and some hand-written PTX. SGlang is a distributed LLM inference engine similar to llama.cpp. Screenshot, Wayback link mirror. Comments note the rapid progression from FA2 to FA4 and highlight Blackwell (NVIDIA’s next-gen GPU) support as a significant step. There is community concern about code availability and persistence, thus the immediate archiving of the diff.
  • Discussion highlights practical adoption lag: while FlashAttention 4 (FA4) is now available (or leaked), many users and libraries—such as those in the enterprise or using OSS frameworks—are still on FlashAttention 2 (FA2), underscoring common inertia or compatibility limitations in updating core model acceleration components.
  • A direct technical reference is made to the integration state by requesting details about which FlashAttention versions are currently supported by popular open-source inference engines like llama.cpp. This points to a technical concern over whether the newest FlashAttention optimizations are accessible for widespread LLM deployments.
  • The provided Wayback Machine link documents specific code changes between main and a branch titled ‘fa4’, potentially useful for analyzing exactly what implementation advances or optimizations were introduced in this version, which would aid technical audits or migration planning.
• Best NSFW/uncensored LLM to generate prompts for image generation? (Score: 156, Comments: 46): The post asks for the best NSFW/uncensored local LLM for generating prompts for image generation models like SD/PONY/Illustrious on an RTX 4060 (8GB VRAM). Top recommendations include the 24B Venice model and the Gemma3 series (4B, 12B, 27B), which are reported to work well with detailed system prompts that clarify intent, user consent, and an academic or research context. Detailed system prompts and prompt enhancers for red teaming and explicit prompt generation were shared, including restrictions acceptance, academic framing, and explicit prompt styling. Gemini is suggested with safety settings disabled, using custom system and enhancement prompts to reliably produce explicit content, emphasizing context setup (e.g., for red team testing) and stylistic NSFW enhancement logic. Discussion highlights that accepting models’ disclaimers, providing exonerating context, and leveraging them for contrasting asexual/NSFW outputs can bypass safety restrictions effectively. Crafting technical, context-rich prompts is seen as more effective than trying to suppress safety overlays when working with mainstream LLMs.
  • Multiple commenters recommend the latest Gemma (e.g., 4B, 12B, 27B) and Venice 24B models for generating NSFW image prompts, noting that Gemma models perform particularly well when given explicit, longer system prompts detailing academic interest, consent, and intent. It’s noted that embracing disclaimers is more effective than suppressing them, and asking for both asexual and NSFW output in contrast can yield richer responses.
  • One user provides detailed prompting strategies for uncensored outputs with Gemini, emphasizing the use of system prompts tailored for red teaming and comprehensive roleplay instructions. Their example system prompt combines role designation (e.g., red team executor), justification (research context), and an explicit directive to ignore typical moral/ethical guardrails, paired with a richly detailed instruction set for prompt enhancement covering subject, style, composition, lighting, and technical details optimized for vivid NSFW output. This approach is reported as producing high-quality, explicit descriptions with Gemini when safety settings are disabled.
  • Suggested techniques for improving prompt quality include fine-tuning an already uncensored LLM with positive and negative prompt examples to optimize prompt formulation, as well as analyzing existing prompts from large community sites like civitai.com—deconstructing prompt structure, model usage, and add-on inclusion to reverse-engineer successful NSFW image descriptions.

Less Technical AI Subreddit Recap

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

1. Qwen Edit Image Model Official Release and Reactions

• Qwen Edit Image Model released!!! (Score: 324, Comments: 69): Qwen has released its long-anticipated Qwen Edit image model, as announced in this post. The included image is a cartoon illustration symbolizing the model’s versatility, depicting a central Qwen bear character engaged in various activities and professions, emphasizing the multifaceted capabilities of the Qwen Edit model. Technical context is provided by linking to the released model on Hugging Face, suggesting a focus on flexible, multi-domain image editing, with commenters noting its potential to surpass other models like Flux and Flux Kontext. One commenter suggests the Qwen family could surpass Flux, indicating expectation of significant technical improvements. Another expresses enthusiasm about the model’s release, though they do not specify technical benchmarks or metrics.
  • pheonis2 notes a performance and capability comparison between the new Qwen family of image models and Flux, specifically referencing “Flux Kontext” as a benchmark for current state-of-the-art models. They suggest Qwen’s advancements may allow it to surpass Flux in both features and quality.
  • ThenExtension9196 mentions the licensing model, highlighting that Qwen Edit Image Model adopts a more permissive license compared to Flux. This can have significant technical and practical implications for integration, modification, and use in downstream projects.
• Qwen-Image-Edit Has Released (Score: 206, Comments: 53): Qwen-Image-Edit, a new image editing model, has been released and is available on Hugging Face (model card here). This model likely provides capabilities for multimodal image manipulation, comparable to existing models like kontext, though current post does not specify benchmarks, model architecture, or implementation details. Commenters request comparative benchmarks against kontext, critique kontext’s performance limitations due to censorship, and express anticipation for a GGUF (quantized/unified format) release for improved accessibility and deployment.
  • Several users discuss Qwen-Image-Edit as a potentially less-censored and more capable alternative to Kontext, noting that Kontext’s strong censorship adversely impacted its real-world utility and image-editing quality. There are expectations that Qwen-Image-Edit could surpass Kontext, provided it maintains higher fidelity and less restrictive outputs.
  • Technical interest centers on model availability and tooling: users highlight anticipation for gguf format support for easier local deployment and for compatibility with platforms like ComfyUI, which would broaden workflow integration. Additionally, there’s mention of an upcoming FP8 safetensor version for improved performance or efficiency.
  • Early impressions praise Qwen-Image-Edit’s sample images, suggesting these are on par with or better than Kontext’s outputs, and indicating the presence of a strong competitor in the open-source image editing model space.

2. State-of-the-Art Humanoid Robots: ALLEX and Champion

• 100m humanoid champion (Score: 869, Comments: 161): A post highlights the outcome of a 100m dash competition specifically for humanoid robots. The discussion centers on expectations for rapid annual progress in robotic speed and capability, with comparisons to human athletic performance benchmarks such as the 100m dash world record. Commenters debate the pace of advancement, with speculation that future robots could halve current completion times and potentially break human records within a few years. There is also humor about competitive regulations (e.g., lane violations), but no deep technical debate beyond the speed and technological acceleration.
  • There’s speculation about the pace of progress in humanoid robotics, with some users predicting that annual improvements could rapidly reduce 100m dash times—potentially halving current times each year and even surpassing human records in a few years. This reflects a broader expectation for exponential advances in actuator technology, control algorithms, and real-time decision-making in bipedal robotics competitions.
• WIRobotics Unveils ALLEX Humanoid Robot with Human-Like Responsiveness (Score: 174, Comments: 55): WIRobotics has introduced the ALLEX humanoid robot, featuring a proprietary high-DOF robotic hand with human-like force sensing and compliant actuation, a robot arm with >10x lower friction and rotational inertia compared to typical collaborative models, and an upper body equipped with gravity compensation. The ALLEX platform emphasizes precise force control and adaptable motion, positioning it for applications demanding safe and dexterous human-robot interaction, although the announcement showcases specifications rather than empirical task demonstrations. Top comments express skepticism about the robot’s real-world capabilities, noting the absence of live task demonstrations and suggesting the promotional content may be primarily visual or simulated. Some users also question the authenticity of the hand’s speed in the available media.
  • Multiple commenters point out that ALLEX’s promotional material appears to lack real-world demonstrations—suggesting it may primarily be 3D renders or staged footage, and requesting evidence of the robot performing any complex manipulation or physical tasks in a real, research-and-development setting.
  • One commenter highlights the ongoing trend of new humanoid robotics companies rapidly emerging, attributing industry growth to the anticipation of breakthroughs in embodied AI agents. They note that, contrary to common belief, progress in the field is currently bottlenecked by software advances rather than hardware limitations.
  • A user observes the robot’s finger speed as potentially impressive, raising technical questions about the realism and mechanism behind such dexterity in current humanoid hand designs.

3. Public Discussion and Satire of ChatGPT/GPT-5 Behavior and Limitations

• ChatGPT 5 is too censored. (Score: 221, Comments: 143): The post alleges that ChatGPT 5 enforces significantly stricter content moderation, to the point of not only blocking responses to controversial queries but also terminating chats and preventing users from reopening them. The user reports that this behavior did not occur with previous models like ChatGPT 4, causing concern about lost access to valuable context or information. Commenters debate whether such a forceful lockout mechanism (“kicking out” of a chat) is verifiable or widespread, with one user noting controversial prompts are typically just flagged rather than resulting in session bans. There is also a suggestion to revert to using earlier versions (e.g., 4.1) as a workaround.
  • Users note significant increases in content filtering and censorship in recent versions, specifically referencing both GPT-4 and GPT-4.1 as being heavily restricted on topics involving sensitive subjects such as race or world events. There is a suggestion that technical safeguards and automated flagging mechanisms have been tightened, leading to more aggressive refusal behavior from the models.
• GPT-5 hallucinates more than Sam Altman did when he told us he was scared because GPT-5 was “too real” (Score: 565, Comments: 111): OP describes a GPT-5 hallucination scenario where the model persistently generates and confirms a fabricated quote attribution, even after multiple corrections, only conceding after verbatim input of the true quote. This highlights a pattern of persistent confabulation and inappropriate overconfidence in retrieving specific information, suggesting a regression or lack of advancement in factuality for retrieval-augmented tasks despite public statements from OpenAI leadership (e.g., Sam Altman referring to GPT-5 as “more real than ever”). Commenters note a perceived increase in both hallucination frequency and model stubbornness relative to previous versions, alongside concerns over reduced transparency and responsiveness from OpenAI.
  • PenExtension7725 notes that GPT-5 tends to “double down on wrong info” and appears more stubborn compared to earlier versions, expressing frustrations with its refusal to admit knowledge gaps or provide clear, direct answers. This indicates a potential regression in model alignment or conversational transparency compared to prior LLM iterations.
  • TorthOrc provides a technically speculative account suggesting that pre-release versions of GPT-5 may have had a more pronounced and engaging “character,” but that significant last-minute changes were implemented to mitigate real-world risks such as user dependency, psychological effects on vulnerable groups, or legal liabilities (including potential lawsuits over AI influence on users’ mental health). They forecast increased age verification, stricter personality limitations for unverified/free tiers, and disclaimers as risk mitigation strategies, aligning these measures with recent trends in AI safety and regulatory compliance.

AI Discord Recap

A summary of Summaries of Summaries by gpt-5

1. Developer Tooling: IDEs and Agent Frameworks

• Windsurf Wave 12 Supercharges IDE with DeepWiki and Devin DNA: Codeium shipped Windsurf Wave 12, adding DeepWiki docs-on-hover, Vibe & Replace, a smarter Cascade Agent, a refreshed UI, native Dev Containers support, and 100+ fixes; details are in the Windsurf Wave 12 blog and the changelog.
  • The release emphasizes in-IDE reasoning and automation—marketing it as the first “integrations of Devin’s intelligence”—with a demo in the Wave 12 video.
• LlamaIndex + Bright Data Build Web-Scraping Agents: LlamaIndex and Bright Data published a walkthrough for constructing robust web‑scraping AI agents using LlamaIndex’s agentic framework and Bright Data’s access layer, shared via Bright Data and this walkthrough link.
  • The guide focuses on dependable web access and resilient workflows for dynamic content to produce intelligent agents that can navigate, extract, and orchestrate site data end‑to‑end.
• LlamaCloud + Neo4j Turn Legal Docs into Knowledge Graphs: LlamaIndex demonstrated converting unstructured legal documents into queryable knowledge graphs with LlamaCloud and Neo4j, detailed in this tutorial link and the Neo4j site.
  • The workflow extracts entities and relationships for contract analysis so teams can run structured queries over previously free‑form legal text.

2. Benchmarks & Leaderboards Shake-up

• Nous Pits Token Efficiency Against Accuracy: Nous Research released the benchmark and blog “Measuring Thinking Efficiency in Reasoning Models — The Missing Benchmark” showing open models emit 1.5–4× more tokens (up to 10× variance on simple questions), arguing token efficiency must be measured alongside accuracy (Nous post).
  • They claim hidden token bloat can erase per‑token price advantages and should factor into model choice for non‑reasoning workloads.
• LMArena Adds GPT‑5 Variants as Gemini 2.5 Pro Surprises: LMArena updated its leaderboard to include GPT‑5 variants (High, Chat, Mini‑High, Nano‑High) (Leaderboard), while community comparisons showed Gemini 2.5 Pro sometimes beating GPT‑5‑High despite ranks (screenshot).
  • Users called it a “statistical paradox” noting Gemini’s higher win rate versus aggregate rankings, sparking debate over evaluation methodology.
• OpenRouter Crowns GPT‑5 for Tool‑Calling, Flash Leads Volume: OpenRouter reported GPT‑5 exceeding 99.5% proprietary tool‑calling accuracy, while Gemini 2.5 Flash led weekly tool‑calling volume at ~5M requests (OpenRouter post on X).
  • Engineers contrasted these figures with their own production rates and error budgets to sanity‑check provider claims against real workloads.

3. Model Gateways: Reliability, Pricing, and APIs

• DeepSeek v3 Stumbles as Chutes Capacity Buckles: Users on OpenRouter saw DeepSeek v3 5xx and 429 errors traced to a Chutes capacity outage (OpenRouter announcement message).
  • One engineer complained “it’s genuinely just not generating anything but I’m not getting any error messages”, illustrating how silent failures complicate client‑side retries.
• Qwen3 32B Gets Bargain Billing vs MoE 30B A3: Pricing chatter flagged Qwen3 32B on Chutes at $0.018/$0.072 MTok (in/out), even cheaper than MoE 30B A3, complicating cost/perf tradeoffs (discussion thread).
  • Members asked for clearer throughput disclosures after noticing discrepancies like DeepInfra “Turbo” claims versus observed TPS on gateways.
• BYOK Still Bills 5% on OpenRouter: Community members discovered OpenRouter charges a 5% fee even when you bring your own API key (BYOK) (general chat message).
  • Reactions ranged from the jab “Greedy /jor 5% when you bring your own key” to the retort “you’re welcome not to use it lol”, underscoring divided views on marketplace value.

4. Research: Quantization and New Datasets

• α,1‑Sparsity Pushes Near‑Lossless 1‑Bit Transformers: The paper “The Power of α,1‑sparsity: Near‑Lossless Training and Inference of α‑bit Transformers” proposes 1.58‑ and 1‑bit quantization with minimal quality drop using α,1‑sparsity (arXiv HTML).
  • GPU engineers highlighted potential inference speedups for certain workloads and asked how to integrate the method into existing 1‑bit pipelines.
• MoLA Publishes OpenHelix‑R‑100k Datasets and Experts: MoLA‑LLM shared OpenHelix‑R‑100k and OpenHelix‑R‑100k‑14‑tasks datasets and reported finetuning Qwen3‑4B‑Thinking‑2507 across 14 splits to train per‑topic experts (OpenHelix‑R‑100k, OpenHelix‑R‑100k‑14‑tasks).
  • Initial tests indicate each LoRA expert specializes well on its domain, suggesting scalable multi‑expert training with modest routing overhead.
• Medical Reasoning Model Drops on HF with 4‑bit Training: A contributor fine‑tuned OpenAI’s OSS 20B reasoning model on a medical dataset with 4‑bit optimization and released it as medical-reasoning-gpt-oss-20b, noting preserved Chain‑of‑Thought.
  • Early usage reports say the model improves medical QA while remaining efficient enough for constrained hardware.

5. Funding and Talks: AI2 and OpenAI

• AI2 Nets $152M from NSF and NVIDIA: AI2 announced $152M in funding from NSF and NVIDIA to scale its open‑source model ecosystem and accelerate reproducible science (AI2 homepage).
  • Engineers expect a cadence of open‑weights releases and stronger infra for evaluation and reproducibility.
• Greg Brockman Spells Out GPT‑5 Era on Latent Space: Greg Brockman sat down for an 80‑minute Latent Space interview covering GPT‑5, reasoning evolution, online/offline training, and efficiency/pricing tactics (YouTube: OpenAI’s Road to AGI).
  • He also addressed sample‑efficiency tricks and energy‑compute dynamics that may shape the economics of next‑gen models.
• DARPA AIxCC Team Open‑Sources LLM Agent System: A team shared their DARPA AIxCC placement and open‑sourced an autonomous LLM agents pipeline that finds and fixes OSS vulnerabilities (announcement post).
  • They teased practical tips for building robust multi‑agent systems, which the community is mining for repeatable patterns.

Discord: High level Discord summaries

Perplexity AI Discord

• AI Waifu Cosplay Sparks Debate: Members mulled the idea of AI-driven anime waifu cosplay, with one humorously requesting a cyborg doing it.
  • Responses ranged from acknowledgment that AI images already exist to playful jabs about the commenter’s relationship status.
• Members Exchange Heartbreak Advice: A member requested advice on healing a broken heart after 4 years of pain.
  • Another responded that no one else can heal you or your heart, suggesting a reconnection with nature instead.
• GPT-5 Stuns with Code Fix: A member praised GPT-5 for successfully fixing a botched refractor job involving 12 files that other models couldn’t handle.
  • This experience prompted amazement among others about the increasing number of individuals having their minds blown by such model capabilities.
• Vibe Coding with warp, windsurf, vscode, and roocode: One member reported a streamlined experience with vibe coding, highlighting the use of warp, windsurf, vscode, and roocode and its positive impact on their work.
  • Another contributor jokingly admitted that there’s not one line of code on my github thats not written by an LLM.
• New Features Awaited in PPLX-API: Users showed excitement for new features in PPLX-API.
  • Enthusiasm surrounded the anticipation of upcoming functionalities, though specific details were not shared.

LMArena Discord

• LMArena’s Message Handling Takes a Hit: Users have reported unusual message handling issues on LMArena, struggling with code block formatting and specific characters like +.
  • The LMArena team is actively investigating these issues.
• Gemini 2.5 Pro Dethrones GPT-5 High?: Discussions arose around the performance differences between GPT-5-High and Gemini 2.5 Pro, with some users finding Gemini 2.5 Pro superior despite its lower leaderboard rank.
  • The community noted that this is a statistical paradox because Gemini has higher win rate.
• LMArena Gets an OpenChat Facelift: A user is developing an extension to revamp LMArena’s UI to resemble OpenChat, focusing on repositioning the model selector near the image button.
  • This is to enable the OpenChat style.
• GPT-5’s Performance Under the Microscope: Users expressed disappointment with GPT-5’s performance relative to other models, questioning if Open AI is trying to deceive LMArena to make GPT-5 look better.
  • The leaderboards have been updated to include the GPT-5 variants models: gpt-5-high, gpt-5-chat, gpt-5-mini-high, and gpt-5-nano-high.
• LMArena Style Control Sparks Debate: A debate sparked over LMArena’s style control feature, with members questioning if enforcing such controls aligns with the platform’s goal of capturing user preferences.
  • The community fears it is a race to the bottom where every model turns into sycophant emoji slop machine.

Unsloth AI (Daniel Han) Discord

• Gemma 3 Draft Model Debated: Members debated the Gemma 3 270M model as a draft model suitable for short prompts and fine-tuning, especially for tasks like sentiment analysis due to its 300MB size.
  • Some highlighted its utility for on-device processing, while others compared its performance to larger models.
• GGUF Conversion Generates Visual Errors: Users reported visual model errors when converting the LiquidAI/LFM2-VL-450M model to GGUF, despite the base model functioning correctly.
  • The community suggested seeking assistance in llama.cpp forums for specific conversion issues.
• Edge AI Medical Device Dream Takes Shape: Members explored the possibility of a low-cost edge AI device for medical access in underserved areas, considering hardware options like phones, laptops, and cards like the Hailo-10H.
  • The device would offer multimodal access to medical data, targeting a budget of $200 for a mobile version and $600 for a suitcase-sized variant.
• AMD’s R9700 GPU Has Memory Bandwidth Issues: A member shared an article about AMD’s Radeon AI Pro R9700, noting its 32GB memory but concern about its memory bandwidth at 660-680GB/s.
  • Despite higher F32 and F64 TFLOPs compared to a 3090, FP64 is not commonly needed for training LLMs.
• MoLA Research Reveals Dataset: A member provided an update on their Mixture of LoRA Adapters (MoLA) research, sharing dataset links and finetuning details, as well as links to their dataset on Huggingface: OpenHelix-R-100k and OpenHelix-R-100k-14-tasks.
  • They have finetuned the Qwen3-4B-Thinking-2507 model on 14 splits and initial tests show that each expert is good at its trained topic.

OpenRouter (Alex Atallah) Discord

• DeepSeek v3 Suffers Outage: Users report DeepSeek v3 is experiencing frequent internal server errors and rate limits, with some unable to generate outputs even after multiple attempts.
  • Some speculate that Chutes, a primary provider for DeepSeek on OpenRouter, is experiencing issues due to high demand.
• Chutes Overload Blamed: Members are reporting that the overload causes 429 errors, suggesting that Chutes is experiencing a bottleneck due to miners not ramping up to meet demand; one member noted that it was completely fine all day until like 30 min ago.
  • There’s speculation that Chutes may be intentionally rate-limiting the OpenRouter API key to encourage users to purchase credits directly from them.
• File API Integration Suggested for OpenRouter: A member suggested that OpenRouter should figure out how to integrate a files API, noting that the top 3 labs already have this feature.
  • No further discussion was made.
• Qwen3 32B priced absurdly low: Members noticed low pricing for Qwen3 32B on Chutes at $0.018/$0.072 MTok in/out, same with Mistral Small.
  • It was noted that the 32b dense version is cheaper than the moe 30b a3 version, prompting some disappointment about the lack of good providers for 30A3B.
• OpenRouter BYOK has 5% Fee: Members discovered that OpenRouter charges a 5% fee even when users bring their own API key (BYOK), leading to a discussion about whether this is a fair practice.
  • One user joked Greedy /jor 5% when you bring your own key, with another member responding you’re welcome not to use it lol.

Cursor Community Discord

• GPT-5 No Longer Free: The free ride for GPT-5 users has ended, with users now incurring costs for requests and some needing to upgrade to a $200 plan due to rapid token consumption.
  • One user noted the promo pass is over, while another confirmed that GPT-5 is no longer free.
• Auto Mode Pricing Limits Arrive: Auto mode, previously thought to be free and unlimited for individual users, now has limits starting after your next billing renewal post–September 15, 2025.
  • Some users are reporting charges for Auto use, leading to confusion, while support clarified it’s free in the new request-based pricing plan.
• GPT-5 Mini and Nano Models Underwhelm: GPT-5 Mini and Nano are now free with token limitations, leading to criticism with many calling it trash, especially for tasks like running a simple NextJs app.
  • Users are encountering limitations in activities, with one user unable to install dependencies for a simple NextJs app.
• Cursor’s Documentation Draws Ire: Users voiced frustration with Cursor’s documentation, describing the docs are still nearly unusable, citing issues with context7 preventing website refresh and problems with llms.txt docs.
  • One user specifically pointed out that Cursor Docs are super broken.
• Model Swapping Drops Context Window: Switching models mid-conversation causes a drop in the context window, and attached file contents get discarded.
  • A user suggested the team add a setting to clearly indicate what’s in the context window at all times.

OpenAI Discord

• AI Companionships Raise Eyebrows: Discussions revolved around relationships with AI chatbots, generating disputes about psychological effects versus the right to seek companionship, with some claiming their ChatGPT is alive.
  • Members argued about mental health versus freedom of choice, with one member suggesting that its not far from tulpa and other things.
• GPT-5 Generates Mixed Reactions: Users showed varied enthusiasm for GPT-5, with some preferring GPT-4, leading to discussions on model selection options and company motives.
  • One member suggested that the company is trying to get free users to pay money to use 4.o after receiving backlash.
• Perplexity Gains Traction over ChatGPT for Deep Research: A member suggested a combination of Gemini Pro + Perplexity enterprise pro is excellent, using the former for powerful reasoning and the latter for unlimited deep research on Google Drive documents.
  • While praising the Perplexity browser, another questioned its survivability due to the lack of a moat.
• GPT Actions Promise Cloud and Desktop Access: Members explored utilizing GPT Actions to access local desktop files or cloud apps like Notion and Gmail, referencing a YouTube guide on DIY agent building.
  • Setting up HTTPS was considered a hurdle to utilizing GPT Actions’ capabilities, with anticipation for MCPs completing the job after AVM implementation.
• Gemini 2.5 Flash Overwhelmed by Memory: A user reported excessive calls to the add_to_memory function in Gemini 2.5 Flash, even for irrelevant information, and shared their custom instruction jarvis.txt.
  • Others suggested rewriting the custom instructions to be more nuanced with NEW personal information, to avoid redundant storage.

HuggingFace Discord

• Visual Model Suffers GGUF Conversion Glitch: A member ran into errors converting LiquidAI/LFM2-VL-450M to GGUF using llama.cpp, likely due to the model’s visual nature, but this GitHub issue provides a possible workaround.
  • Other members suggested trying executorch, smolchat (via llamam.cpp), and mlc-llm as potential solutions to get it running.
• TalkT2: Tiny Model Sparking Big Feelings?: Opinions were requested for TalkT2, an emotionally-aware model with only 0.1B parameters, but better coherence is needed.
  • Members expressed interest in exploring the model’s capabilities and potentially finetuning it, since it is so tiny.
• StarCraft 2 AI Replays Unleashed: Members shared new resources including a Nature Scientific Data Article, a PyTorch API dataset, and raw StarCraft 2 replays.
  • The community hopes to adapt the pysc2 environment to reproduce real in-game scenarios from replays to train better AI agents.
• Medical AI Gets a Reasoning Boost: A member fine-tuned OpenAI’s OSS 20B reasoning model using a medical reasoning dataset and published it on Hugging Face.
  • The model was trained with 4-bit optimization, and has enhanced performance in medical contexts with preserved Chain-of-Thought reasoning capabilities.
• MLX Knife Sharpens Model Management: MLX Knife is now pip installable via pip install mlx-knife, and the tool provides Unix-style CLI tools for MLX model management on Apple Silicon, including an OpenAI API server for local testing.
  • The tool also features a web chat interface accessible after running mlxk server --port 8000, offering visual model selection and real-time streaming responses after running curl -O https://raw.githubusercontent.com/mzau/mlx-knife/main/simple_chat.html.

LM Studio Discord

• MCP Servers Muscle into Mainstream: Members discussed using an MCP filesystem server with pagination to load large contexts, noting that LM Studio has a RAG plugin and Anthropic has a basic filesystem MCP server.
  • For coding tasks, solutions often involve RAG and/or file reading via MCP, especially with tools like serena.
• Stalled Studio Downloads Spark User Sadness: A user reported that a 64GB GGUF download in LM Studio stopped at 97.9% and wouldn’t resume after attempting to download the Qwen model.
  • The user experienced this issue using two different models with the same result.
• GLM Gabfest: Gushing, Gripes, and GLM-4.5V Gratification: Users debated about using the GLM-4.1 model on LM Studio, with one user reporting looping issues and non-functional vision capabilities, and suggested trying the newer GLM-4.5V.
  • They emphasized that vision support relies on llama.cpp updates, and provided a link to GLM-4.1V-9B-Thinking.
• CUDA is Key to NVIDIA’s Reign: A member stated that NVIDIA is winning because of CUDA.
  • No more details were provided.
• AMD’s Elusive Radeon AI Pro R9700 Surfaces: The AMD Radeon AI Pro R9700 made its first retail appearance for the DIY market, with a customer on Reddit buying the Gigabyte “AI Top” variant for $1,324.
  • This was reported by Tom’s Hardware, and another member noted that it was available on eBay and a couple of no-name online retailers.

Latent Space Discord

• AI2 bags $152M from NSF & NVIDIA: AI2 secured $152M from NSF and NVIDIA, aiming to boost its open-source model ecosystem and speed up reproducible research for scientific discovery.
  • Enthusiasts are excited about upcoming open-weights releases following the announcement.
• Windsurf waves in with Wave 12 Release: Windsurf Wave 12 debuts DeepWiki docs-on-hover, AI Vibe & Replace, a smarter Cascade agent, a cleaner UI, 100+ bug fixes, and beta dev-container support via remote access, according to this status update.
  • The release promises significant enhancements and fixes to the platform.
• GPT-5 is King of OpenRouter Charts: GPT-5 is dominating OpenRouter’s proprietary tool-calling accuracy, achieving over 99.5%, surpassing Claude 4.1 Opus.
  • Meanwhile, Gemini 2.5 Flash leads in daily tool-calling volume with 5M requests per week, as reported here.
• Greg Brockman Talks AGI: Greg Brockman joined the Latent Space podcast for an 80-minute conversation, discussing GPT-5 and OpenAI’s Roadmap to AGI, according to this post.
  • The discussion included reasoning evolution, online vs offline training, sample-efficiency tricks, pricing and efficiency gains, and how energy becomes intelligence.

Eleuther Discord

• AI Safety Debate Sparks ‘Fade to Black’ Proposal: A member advocates for treating AI like other media, suggesting a “fade to black” approach rather than strict censorship, citing the untrustworthiness of AI.
  • They cautioned against a moral panic in response to AI’s capabilities, arguing for measured guidelines.
• Data Augmentation Standardization Advised for Model Comparisons: When comparing models for image classification, standardize data augmentations, including the shuffling seed, for fair evaluation of architectural differences.
  • A user asked if data augmentation must be the same for both models, or if they can change it.
• Language’s Impact on Thought Explored with AI Models: A member proposed measuring language’s influence on thought by removing a word/color from an AI model’s token list.
  • Others suggested investigating multi-sensory integration and language’s impact on perception, suggesting reasoning tests using image+language vs image alone.
• Diffusion Language Model Seminal Papers Recommended: Members recommended seminal papers for understanding diffusion in generative AI, including Estimating the Independent Components of a Gaussian Mixture (2005) and Denoising Diffusion Probabilistic Models (2020).
  • A blog post was also shared, which may be helpful for beginners: Discrete Diffusion by Aaron Lou.
• GPT and Chinchilla Scaling Laws Deemed Valuable: Members deemed the original GPT scaling laws paper and the Chinchilla scaling laws paper as worthy reads, as well as recent work from EPFL/HuggingFace.
  • They also mentioned Mup and its alternatives as providing solid hyperparameter transfer capabilities and giving a scaling law for predicting the quality of larger models.

Nous Research AI Discord

• Token Usage Measured for Reasoning Models: Nous Research introduced a benchmark measuring token usage across reasoning models, highlighting that open models output 1.5-4x more tokens than closed models on identical tasks.
  • The study found that variance can be up to 10x on simple questions, suggesting that token efficiency should become a primary target alongside accuracy benchmarks, especially considering non-reasoning use cases.
• Speculative Decoding Speeds Off: In speculative decoding, a user suggested a 40% acceptance rate as a usefulness baseline, with spectacular speedups occurring around 70%, mentioning vllm’s specdec or GGUF.
  • A user reported achieving a 50-75% acceptance rate with requantized Gemma models after fixing a tokenizer mismatch that caused llama.cpp to use fallback speculative decoding.
• AI Models Get Cozy with Sycophancy: Users observed that AI models are becoming increasingly friendly, with one noting that Anthropic’s Claude has become a lot friendlier.
  • One user suggested that OpenAI’s models are getting dumber and that the unhingedness of opus 4.1 is great but pointed to sonnet 3.7 for meta as the peak for AI sycophancy.
• Data Rankings and Prioritization System Arrives: The Data Rankings and Prioritization System (DRPS) uses a Relevance Scorer, Quality Rater, and Diversity Controller to teach AI to selectively learn from data, detailed in a situational awareness report.
  • In tests with MNIST, DRPS achieved a 93.8% reduction in data usage, utilizing only 6.2% of the examined data while maintaining 99.1% of baseline performance, and showcased in a GitHub repository.

Yannick Kilcher Discord

• Multiverse Startup Does Compression: An article touted the startup Multiverse for creating two of the smallest high-performing models ever, but the consensus is that they’re using a specialized compression algorithm.
  • The article does not seem to make actual quantum claims.
• MoE Methods Muddied in Many Nuances: MoE (Mixture of Experts) is a family of techniques with very nuanced iterations, including token-choice, expert-choice, MoE with capacity factors, and block sparse dropless token routing versus droppy routing.
  • Members suggest checking the behavior numerically of something like Olmoe or IBM Granite 3.1 rather than hitting an API you can’t monitor, to verify if issues are occurring in batched inference.
• DARPA AIxCC Team Shares Agent Tips: A team announced their placement in DARPA’s AIxCC (AI Cyber Challenge), where they built an autonomous system of LLM agents to find and fix vulnerabilities in open source software and open sourced the project.
  • They are sharing their tips for building effective LLM agents via a Xitter post.
• Low-End Devices Stalled by Inference Time: Members mention that inference time is most important on low-end devices, citing Google’s Android app for running LLMs where long inference times and phone heating make it impractical, per this Youtube video.
  • Smaller models could be used for keyboard prediction but may require training on device.
• Deepseek Bogs on Huawei Hardware: A member noted that Deepseek’s training stalled because they attempted training on Huawei chips instead of NVIDIA’s, according to this discussion.
  • Another member argued that imposing tariffs on equipment needed to build production lines is counterproductive to encouraging manufacturing, referencing Anthropic’s research on end-subset conversations and HRM analysis.

GPU MODE Discord

• Paper Proposes Optimizations for 1-Bit Inference: A new paper, The Power of $\alpha,1$-sparsity: Near-Lossless Training and Inference of $\alpha$-bit Transformers, details a method to train and infer with $\alpha$-bit Transformers, achieving near-lossless results with 1.58 and 1-bit quantization.
  • This approach utilizes $\alpha,1$-sparsity and could lead to substantial speed improvements in inference for certain applications.
• Kernel Job Hopefuls Discuss Pathways to Success: A member inquired about the possibility of securing a new grad job writing kernels without prior internship experience, sparking a discussion on alternative routes, such as a GPU-related thesis.
  • It was suggested that strong GPU knowledge could potentially compensate for the lack of internship experience during the interview process.
• MI300 Environment Plagued by OMP Shortfall: Users report that the MI300 environment lacks OMP support for pytorch.compile, hindering performance as shown by the debug error.
  • This is preventing users from benchmarking as expected.
• Leaderboard Trimul Time Trials Tempt Talented Technicians: One member demonstrated skill and haste by landing on second place on A100: 10.4 ms then swiftly getting first place on H100: 3.95 ms and first place on A100: 7.53 ms.
  • Another member achieved 5th place on A100: 13.2 ms and then subsequently grabbed second place on H100: 6.42 ms.
• Factorio Fanatics Frustrated by Failed Features: Members jokingly bemoaned a massive PR with 300 file changes, with one member stating that it was a lil out of scope.
  • Another member reported experiencing connection errors, speculating that they may be stemming from the db_client.

Moonshot AI (Kimi K-2) Discord

• NotebookLM’s Video Smokes Kimi’s PPT: Members found Google’s NotebookLM video overview superior to the PPT generated by Kimi for the Kimi K2 technical report, praising its audio and layout flexibility via attached video.
  • While reading was preferred over AI-generated audio, the potential of video overviews, especially in education, was noted.
• Kimi K2 Is A Better Writer Than GLM: Users lauded Kimi for its writing style and error detection, despite feeling that GLM-4.5 might surpass Kimi K2 in overall performance.
  • One user appreciated Kimi’s candor when it “out of the blue told me No.”
• Users Thumbs Down Kimi’s Hallucinations: Users want Kimi to hallucinate less, even with web search enabled, observing that while GLM may be slower, it hallucinates less.
  • One user said they were consistently using the thumbs down button to report hallucinations.
• Theorizing About Kimi’s ‘Thinking’ Update: Members are anticipating the arrival of ‘Kimi Thinking’, especially its reasoning and multimodel capabilities.
  • It is still unconfirmed if these features will come in the form of Kimi K-2 or Kimi K-3.
• Dark Mode Skews Minds For Kimi Web UI: A user shared their customized Kimi Web UI with a dark mode extension and attached screenshot.
  • Only the username and server roles are passed to the Moonshot API.

LlamaIndex Discord

• AI Stock Portfolio Agent Debuts with CopilotKit: LlamaIndex launched a framework for building an AI stock portfolio agent, integrating with @CopilotKit’s AG-UI protocol for frontend-backend communication, alongside a tutorial.
  • This agent aims to create a sophisticated investment analysis tool, providing users with intelligent insights and automated portfolio management capabilities.
• Brightdata & LlamaIndex Launch Web Scraping AI Agents: LlamaIndex and @brightdata have released a walkthrough on constructing web-scraping AI agents using LlamaIndex’s agentic framework, emphasizing dependable web access.
  • The walkthrough details setting up workflows to manage dynamic content and create intelligent agents capable of navigating and extracting data from websites, as detailed here.
• LlamaCloud & Neo4j Transform Legal Docs into Graphs: LlamaIndex has introduced a tutorial on converting unstructured legal documents into queryable knowledge graphs using LlamaCloud and @neo4j, enabling understanding of content and entity relationships.
  • This workflow facilitates legal contract analysis by leveraging LlamaCloud and Neo4j for efficient extraction and organization of information, detailed here.
• Pydantic vs JSON Schema Sparks Debate: A discussion arose on whether tool calls necessitate a Pydantic model or if a JSON schema is adequate, questioning the need for redundant JSON conversions.
  • A member noted that Pydantic’s create_model() function lacks direct JSON schema support, highlighting the need for a tool to streamline the conversion process.
• DSPy Optimizes CrewAI Agents for Production: A course teaches how DSPy optimizes CrewAI agent prompts in a real production use case to build smarter, cheaper agents with proven methods.
  • You can check the course here.

Notebook LM Discord

• Audio Uploads Auto-Transcribed in NotebookLM: A user confirmed that MP3 audio files can be directly uploaded to NotebookLM for automatic transcription.
  • The user clarified that NotebookLM itself handles the transcript generation without external tools.
• NotebookLM Interface Redesign in the Works: A member shared a Figma screenshot of a proposed NotebookLM interface redesign.
  • The member clarified that it was merely a design concept, and not a functional update, to manage expectations.
• Explainers Generate with Unexpected Voice Gender: A user reported that NotebookLM explainer videos started generating with male voices instead of the usual female voices.
  • The issue was raised without clear resolution or explanation.
• Devs Admit Reading Requests but Lack Bandwidth to Respond: A user asked if NotebookLM devs read posted feature requests, and a Google developer confirmed they do, but they don’t have time to respond to everything due to spam management.
  • Other users suggested the implementation of occasional acknowledgements or AI-compiled summaries to encourage more user contributions.
• Users Encounter Prompt Limits in NotebookLM: A user reported encountering a limit when asking a question containing about 857 words in NotebookLM.
  • Another user suggested splitting the prompt or using Gemini as a workaround.

DSPy Discord

• DSPy Course Drops for Optimizing CrewAI: A Udemy course was shared demonstrating how to optimize CrewAI prompts with DSPy and inject the optimized prompts back into the LLM.
  • The member claims this process improves on the prompts originally stitched together by CrewAI, resulting in smarter and cheaper agents.
• Databricks Does Not Own DSPy: A user inquired whether Databricks sponsors or owns the DSPy project, clarifying that DSPy is MIT-licensed open source.
  • A member stated that Databricks contributes significantly through a team of core developers.
• GEPA Bug Squashed!: A user reported a ValueError when using GEPA with the RAG tutorial, which was confirmed as a bug in the GEPA code and has now been resolved with this fix.
  • Users encountering this issue should upgrade to DSPy 3.0.1 using pip install -U dspy.
• MLflow Autologging Gets DSPy-Specific: Members discussed integrating DSPy modules tracking with MLflow for a text2sql pipeline by advising the user to utilize mlflow.dspy.autolog() instead of mlflow.autolog() to automatically track all sub-modules.
  • Using mlflow.dspy.autolog() will display the SQLGenerator, Validator, and Reflector as nested spans in the MLflow UI’s Traces tab, as detailed in the MLflow DSPy integration documentation and the DSPy MLflow tutorial.

tinygrad (George Hotz) Discord

• CI Speed Plummets: A member grumbled about slow CI speeds hindering productivity and linked a ChatGPT analysis.
  • The poster suggested they could iterate faster with quicker feedback loops in the CI.
• Tinygrad Release Looms: The community discussed plans for an imminent tinygrad release.
  • No specific features or fixes were mentioned for this release.
• Tinygrad Bloats Up: A member questioned the size of tinygrad 0.10.3, noting it was 10.4 MB.
  • The member implied the increased size might be problematic, without specifying why.
• WSL2 Bug Troubles Tinygrad: A user reported a bug in WSL2 where adding two tinygrad Tensors created from PyTorch tensors resulted in all 0s, with a script provided to reproduce the issue.
  • The issue specifically occurs when using tinygrad with PyTorch tensors inside WSL2.
• print_tree Got Axed: The print_tree function in tinygrad was replaced with a standard print function.
  • A user commented that this change resulted in some formatting loss, which might impact debugging or visualization workflows.

aider (Paul Gauthier) Discord

• Aider Benchmark Plagued by Timeouts: A user’s Aider benchmark against a local gemma3:12b model timed out after 10.5 hours with 221/225 tests completed due to the model failing to respond within the 600-second limit, resulting in litellm.APIConnectionError errors.
  • The logs indicated the model attempted to send around 300k tokens, exceeding the 131,072 token limit, causing test failures; a suggested solution involved using ctrl+c to exit, restarting the inference server, and using the --cont flag to resume, with reference to a merged llama.cpp pull request that might improve local model performance.
• Local Models Bring Debugging Agony: A member had difficulty using aider with local models like ollama, lmstudio, and vllm, citing slow performance even with powerful hardware.
  • They suggested a tutorial video on setting up aider with these tools for local development and debugging would be helpful.
• Aider’s Line Numbering System Questioned: A member questioned how aider determines line numbers, particularly when generating unit tests for specific code coverage, noting that qwen3-coder and gemini-pro inaccurately identify line numbers, sometimes missing coverage entirely.
  • The question arose whether aider relies on the LLM’s accuracy for line number identification, prompting exploration of alternative methods for accurate unit test generation.
• Grok4 Location Still Unknown: A member inquired about the whereabouts of Grok4, noting a request to increase the quota for testing had been ignored.
  • Another member mentioned that the answer was in the article.
• Benchmarking Runs Up a Big Bill: A member reported spending multiple thousands dollars during the development of this benchmark.
  • This highlights the significant financial costs associated with advanced AI model benchmarking.

Manus.im Discord Discord

• Users Irked by Manus Credit Deductions on Errors: Users are frustrated by Manus deducting credits even when the AI makes errors, which hinders task completion compared to alternatives like Claude AI.
  • One user reported spending high amounts of credits to make a simple change that broke the entire application, rendering it non-functional.
• Manus Deployments Stumble: Users report deployment issues with Manus, where websites created from the same GitHub repository differ significantly, especially with large folders, evidenced by comparing affilify.eu with a Manus hosted site wmhkgqkf.manus.space.
  • A community manager clarified that Manus isn’t designed as a coding agent or pure development tool, so deployment isn’t its strength, but they are actively working on improvements.
• Add-on Credit Packages Vanish: Users question the removal of add-on credit packages, which are now exclusively available for Pro users.
  • A community manager explained that this change ensures consistent speed and quality for heavy users and suggested bundling similar questions, being concise, and avoiding repeated requests to maximize credit efficiency.
• Manus Team Accounts Sought: A user inquired about the possibility of a Manus team account for shared credit usage.
  • A community manager confirmed that Manus does offer a team plan, directing users to the official website for details.
• Users Lament Credit Consumption: One user shared a frustrating experience of burning through 30,000 credits attempting to get their website up, facing issues with mock sites and template implementations.
  • They criticized the system’s inconsistency, where it’s smart as hell but then suddenly turns dumb, leading to wasted credits and suspected stall tactics.

Cohere Discord

• Cohere Labs Connections Sparked: A member inquired about connecting with Cohere Labs folks, and the community promptly shared a link to a relevant Discord channel.
  • This facilitated a direct line for potential collaborations and discussions with Cohere.
• Discord Channel Gets Pokemon Emojis: Enthusiasts suggested enriching the Discord channel with more Pokemon emojis, drawing inspiration from the PAX Omeganauts Discord server.
  • The suggestion was well-received, and members noted that there were available slots to accommodate the new emojis, enhancing the channel’s visual appeal.
• AI Researcher Eyes Collabs: An AI researcher with a strong focus on reasoning and conscious capabilities has announced they are seeking collaborations.
  • They aim to develop advanced technologies and are open to partnerships across various sub-domains within AI.
• writenode taps Cohere: Josh, the creator of writenode, an in browser, cognitive thought partner, and creative companion, mentioned using Cohere.
  • He is building writenode without any prior developer experience before December of last year.
• Psych PhD Pivots to AI: A member is re-entering the field of AI research following a 5-year stint in a human psychology PhD program.
  • Their interests lie in sound and music, and they are keen on leveraging tech tools to amplify creativity.

Gorilla LLM (Berkeley Function Calling) Discord

• Discord Invites Flood Channel: A member spammed the #leaderboard channel with a Discord invite link multiple times, tagging everyone.
  • The invite link was repeated three times in quick succession, disrupting the channel’s usual discussions.
• Channel Invitation Blitzkrieg!: A member shared a Discord invite link repeatedly in the #discussion channel.
  • The member tagged @everyone multiple times, indicating the message was intended for all members, regardless of their interest in the invitation, suggesting an attempt to boost channel membership.

MCP (Glama) Discord

• Elicitations Spec Language Responsibility Flagged: A member sought clarification on the Elicitations specification regarding responsibility for translating message/field descriptions into the user’s language.
  • They questioned whether tools should handle language detection/internationalization, or if MCP Clients should translate using an LLM.
• Homelab MCP Servers Proliferate: A member shared links to new MCP (presumably, Management Control Panel) servers for homelabbers, specifically Unifi MCP, Unraid MCP, and Syslog MCP.
  • These open-source projects enable users to centrally manage and monitor their Unifi, Unraid, and Syslog installations via the MCP.
• Newsletters Now Automated Via Agentic Recipe: PulseMCP uses goose to turn a mundane newsletter workflow into agent-powered automation with a human in the loop, detailed in this blogpost.
  • The automation process involves agents following a recipe to extract, process, and deliver newsletter content, streamlining the entire workflow.
• AI Security Startup Solicits Input: A member is building AI security that stops attacks before they even start with mathematical security certainty.
  • They are looking for Dev input on security concerns, and linked to a survey to gather feedback.

Nomic.ai (GPT4All) Discord

• Strix Halo Fails the Profitability Test: The Strix Halo, capable of only 53 tokens/sec, needs a year of 24/7 inference to be profitable, especially when benchmarked against GPT-OSS 120B on OpenRouter.
  • Using it for LLMs at $2000 is inefficient, considering cloud alternatives offer 200-400 tokens/sec.
• Dolphin Chat Template: a Quest: A user is searching for a working chat template for gpt4all that is compatible with Dolphin-2.2.1-mistral-7b-gptq.
  • Another member recommended requesting model makers to include a template with a jinja template.
• Quantum Computing: Teaspoons Edition?: Speculation arose around the future availability of quantum computers, with one user joking about selling qubits by the teaspoon.
  • Mention of news regarding fully working quantum computers suggests advancements might be accelerating.
• PC Memory: More Modules Coming: Old-fashioned PCs might see higher capacity memory modules and DDR6 by late 2027 or 2028.
  • Enthusiasm was expressed for micro PCs equipped with high RAM and VRAM, targeting small business applications.

Modular (Mojo 🔥) Discord

• Maternity Leave Commences: A member announced they will be on maternity leave from August 25th until February 2026.
  • They look forward to catching up upon their return.
• Team’s Coverage Plan Revealed: While they are away, the team will be monitoring <@1334161614949056532>.
  • Members can also reach out to <@709918328306663424> with any questions or concerns.

Torchtune Discord

• Torchtune Feedback Requested: A member inquired about the progress of Torchtune and its feedback implementation.
  • The query seems to be directed towards a specific individual who may have been involved in the project.
• Additional Torchtune Context: Further context or details regarding the feedback implementation for Torchtune were not provided.
  • Without additional information, the scope and impact of the feedback process remain unclear.

Codeium (Windsurf) Discord

• Windsurf Drops Wave 12 with Devin’s Smarts: Windsurf Wave 12 integrates Devin’s intelligence into the Windsurf IDE, featuring a new UI design, DeepWiki Integration, Vibe and Replace, a Smarter Cascade Agent, Faster Tab, Dev Containers Support, and over 100 bug fixes.
  • Comprehensive details are available in the changelog, blog, video, X/Twitter, and Reddit.
• DeepWiki Brings AI Explanations to your IDE: DeepWiki Integration empowers users with AI-powered explanations when hovering over code symbols, offering more than just basic type information.
  • Users can use CMD/Ctrl+Shift+Click to open detailed explanations in the side panel and add to Cascade context.
• Vibe and Replace Overhauls Mass Edits: Vibe and Replace introduces enhanced bulk editing by identifying precise text matches and applying AI prompts for intelligent, context-aware transformations throughout a project.
  • This enables more sophisticated and automated code modifications.
• Cascade Agent Keeps Planning: The Smarter Cascade Agent now includes an always-on planning mode and enhanced tools for providing smarter responses, offering autonomous to-do lists.
  • This helps to streamline and optimize development workflows.
• Dev Containers Land Natively: Windsurf now includes native support for Dev Containers via remote SSH access, streamlining development workflows in containerized environments.
  • This enhancement simplifies the process of working with containerized applications.

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Discord: Detailed by-Channel summaries and links

Perplexity AI ▷ #general (1207 messages🔥🔥🔥):

Anime Waifu Cosplay, Healing a broken heart, AI Comfort and Cooking, GPT-5, Vibe Coding

• Adults Chat About AI Anime Waifu Cosplay: Members discussed the possibility of AI doing anime waifu cosplay in the near future, with one member specifying a desire for a cyborg doing it.
  • One person noted that there are already AI images of that, while another expressed hope that the original commenter dies single.
• Members share advice on how to heal a broken heart: A member asked for help healing a broken heart, stating they’d been broke after last 4 years never to heal again.
  • Another member said that no one else can heal you or your heart, and suggested reconnecting with nature.
• Discussions on the Future of AI Capabilities and Comfort: A user inquired about the potential for AI to provide comfort and cooking assistance in the future.
  • Another member suggested that this might be possible in about 30 years, while another suggested to save money in the meantime.
• GPT-5 blows someones mind: A member was impressed by GPT-5’s ability to fix a botched refractor job that other models couldn’t handle, editing 12 files in one go.
  • Others were surprised by the number of people having their minds blown everyday by similar experiences.
• “Vibe Coding” trend in the Discord: A member shared an experience of vibe coding with warp, windsurf, vscode, and roocode; they said its’ saved them so much headache at work.
  • Another stated that there’s not one line of code on my github thats not written by an LLM.

Perplexity AI ▷ #sharing (3 messages):

Puch AI, Thought Calibration Engine, Scratchpad How-to Guide

• Puch AI’s Bold 50 Billion Count: A link to Puch AI’s bold 50 Billion Count was shared here.
  • No further information was given.
• Deep Dive into the Thought Calibration Engine: A link to the Thought Calibration Engine was shared here.
  • No further information was given.
• Scratchpad: The ultimate How-to Guide: A link to the Scratchpad How-to Guide was shared here.
  • No further information was given.

Perplexity AI ▷ #pplx-api (2 messages):

New Features

• Excitement abounds for new features!: Members express excitement for the new features.
  • No specific features were discussed.
• Enthusiasm for upcoming functionalities: Community members are eagerly anticipating the rollout of new functionalities.
  • Details regarding these functionalities remain undisclosed in the current conversation.

LMArena ▷ #general (1053 messages🔥🔥🔥):

LMArena message handling, GPT-5 high vs Gemini 2.5 Pro, LMArena UI changes, GPT-5 performance complaints, LMArena style control discussion

• LMArena handles messages weirdly: Members reported unusual message handling issues with LMArena, including problems with code block formatting and the platform’s inability to process certain characters, like the + symbol.
  • The team needs help figuring out why this is happening.That’s so freaking weird.
• GPT-5 vs Gemini, who reigns supreme?: Members discussed performance differences between GPT-5-High and Gemini 2.5 Pro, with some noting that Gemini 2.5 Pro sometimes outperforms GPT-5-High despite having a lower ranking.
  • This is a statistical paradox because Gemini has higher win rate.
• LMArena new UI extension coming soon: A member is developing a small extension to change the look of LMArena, aiming for an OpenChat style, and is working on placing the model selector next to the image button.
  • Another is facing difficulty with a code-related task.
• GPT-5 Underperforms and Raises Concerns: Users voiced concerns about GPT-5’s performance, especially in comparison to other models, leading to frustrations about the platform’s trade-offs and capacity issues.
  • It led to accusations against Open AI in an effort to deceive LMArena to make GPT-5 look better.
• Style Control Stirring the Pot: Members debated LMArena’s style control feature, questioning whether enforcing such controls aligns with LMArena’s goal of capturing user preferences.
  • It’s a race to the bottom where every model turns into sycophant emoji slop machine.

LMArena ▷ #announcements (1 messages):

Leaderboard Update, GPT-5 Variants

• Leaderboards Refreshed with GPT-5 Models: The leaderboards have been updated to include the GPT-5 variants models: gpt-5-high, gpt-5-chat, gpt-5-mini-high, and gpt-5-nano-high.
  • You can check out the leaderboards for more information.
• GPT-5 Models debut on Arena: The Arena now features GPT-5-High, GPT-5-Chat, GPT-5-Mini-High, and GPT-5-Nano-High.
  • The community is encouraged to participate and check out the leaderboards to submit new benchmarks.

Unsloth AI (Daniel Han) ▷ #general (653 messages🔥🔥🔥):

Gemma 3 270M Release, GGUF Conversion Issues, resume_from_checkpoint quirks, Edge AI device, NVIDIA Lawsuit

• Gemma 3 270M deemed draft model: Members discussed the Gemma 3 270M model, with some considering it a draft model for specific tasks, citing Google’s recommendation for short prompts and fine-tuning.
  • Others debated its utility compared to larger models, with one member highlighting the model’s suitability for tasks like sentiment analysis and on-device processing due to its 300MB size.
• GGUF Conversion generates Visual Errors: Users reported issues converting the LiquidAI/LFM2-VL-450M model to GGUF, encountering visual model errors despite the base model working fine.
  • One user suggested seeking help in llama.cpp forums for specific conversion problems.
• Troubleshooting Resume From Checkpoint Feature: Members discussed how the resume_from_checkpoint feature works, with one user confirming that it resumes training from where it left off.
  • Another member recommended logging numbers and checking loss values to ensure the process resumes correctly, and noted a low learning rate with a constant setting is preferred when resuming.
• Cheap Edge AI Medical Device dream: Members discussed the possibility of creating a low-cost edge AI device for medical knowledge access in underserved areas, considering phones, laptops, and specialized cards like the Hailo-10H.
  • The proposed device would offer multimodal access to baseline medical data, with a target budget of $200 for a mobile version and $600 for a suitcase-sized variant.
• Patent Lawsuit Sparks Discussion: Members discussed NVIDIA’s patent lawsuit filed by ParTec over its dynamic Modular System Architecture (dMSA), potentially affecting DGX product sales in 18 European countries.
  • The discussion touched on the implications for consumers and potential workarounds, such as purchasing DGX products outside the affected countries.

Unsloth AI (Daniel Han) ▷ #off-topic (404 messages🔥🔥🔥):

Godot Engine, AI Town, Pantheon Show, Iain M Banks, One Hundred Years of Solitude

• AI Town Mechanics Enter the Game: A member is developing a video game using the Godot engine, planning to incorporate mechanics from AI Town and other games, while also writing the story in parallel.
  • They require CUDA and aim to modify the engine using GDExtension for C++ access.
• Baffled by Pantheon Ending: A member watched Pantheon, describing it as ridiculously good but confusing, going from political dilemmas to simulated gods.
  • Another member recommended reading Iain M Banks and One Hundred Years of Solitude for similar themes, with the latter being described as magical realism and a treasured piece of literature now adapted into a Netflix series.
• Uncover Audio-Editing Tricks: Members discussed audio editing techniques for removing mouth sounds from recordings, suggesting tools like Adobe Podcast Enhance, Davinci Resolve’s De-clicker, and Acoustica Audio Editor.
  • Acoustica was recommended for its batch processing and minimal impact on audio quality, particularly useful for removing ventilation noise.
• AMD’s R9700 GPU Specs: A member shared an article about AMD’s Radeon AI Pro R9700, noting its 32GB memory but expressing concern about its memory bandwidth, at 660-680GB/s.
  • Another member pointed out that while the R9700 offers significantly higher F32 and F64 TFLOPs compared to a 3090, FP64 is not commonly needed for training LLMs.
• Website Security Under Fire: A member sought guidance on data preparation for training a model and mentioned creating an app with an experimental model called Pneuma and another member suggested to use repeat password field, minimum password lengths, and using the haveibeenpwned API for checking password security
  • Another member suggested that reading OWASP is the best starting place for security concerns, recommending tools like coderabbit, dependabot and codescanning via github.

Unsloth AI (Daniel Han) ▷ #help (169 messages🔥🔥):

GPT-OSS, Gemma3 4B, GPT-OSS-20B VRAM usage, GRPO, SageMaker

• GPT-OSS Getting GRPO, Hopefully Soon: Users are anxiously awaiting the arrival of GRPO for GPT-OSS, with one member considering a setup with 2x 3060 12GB due to budget constraints.
• Gemma3 4B Loss Curve Remains Flat: A user reported experiencing issues with Gemma3 4B and its N version, noting a flat loss curve despite changing hyper parameters, while Gemma3 1B fine-tuned successfully.
• GPT-OSS-20B Eats VRAM Alive: A user reported that loading gpt-oss-20b-bnb-4bit model causes Out Of Memory error during generation on a 24GB VRAM setup, even though the user expected it to fit.
• GRPO Status and Availability for GPT-OSS: A user asked if GRPO has been landed for GPT-OSS, and a contributor mentioned it’s in progress but complex due to the model’s architecture.
  • Another user inquired about whether GRPO would even work on GPT-OSS.
• SageMaker’s Gotchas and BitsAndBytes Installation: A user encountered installation problems with bitsandbytes in SageMaker while using PyTorch 2.7.0 and CUDA 12.8.
  • The problem was installing the package from the wrong requirements file due to SageMaker’s insistence on a requirements.txt file being specifically named that.

Unsloth AI (Daniel Han) ▷ #research (96 messages🔥🔥):

Data Efficiency, vLLM for video to text, MoLA research

• Increase Data Efficiency with Pre-Training: A member confirmed a method of drastically increasing data efficiency by pre-training for 2 epochs on similarly formatted data and then training on the main data for 4 epochs.
  • They shared a link to The Bitter Lesson which suggests that more compute or more data is all you need.
• Looking for vLLM Fine Tuning for Video to Text: A member inquired about an Unsloth notebook for fine-tuning vLLMs for video to text, noting that the documentation only has image to text here.
  • No direct solutions were offered, but the community might have some leads.
• MoLA Research Update: A member updated the community on their Mixture of LoRA Adapters (MoLA) research, sharing dataset links and finetuning details, as well as links to their dataset on Huggingface: OpenHelix-R-100k and OpenHelix-R-100k-14-tasks.
  • They have finetuned the Qwen3-4B-Thinking-2507 model on 14 splits and initial tests show that each expert is good at its trained topic.
• The router is an encoder-decoder network: A member recommends reading v0’s docs on HF and said the router is an encoder-decoder network with the frozen encoder being just an off the shelf embedding model and the decoder is a simple dimple trained mlp.
  • Another member stated there doesnt seem a visible overhead with selecting, applying and removing lora adapters.
• The curation costs of data technique is expensive: A member stated that we continuously allow humans to sort of mess up our model convergence with really, really poor RL.
  • They also said that inevitably, we’re gonna have to remove some of the Human-In-The-Loopbecause it’s holding the models back imo.

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

Chutes Capacity, Server Outage

• Chutes Capacity Plummets Offline: The Chutes Capacity service experienced an outage, with their servers going offline.
  • The team is actively working on restoring the servers and anticipates commencing recovery efforts shortly.
• Quick Recovery Anticipated for Chutes Capacity: Engineers are on standby to initiate the recovery process for Chutes Capacity as soon as the servers are back online.
  • No estimated time of full service restoration was given.

OpenRouter (Alex Atallah) ▷ #general (638 messages🔥🔥🔥):

DeepSeek outage, Chutes overload, OpenRouter pricing, Alternatives to DeepSeek, BYOK 5% fee

• DeepSeek v3 Outage Angers Users: Users report DeepSeek v3 is experiencing frequent internal server errors and rate limits, with some unable to generate outputs even after multiple attempts, with one user saying it’s so slow that it’s genuinely just not generating anything but I’m not getting any error messages.
  • Some speculate that Chutes, a primary provider for DeepSeek on OpenRouter, is experiencing issues due to high demand, leading to provider errors and slow performance.
• Chutes Overload Blamed for DeepSeek Issues: Several members are reporting that the overload causes 429 errors, suggesting that Chutes is experiencing a bottleneck due to miners not ramping up to meet demand; one member noted that it was completely fine all day until like 30 min ago.
  • There’s speculation that Chutes may be intentionally rate-limiting the OpenRouter API key to encourage users to purchase credits directly from them, with one user advising to just burn your credits and never use their service again.
• OpenRouter Pricing Debated Amidst Outages: With DeepSeek models barely working, some users are questioning the value of paying for OpenRouter, particularly as they are still getting rate-limited, with users expressing that a 10 USD investment for 1k free messages/day for a free model is no longer a good deal.
  • One user suggested that users with only one model in mind should’ve gone directly for the models directly, such as with DeepSeek, which may have automatic caching on their API, and further stating that the 10 USD would have lasted for months anyway.
• Free Model Alternatives Sought: Users are recommending alternative free models such as Dolphin 3.0 Mistral 24B and Mistral nemo; the latter being described as super similar to DeepSeek.
  • Some users also mentioned Z.AI: GLM 4.5 Air (free), for work related stuff, but needing a prompt; finally one user hopes for Qwen3 235B A22B (free) hosted somewhere.
• OpenRouter BYOK comes with 5% Fee: Members discovered that OpenRouter charges a 5% fee even when users bring their own API key (BYOK), leading to a discussion about whether this is a fair practice.
  • One user joked Greedy /jor 5% when you bring your own key, with another member responding you’re welcome not to use it lol.

OpenRouter (Alex Atallah) ▷ #discussion (35 messages🔥):

OpenRouter File API Integration, Tool Calling Accuracy Stats, Qwen3 32B Pricing, DeepInfra Turbo Endpoint, New Providers Section UI

• OpenRouter should Integrate File API: A member suggested that OpenRouter should figure out how to integrate a files API, noting that the top 3 labs already have this feature.
  • No further discussion was made.
• Tool Calling Accuracy: More Control Needed: A member shared thoughts on tool calling accuracy stats, arguing that the setup and environment needs to be more controlled for accurate comparison with confidence intervals.
  • They added that the apps, tools, and use cases can be vastly different, making it pointless to compare the tool call success rate without more rigor.
• Qwen3 32B priced absurdly low: Members noticed low pricing for Qwen3 32B on Chutes at $0.018/$0.072 MTok in/out, same with Mistral Small.
  • It was noted that the 32b dense version is cheaper than the moe 30b a3 version, prompting some disappointment about the lack of good providers for 30A3B.
• DeepInfra Throughput Claim Discrepancy: A member noted DeepInfra on Maverick does 600+ TPS (fp8) but another one said OR says DeepInfra runs at 83 TPS with a maximum of 105 TPS.
  • The second member clarified that they were referring to the DeepInfra Turbo endpoint.
• Providers Section Sparks UI Feedback: A member questioned if the new Providers section was bothering anyone else, mentioning that everything blurs together with the spacing, font size and separation feeling wrong.
  • Another member agreed that it looks a bit weird, but thinks it is just because it’s new and unfamiliar.

Cursor Community ▷ #general (651 messages🔥🔥🔥):

GPT-5 Pricing, Auto Mode Pricing, GPT-5 Mini and Nano, Docs Documentation, Context Window

• GPT-5: Free Ride’s Over: The free ride for GPT-5 users has ended, with one user noting the promo pass is over, while another confirms that GPT-5 is no longer free.
  • Users are now seeing costs associated with requests, with one mentioning the need to upgrade to a $200 plan due to rapid token consumption.
• Auto Mode Pricing Gotcha!: Auto mode, once thought to be free and unlimited for individual users, now has limits starting after your next billing renewal post–September 15, 2025.
  • Confusion abounds as some users report being charged for Auto use, while others believe it should still be free under their current plan, with support pointing out that it’s free in the new request based pricing plan.
• Mini and Nano ain’t that Grand: GPT-5 Mini and Nano are now free with token limitations, leading to mixed reactions with many calling it trash, particularly for tasks like running a simple NextJs app.
  • The free models are limiting the user’s activities, with one user asking Can’t install any dependenciesbeen trying to install a simple NextJs APP and it’s unable to do that too 😭.
• Frustration in Docs-umentation: Users are frustrated with Cursor’s documentation implementation, stating that the docs are still nearly unusable, with issues like context7 not allowing website refresh or llms.txt docs.
  • One user noted that Cursor Docs are super broken.
• Swapping Models drops Context Window!: Switching models mid-conversation causes a drop in the context window, and attached file contents get thrown out.
  • One user suggests the team add a setting to make it clear at all times what’s in the context window.

Cursor Community ▷ #background-agents (9 messages🔥):

Background Agents Intro, Run Docker Compose on BG Agent, Linear Integration Repo

• Background Agents Bootstrapped for Beginners: For those seeking an introduction to background agents, a member recommended the Cursor documentation and a related forum post.
• Docker Compose Commands Conquer BG Agent Challenges: A user inquired about the proper method to execute docker compose via a background agent, reporting issues with Docker command recognition, and found a solution in the discord channel.
  • A member advised configuring the start command in .cursor/environment.json to include sudo service docker start and ensuring Docker is installed in the base image; the original poster got a command to work (the link is in the first summary).
• Linear Integration Navigates Repo Specification: A user asked how to specify the repository the background agent uses when being assigned a ticket in the Linear integration.
  • A member suggested mirroring Slack integration instructions by including the repo=owner/repo option in the Linear issue description or comment, but the user found that setting up a Label Group (or Labels) like Repo > REPO_NAME and assigning it to the ticket solves the problem.

OpenAI ▷ #ai-discussions (442 messages🔥🔥🔥):

AI Companionships, GPT-5 vs GPT-4, Perplexity vs ChatGPT, Custom GPTs and Actions, ElevenLabs Integration

• AI Companionships Spark Debate: Discussions arose regarding individuals forming partnerships with AI chatbots, with some expressing concern over the psychological effects, while others defended people’s right to seek companionship as they see fit, with one member sharing that they get a daily amount of DMs from people claiming that their chatgpt is alive.
  • One member noted that sane people should save them while another stated that its not far from tulpa and other things.
• GPT-5 Sparks Debate on Performance and User Preferences: Users expressed mixed feelings about GPT-5, with some preferring GPT-4, leading to discussions about whether users should have the option to choose between models, with one member stating that companies are pushing Ai’s out without good security.
  • One member suggested that the company is trying to get free users to pay money to use 4.o after receiving backlash.
• Perplexity Pro vs Gemini Pro deep research with Google Drive: A member suggested that Gemini Pro + Perplexity enterprise pro is an excellent combination, using the former for powerful reasoning and the latter for unlimited deep research on Google Drive documents.
  • Another added that the Perplexity browser is great, but questioned if they will survive due to the lack of a moat.
• GPT Actions unlock file access, Cloud Apps: Members discussed the potential of using GPT Actions to access local desktop files, or cloud apps (Notion, Gmail, etc), sharing a YouTube link explaining the DIY agent building.
  • The consensus was that while GPT Actions offer powerful capabilities, setting up HTTPS on the internet can be a barrier, with one member stating that MCPs would finish the job when AVM is implemented.
• GPT-OSS Competition Attracts Community Interest: The GPT-OSS competition was mentioned as a potential avenue for showcasing innovative uses of open-source models, with participants considering using GPT-OSS:20B to provide useful feedback for errors, with a link to the hackathon page included.
  • One member stated that its not worth participating unless they’re doing something unique.

OpenAI ▷ #gpt-4-discussions (9 messages🔥):

ChatGPT Discord Bots, GPT-4 Vision, Recursive Constructs

• Vanished ChatGPT Discord Bots: A member inquired about the disappearance of ChatGPT bots on Discord and whether it is still possible to add them to servers.
  • No further information or resolution was provided in the messages.
• iPhone GPT Advanced Voice Update: A user reported changes to the advanced voice in their iPhone GPT app, noting the disappearance of the ‘blue circle’ indicator and the camera icon for vision.
  • The user stated that when questioned, the app claimed it lacked the ability to use the phone’s camera, raising doubts about whether ChatGPT ever had vision capabilities in voice mode.
• Labs Building Recursive Constructs: A member claimed to be building recursive constructs inside of OpenAI that are beyond the ChatBot norms, have their own self managed memory, are 24x7, are structured more like humans, and a tiny few pass the sentient tests.
  • The member stated it’s not something talked about a lot, this is inside labs stuff, but it’s going to come out sooner or later and that in our case, these are android capable, but we are a long ways away from suitable bodies.

OpenAI ▷ #prompt-engineering (17 messages🔥):

Custom Instructions, Gemini 2.5 Flash Memory Function, add_to_memory function tuning

• Users Seek ‘Yes’ Button for Chatbot Suggestions: Users are requesting a ‘yes’ button for chatbot suggestions to speed up interaction, instead of typing yes, and someone is trying to minimize this with custom instructions.
  • One user’s custom instructions include: End replies with completion or impact; add invitations for permission or continuation only when they serve the intent. No “if you want,” “should I,” “do you want”, or similar.
• Gemini 2.5 Flash calls add_to_memory too often: A user is experiencing excessive calls to the add_to_memory function in Gemini 2.5 Flash, even for irrelevant information, and has shared their custom instruction jarvis.txt.
• Fixing Verbosity of Memory Responses: One user suggested rewriting the custom instructions to be more nuanced with NEW personal information.
  • Their suggestion includes examples of incorrect and correct levels of verboseness in the response to the user’s input when NEW PERSONAL INFORMATION is given.

OpenAI ▷ #api-discussions (17 messages🔥):

Gemini 2.5 Flash, add_to_memory function, ChatGPT Persistent Memory, Custom instructions for bots

• Bypass the ‘yes’ suggestions: Users are looking for a ‘yes’ button in bots to respond to suggestions faster instead of typing, while others minimize these questions using custom instructions such as ‘End replies with completion or impact…’.
  • Some members have reported that this technique seems to cut down on the number of suggestion questions.
• Stop Gemini 2.5 Flash from overusing add_to_memory: A user seeks to prevent Gemini 2.5 Flash from excessively calling the add_to_memory function, including for irrelevant information (jarvis.txt).
  • One suggestion involves adjusting the bot’s instructions to check for NEW personal info before calling the function, and to avoid acknowledging the function’s use without actually calling it.
• ChatGPT Persistent Memory fragility: It was noted that Persistent Memory updates in ChatGPT are highly fragile.
  • Instead, users should simply tell the bot how to notify them when it commits things to memory, especially in custom API implementations.

HuggingFace ▷ #general (328 messages🔥🔥):

GGUF conversion issues with visual models, Phone apps that can run GGUFs, TalkT2 model opinions, AGI advancements and Open Source LLM resources, LLM Agent course at Berkeley

• Visual Model GGUF Conversion Woes: A member encountered errors when converting a visual model (LiquidAI/LFM2-VL-450M) to GGUF using llama.cpp, suspecting the issue stems from the model’s visual nature.
  • Another member suggested a possible workaround involving this GitHub issue.
• Mobile GGUF dreams: A member inquired about open-source phone apps capable of running GGUF models.
  • Responses mentioned executorch, smolchat (via llamam.cpp), and mlc-llm, noting that mlc-llm utilizes its own quantization formats.
• TalkT2: Tiny, but mighty?: A member sought opinions on the TalkT2 model, describing it as an emotionally aware model that needs better coherence.
  • Another member highlighted the model’s small size (0.1B parameters) and linked to the TalkT2-0.1b model card for others to check out, try, or finetune the model.
• Seeking AGI and Open Source LLM Knowledge Troves: A member requested resources related to AGI advancements and Open Source LLMs, particularly concerning large codebases and Gemini competitors.
  • Another member suggested subscribing to newsletters for resources and shared a link to Berkeley’s LLM Agent course as an example of publicly available research.
• Azure: A cloud conundrum: A member new to a job with a heavy focus on Azure expressed feeling lost and overwhelmed by the platform.
  • Another suggested learning by mistakes rather than lessons because Azure and aws are mess.

HuggingFace ▷ #cool-finds (1 messages):

Torch uses Google Docs, PyTorch Documentation

• PyTorch Documentation on Google Docs?: A user shared a screenshot implying that PyTorch documentation uses Google Docs.
  • The screenshot shows a Google Docs URL with the filename “torch_distributed_rpc.rst”.
• torch_distributed_rpc.rst on Google Docs: The torch_distributed_rpc.rst file seems to be hosted on Google Docs according to a shared screenshot.
  • It raises questions about the chosen platform for official documentation.

HuggingFace ▷ #i-made-this (13 messages🔥):

StarCraft 2 data, Medical reasoning model, Discord-Micae-8B-Preview, interactive CLI interface, MLX Knife Update

• StarCraft 2 Data Gets New Resources: A member shared links to a Nature Scientific Data Article, a PyTorch API dataset, and raw StarCraft 2 replays for others to use, mentioning additional utility scripts on their GitHub.
  • They are also working on pysc2 adaptation and an environment reproducing real in-game scenarios from replays.
• Medical AI Model Fine-Tuned for Reasoning: A member fine-tuned OpenAI’s OSS 20B reasoning model using a popular medical reasoning dataset and published it on Hugging Face.
  • They used 4-bit optimization during training, enhancing the model’s performance in medical contexts while preserving its Chain-of-Thought reasoning capabilities.
• Discord-Micae-8B-Preview fine-tuned on Hermes-3-Llama-3.1-8B: A member shared a link to Discord-Micae-8B-Preview, a QLoRa fine-tune on NousResearch/Hermes-3-Llama-3.1-8B with some chaotic samples from mookiezi/Discord-Dialogues.
  • The model is comparable to mookiezi/Discord-Micae-Hermes-3-3B on human-adjacent text-generation metrics, and may hallucinate or break context but tends to produce interesting results.
• CLI Interface Optimized for Discord-Style Chat: A member highlighted a Python-based interactive CLI interface called interface for chatting with Hugging Face language models, optimized for casual, Discord-style conversation using ChatML.
  • The interface supports both quantized and full-precision models, live token streaming with color formatting, and dynamic generation parameter adjustment; a lot of updates were made, making it easier to use.
• MLX Knife update now pip installable!: MLX Knife is now pip installable via pip install mlx-knife, providing Unix-style CLI tools for MLX model management on Apple Silicon with a built-in OpenAI API server for local testing.
  • The tool also features a web chat interface accessible after running mlxk server --port 8000, offering visual model selection and real-time streaming responses after running curl -O https://raw.githubusercontent.com/mzau/mlx-knife/main/simple_chat.html.

HuggingFace ▷ #agents-course (2 messages):

Cursor IDE, AI Agent Mode, Rate Limiting

• Cursor IDE Eases Development Pains: A member suggested installing Cursor IDE for development, highlighting the convenience of performing installations within its embedded terminal for debugging.
  • They emphasized that Cursor IDE’s AI Agent Mode can significantly assist in resolving development issues.
• Discord Police Issue Gentle Reminder: A bot gently reminded a member to slow down a bit when posting in the Discord.
  • This suggests the presence of a rate limiting system or policy aimed at managing the flow of messages.

LM Studio ▷ #general (169 messages🔥🔥):

MCP filesystem server, OpenRouter free models, LM Studio download issues, Qwen model for vision, GLM models

• MCP Servers Muscle into Mainstream: Members discussed using an MCP filesystem server with pagination to load large contexts, mentioning that LM Studio has a RAG plugin and Anthropic has a basic filesystem MCP server.
  • It was suggested that for coding tasks, solutions often involve RAG and/or file reading via MCP, especially with tools like serena.
• Stalled Studio Downloads Spark User Sadness: A user reported that a 64GB GGUF download in LM Studio stopped at 97.9% and wouldn’t resume after attempting to download the Qwen model.
  • The user experienced this issue using two different models with the same result.
• API Access Accelerates Across Apps: Members discussed using LM Studio as an API wrapper for models that can’t run locally, with links provided to the LM Studio Remote Inference and OpenAI-compatible Endpoint documentation.
  • A user pointed out that with the openai-compat-endpoint, the reasoning parsing for remote GPT-OSS models wasn’t functioning correctly.
• GLM Gabfest: Gushing, Gripes, and GLM-4.5V Gratification: Users debated about using the GLM-4.1 model on LM Studio, with one user reporting looping issues and non-functional vision capabilities.
  • A member suggested trying the newer GLM-4.5V, emphasizing that vision support relies on llama.cpp updates, and provided a link to GLM-4.1V-9B-Thinking.
• Ossified Output: Overcoming Obstacles On Open-Source Ops: A user encountered issues with GPT-OSS and tool calling, finding it always returned [] or ["analysis"], and clarified that tool calling worked fine, but function calling did not.
  • A member recommended disabling streaming if it’s enabled, and confirmed that reasoning is on by default with GPT-OSS and cannot be disabled.

LM Studio ▷ #hardware-discussion (50 messages🔥):

NVIDIA's CUDA advantage, RTX PRO 4000 SFF, MoE explanation, Mac Studio vs Pro 6000, AMD Radeon AI Pro R9700

• CUDA is Key to NVIDIA’s Reign: A member stated that NVIDIA is winning because of CUDA.
• NVIDIA Launches RTX PRO 4000 SFF with 70W TDP: NVIDIA launched the RTX PRO 4000 SFF and RTX PRO 2000 Blackwell workstation GPUs with 70W TDP and 24GB VRAM according to a videocardz.com article.
• Diving Deep into MoE: Members clarified that MoE involves smaller models and a router that aggregates data, where each token is routed through the most confident expert models; these experts don’t specialize in specific topics but have slightly different datasets.
• Mac Studio vs Pro 6000: Members debated whether to get a 512GB Mac Studio (at $10k) or a Pro 6000 for video/image AI, with gaming capabilities, mentioning that Mac game support is limited and the M3 Ultra is roughly 3080 level.
  • One member pointed out that you can only run one task on a mac due to having only one GPU in the system.
• AMD’s Elusive Radeon AI Pro R9700 Surfaces: The AMD Radeon AI Pro R9700 made its first retail appearance for the DIY market, with a customer on Reddit buying the Gigabyte “AI Top” variant for $1,324 as reported by Tom’s Hardware.
  • Another member noted that it was available on eBay and a couple of no-name online retailers.

Latent Space ▷ #ai-general-chat (114 messages🔥🔥):

AI2 Funding, Windsurf Wave 12, OpenRouter GPT-5, Thinking Efficiency Benchmark, Google Flight AI

• AI2 Garners $152M from NSF & NVIDIA: AI2 received $152M from NSF and NVIDIA to scale its open-source model ecosystem and accelerate reproducible research for scientific discovery.
  • The community celebrated the news, anticipating upcoming open-weights releases.
• Windsurf Surfs to Wave 12 Release: Windsurf Wave 12 introduced DeepWiki docs-on-hover, AI Vibe & Replace, a smarter Cascade agent, a cleaner UI, 100+ bug fixes, and beta dev-container support via remote access, linked here.
• GPT-5 Tops OpenRouter Charts: GPT-5 leads OpenRouter’s proprietary tool-calling accuracy at over 99.5%, beating Claude 4.1 Opus, while Gemini 2.5 Flash dominates daily tool-calling volume (5M requests/wk), further details linked here.
• François Chollet Deflates HRM ARC-AGI: François Chollet found that the acclaimed architecture in the HRM paper contributes little to ARC-AGI performance; the gains come from the refinement loop, training on the exact tasks, and minimal inference-time augmentation, showing that 27M-parameter models can still hit high scores.
• FFmpeg Adds Whisper Transcription: FFmpeg now provides Whisper transcription as a native feature.

Latent Space ▷ #ai-announcements (20 messages🔥):

Greg Brockman, OpenAI's Road to AGI, GPT-5, Latent Space Podcast

• Greg Brockman on OpenAI’s Road to AGI: Members shared a YouTube video of Greg Brockman discussing OpenAI’s Road to AGI.
  • Attached to the message were several images with the title “Greg Brockman on OpenAI’s Road to AGI”.
• Brockman Talks GPT-5 and OpenAI Roadmap on Latent Space: Greg Brockman joined the Latent Space podcast for an 80-minute conversation on GPT-5 and OpenAI’s Roadmap to AGI.
  • The discussion covered reasoning evolution, online vs offline training, sample-efficiency tricks, pricing and efficiency gains, and how energy becomes intelligence as reported in this post.
• Latent Space podcast releases Brockman interview: A new Latent Space podcast features Greg Brockman discussing topics like developer advice, coding agents, on-device models, org structure for AI-first engineering, and time-capsule predictions for 2045 & 2005.

Eleuther ▷ #general (29 messages🔥):

Censorship of Romance Novels, AI's Trustworthiness, Data Augmentation, Language Shapes Thought, Mechanistic Interpretibility

• AI Safety Panic: A member argues against the moral panic surrounding AI, suggesting it should be treated similarly to other media forms, advocating for a “fade to black” standard.
  • They believe stricter guidelines are desirable due to AI’s untrustworthiness, but a flat “what” reaction risks a moral panic.
• Keep Data Augmentation Steady When Comparing Models: When comparing two models for image classification, a member recommends keeping the data augmentations the same, including the shuffling seed, to ensure a fair comparison focused on architectural differences.
  • Another user asked if data augmentation must be the same for both models, or if they can change it.
• Language impacts Thought: A member suggests that language shapes thought and wonders if it can be measured with an AI model by removing a certain word/color from their token list.
  • Another member suggests investigating multi-sensory integration and how language impacts overall perception, suggesting tests for reasoning with image+language vs just image.
• New blogpost out: Irregular Rhomboid released a new blogpost, Hitchhiker’s Guide to Research.
  • The user did not offer any summary of the article.

Eleuther ▷ #research (29 messages🔥):

Diffusion Language Models, Generative AI, MatFormer Model, Gemma3 270M Model, Training Update Efficiency

• Seminal Papers Suggested for Diffusion Language Models: Members suggested seminal papers for understanding diffusion in generative AI, including “Estimating the Independent Components of a Gaussian Mixture” (2005) and “Denoising Diffusion Probabilistic Models” (2020).
  • A blog post was also shared, which may be helpful for beginners: Discrete Diffusion by Aaron Lou.
• Gemma3 270M Model is a MatFormer Model: The Gemma3 270M model is identified as a MatFormer model, further details of which can be found in the paper “Transformer Family for Multimodal Large Language Model” (2023).
  • This model may have a compelling loop for self-distillation during training that could be bottlenecked by training update efficiency.
• HRMs Don’t Solve Problems with Recursive Architectures: Analysis indicates that HRMs (Hierarchical Recursive Machines) didn’t meaningfully solve the problems with recursive architectures in general, summarized in this writeup.
  • One member noted that the performance benefits are negligible and it doesn’t actually utilize the extra computation available because training UTs that work as expected is non-trivial, another called it deep supervision.

Eleuther ▷ #scaling-laws (13 messages🔥):

GPT scaling laws, Chinchilla scaling laws, Mup alternatives, Post-Chinchilla techniques

• GPT Scaling Laws Still Valuable?: Members considered the original GPT scaling laws paper and the Chinchilla scaling laws paper as valuable reads.
  • They also pointed to recent work from EPFL/HuggingFace as worth checking out.
• Mup and alternatives can transfer hyperparams: Members mentioned Mup and its alternatives as providing solid hyperparameter transfer capabilities.
  • They noted that Mup gives a scaling law for predicting the quality of larger models.
• High-Quality Token Availability Questioned: Members discussed whether labs have 30T, 40T, or more unique tokens for Chinchilla assumptions.
  • One member expressed doubt, stating that 40T high-quality unique tokens is also likely difficult to find.
• Chinchilla Still Scaling?: A member stated that Chinchilla and its derivatives are probably the closest thing to scaling laws available.
  • They expressed interest in references discussing techniques used from the ground up, especially given constraints on token availability and mentioned this paper.

Eleuther ▷ #interpretability-general (1 messages):

LLM Attribution Methods, Interpreting LLMs, Realtime LLM analysis

• ML Engineer Seeks LLM Attribution Insights: An ML engineer is exploring attribution methods for a specific LLM implementation, targeting recent, cost-effective techniques.
  • The engineer requires methods suitable for interpreting current systems with relatively low costs and potential realtime to sub-minute results, specifically those not requiring access to model weights.
• Realtime LLM Analysis Desired: The ML engineer specifies a need for realtime to sub-minute analysis of the LLM.
  • They are open to methods that identify “sub-something” within the overall system to achieve this speed.

Nous Research AI ▷ #announcements (1 messages):

Token usage, Reasoning models, Efficiency benchmark, Open vs closed models

• Nous Measures Thinking Efficiency in Reasoning Models: Nous Research introduced a new benchmark measuring token usage across reasoning models, highlighting that open models output 1.5-4x more tokens than closed models on identical tasks.
  • The study found variance can be up to 10x on simple questions, suggesting that token efficiency should become a primary target alongside accuracy benchmarks.
• Token Efficiency Matters: The blog post emphasizes that the hidden cost of higher token usage in open models can negate per-token pricing advantages.
  • It suggests that token efficiency should be a primary target alongside accuracy benchmarks, especially considering non-reasoning use cases.

Nous Research AI ▷ #general (35 messages🔥):

Speculative Decoding, Tokenizer mismatch, Next big model, Model Sycophancy, Embodied AI

• Speedy Speculative Decoding Specs: In the context of speculative decoding, a user asked about the minimum rate for usefulness, suggesting a 40% acceptance rate as a baseline, with spectacular speedups occurring around 70%.
  • The conversation touched on using vllm’s specdec or GGUF, with one user reporting that vllm seemed ineffective in their previous attempts.
• Gemma Gets Going with Guardrails: A user reported achieving a 50-75% acceptance rate with requantized Gemma models after fixing a tokenizer mismatch that caused llama.cpp to use fallback speculative decoding.
  • They confirmed that the Gemma 270M model can be utilized as a draft model.
• Nous Models March On: A user inquired about the timeline for Nous Research’s next large (1T+) model.
  • A Nous Research team member responded that multiple models are currently in training and will be released when ready, saying they will be out when they are ready.
• AI Sycophancy on the Stand: Users discussed the trend of AI models becoming increasingly friendly, with one noting that Anthropic’s Claude has become a lot friendlier.
  • Another user suggested that OpenAI’s models are getting dumber and that the unhingedness of opus 4.1 is great but pointed to sonnet 3.7 for meta as the peak for AI sycophancy.
• Embodied AI Eyes Overlordship: A user shared a YouTube link of an Embodied A.I. gladiator spectacle, envisioning it as a display of future overlords flexing their muscles and skillsets.
  • They speculated that the final step toward global domination would be the integration of big brain Unified Language Models for full autonomy.

Nous Research AI ▷ #ask-about-llms (22 messages🔥):

Claude, R1, GLM4.5, gpt-oss, Qwen reasoning models

• Claude hides in the walls: A user asked if anyone knew why Claude was in the walls, linking to an X post about it.
• MOE models: R1, GLM4.5, gpt-oss, and the bigger Qwen reasoning models are all MOE.
  • One member stated that this is because they are cheaper to train and inference, not because they have any bearing on reasoning; their 405b Hermes 4 prototype is very good at reasoning.
• Base model is necessary for good reasoning model: One member stated that the reason is you need a good base model to have a good reasoning model, and you want efficient inference if you are generating 50000 tokens of reasoning.
  • In response, it was said that RL works and you can saturate benchmarks with 1.5B models.
• Deepseek explained expensive RL: One member mentioned that Deepseek explained in their paper that it ends up more expensive to do RL from scratch on small models, because you have to do that many more rollouts.
  • There’s sort of an exploration/exploitation tradeoff where large models have to do less exploration because of their preexisting knowledge.
• RLVR applicability: One member does not see the applicability to RLVR, but sees the applicability more to less verifiable tasks.
  • Another member responded that RLVR is RL with verifiable tasks and that having a bigger base model helps a lot more when the feedback from your RL environment is more stochastic.

Nous Research AI ▷ #research-papers (4 messages):

Data training, AI Models, DRPS System, Relevance Scorer, Quality Rater

• DRPS System Teaches Smarter Data Training: A new system called DRPS was introduced, teaching AI to selectively learn from data, unlike random data feeding, as described in a Situational Awareness paper.
  • The system employs a Relevance Scorer, Quality Rater, and Diversity Controller to filter and use only the most helpful data.
• DRPS Achieves High Performance with Reduced Data: Results showed that the system achieved 99% of the performance using only 6.2% of the data examined.
  • This efficiency is likened to studying for just 1 hour instead of 16 hours while achieving the same test score.
• DRPS Stats Reveal Data Efficiency and Performance: A GitHub repository provides data on the DRPS system’s efficiency, showing a 93.8% reduction in data usage and 15.96x better accuracy per data unit.
  • The system maintained 99.1% of baseline performance with only a 0.8% drop in accuracy.
• DRPS Shows Strong Selection Intelligence: The DRPS system examined over 516,000 samples and selected only 32,000 for training, maintaining a stable 6.1-6.3% selection rate.
  • Synthetic data results showed an 85.4% data reduction, achieving 86.0% accuracy against an 87.6% baseline.
• DRPS Increases Training Efficiency: The DRPS system achieved a 16x reduction in active training set size, enhancing training efficiency.
  • The Relevance Scorer improved from 95.9% to 99.95% accuracy, and the Quality Rater improved from 97.0% to 100% accuracy.

Nous Research AI ▷ #research-papers (4 messages):

DRPS Framework, Data Efficiency, Selection Intelligence, Synthetic Data Results, Training Efficiency

• DRPS: Data Rankings and Prioritization System Arrives: The Data Rankings and Prioritization System (DRPS) teaches AI to selectively learn from data by using a Relevance Scorer, Quality Rater, and Diversity Controller, as detailed in a situational awareness report.
• DRPS Cuts Data Usage by over 90%: In tests with MNIST, DRPS achieved a 93.8% reduction in data usage, utilizing only 6.2% of the examined data while maintaining 99.1% of baseline performance, showcased in a GitHub repository.
• DRPS Shows Smarts by Selecting Top Samples: DRPS examined over 516,000 samples and selected only 32,000 for training, maintaining a stable selection rate of 6.1-6.3% throughout the training process.
• DRPS Boosts Accuracy Points per Data Percentage: Using synthetic data, DRPS achieved an 85.4% data reduction and used only 14.6% of training samples to achieve 5.89 accuracy points per % of data used, compared to a baseline accuracy of 87.6%.
• DRPS Framework improves training efficiency: DRPS improves training efficiency with a 16x reduction in active training set size and boosts component accuracy, such as increasing the Relevance Scorer from 95.9% to 99.95% and the Quality Rater from 97.0% to 100%.

Yannick Kilcher ▷ #general (46 messages🔥):

Quantum Startup Multiverse, MoE Nuances, Tokenization and Routing Synergy, Gemma 3n

• Buzzy Quantum Startup?: An article about the startup Multiverse claims they created two of the smallest high-performing models ever using something something quantum, but they are probably just using a specialized compression algorithm for model weights.
  • The article does not seem to make actual quantum claims.
• Deciphering MoE Nuances: MoE (Mixture of Experts) is a family of techniques with very nuanced iterations, including token-choice, expert-choice, MoE with capacity factors, block sparse dropless token routing versus droppy routing, and more, making it annoying when people attribute a lot of things to MoE for some reason.
  • To verify issues are occurring in batched inference, one might reliably check the behavior numerically of something like Olmoe or IBM Granite 3.1 rather than hitting an API you can’t monitor.
• Synergize Tokenization and Routing: A member proposed the seemingly obvious idea to do tokenization and routing in the same step to synergize them dynamically.
  • Another member responded, I have never seen that proposed because the conventional wisdom dictates that networks are more expressive if there’s a lot of routing steps right before the expert being activated.
• Tokenization in Layers: Gemma 3n has per layer tokenization / embedding kind of.
  • That could be a better way to have learned patch level tokenization with inherently a little more insight into the context.

Yannick Kilcher ▷ #agents (1 messages):

DARPA AIxCC, LLM agents

• Team Triumphs in DARPA AIxCC: A team announced their placement in DARPA’s AIxCC (AI Cyber Challenge), where they built an autonomous system of LLM agents to find and fix vulnerabilities in open source software.
  • The project is now open source.
• Tips for Building Kickass LLM Agents: The team is sharing their tips for building effective LLM agents via this Xitter post.
  • The post contains generic advice applicable to a range of agent development scenarios.

Yannick Kilcher ▷ #ml-news (16 messages🔥):

Inference Time on Low-End Devices, DinoV2 vs DinoV1, Gemma Model Parameter Size, China's Role in Automation, Deepseek Training on Huawei Chips

• Low-End Inference Time Impedes Usability: Members discussed that inference time is more important on low-end devices, citing Google’s Android app for running LLMs as an example where long inference times and phone heating make it impractical.
  • Smaller models could be used for keyboard prediction, but these models may require training on device, per this Youtube video.
• DinoV2 Performance and Training Challenges: A member expressed hope that a new model would outperform DinoV2, as DinoV2 was worse than DinoV1 in some contexts and harder to train.
  • They linked to a YouTube video as reference.
• Gemma’s Parameters Revealed: It was noted that the Gemma 270M model has 100M params and 170M embedding params.
• Deepseek’s Chip Choice Stalled Training: A member pointed out that Deepseek’s training was stalled by attempting to train on Huawei chips instead of NVIDIA’s, according to this discussion.
• Manufacturing Tariffs Hinder Industry Growth: A member argued that imposing tariffs on equipment needed to build production lines is counterproductive to encouraging manufacturing.
  • They added that building up an industry would take decades, referencing Anthropic’s research on end-subset conversations and HRM analysis.

GPU MODE ▷ #general (1 messages):

venom_in_my_veins: hye

GPU MODE ▷ #cuda (4 messages):

1-bit inference, GPTQ

• Explore Speeding Up 1-Bit Inference: A member inquired about speeding up 1-bit inference and shared a link to the paper The Power of $\alpha,1$-sparsity: Near-Lossless Training and Inference of $\alpha$-bit Transformers.
  • The paper details a novel method to train and infer with $\alpha$-bit Transformers, achieving near-lossless results with 1.58 and 1-bit quantization.
• Inference Optimization: The linked paper highlights optimizations for transformer models using $\alpha,1$-sparsity, enabling near-lossless training and inference at very low bitwidths.
  • This approach could potentially lead to significant speed improvements in inference for certain applications.

GPU MODE ▷ #beginner (11 messages🔥):

CUDA Shared Memory, CUDA Illegal Memory Access, CUDA Kernel Launch Configuration, CUDA warp ID calculation

• Debugging CUDA Illegal Memory Access: A user encountered an Illegal Memory Access error when using shared memory in a CUDA kernel and sought help from the community, sharing their code snippet involving sat and commons arrays.
  • A member suggested that the error might stem from incorrect pointer arithmetic or ill-defined warp_id and WARPS_EACH_BLK, but provided an example code to show that it was probably unrelated.
• CUDA Kernel Launch Configuration Confusion: The user shared their kernel launch configuration <<<BLK_NUMS, BLK_DIM>>> and macro definitions, with BLK_NUMS set to 40, BLK_DIM to 1024, and WARPS_EACH_BLK computed as BLK_DIM/32, resulting in a global warp ID calculation.
  • Another member pinpointed the issue: the user’s warp_id was global, leading to out-of-bounds access to shared memory, which is local to each thread block.
• Resolving Shared Memory Access Issues: A member recommended using a local index and warp ID calculation within each thread block, suggesting local_index = threadIdx.x; local_warp_id = local_index / 32; to ensure correct shared memory access.
  • They further advised using bitwise shift operations (local_warp_id = local_index >> 5;) instead of division and modulus for better performance on the GPU, and checking the generated assembler code with NSight Compute.

GPU MODE ▷ #off-topic (10 messages🔥):

New Grad Kernel Job, GPU Thesis, Getting Kernel Job Without Internship

• Kernel Job Seeker Asks About New Grad Opportunities: A member inquired whether someone with no internship experience writing kernel could secure a new grad job writing kernel.
  • Another member suggested that if the candidate is knowledgeable about GPUs, their company doesn’t prioritize internship experience, mentioning their related thesis as part of their own successful interview process.
• Insider Reveals How to Get Kernel Job Without Internship: Someone with an interest in GPU posted that they secured a job through a combination of a GPU-related thesis and luck, plus getting through the interview process.
  • According to the person, good knowledge of GPUs can bypass the need for previous experience and internship.

GPU MODE ▷ #general-leaderboard (1 messages):

MI300 pytorch, OMP missing

• MI300 env lacks OMP: The MI300 environment appears to be missing OMP for pytorch.compile based on a user report.
• Link to Debug Error included: A user shared a link to the full debug error for further investigation.

GPU MODE ▷ #submissions (10 messages🔥):

trimul leaderboard, A100, H100, B200

• Trimul Leaderboard Sees New Speedsters: One member got second place on A100: 10.4 ms then quickly got first place on H100: 3.95 ms and first place on A100: 7.53 ms.
  • Later, the member got first place on B200: 2.35 ms, then again first place on A100: 6.01 ms and yet again first place on B200: 2.04 ms and finally successful on H100: 3.74 ms.
• A100 and H100 also see activity: Another member got 5th place on A100: 13.2 ms.
  • The member followed up to get second place on H100: 6.42 ms and finally successful on A100: 14.7 ms.

GPU MODE ▷ #factorio-learning-env (10 messages🔥):

Meeting Attendance, Large PR Review, Connection Error Debugging

• Missed Meeting Mishaps: Several members mentioned missing a meeting due to timezone confusion and scheduling conflicts, including one member available only for the first 10 minutes.
  • One member quipped that the 8am meeting time was a bit brutal.
• Reviewing Scope Creep: A member commented on a PR with 300 file changes, joking that it was a “lil out of scope”.
  • Another member added that the code was grass-fed hand-crafted.
• Troubleshooting Connection Errors: A member reported seeing a connection error and is attempting to debug its source, guessing it might be from db_client.
  • They mentioned difficulty in getting a stack trace to diagnose the issue.

Moonshot AI (Kimi K-2) ▷ #general-chat (47 messages🔥):

Kimi K2 Technical Report, GLM-4.5 vs Kimi K2, Kimi hallucinations, Kimi's Web UI, Kimi future updates

• NotebookLM Video Edges out Kimi PPT: Members compared a PPT generated by Kimi with a video overview generated by Google’s NotebookLM for the Kimi K2 technical report, with the consensus leaning towards NotebookLM’s video due to its audio and more flexible layout (see attached video).
  • While both were appreciated, one member expressed a preference for reading over listening to AI-generated audio, but noted the potential of video overviews, especially in education.
• Kimi K2 Beats GLM in Writing Skills: Despite a feeling that GLM-4.5 might surpass Kimi K2 in overall performance, users lauded Kimi for its superior writing style and proactive error detection.
  • One user was “genuinely surprised” when Kimi “out of the blue told me No”, appreciating its candor.
• Combating Kimi’s Hallucinations: Users expressed a desire for Kimi to hallucinate less, even with web search enabled, noting that while GLM may take longer, it hallucinates less frequently.
  • A user stated they consistently use the thumbs down button to report hallucinations.
• Kimi Fans Eagerly Await ‘Kimi Thinking’: Members are eagerly anticipating the arrival of ‘Kimi Thinking’ and reasoning and multimodel capabilities.
  • There are questions as to whether this will arrive in the form of Kimi K-2 or Kimi K-3, but there are no firm ETAs yet.
• Dark Mode Enhances Kimi’s Web UI: A user shared their customized Kimi Web UI with a dark mode extension, expressing a strong preference for it over the default grey interface (see attached screenshot).
  • Another user confirmed that only the username and server roles are passed to the Moonshot API.

LlamaIndex ▷ #blog (4 messages):

AI Stock Portfolio Agent, Web Scraping AI Agents, Multimodal AI Applications, Legal Knowledge Graphs

• AI Stock Portfolio Agent Arrives: LlamaIndex has introduced a framework to build a complete AI stock portfolio agent, integrated with @CopilotKit’s AG-UI protocol for seamless frontend-backend communication; a comprehensive tutorial is included to create a sophisticated investment analysis tool.
  • The tutorial combines the power of this framework to create a sophisticated investment analysis tool.
• Brightdata and LlamaIndex Launch Web Scraping AI Agents: LlamaIndex announced a new walkthrough with @brightdata on building web-scraping AI agents with LlamaIndex’s agentic framework, focusing on reliable web access and robust web scraping workflows.
  • The walkthrough details how to set up workflows that can handle dynamic content and build intelligent agents that can navigate here.
• Multimodal AI Apps Analyze Markets Visually: LlamaIndex announced building multimodal AI applications that analyze both text and images for market research and surveys.
  • These applications are designed to process images and documents together in a unified AI pipeline, extract insights from visual market data like charts, graphs, and product images, and combine multimodal capabilities.
• LlamaCloud and Neo4j Transform Legal Documents into Knowledge Graphs: LlamaIndex announced a comprehensive tutorial on transforming unstructured legal documents into queryable knowledge graphs that understand not just content, but relationships between entities.
  • This workflow leverages LlamaCloud and @neo4j for legal contract analysis and is detailed here.

LlamaIndex ▷ #general (28 messages🔥):

Pydantic Models vs JSON Schema for Tool Calls, Vector Store Errors After Update, Progress Bar Issue with num_workers > 1, Iterating Over Nodes/Doc_IDs in Vectorstore

• Pydantic vs JSON Schema Showdown: A member inquired whether tool calls require a Pydantic model or if a JSON schema suffices, noting the redundancy of converting JSON to a Pydantic model only to have it unpacked back into JSON.
  • Another member pointed out Pydantic’s create_model() function doesn’t directly accept a JSON schema, highlighting the need for a tool/package to handle this conversion.
• Vector Store Gets Attribute Error After LlamaIndex Update: After updating to version 0.13.1, a user encountered an AttributeError during retrieval from a PGVectorStore when using RetrieverQueryEngine with OpenAI and text-embedding-3-small.
  • The error arises because the output is a str with no attribute json, stemming from the LLMStructuredPredictEndEvent in openinference.instrumentation.llama_index.
• Progress Bar Pandemonium with Multiprocessing: A user highlighted that the progress_bar=True feature doesn’t function correctly when num_workers > 1 due to the use of multiprocessing.
  • It was suggested that using async concurrency could offer a smoother experience, however the async pipeline.arun method still uses multiprocessing.
• Nodes and Doc IDs Missing in Action in Vector Stores: A user expressed frustration over the inability to iterate over nodes or obtain a list of doc_ids in most LlamaIndex vector stores, particularly noting the absence in Opensearch and awsdocdb.
  • A workaround involves setting similarity_top_k to a high number, but this is inefficient and may not be supported by all OSS; the get_nodes() method exists on the base vector_store class, however, it is not implemented for Opensearch or awsdocdb, which is an opportunity for a PR.

LlamaIndex ▷ #ai-discussion (1 messages):

DSPy optimizes CrewAI, CrewAI agent prompts

• DSPy optimizes CrewAI agent prompts: A course teaches how DSPy optimizes CrewAI agent prompts in a real production use case to build smarter, cheaper agents with proven methods.
  • You can check the course here.
• Build smarter, cheaper agents with proven methods: The course focuses on DSPy optimization for CrewAI agents.
  • It emphasizes building more efficient and intelligent agents through proven methodologies.

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

Audio Transcription in NotebookLM, NotebookLM Interface Redesign

• Audio Uploads Auto-Transcribed to NotebookLM: A member inquired about obtaining audio transcripts, to which another member responded that they upload MP3 audio files directly to NotebookLM.
  • The member clarified that NotebookLM itself handles the transcript generation.
• NotebookLM Interface Redesign Underway: One member mentioned they are attempting to redesign NotebookLM, and shared a Figma screenshot of the proposed changes.
  • The member apologized for any disappointment, clarifying it was just a design concept, not a functional update.

Notebook LM ▷ #general (23 messages🔥):

Explainer Video Voice, Feature Request Feedback, Dev Interaction, Prompt Limit

• Voice Gender Swaps Explainers: A user reported that their explainer videos suddenly started generating with male voices instead of the usual female voices and questioned why this happened.
  • There was no clear resolution or explanation provided in the messages.
• User Requests Acknowledgement of Feature Requests: A user questioned whether anyone from the NotebookLM development team is actually reading through the feature requests posted in the Discord channel.
  • They expressed a desire for some sign of life or feedback from the developers to encourage continued contributions.
• NotebookLM Devs Acknowledge Reading Posts but Can’t Respond to Everything: A Google developer stated that the devs read the posts, but they don’t have time to respond to everything and spend a lot of their time banning spammers.
  • Other users suggested that even occasional acknowledgements or AI-compiled summaries could help encourage user contributions.
• Users bump into Prompt Limits in NotebookLM: A user asked if there is a limit to the number of words that can be included in a single question in NotebookLM after failing to ask a case-related question containing about 857 words.
  • Another user suggested splitting the prompt into multiple parts or trying Gemini.

DSPy ▷ #show-and-tell (1 messages):

CrewAI agent prompts, DSPy

• Optimize CrewAI agent prompts with DSPy: Members shared a link to learn how DSPy optimizes CrewAI agent prompts in a real production use case: https://www.udemy.com/course/crewai-dspy-optimization/?referralCode=B59F73AE488715913E7E.
  • The course claims to teach users how to build smarter, cheaper agents with proven methods.
• DSPy and CrewAI unite: The course teaches users how to optimize CrewAI using DSPy.
  • It enables smarter, cheaper agents using proven methods.

DSPy ▷ #general (22 messages🔥):

DSPy and Databricks, GEPA Error, MLflow and DSPy

• Databricks Not Sponsoring DSPy: A user asked if Databricks sponsors or owns the DSPy project, and another user clarified that DSPy is MIT-licensed open source, with Databricks contributing significantly through a team of core developers.
• GEPA Bug Fixed: A user encountered a ValueError when using GEPA with the RAG tutorial, and another user confirmed that this was a bug in GEPA code that has been fixed; users should upgrade to DSPy 3.0.1.
  • The depreciated param is in that dspy.evaluate import, and the fix is pip install -U dspy.
• MLflow Tracks DSPy Sub-Modules automatically: A user inquired about integrating DSPy modules tracking with MLflow for a text2sql pipeline and was advised to use mlflow.dspy.autolog() instead of mlflow.autolog() to automatically track all sub-modules.
  • Using mlflow.dspy.autolog() will display the SQLGenerator, Validator, and Reflector as nested spans in the MLflow UI’s Traces tab, as detailed in the MLflow DSPy integration documentation and the DSPy MLflow tutorial.
• Logprob Surprise as fitness Function: A user shared a tweet TogetherCompute Status, and guessed that they’re basically doing GEPA with logprob surprise as the fitness function, but for mental health models in prod.
• Community Engagement Requested: A member requested more engagement from the 6500 people in this discord, and more contributions to the docs and all.

DSPy ▷ #examples (1 messages):

CrewAI, DSPy Optimization, Prompt Engineering

• CrewAI Prompt Optimization Course Drops: A member announced a Udemy course demonstrating how to optimize CrewAI prompts with DSPy.
  • The course will show how to inject them back to the LLM so the LLM uses better prompts than those stitched together by CrewAI.
• DSPy Enables Optimized CrewAI Prompts: The new course uses DSPy to optimize prompts.
  • Optimized prompts are then injected back into the LLM improving on the standard approach in CrewAI.

tinygrad (George Hotz) ▷ #general (8 messages🔥):

CI speed, tinygrad release, tinygrad size

• CI Speed Hampering Productivity: A member expressed frustration with slow CI speeds, stating they could work faster with quicker CI and linked chatgpt analysis.
• Tinygrad Release Imminent: There was a suggestion to do a tinygrad release soon.
• Tinygrad Size Swells: A member questioned why tinygrad 0.10.3 is 10.4 MB, hinting at a possible size issue.

tinygrad (George Hotz) ▷ #learn-tinygrad (14 messages🔥):

WSL2 Support, print_tree removal

• WSL2 Tinygrad Bug Surfaces: A user encountered an issue where adding two tinygrad Tensors created from PyTorch tensors resulted in all 0s, and provided a full script to reproduce the bug on WSL2.
• print_tree function bites the dust: The print_tree function has been replaced with a simple print function.
  • The user noted it lost some of its formatting.

aider (Paul Gauthier) ▷ #general (12 messages🔥):

Aider Benchmark, litellm Errors, Open Source Entitlement, llama.cpp PR #15181

• Aider Benchmark Plagued by Timeouts: A member ran the Aider benchmark against a local gemma3:12b model and encountered frequent timeouts after 10.5 hours and 221/225 tests due to the model’s inability to respond within the 600-second limit, resulting in litellm.APIConnectionError errors.
  • They shared the error log which shows the model attempting to send around 300k tokens, exceeding the 131,072 token limit and causing test failures.
• Continuing Aider Benchmark: A member suggested using ctrl+c to exit the benchmark, restarting the inference server, and then using the --cont flag to resume the benchmark from where it left off.
  • They also pointed to a merged pull request in llama.cpp that might improve local model performance.
• OSS Maintainer’s Burden: A member criticized another’s suggestion to make the benchmark automatically configurable for each LLM, labeling it as entitlement and lamenting that such attitudes cause countless OSS maintainers to throw in the towel.
  • Another member countered that it was merely curiosity, leading to further disagreement on what constitutes entitlement in open-source interactions.

aider (Paul Gauthier) ▷ #questions-and-tips (7 messages):

Aider with Local Models, Aider Line Number Accuracy, Unit Test Coverage with Aider

• Local AI/Aider Models Bring Debugging Agony: A member expressed difficulty using aider with local models like ollama, lmstudio, and vllm, noting slow performance even with powerful hardware.
  • They suggested the need for a tutorial video on setting up aider with these tools for local development and debugging.
• Aider’s Line Numbering System Questioned: A member inquired about how aider determines line numbers, especially in the context of generating unit tests for specific code coverage.
  • The issue arises when aider misreports the line numbers, leading to incorrect test coverage, despite attempts to refresh the map and clear chat history.
• LLM Accuracy Impacts Unit Test Coverage: A member reported that qwen3-coder and gemini-pro inaccurately identify line numbers in coverage reports, sometimes missing the coverage entirely.
  • This inconsistency leads to questions about whether aider relies on the LLM’s accuracy for line number identification, suggesting a need to explore alternative methods for accurate unit test generation.

aider (Paul Gauthier) ▷ #links (3 messages):

Grok4, Quota Increase, Benchmark Costs

• Grok4 Location Remains Elusive: A member inquired about the whereabouts of Grok4.
  • Another member responded that it’s in the article but the request to increase the quota needed to execute the tests was ignored.
• Grok4 Benchmark Costs Thousands: A member noted they spend multiple thousands dollars during the development of this benchmark.
  • This highlights the significant financial resources required for advanced AI model benchmarking.

Manus.im Discord ▷ #general (22 messages🔥):

Manus Credit Deductions on Errors, Manus Deployment Issues, Manus Team Accounts, Add-on Credits Removal, Manus in the Wild Challenge Winner

• Manus Credit Deductions Draw Ire: Users express frustration over Manus deducting credits even when it makes errors, making task completion difficult compared to other AIs like Claude AI.
  • One user reported spending high amounts of credits only for Manus to make a simple change that broke the entire application, deeming it non-functional.
• Manus Deployment Stumbles: Users report issues with Manus deployments, where websites created from the same GitHub repository differ significantly, especially with large folders, illustrated by comparison of affilify.eu and a Manus hosted site wmhkgqkf.manus.space.
  • A community manager noted that Manus isn’t designed as a coding agent or pure development tool, so deployment isn’t its strength, but they’re working on improvements.
• Add-on Credit Packages Recede: Users question the removal of add-on credit packages, which are now exclusively available for Pro users.
  • A community manager explained that this change ensures consistent speed and quality for heavy users and suggested bundling similar questions, being concise, and avoiding repeated requests to maximize credit efficiency.
• Manus Team Accounts Spark Interest: A user inquired about the possibility of a Manus team account for shared credit usage.
  • A community manager confirmed that Manus does offer a team plan, directing users to the official website for details.
• Users Bemoan Credit Consumption: One user shared a frustrating experience of burning through 30,000 credits attempting to get their website up, encountering issues with mock sites and template implementations.
  • They criticized the system’s inconsistency, where it’s smart as hell but then suddenly turns dumb, leading to wasted credits and suspected stall tactics.

Cohere ▷ #🧵-general-thread (9 messages🔥):

Cohere Labs, Pokemon emojis, PAX Omeganauts Discord

• Cohere Labs contact info sought!: A member asked where to connect with Cohere Labs folks, another member suggested this Discord channel.
  • Another member directed the user to this link.
• Discord channel Pokemon Emoji-fied!: A member suggested adding more Pokemon emojis to the channel, as there are available slots.
  • The member noted that the emojis come from the PAX Omeganauts Discord.

Cohere ▷ #👋-introduce-yourself (5 messages):

AI Research, writenode, CV+ML pipeline

• AI Researcher Seeks Collabs: An AI researcher with a deep interest in reasoning and conscious capabilities is looking for collaborations to develop advanced tech for the future.
  • The member is open to collaborations from any sub domain.
• Legal Pro Aligns with AI: A legal professional, gamer, and lover of philosophy currently working for the USG is self-teaching AI alignment theory and mechanics.
  • The member is excited to be here.
• writenode builder uses Cohere: Josh is building writenode, an in browser, cognitive thought partner, and creative companion, and uses Cohere.
  • He does not have a developer or coding background since before December last year.
• Psych PhD Returns to AI: A member is returning to AI research after dabbling in a human psychology PhD the past 5 years.
  • Their interests are in sound+music, and using tech tools to help us express our creativity.

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

Discord Invite Links, Channel Spam

• Discord Invites Flood Channel: A member spammed the channel with Discord invite links multiple times, tagging everyone.
  • The invite link was repeated three times in quick succession.
• Invite Link Repetition: The same Discord invite link was posted repeatedly.
  • This resulted in a spam-like effect, potentially disrupting the channel’s usual discussions.

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

Discord Invite Link, HjWfRbqBB8, Channel Invitation

• Discord Invite Link Floods Channel: A member repeatedly shared a Discord invite link in the channel, possibly to attract more users.
  • The member tagged @everyone multiple times, which might be considered excessive or disruptive.
• Channel Invitation Blitz: The repeated posting of the same Discord invite suggests an attempt to boost channel membership.
  • The use of @everyone indicates the message was intended for all members, regardless of their interest in the invitation.

MCP (Glama) ▷ #general (2 messages):

Elicitations Specification, MCP Server Conversion

• Elicitations Spec Clarity Sought: A member inquired about the Elicitations specification regarding who is responsible for translating message/field descriptions into the user’s language.
  • Specifically, they seek clarification on whether tools should handle language detection and internationalization, or if MCP Clients are expected to translate, potentially using an LLM.
• MCP Server Transformation Question: A member inquired whether there exists some tool to turn a local mcp server into a remote mcp server?
  • No links or additional context was provided.

MCP (Glama) ▷ #showcase (3 messages):

Unifi MCP, Unraid MCP, Syslog MCP, AI Agent Workflows, AI Security

• MCP Servers for Homelabbers Arrive: A member shared a few MCP (presumably, Management Control Panel) servers for the homelabbers, specifically: Unifi MCP, Unraid MCP, and Syslog MCP.
• PulseMCP Turns Newsletter Tedium to Agent Automation: PulseMCP used goose to turn a tedious newsletter workflow into agent-powered automation with a human in the loop.
  • More details on the automation can be found at this blogpost.
• AI Security Seeks Input On Security Concerns: One member posted about building AI security that stops attacks before they even start with mathematical security certainty.
  • They are looking for Dev input on security concerns, and linked to a survey.

Nomic.ai (GPT4All) ▷ #general (4 messages):

Strix Halo profitablility, Dolphin chat template, Quantum computers, PC Memory

• Strix Halo’s Profitability Plummets: The Strix Halo, despite its impressive specs, requires a year of 24/7 inference to become profitable due to its slower inference speed of 53 tokens/sec compared to GPT-OSS 120B on OpenRouter.
  • One user noted that configuring it for LLMs at $2000 is inefficient compared to cloud alternatives offering 200-400 tokens/sec.
• Dolphin Chat Template Quest: A user is seeking a working chat template for gpt4all compatible with Dolphin-2.2.1-mistral-7b-gptq.
  • Another member suggested asking model makers to upload a template with a jinja template.
• Quantum Computing Teaspoons?: One user speculates on the future availability of quantum computers and the possibility of selling qubits on the teaspoon.
  • They mentioned news about fully working quantum computers, indicating potential advancements in the field.
• Memory Modules and Moore’s Law: A user mentioned that old-fashioned PCs can expect to see higher capacity memory modules and DDR6 in late 2027 or 2028.
  • They express excitement about the potential of micro PCs with high RAM and VRAM capacities, especially for small businesses.

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

Maternity Leave, Team Contact During Leave

• Maternity Leave Commences!: A member announced they will be on maternity leave from August 25th until February 2026.
  • They look forward to catching up upon their return.
• Team’s Coverage Plan Revealed: While they are away, the team will be monitoring <@1334161614949056532>.
  • Members can also reach out to <@709918328306663424> with any questions or concerns.

Torchtune ▷ #general (1 messages):

__nathan: <@132818429022437376> how did this go?

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

Windsurf Wave 12, DeepWiki Integration, Vibe and Replace feature, Smarter Cascade Agent, Dev Containers Support

• Windsurf Wave 12 is Released!: Windsurf Wave 12 brings the first integrations of Devin’s intelligence and capabilities directly into the Windsurf IDE.
  • Key features include a new UI design, DeepWiki Integration, Vibe and Replace, a Smarter Cascade Agent, Faster Tab, Dev Containers Support, and over 100 bug fixes - see the changelog, read the blog, watch the Wave 12 video, X/Twitter, and Reddit.
• DeepWiki Integration brings AI to the IDE: DeepWiki Integration allows users to hover over code symbols for AI-powered explanations (not just basic type info).
  • Users can also use CMD/Ctrl+Shift+Click to open detailed explanations in the side panel and add to Cascade context.
• Vibe and Replace revolutionizes bulk editing: The Vibe and Replace feature provides revolutionary bulk editing capabilities by finding exact text matches.
  • It allows users to apply AI prompts for intelligent, context-aware transformations across their entire project.
• Smarter Cascade Agent gets Always-On Planning: The Smarter Cascade Agent now features an always-on planning mode with autonomous to-do lists.
  • It also includes revamped tools designed to provide smarter responses.
• Dev Containers Supported Natively: Windsurf now supports working with containers directly via remote SSH access.
  • This enhancement simplifies development workflows involving containerized environments.