a quiet day

AI News for 8/22/2025-8/25/2025. We checked 12 subreddits, 544 Twitters and 29 Discords (229 channels, and 18470 messages) for you. Estimated reading time saved (at 200wpm): 1488 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!

If you browse the Twitter and Reddit sections you’ll know this week is about to be a big GDM week, but not today :)

AI Twitter Recap

Open-weights model drops: xAI’s Grok-2/2.5, Microsoft VibeVoice, and Motif-2.6B

• xAI released Grok-2 (and says Grok-2.5) open weights on Hugging Face. Files are ~500 GB and the config shows μP usage and an unusual “MoE residual” path that acts like a shared expert. Community reactions span excitement to licensing concerns: @elonmusk claims Grok 3 will be open-sourced in ~6 months and that 2.5 was their best model last year (tweet); @HuggingPapers summarized the drop (tweet); @ClementDelangue shared the repo (tweet); @rasbt highlighted the residual MoE block with a side-by-side arch note (tweet); @QuanquanGu noted explicit μP scaling in the config (tweet). Others flagged the license as highly restrictive, “dead on arrival” for true open use (tweet). Repo: https://huggingface.co/xai-org/grok-2
• Microsoft open-sourced VibeVoice-1.5B (MIT license) for long-form TTS: multi-speaker conversations, up to 90 minutes continuous synthesis, with streaming support on the way and a 7B variant coming. Demos and Spaces are already live via Gradio and community repos. See @MaziyarPanahi’s overview (tweet), @Gradio’s announcement (tweet), and the model card (tweet). Repo: https://huggingface.co/microsoft/VibeVoice-1.5B
• Motif Technology released a detailed tech report for Motif-2.6B (trained on 2.5T tokens) featuring Differential Attention and PolyNorm at scale, WSD with simple moving average ensembling (last 6 checkpoints), and extensive finetuning data curation (Finemath, Fineweb2, DCLM, TxT360). They also published Muon optimizer and PolyNorm kernels compatible with FSDP2/HF stacks; training reportedly used AMD MI250 GPUs. Good technical thread by @eliebakouch (tweet) and follow-ups with paper/model links (tweet, tweet).

Coding and agent toolchains: GPT-5 momentum, Qwen-Code, DSPy/GEPA, MCP

• The center of gravity for AI coding workflows appears to be shifting toward GPT‑5-backed tooling. Developers report strong results with codex-cli gpt‑5-high (pair programming, API design feedback, subtle bug hunts) and are downgrading Claude Code for certain tasks: see @gdb (tweet), @ericmitchellai (tweet), @ivanfioravanti (tweet), @deanwball (tweet), and @giffmana’s detailed workflow notes (tweet).
• Alibaba’s Qwen-Code v0.0.8 dropped major integrations: deep VS Code support (context-aware suggestions, inline diffs), robust MCP CLI (add/remove/list), responsive TUI, reverse search, context compression controls, multi-directory auto-load, and more. Thread with specifics from @Alibaba_Qwen (tweet).
• MCP ecosystem is accelerating:
  • LiveMCP-101: stress-testing and diagnosing MCP-enabled agents on challenging queries (tweet).
  • “Rube,” a universal MCP server that connects agents to hundreds of apps (Zoom, Gmail, GA, YouTube, etc.), with smooth demos inside Claude Code (tweet).
  • LangGraph Platform ships rollbacks and revision queueing (tweet, tweet) and announced an integration with ART to train LangGraph agents via RL for improved tool use and reasoning (tweet).
• DSPy’s GEPA optimizer landed in v3.0 and is getting strong results across use-cases (e.g., 40% gain in 500 metric calls; listwise reranking tutorials). See @DSPyOSS (tweet), @CShorten30’s walkthrough (tweet), and @MaximeRivest’s end-to-end course (tweet).

Systems and infra: TPU vs GPU, NVFP4, vLLM scale-up, OpenRouter growth

• TPU pods vs GPU islands: multiple engineers highlighted that TPU v3/v4 pods offer near NVLink-tier bandwidth across the pod with clean scaling on a 2D torus, easing parallelism pressure (less need for PP at K2/DeepSeek scale). See @JingyuanLiu123’s cross-ecosystem thread (tweet), @gallabytes on topology (tweet), and @mr_besher’s DP/TP/PP heuristics (tweet).
• NVIDIA’s NVFP4 pretraining improvements continue apace; @ctnzr posted a succinct update (tweet).
• vLLM momentum:
  • New sampling control PRs powering state-of-the-art reasoning evals (tweet).
  • Shanghai meetup deep-dived distributed inference, ERNIE integration, caching, and hardware support; slides/notes linked by @vllm_project (tweet).
  • Tinybox demo of gpt-oss-120B via vLLM for a local OpenAI-compatible API (tweet).
• Mac MLX: practical “large model locally” tinkering—RAID0 over TB4 to load Qwen3-480B in ~25–46s TTFT; detailed build notes and performance numbers from @TheZachMueller (tweet, tweet).
• Platform/data:
  • OpenRouter throughput exploded from ~111B to 3.21T tokens/week in a year (tweet).
  • EpochAI renamed its “AI Supercomputers” dataset to “GPU Clusters” and added 32 entries (tweet, tweet).

Video and multimodal editing: Veo-3 free weekend, Kling-2.1 keyframes, Qwen-Image-Edit

• Google ran a Veo-3 open weekend in Gemini with expanded generation limits (free users 6 total; Pro 6/day; Ultra 10/day) and prompt tips; @sundarpichai (tweet), @GeminiApp (tweet).
• ByteDance’s Kling 2.1 added “Start/End frame” keyframing, enabling multi-view-consistent transitions and cinematic camera moves with consistency across frames; now in Higgsfield. Strong creator demos: @renataro9 (tweet), @EHuanglu (tweet).
• Qwen-Image-Edit is getting traction for outpainting/edits and fun “merch mockups” (turn memes into physical figures). See @Alibaba_Qwen (tweet), @linoy_tsaban (tweet), and @jon_durbin for API playground use (tweet).

Research and evals: programming benchmarks, RL vs SFT, biomedical agents, safety

• New programming competition benchmark AetherCode (IOI/ICPC-style) with expert-curated test suites; only o4-mini-high and Gemini-2.5-Pro solve at “Extremely Difficult” level. See @iScienceLuvr for details and links (tweet).
• “RL Is Neither a Panacea Nor a Mirage”: spectrum-aware analysis suggests RL often counteracts SFT-induced drift; cheap recovery knobs (low-rank UV merges, shallow-layer resets) can precede costly RL finetuning. Summary by @iScienceLuvr (tweet).
• DuPO (Dual Preference Optimization) proposes annotation-free feedback via reconstructing hidden input parts (xu) from model outputs + context (xk), providing a self-supervised reward pathway compatible with PPO/GRPO. Results show gains in translation, math reasoning, and inference-time reranking across small-to-mid models (tweet).
• OwkinZero introduces an 8-dataset benchmark (300k+ verifiable Q&A) across the drug discovery pipeline; specialist models post-trained with RL outperform larger commercial LLMs and show cross-task generalization (tweet).
• Prompt-security watch: a live PoC shows browser-based prompt insertion/prompt-injection risks—e.g., doomscrolling Reddit triggering tool-use flows—highlighting the need for rigorous sandboxing and tool-scoping in “AI browsers” (tweet).
• ByteDance’s recent CoT behavior: special tokens periodically budget/track “thinking” tokens during reasoning steps (tweet).
• Token cost engineering for code: removing cosmetic formatting cut input tokens ~24.5% with no quality loss and modest output savings via instruction/fine-tuning; shipping tools can strip/restore formatting transparently (tweet).

Ecosystem and products: Perplexity iOS, Genspark IDE, RL envs reality check

• Perplexity shipped a redesigned iOS app with gestural navigation, SuperMemory integration on the way, and standout voice dictation UX; widely praised by @AravSrinivas (tweet, tweet) and others.
• Genspark launched a browser IDE for “describe → iterate” coding with multi-model backends; @fchollet emphasized low-barrier tools for non-experts (tweet).
• RL environments discourse: @rosstaylor90 argues we lack high-quality, domain-authentic RL envs/evals; advises prioritizing expert-built, high-construction-difficulty tasks over verifiability fetishism and notes that “scaling envs” ≠ recreating internet-scale diversity (tweet).

Top tweets (by engagement)

• xAI: Grok 2.5 open weights now, Grok 3 in ~6 months (tweet, 54k+ engagement)
• SpaceX: Starship Flight 10 broadcast and “Standing under Starship” photos (tweet, tweet, 13k–282k+)
• Google Veo-3 free weekend + doubled limits (tweet, 2.3k+)
• Waymo: 85% fewer serious injuries, 79% fewer injuries overall vs human drivers (57M miles) with calls for policy response (tweet, 7.4k+)

AI Reddit Recap

/r/LocalLlama + /r/localLLM Recap

1. Open-source Multimodal Launches: InternVL3.5 and WAN 2.2-S2V

• InternVL3.5 - Best OpenSource VLM (Score: 309, Comments: 61): **InternVL3.5 introduces expanded multimodal “agency” features (e.g., GUI and embodied agents) and claims its** InternVL3.5-241B-A28B checkpoint achieves state-of-the-art aggregate scores across multimodal general, reasoning, text, and agency tasks among open-source VLMs, reportedly narrowing the gap with leading closed models (cited as “GPT-5”). Multiple checkpoints are released, including small (e.g., 2B/4B) variants and intermediate/base training snapshots to enable reproducibility and downstream fine-tuning. Commenters highlight appreciation for releasing checkpoints at multiple training stages and note that while InternVL3.5 reports gains over bases, vision-centric models can underperform on pure text tasks—suggesting community benchmarking is needed. Enthusiasm is strong for the 2B/4B variants’ efficiency-to-performance ratio, while some point to Qwen 3 fine-tuning as a likely contributor to non-vision quality improvements.
  • Model release strategy: commenters highlight that InternVL publishes checkpoints at multiple training stages (including the base), which enables rigorous ablations, reproducibility, and downstream fine-tuning comparisons. Having base and intermediate snapshots is valuable for isolating gains from instruction tuning vs continued pretraining and for benchmarking scaling behavior across the same data/architecture.
  • Backbone and task trade-offs: one commenter notes InternVL3.5 reportedly finetunes a Qwen 3 backbone, and flags the common issue that VLMs are usually weaker on pure text tasks than their text-only bases. Early numbers are described as “some better and some worse … overall slightly better” versus base models, suggesting the need for hands-on evaluation across non-vision tasks to validate whether the finetuning improves general NLP without regressing compared to Qwen 3 baselines.
  • Scaling and MoE details: users call out that the 2B and 4B variants perform “amazing for their size,” and ask about the speed of the MoE 30B. A linked checkpoint, InternVL3_5-241B-A28B (Hugging Face), implies ~241B total parameters with ~28B active per token (typical MoE notation), so expected throughput may be closer to a ~28B dense model plus routing overhead; this contextualizes latency/throughput expectations for the larger MoE variants.
• InternVL3_5 series is out!! (Score: 222, Comments: 82): Announcement of the InternVL3.5 series from InternLM surfaced on Hugging Face’s org activity page (link), but at the time of posting there were no public benchmark results or detailed model cards, and the artifacts appear to have been taken down shortly after. Technical specifics (model sizes, training data, evaluation suites) were not disclosed in the thread; commenters reference ~9Bscale visual models from prior InternVL lines as context, but no v3.5 metrics are available. Top comments praise InternLM as a “dark horse,” highlighting strong yet underrated ~9B visual models, while others question the lack of benchmarks and note the release was quickly removed.
  • Benchmarking/documentation gap: commenters ask for public evals and technical details, but there are no released benchmarks or model cards yet for InternVL3.5. Without weights, the community can’t run standard MLLM evals (e.g., MMBench, MMMU, MME, LLaVA-Bench), so claims—especially around the 9B visual variant—remain unverified.
  • Release status/availability: multiple reports say the model was posted then taken down, and there are currently no files/weights available. This blocks reproducibility, independent fine‑tuning, and third‑party latency/throughput testing until artifacts and a license are re-published.
  • Model class focus: a commenter highlights the lab’s 9B visual models as strong/underrated, suggesting a compact VLM targeting the 7B–13B efficiency band. If confirmed, a 9B VLM would be attractive for lower‑latency inference versus 13B–34B classes while aiming to maintain competitive multimodal accuracy—pending public benchmarks.

• Qwen Wan2.2-S2V is coming soon (Score: 378, Comments: 35): Alibaba’s WAN team teased “WAN 2.2‑S2V” via an X post, positioning it as an open‑source, audio‑driven cinematic video generation system (“sound/speech‑to‑video”) that’s “coming soon.” The teaser provides no model specs, benchmarks, or code, but implies a new modality for the WAN 2.2 family that conditions video generation directly on audio, complementing existing T2V work. Link: https://x.com/Alibaba_Wan/status/1959963989703880866 Comments are largely hype; one highlights interest in an integrated T2V + audio pipeline (“T2V+A”), implying demand for multimodal conditioning beyond text alone.

2. Training Method & Tooling: GTPO vs GRPO and llama.ui Privacy Chat

• GRPO please stop punishing your correct token (Score: 163, Comments: 19): OP introduces GTPO (Group-relative Trajectory-based Policy Optimization) as a modification to GRPO to avoid gradient conflicts and policy collapse: it skips negative updates for “conflict tokens” and replaces KL-to-reference regularization with filtering out high-entropy trajectories. They report more stable training without a reference model (lighter runs; e.g., Colab + Unsloth) and better pass@k on reasoning datasets (GSM8K, MATH, AIME 2024) for LLaMA-8B and Qwen-3B versus GRPO and SFT, illustrated by two line plots (Qwen and LLaMA) showing GTPO curves above GRPO across k. Links: arXiv, GitHub, Colab. Commenters ask for a concrete explanation of the “conflict tokens” gradient issue (tokens vs parameter updates) and how GTPO compares against Qwen’s GSPO; another offers quick positive feedback.
  • Policy-gradient credit assignment concern: In PPO/GRPO-style updates, gradients look like ∑_t A_t ∇θ log π_θ(x_t | x<t). When training on multiple completions per prompt (grouped), a token that appears in both a high-reward and low-reward trajectory receives opposing advantages (positive vs negative), creating push–pull on the same logits even if that token is part of a correct shared prefix. This can misattribute blame to early tokens when the actual error occurs later. Common mitigations discussed in RLHF include masking updates before the first divergence point between pairs, applying per-token baselines/group-normalization, or emphasizing a reference KL on the shared prefix to reduce collateral gradient on correct tokens (see PPO: https://arxiv.org/abs/1707.06347).
  • Benchmarking ask vs Qwen’s GSPO: A commenter requests head-to-head evaluation of GRPO against Qwen’s GSPO, ideally controlling for prompt set, group size, reward model, and compute. Useful axes include sample efficiency (steps to reach target reward), stability (advantage/clip fraction, reward variance), alignment–capability tradeoff (KL to reference vs pass@k on GSM8K/MATH/HumanEval), and rejection-accuracy (win-rate of chosen over rejected). Reporting per-token advantage distributions and the effect of divergence-point masking would clarify whether GSPO/GRPO differ in how much they penalize shared-prefix tokens.
• llama.ui - minimal privacy focused chat interface (Score: 183, Comments: 61): Screenshot shows “llama.ui,” a minimal, privacy‑focused chat client with a sparse chat pane, four preset quick actions (fun fact, summarize text, team‑building ideas, professional email), a left sidebar of recent conversations grouped by time, and a bottom input box—suggesting a lightweight UI intended for local/self‑hosted LLM workflows (e.g., llama) rather than a feature‑heavy cloud assistant. The emphasis is on simplicity and privacy, mirroring default LLM chat clients with history and prompt templates but little else. Commenters question novelty: one argues that chatgpt.com already provides a minimal privacy mode, another notes the title’s missing comma (“minimal, privacy‑focused…”) to avoid implying “minimal privacy,” and a third asks what this offers beyond the default llama‑server client.
  • Requests for a technical comparison with the llama.cpp/llama-server default web client: commenters ask what capabilities this UI adds beyond the built-in server client (e.g., multi-backend support, OpenAI/llama.cpp API compatibility, streaming/token-by-token updates, chat history persistence, auth, configurable sampling params, or tool/function-calling). Reference: llama.cpp server and its default UI at https://github.com/ggerganov/llama.cpp/tree/master/examples/server.
  • Several ask for the concrete benefit over Open WebUI, implying a need to justify tradeoffs like footprint and features. Open WebUI provides rich integrations (RAG/vector DBs, multi-user auth, model management, TTS/STT, extensible plugins) at the cost of heavier dependencies; a “minimal privacy-focused” UI would need to demonstrate lower resource usage (small static bundle, no telemetry, strict CSP, offline assets) and simpler deployment to be compelling. Reference: https://github.com/open-webui/open-webui.
  • Missing repository link blocks technical evaluation of the privacy claim; commenters want to inspect source for external network calls, analytics, CDN assets, and storage behavior (e.g., local-only persistence, export/import, encryption). They also want to verify backend compatibility (OpenAI-compatible REST, llama.cpp server, vLLM/Ollama) and licensing to assess integration risk.

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. Google Gemini 3 Teaser Week (Three-Ship Hints) + Google AI Quirks and Industry Headlines

• Gemini 3? Following a 3 ship emoji from one of the devs just 4 hours ago (Score: 444, Comments: 54): A screenshot of a developer (Patrick Loeber) urging people to follow @googleaistudio “this week,” combined with a prior post showing three ship 🚢 emojis, is fueling speculation about imminent Google AI Studio updates rather than a core model release. Commenters note that a true foundation model launch like “Gemini 3” would likely surface first via third‑party benchmarking/mystery evals (e.g., LMArena) and not be teased specifically through the AI Studio channel, suggesting the tease points to multiple feature/product rollouts inside AI Studio instead. Skeptics in the thread say, “If it’s Gemini 3 I’ll eat my hat,” and argue that directing attention to AI Studio implies tooling/product changes, not a base-model jump, and that a big model would be preceded by a week of mystery tests on LMArena.
  • Several note that a true Gemini 3 base-model release would typically be preceded by LMSYS Arena “mystery model” runs and public benchmarking chatter; the teaser specifically pointing to Google AI Studio implies a platform/tooling update rather than new core model weights. As one puts it, “wouldn’t happen without a week of great mystery model tests on LMArena”—i.e., the absence of Arena entries (https://lmsys.org/arena/) or community eval signals makes a 3generation model drop unlikely, while an AI Studio focus (https://aistudio.google.com/) cues SDK/console/API changes instead of a base-model upgrade.
• Ok so nano banana and gemini 3 (cause of three ships) (Score: 276, Comments: 90): A verified user “Simon (@tokumin)” posted a teaser tweet — “Buckle up! Going to be quite the week!” — with three ship emojis, prompting speculation about upcoming Google/AI releases, but the post contains no technical details, benchmarks, or release notes. Most commenters interpret the three ships as three product “ships” (features/modes), not a new model like “Gemini 3,” with guesses pointing to three modes: Agent, Go, and Immersive. This is a hype tease rather than a technical announcement; see the screenshot: https://i.redd.it/a7dl6f5yp6lf1.png Top comments express skepticism toward hype-y teaser marketing and mock over-interpretation (e.g., jokes about emojis implying parameter counts), while cautioning not to conflate emoji with a major model release.
  • The “three ships” teaser is interpreted as 3 product modes shipping — Agent, Go, and Immersive — rather than a new foundation release like “Gemini 3” or parameter-count rumors (e.g., 3T). There’s no concrete benchmark/model-card evidence of a Gemini v3-class model; expectations should be for feature rollouts, not a base-model upgrade.
  • Developer-leaning commenters critique the teaser-driven cadence versus prior practice of quietly dropping models on AI Studio, arguing it impedes technical evaluation without tangible artifacts (API access, model IDs, release notes, or evals). Preference is for immediately usable releases over ambiguous marketing hints.
• Google AI 😩… somehow dumber each time you ask (Score: 252, Comments: 45): Screenshot of Google Search’s AI Overview for the query “was 1995 30 years ago?” shows contradictory temporal reasoning: it first answers “No,” then cites a reference date of July 25, 2025 (“today”) and concludes “Yes,” revealing broken date-grounding and self-consistency in a single response. Technically, this highlights weak temporal context handling and lack of validation passes in the AI Overview pipeline, likely due to using a lightweight/low-latency model with limited reasoning depth rather than robust tool-based date arithmetic. Comments suggest AI Overview runs on a very cheap/small model—possibly even smaller than Gemini Flash Lite—which could explain the shallow reasoning and inconsistency; others note the image has been widely circulated.
  • One commenter argues AI Overview is backed by an ultra-cheap, very small model—“maybe smaller than Gemini Flash Lite”—which would prioritize latency/cost over reasoning quality and thus explain brittle, inconsistent answers across turns. While speculative, this aligns with how smaller, aggressively quantized models often underperform on ambiguous prompts and multi-turn coherence compared to larger variants like Gemini 1.5 Pro/Flash (see Google’s model lineup: https://ai.google.dev/gemini-api/docs/models/gemini).
• I found this amusing (Score: 2076, Comments: 141): A clickbait-style optical-illusion puzzle: a grid of “79”s with a single hidden “76” that is visibly circled (row 5, column 6) in the screenshot image. The technical angle emerges from a quoted response by Gemini 2.5 Flash that confidently denies the presence of “76,” showcasing a basic VLM hallucination/grounding failure in visual question answering—overconfident text output contradicting the image’s content. Comments frame this as AI “gaslighting,” while one lengthy edit challenges the “stochastic parrot” critique, arguing LLMs mirror human predictive mechanisms and are limited mainly by guardrails—an opinionated defense that sparked debate rather than adding empirical evidence.
  • Multiple users share multimodal failure cases: Gemini 2.5 Flash confidently asserted the number 76 was absent in a “spot-the-different-number” grid and produced a templated explanation about optical illusions, indicating language-prior-driven pattern matching rather than grounded visual parsing/OCR. This is a classic VLM hallucination where fluent rationales mask pixel-level errors; similar issues are documented in VQA/image-captioning hallucination literature (e.g., object/text hallucination), and may be exacerbated in fast, low-latency variants like “Flash.”
  • Another report notes the model “added a row and took away a column” and insisted target words existed, even offering to “outline them,” implying confident yet incorrect region proposals/bounding boxes. This highlights poor calibration between detection confidence and accuracy in multimodal UIs; safer designs would expose uncertainty, gate region-annotation features behind OCR thresholds, or provide attention/heatmap sanity checks before drawing boxes.
  • One commenter pushes back on the “stochastic parrot” framing, arguing LLMs are next-token predictors analogous to brain predictive coding and that alignment/guardrails (e.g., RLHF-style safety layers) constrain observable behavior despite latent capability. For context, the critique originates with Bender et al. 2021 (“On the Dangers of Stochastic Parrots” — https://dl.acm.org/doi/10.1145/3442188.3445922); the counterpoint emphasizes predictive modeling and massive pretraining data, with post-training safety layers shaping outputs without altering base competence.
• Elon on AI replacing workers (Score: 4859, Comments: 1948): Screenshot shows Elon Musk replying to a question about AI-driven job displacement, asserting society will have a “universal high income” (beyond basic) so everyone gets essentials (medical care, food, transport), yielding “sustainable abundance.” No technical plan, metrics, models, or implementation details are provided—this is an economic-policy prediction tied to AI automation, not a technical announcement. Image: https://i.redd.it/o6l79opq55lf1.png Top comments are skeptical, arguing Musk’s claim conflicts with policies/people he supports and questioning feasibility/credibility of a billionaire promising broad income distribution.
• Microsoft launches Copilot AI function in Excel, but warns not to use it in ‘any task requiring accuracy or reproducibility’ (Score: 211, Comments: 42): Microsoft launched Copilot for Excel, an LLM‑powered assistant that can generate formulas, summarize tables, and run natural‑language analyses inside spreadsheets, but Microsoft’s guidance warns against using it for “any task requiring accuracy or reproducibility” (e.g., numerical calculations, financial reporting, or legal documents) due to non‑deterministic outputs. In effect, Copilot is positioned as an exploratory/authoring aid (brainstorm queries, draft formulas, outline pivot analyses) with human verification, not a replacement for Excel’s deterministic calculation engine or auditable reporting workflows. For product context, see Microsoft Copilot. Top comments suggest this is standard legal/AI safety boilerplate across vendors, while others question the utility in Excel if accuracy‑critical scenarios are discouraged, comparing it to “Clippy” and asking what valid use cases remain beyond low‑stakes exploration.
  • Commenters highlight Microsoft’s explicit warning to avoid using Copilot in Excel for “any task requiring accuracy or reproducibility,” including “numerical calculations” and “financial reporting, legal documents, or other high-stakes scenarios.” Technically, this underscores that the LLM-driven assistant generates suggestions that can be incorrect and are not deterministic, so it should not be treated as the calculation engine. Safer uses are drafting or exploring formulas/approaches that a human then verifies with Excel’s deterministic functions before relying on results.
  • A technical counterpoint notes that while Copilot shouldn’t be trusted for correctness, “it can set up tasks that require accuracy and repeatability.” In practice, this means using it to scaffold repeatable workflows or spreadsheet logic that, once validated by the user, Excel will execute deterministically; the non-reproducibility applies to the generation phase, not the final, locked-down formulas. This positions Copilot as a scaffolding/boilerplate tool, with human-in-the-loop verification ensuring reproducible execution.
• Elon Musk’s xAI secretly dropped its benefit corporation status while fighting OpenAI (Score: 245, Comments: 17): **CNBC reports xAI terminated its Nevada public benefit corporation status by** 2024-05-09 and remained non‑PBC after a 2025-03-28 merger with X, while Elon Musk was suing OpenAI over mission/structure. The shift removes PBC mission‑balancing and impact‑reporting expectations under Nevada law (noted for weak shareholder enforcement), coinciding with scrutiny of a Memphis gas‑turbine data center lacking promised pollution controls and the release of Grok 4 on 2025-07-09 without pre‑release safety disclosures; xAI added a model card update on 2025-08-20 after inquiries. Records indicate xAI never filed PBC impact reports and a Musk attorney referenced outdated PBC status in 2025-05. Comments argue dropping PBC status signals prioritizing profit over a formal social mission and could ease fundraising and competition with OpenAI. Some highlight perceived inconsistency with Musk’s criticism of OpenAI’s governance, though this is framed as normative rather than technical.
  • Dropping a Public Benefit Corporation (PBC) charter removes directors’ statutory duty to “balance” shareholder returns with a stated public benefit (see Delaware PBC framework under 8 Del. C. §§362, 365). Converting to a standard C‑corp reverts fiduciary focus to shareholder value, which typically simplifies venture financing, secondary sales, and M&A by eliminating mission‑driven constraints and potential litigation over “balancing” trade‑offs. Practically, this is a capital‑raising and competitive speed optimization move; it signals, but doesn’t guarantee, a shift in prioritization away from mission commitments. Useful overviews: Cooley on PBCs and Delaware code §362/§365.
  • Several commenters contrast this with OpenAI’s governance: OpenAI is not a PBC; it’s a non‑profit parent (OpenAI, Inc.) controlling a capped‑profit subsidiary (OpenAI LP) with a mission‑oriented charter. Thus, criticisms that OpenAI “abandoned” a social mission differ legally from xAI’s move, which removes any formal public‑benefit obligation from its corporate form. References: OpenAI’s LP structure explainer and Charter.

2. OpenAI GPT-5: Pokémon Crystal Run, 4o-vs-5 Routing Debunk, User Reports, Deep Research/AI Studio Anecdotes

• GPT-5 completes Pokémon Crystal - Defeats final boss in 9,517 steps compared to 27,040 for o3 (Score: 363, Comments: 72): An X post by Clad3815 claims GPT‑5 completed Pokémon Crystal and beat the final boss (RED) in 9,517 steps vs 27,040 for o3 (~3× action efficiency), allegedly while under‑leveled, suggesting stronger world modeling/strategy beyond typical benchmarks. This is not an official benchmark; details on experimental setup (action definition, RNG, resets, tool assistance, or rules) aren’t provided; stream plans further goals like legendary catches and Pokédex completion. Source: https://x.com/Clad3815/status/1959856362059387098 Comments report GPT‑5 (Thinking Mode) outperforming o3 in legal workflows (fewer hallucinations, better issue spotting), while others note Pokémon is a favorable RL environment and inject some skepticism/sarcasm about hype.
  • Benchmark-wise, the post title reports GPT-5 clearing Pokémon Crystal’s final boss in 9,517 steps vs o3 at 27,040, implying ~2.8× fewer steps (27,040/9,517) and markedly better long‑horizon planning/sample efficiency than o3 (o3). This suggests superior search/pruning or state abstraction, since fewer environment interactions typically reflect better exploration–exploitation balance and credit assignment over long sequences.
  • Practitioner feedback highlights GPT-5’s “Thinking Mode” yielding substantially fewer hallucinations and more accurate legal issue spotting in document analysis workflows. For coding/engineering, users report stronger problem decomposition and implementation guidance, implying improved multi‑step reasoning and constraint tracking compared to o3, with fewer off-target suggestions and corrections required.
  • One commenter notes Pokémon as a near-ideal reinforcement learning environment: discrete, turn-based, and long-horizon with inventory/state management and sparse rewards. Success here is informative because it stresses planning under partial observability and long-term credit assignment, making step-count efficiency a meaningful proxy for reasoning quality rather than mere reaction speed.
• 4o is not secretly 5. Stop using LLMs to back up your paranoia. (Score: 151, Comments: 73): OP debunks the rumor that prompts to GPT-4o are secretly routed to GPT-5, citing OpenAI docs: GPT-5 is the ChatGPT default and uses an internal router among GPT-5 variants (e.g., fast vs thinking/pro) within the GPT-5 family, while GPT-4o remains a separate, selectable model (and its API alias maps to its own family/snapshots). Docs note that aliases like gpt-4o may advance to newer 4o snapshots and recommend pinning dated snapshots for stability; any cross-family remap would appear in official deprecations/release notes, which currently show no notice of 4o→5 routing (Models, Deprecations, Release notes). Technical commenters add that with Reference Chat History enabled, style/tone can “bleed” between sessions: using GPT-5 can influence how GPT-4o responds due to shared context memory across chats, potentially explaining perceived similarity. Others argue both models serve distinct roles (e.g., GPT-5 thinking for coding/architecture; 4o for expressive creative writing).
  • Multiple commenters provide a technical explanation for perceived “model blending”: with Reference Chat History (RCH) enabled, the system leverages shared context across sessions, so style/tone from chats with GPT‑5 can “bleed” into GPT‑4o responses. They report that archiving/deleting GPT‑5 sessions or disabling RCH restores 4o’s baseline style; this reflects a shared context memory that doesn’t strictly attribute who said what across sessions and optimizes for continuity, blurring “personalities” rather than indicating covert model routing. Quote: “If you have RCH on, any sessions that use 5 will bleed into how 4o responds… 4o will start talking more like 5 with RCH on, so if you prefer 4o get rid of the 5 sessions.”
  • Several replies critique claims that “4o is secretly routed to 5” as non-evidence-based, noting that conversational anecdotes or “reverse engineering by chatting” are not valid diagnostics. A rigorous approach would use controlled prompts, inspect explicit model identifiers/versions in API logs, and compare reproducible metrics (e.g., latency distributions, output length/style statistics) instead of subjective impressions. Thread consensus leans toward requiring instrumentation before asserting model swaps.
  • A practitioner notes differing strengths: GPT‑4o is “more expressive” and preferred for creative writing and thought experiments, while GPT‑5 serves other purposes—arguing to keep both available. This frames a task-dependent performance trade-off between models rather than one universally superior option, though no quantitative benchmarks are provided.
• It took me a while. But now I also hate ChatGPT 5. (Score: 560, Comments: 261): OP reports a regression from GPT‑4o to GPT‑5 in strict instruction adherence for code generation within a proprietary framework: GPT‑5 repeatedly ignores explicit I/O and Node Class schema constraints, hallucinates non‑existent integrations/ergonomics, and proposes unchangeable engine‑level modifications, requiring frequent re-prompting. Commenters corroborate issues including rigid, repetitive follow‑up questions, degraded constraint memory, shorter low‑effort outputs, factual errors and even spelling mistakes, plus intra‑turn context loss (e.g., the model attributing to the user a list it generated itself). Overall pattern: weaker schema binding, higher hallucination rate for API surfaces, and increased assistant‑initiated scope creep versus 4o/4.5. Technically oriented complaints emphasize degraded instruction-following and increased prompt friction, with some attributing the change to product direction (e.g., push toward guided follow‑ups) and speculating about cost/usage optimization; others note seeking alternatives (e.g., Grok) but finding them inferior to prior 4o/4.5 behavior.
  • Users report a regression in instruction-following and response quality with GPT‑5: it often ignores explicit directions, asks repetitive clarifying questions, and returns shorter, poorly researched, or incorrect answers (even with occasional spelling errors). Compared to GPT‑4o/4.1 and o3, which understood intent with minimal prompting, GPT‑5 feels rigid and increases the “prompt tax,” harming throughput for production work.
  • A notable failure mode: within a single response, GPT‑5 generated a list and then praised the user for the very list it had produced—evidence of intra-turn state confusion. This suggests a coherence/control bug where assistant/user roles get conflated during decoding or RLHF-driven templating injects misattributed praise, not merely long-context drift.
  • Perceived capability/style trade-offs: GPT‑5 is described as constrained and formulaic (e.g., repetitive “Do you want me to…” follow-ups), while GPT‑4o was more conversational and creative. Prior models (4o, 4.1, o3) reportedly required fewer iterations to capture intent; alternatives like Grok are said to underperform those earlier baselines, reinforcing concerns that tighter guardrails may be suppressing useful generative behavior.
• noooo not gpt-5 as well (Score: 428, Comments: 56): Non-technical meme: a screenshot highlighting “codex” and the canned reply fragment “You’re exactly right —” jokes that even “GPT‑5” inherits the same LLM catchphrase/style tics seen in prior OpenAI models (e.g., GPT‑4/ChatGPT), rather than any new technical capability. The title and image play on recurring jokes about system prompts and boilerplate acknowledgments, not any real evidence of model internals or benchmarks. Comments lean into the running gag about LLMs overusing phrases like “you’re absolutely/exactly right,” and a tongue‑in‑cheek claim that OpenAI “got caught using Claude code,” implying shared stylistic tics or prompt reuse rather than substantive technical overlap.
• Before GPT-5 was released (Score: 356, Comments: 73): Meta thread about recurring claims that new ChatGPT releases are “nerfed,” projecting the same cycle for GPT-5 and later GPT-6. No benchmarks or implementation details are discussed; the referenced gallery is inaccessible (HTTP 403) via the provided link (gallery). Top comments argue this pattern is perennial and that prior versions get nostalgically praised once a newer model ships; several note r/ChatGPT has shifted from use-case sharing to complaints, with a pragmatic stance of “don’t use it” if dissatisfied.
  • Several users note a recurring release pattern: OpenAI ships major models (e.g., o1, GPT‑4o, and even base GPT‑4) initially with conservative settings—smaller context windows and stricter/max-token truncation—leading to early ‘underbaked’ impressions; these are then relaxed or tuned over subsequent weeks, improving perceived quality. One example cited is the o3 release, which drew negative posts at launch but later became ‘almost universally’ praised, suggesting staged rollouts and post-deploy calibration rather than true capability regressions. Example screenshot.
  • Veteran users argue that claims of random ‘lobotomization’ have appeared since week one of ChatGPT and should be treated skeptically absent longitudinal benchmarks or A/B comparisons; if such cumulative nerfs were literal, we’d see a reversion to GPT‑1level performance by now. The takeaway is to rely on reproducible tests (e.g., fixed prompts, controlled temperature, and context parity) across time rather than anecdotal impressions.
• Sammy,you did it dirty! (Score: 185, Comments: 22): Non-technical meme: a two-panel “bus selfie” compares GPT-4 (intact bus) vs GPT-5 (overturned bus), implying GPT-5 is a downgrade/regression. The title/selftext express disappointment and missing GPT-4; no benchmarks, logs, or technical details are provided. Image: https://i.redd.it/ar1nq7wl57lf1.png Comments echo a perception that “4 was better than 5” and note GPT-4 being removed as an option, while others criticize the proliferation of 4-vs-5 memes; no measurable evidence is cited.
  • A user claims the ChatGPT UI has removed the GPT‑4 selection option (“removed the 4 from the option”) and asserts 4 performs better than 5. For technical workflows, this implies a model-availability change or forced default to newer releases, affecting reproducibility and evaluation baselines; see OpenAI’s model availability/deprecation notes: https://platform.openai.com/docs/models.
  • Another commenter reports a strict chat cap of 10–15 messages for the current model, after which the session “returns to a previous model,” and asks if this could be used to revert to GPT‑4. This suggests server-side session caps with potential automatic model fallback in the consumer UI, but using caps to select a specific model is likely unreliable/unsupported; deterministic control over models is documented for API usage (e.g., specifying the model name): https://platform.openai.com/docs/guides/text-generation.
• Soo uhhh, This just happened? (Score: 166, Comments: 32): OP shows a screenshot from an AI Studio session where a custom “Briarheart” jailbreak (used for ERP role-play) plus an instruction to “focus on thinking mode” triggered the model to emit an extremely long, repetitive, aggressive monologue. Technically, this illustrates how role-play/jailbreak prompts can dominate the model’s behavior and cause verbosity loops or mode collapse-like repetition; the behavior is prompt-induced rather than a spontaneous model failure. Commenters note this isn’t “weird” from the model’s side—overly specific role-play/jailbreak instructions make it act this way—while others just find it amusing.
  • One commenter argues the observed behavior is a byproduct of heavy role‑play prompting and persona conditioning rather than autonomous model drift: “it’s not them that are losing it. It’s y’all.” In instruction‑tuned chat LLMs, the system prompt plus prior turns act as strong priors that bias next‑token probabilities; with long context windows and few‑shot persona examples, the model will remain “in character,” producing anthropomorphic lines like having a “favorite user.” This is expected with RLHF‑trained assistants and can be tested by resetting context, removing persona priming, and controlling sampling params (e.g., temperature, top_p); see Anthropic’s RLHF overview (https://www.anthropic.com/research/rlhf) and OpenAI prompting docs (https://platform.openai.com/docs/guides/prompt-engineering).
• AGI Achieved. Deep Research day dreams about food mid task (Score: 1104, Comments: 56): This is a humorous, non-technical screenshot of a “Deep Research” workflow UI where the model’s surfaced “thoughts” digress to the “twine method for pie crusts” mid numeric analysis, highlighting that the tool exposes intermediate reasoning/trace content that can include off‑topic associations. The title’s “AGI Achieved” is tongue‑in‑cheek; technically it underscores the anthropomorphic feel and potential noisiness of displaying chain‑of‑thought‑style traces rather than any capability leap. One commenter adds the task was algo‑trading number crunching, reinforcing that the digression occurred during a routine, boring computation task. Commenters note the thought stream can be more entertaining than answers, joke about “Python” vs “pie,” and liken the detour to human daydreaming during monotonous work.
  • Multiple reports show Deep Research injecting whimsical “thoughts” (e.g., “Mmmm… pie!” or references to bananas) mid-run, even during quant-heavy/algorithmic trading tasks. Commenters infer this may be an intentionally added persona/UX flourish rather than genuine intermediate reasoning, which reduces the signal-to-noise of audit logs and could hinder reproducibility in numeric workflows; ideally this should be toggleable or filtered.
  • There’s active interest in applying Deep Research to investment analysis/algorithms; a commenter building a stock-focused deep-research tool, deepvalue.tech, solicited use cases and gaps. The mentioned tasks involve large-scale number crunching; evaluation priorities for such tools would include data sourcing transparency, quantitative error rates, and structuring multi-step financial analyses.
  • A user notes preferring the surfaced “thoughts” over final answers, highlighting demand for interpretable intermediate steps. If those “thoughts” include non-task-related filler, they risk misleading users about actual reasoning quality and can confound attempts to audit or benchmark the system’s decision path.
• How do you make AI generated text undetectable from Turnitin and other AI detectors (Score: 301, Comments: 76): OP asks if there’s a way to make AI‑generated text undetectable by Turnitin and other AI detectors, noting such detectors are unreliable. Top replies assert there’s no dependable technical method to guarantee undetectability; the only robust approach is to author the work yourself, optionally using AI strictly for proofreading, and to retain a personal voice (including natural imperfections) rather than attempting detector evasion. Consensus view: ethically and practically, students should write their own work; attempts to bypass detectors are discouraged and seen as contrary to the purpose of university study.
  • Commenters highlight unreliability of current AI-writing detectors (e.g., Turnitin-style tools), citing false positives; one reports a fully human-written short story being flagged as 25% AI-generated. The consensus is that these systems provide heuristic confidence scores that can misattribute authorship, so flags should not be treated as definitive evidence.
  • Others argue that manual paraphrasing and adding a personal voice (keeping wording simple and introducing small imperfections) can reduce detectability, implying detectors rely on stylometric cues like uniformity and low lexical diversity rather than robust semantic attribution. One notes that even prompting a model to make text “undetectable” sometimes works, underscoring brittleness in current detector decision boundaries.
• AGI talk is out in Silicon Valley’s latest vibe shift, but worries remain about superpowered AI (Score: 198, Comments: 55): Thread notes a rhetoric shift in Silicon Valley away from monolithic “AGI” toward domain-specific “superintelligences”—i.e., specialized systems with superhuman capability in constrained domains—while concerns about “superpowered AI” persist. The implicit technical reframing prioritizes verticalized models and products (code, science, robotics) over a single generally capable system, acknowledging that current frontier models remain far from domain-transferable, robust general reasoning despite scaling. See background on AGI vs. narrow AI. Comments debate whether this is a substantive shift or narrative repositioning: one quips, “Someone remind me what the G in AGI stands for?”, another claims the change admits we’re not close to AGI, and a third compares expectations to the Web’s early hype cycle—overestimated short-term progress, underestimated long-term impact.
  • Several comments note a shift from chasing a single, monolithic “AGI” to building domain-specific “superintelligences,” implying an architecture strategy of specialized models (e.g., code, bio, search) orchestrated via tools/agents. This prioritizes domain-tuned data, bespoke evals, and integration layers over a one-size-fits-all foundation, since specialists often outperform generalists on narrow, high-stakes tasks.
  • Skeptics argue current LLM scaling is unlikely to yield AGI due to training objectives (next-token prediction) that don’t enforce grounded world models, long-horizon planning, or reliable tool-use. They point to brittle reasoning, hallucinations, and weak systematic generalization as evidence and argue for hybrid approaches (explicit memory, model-based RL, neuro-symbolic methods, or multimodal world models) if “general” capabilities are the goal.
  • The narrative cooling on AGI is framed as a recalibration of timelines rather than abandonment: capability growth is real but uneven, with persistent bottlenecks (evaluation overfitting, inference cost/latency, and safety/robustness gaps). Expectation-setting moves toward multi-year infrastructure and product cycles, not rapid step-function leaps, echoing early web-era hype-versus-delivery dynamics.

3. Alibaba WAN 2.2 S2V and Qwen Image Editing Demos + Generative Media/Art Parodies

• WAN will provide a video model with sound 👁️‍🗨️🔊 WAN 2.2 S2V (Score: 262, Comments: 62): Alibaba’s WAN team teased “WAN 2.2 S2V” via post 1 and post 2, suggesting upcoming sound-enabled video generation. From the available previews, it appears to be audio-driven video (speech-to-video/lip-sync) rather than end-to-end audio synthesis; no model card, training details, metrics, or release timeline were provided, and the original v.redd.it media is gated (HTTP 403). Technical replies emphasize it looks like an audio-driven lip-sync pipeline, not a model that generates audio. A related workflow is cited: Kijai’s ComfyUI WanVideoWrapper “Infinite Talk” V2V for adding custom voice with lip-sync to existing video, with example workflows here: https://github.com/kijai/ComfyUI-WanVideoWrapper/tree/main/example_workflows.
  • Clarification from users: WAN 2.2 S2V appears to be an audio-driven video pipeline—using an input audio track to drive visual motion (e.g., lip/mouth sync)—and does not synthesize or output audio itself. As one notes, “looks like audio driven video, not a model that produces audio,” implying no V2S (video-to-sound) capability in this release.
  • For adding audio with proper lip sync, Kijai provides a ComfyUI workflow: “V2V infinite talk” in the ComfyUI-WanVideoWrapper examples. It takes an existing video and a user-provided voice/sound track and performs lipsync (a V2V pipeline); see https://github.com/kijai/ComfyUI-WanVideoWrapper/tree/main/example_workflows and search for the “infinitetalk v2v” JSON.
  • Use-case discussion: some prefer V2S over S2V, wanting automatic Foley/SFX generation (e.g., punches, explosions) from video rather than turning sound into video. V2S would synthesize audio conditioned on visual events/timing, whereas S2V consumes audio to condition visual generation; the current announcement seems to deliver the latter, not the former.
• Qwen Image Edit + Wan 2.2 FFLF - messing around using both together. More of my dumb face (sorry), but learned Qwen isn’t the best at keeping faces consistent. Inpainting was needed. (Score: 638, Comments: 69): OP demos a hybrid image/video generation workflow combining Qwen Image Edit with Wan 2.2 FFLF, reporting strong visual quality but noting Qwen’s weak face identity consistency—requiring an inpainting pass to maintain the subject’s face. Compared to a standard Wan 2.2 workflow, viewers observed higher apparent resolution and more coherent outputs; sample video link: v.redd.it/5zizxpo6q3lf1 (403/login required). Commenters ask for the exact Wan 2.2 high‑quality workflow and note the combo “doesn’t magically pull spawn items out of the ether” (i.e., fewer hallucinated insertions), praising the approach as a solid way to pair both models.
  • Combining Qwen Image Edit with Wan 2.2 FFLF appears to produce higher-resolution outputs than a “standard Wan 2.2 workflow,” but identity consistency is a weak point for Qwen without explicit inpainting. The OP indicates inpainting was necessary to keep the same face across edits, implying a workflow where Qwen handles broad edits and targeted inpaint passes lock identity fidelity.
  • Several users request the exact workflow/pipeline for achieving the showcased quality with Wan 2.2 FFLF, noting their own results are lower-res with the default Wan 2.2 setup. There’s specific interest in reproducibility details (e.g., step order, edit vs. inpaint passes) rather than generic prompts, to replicate the higher-fidelity outputs shown.
  • A technical observation highlights that Qwen’s edit pass “doesn’t magically pull spawn items out of the ether” and remains coherent with the source image, suggesting lower hallucination under constrained edits. However, this coherence likely necessitates inpainting for controlled insertions or identity preservation, trading off free-form generation for adherence to the original scene.
• Using AI to play inside Magic the Gathering artworks and worlds (Score: 1436, Comments: 133): The post claims an AI-driven interactive experience that lets users “play inside” Magic: The Gathering card artworks/worlds (i.e., navigable environments derived from 2D art), but the linked media v.redd.it/dd1zfqjqi5lf1 is currently inaccessible (HTTP 403), so model, pipeline, or implementation details cannot be verified from the thread. No code, benchmarks, or named models are provided; the discussion lacks technical specifics beyond interest and requests for attribution/source. Top comments are mostly non-technical hype; one asks for provenance and speculates it may use a Google engine—“Share the source please… Guessing it’s Google’s engine. Can a mortal access it?”—but no confirmation or access details are given.
  • The only technical-leaning thread is a request for the specific engine/model used to generate the playable MTG-style environments; one commenter speculates it might be a Google system and asks if it’s publicly accessible so they can try it on other card art. No implementation details, model names, or performance notes (e.g., latency, FPS, or training/inference setup) were provided in the thread, so readers are seeking attribution and access details rather than debating techniques.
• Nicolas Cage is Barbie (2026) - Trailer (Score: 195, Comments: 30): Reddit post shares a parody trailer titled “Nicolas Cage is Barbie (2026) – Trailer,” but the hosted video at https://v.redd.it/k6vrey0eb3lf1 returns HTTP 403 Forbidden without Reddit authentication, so the underlying media could not be retrieved or analyzed. Consequently, no technical details about the editing/VFX pipeline, potential AI face-swap usage, audio design, or source material can be verified from the link alone. Top comments are non-technical, expressing positive reception (humor and watchability) with no substantive critique of production methods or tools.
• The anti-AI crowd would be less upset if we rebranded it as AI art mining (Score: 222, Comments: 98): Discussion post suggests rebranding AI image generation as “AI art mining” (i.e., exploring/model “latent space”) to defuse backlash against “vibe prompting” (LLM-assisted prompt crafting). The attached image—a whimsical forest scene with a person in leaf attire holding a cat—serves as an example output of text-to-image generation rather than a new model/technique; no implementation details or benchmarks are provided. Comments split: a former pro artist uses AI as a disability aid via open-source tools and emphasizes low energy use (“about three light bulbs”), others argue rebranding is pointless and critique some ‘prompt engineers’ for weak art fundamentals, while some artists say they simply use AI as a complementary tool.
  • A commenter distinguishes two image‑generation workflows: exploratory prompting (akin to scouting photos/screenshots) versus directed composition with positional control. They note that quality depends heavily on tuning diffusion parameters like steps and sampler, and that using tools to control object placement (e.g., ControlNet‑style conditioning: https://arxiv.org/abs/2302.05543) can transform outputs from random exploration to intentional layouts; scheduler choice materially impacts sharpness/speed (see Diffusers schedulers: https://huggingface.co/docs/diffusers/using-diffusers/schedulers). They mention working with “qwen image,” underscoring that not all AI art is just text prompts—some workflows approach full compositional control.
  • Another commenter highlights using open‑source, local tooling for accessibility (assistive/disability use case) with very low power draw (“about three light bulbs”), implying on‑device inference rather than cloud GPUs. This aligns with running Stable Diffusion pipelines on consumer hardware via tools like AUTOMATIC1111 (https://github.com/AUTOMATIC1111/stable-diffusion-webui) or ComfyUI (https://github.com/comfyanonymous/ComfyUI), trading peak throughput for privacy, cost control, and offline availability.
• Asked ChatGPT to show me how to roll a wrap. (Score: 2031, Comments: 166): Non-technical/meme example highlighting an LLM limitation: when asked to show how to roll a wrap, ChatGPT produced a step-by-step diagram that mimics an envelope/letter fold rather than a correct burrito-style roll (side folds, bottom up, then a neat “parcel”), underscoring poor visuospatial/procedural reasoning and unreliable autogenerated diagrams without physical grounding. It illustrates how LLMs can confidently output incorrect action sequences and malformed instructional graphics. Commenters note it looks like “sending a letter,” agree it’s “not much better,” and report that ChatGPT often offers diagrams unprompted and they’re consistently wrong—reinforcing the model’s weakness at diagram synthesis and step ordering.
  • Multiple users highlight a recurrent LLM failure mode with ASCII/diagram generation: models often propose diagrams unprompted and produce structurally incorrect or misaligned visuals. This likely stems from token-level next-word training without geometric constraints, plus whitespace handling and proportional-font rendering that breaks intended layout; even with monospaced code blocks, alignment is brittle and non-deterministic. Practically, users should disable unsolicited ASCII via explicit instructions and prefer tool-assisted outputs (e.g., SVG or image generation with a renderer) if spatial fidelity is required.
  • Examples of contradictory or nonsensical step-by-step instructions (e.g., adding ingredients then discarding them, duplicating tortillas) illustrate planning consistency issues in LLMs, especially for procedural tasks with physical constraints. These are classic coherence errors from weak grounded reasoning and lack of constraint checks; mitigation includes requiring state-tracking, validating steps against constraints, and enforcing structured outputs (checklists with pre/post-conditions) instead of free-form prose. Deterministic decoding (low temperature) reduces variance but does not eliminate logical contradictions without explicit constraints or external validators.
• My attempt at generating a generic ChatGPT response as my dating app opening message (Score: 197, Comments: 104): Non-technical/meme post: a dating app opener formatted like a ChatGPT response that humorously “analyzes” a match’s scenic photos (e.g., attributing them to real Colorado scenery, good lighting, and a DSLR) with a tongue‑in‑cheek disclaimer. The technical angle is purely cultural: it references ChatGPT’s response style as a social icebreaker; no models, benchmarks, or implementation details are discussed. Comments split between finding it funny and calling it cringe; top replies encourage authenticity over optimizing for reactions.
• I am a lazyfck so i built this (Score: 291, Comments: 61): Indie app uses on‑device computer vision via the phone camera to track workouts offline (“no cloud”), auto‑count reps, and flag cheating/bad posture across 28 exercises; it also “roasts” missed sessions and gates social apps (e.g., Instagram/TikTok) behind a quick push‑up task. Early preview only; waitlist is open at https://lazyfcks.vercel.app and the demo video is hosted on Reddit (https://v.redd.it/nhsq3lwcv5lf1) but currently returns HTTP 403 without authenticated access. Focus is privacy and low‑latency on‑device inference rather than cloud processing. One commenter suggests the final rep should not be counted, implying stricter rep‑validation heuristics to discourage form breakdown near failure; other top comments are non‑technical.
  • Form/ROM critique for valid push-ups: one commenter notes you should get the chest to the floor (or very close) and avoid flaring the elbows. Translated into objective criteria, that implies a depth threshold (e.g., chest/shoulder midpoint within ~3–5 cm of floor or upper-arm angle past 90° at the bottom) and an elbow abduction limit of roughly ≤45° relative to the torso to reduce shoulder stress. These cues help ensure full range of motion and more reliable rep validation if you’re automating counting or feedback.
  • Rep-quality and termination logic: feedback like “the last rep shouldn’t count” and “0, 0, 0… terminated” implies adding stricter validity checks and a robust state machine. Require both bottom depth and top lockout thresholds plus temporal hysteresis (e.g., maintain threshold crossing for ≥150–250 ms or ≥5–8 frames) to debounce noisy detections, and invalidate reps that don’t meet minimum amplitude/time-under-tension. Define end-of-set conditions such as N consecutive invalid reps or a timeout T without a valid cycle to gracefully terminate and reset.
• Baby in Colombia Registered as ‘Chat Yipiti,’ Name Inspired by ChatGPT (Score: 2097, Comments: 153): A viral post claims a newborn in Cereté, Colombia was officially registered as “Chat Yipiti,” inspired by ChatGPT, illustrated by a hospital bassinet label in the photo and linked coverage (Colombia One). However, the National Civil Registry stated on 2025-08-19 that “after consulting the databases… there is currently no birth registration under the name ‘Chat Yipiti’,” contradicting the purported 2025-08-15 registration and indicating the story/image is likely staged or unverified. Commenters largely question authenticity and raise practical concerns (e.g., bullying) about novelty AI-branded names; the rest are mostly jokes/puns rather than technical discussion.
  • An official statement from Colombia’s National Civil Registry (Registraduría Nacional del Estado Civil) reportedly says that, after querying its databases, there is currently no birth registration under the name “Chat Yipiti.” This directly contradicts claims the registration occurred on August 15, with the registry’s note dated Tuesday, August 19. Absent a matching record in the civil registry databases, the claim appears unverified and likely misinformation until corroborated by an official entry.

AI Discord Recap

A summary of Summaries of Summaries by X.ai Grok-4

Theme 1. DeepSeek V3.1 Debuts with Mixed Reviews

• DeepSeek V3.1 Enters Arenas, Sparks Hype: DeepSeek V3.1 launched across platforms like LMArena and Cursor, scoring 66 on SWE-bench in non-thinking mode but drawing criticism for weaker creative writing and roleplay. Users noted it’s a slightly worse version of Gemini 2.5 pro yet promising for coding, with pricing rising to $0.25 input on OpenRouter starting September 5, 2025.
• DeepSeek V3.1 Thinks Hard, Integrates Wide: The model supports Anthropic API integration for expanded use, as announced on DeepSeek’s X post, but members in Moonshot AI called it an incremental improvement with regressions, per Hugging Face page.
• DeepSeek V3.1 Quants and Thinking Tested: In Unsloth AI, DeepSeek V3.1 hyped for thinking skills but flagged for lacking instruction-following in hybrid modes, with hybrid models lack the instruction following and creativity in the non-think mode.

Theme 2. ByteDance Seeds New OSS Models

• ByteDance Drops Seed-OSS 36B Vanilla Beast: ByteDance released Seed-OSS-36B-Base-woSyn, a dense 36B model with 512K context trained on 12T tokens without synthetic data, exciting Unsloth AI members for tuning, per Hugging Face model.
• Seed-OSS Architecture Stumps GGUF Fans: In Nous Research AI, Seed-OSS features custom MLP, dropout, and qkv bias but lacks GGUF support due to unsupported architectures: [“SeedOssForCausalLM”], sparking ASIC speculation via X post.
• Seed-OSS Invites Community Tests: Latent Space highlighted Seed-OSS family on GitHub and Hugging Face, urging feedback on models, code, and weights for open-source growth.

Theme 3. Hardware Upgrades and Benchmarks Buzz

• RTX 5090 Price Ignites Upgrade Wars: Unsloth AI debated RTX 5090 at $2000 for VRAM perks in training, but slammed NVIDIA’s missing P2P or NVLink, while GPU MODE eyed Infiniband for 4090-5090 distributed setups.
• AMD Debugger Alpha Steals Spotlight: GPU MODE unveiled an alpha AMD GPU debugger with disassembly and wave stepping, independent of amdkfd KMD, shown in video demo.
• M4 Max Melts GGUF in MLX Benchmarks: LM Studio tests showed MLX GPU hitting 76.6 t/s at 32W versus GGUF CPU at 26.2 t/s on GPT-OSS-20b with 4bit quants and 4k context, proving MLX’s edge in efficiency.

Theme 4. Datasets and Training Tricks Emerge

• WildChat-4M Dataset Dedupes English Prompts: Unsloth AI released WildChat-4M-English-Semantic-Deduplicated on Hugging Face, filtering to <=2000 tokens with semantic methods for cleaner training data.
• GRPO Demands Step-by-Step Datasets: Unsloth AI advised splitting multi-step game datasets for GRPO, noting full PPO suits games better since GRPO works for LLMs that roughly know what to do to begin with.
• Imatrix Calibration Boosts Qwen Scaling: Nous Research AI used Ed Addorio’s datasets for importance matrices, enabling Qwen 2507 to hit 512k context via RoPE scaling and minimize quantization errors.

Theme 5. API Woes and Security Scares

• OpenRouter Keys Leak, Cost Users $300: OpenRouter users reported $300 losses from leaked API keys, with threats using proxies to hide IPs, and no recovery options since users bear responsibility.
• Gemini Bans Send Users Back to 2023: OpenRouter discussed mass Gemini bans reminiscent of AI Dungeon purges, with users lamenting we’re being sent back to 2023 and seeking alternatives.
• Command A Reasoning Tackles Enterprise Needs: Cohere launched Command A Reasoning for agentic tasks, running on single H100 with 128k context, featuring token budgets for cost control, per Cohere blog.

Discord: High level Discord summaries

LMArena Discord

• Nano-Banana Falls Prey to McLau’s Law: Members joked that the Nano-Banana model often underperforms expectations, humorously dubbing this phenomenon “McLau’s Law,” referencing an OpenAI researcher, prompting discussion about AI’s current capabilities as depicted in an attached image.
  • One user suggested Nano-Banana often yields results far below nano-banana.
• Video Arena Plagued by Bot Brain-Freeze: Users reported the Video Arena Bot being down, causing command failures and inability to generate videos, effectively locking access to prompt channels <#1397655695150682194>, <#1400148557427904664>, and <#1400148597768720384>.
  • Moderators confirmed the downtime and ongoing fixes, directing users to the announcements channel for updates and also stating that a login feature will be available soon to prevent future outages.
• DeepSeek V3.1 Enters the Ring: DeepSeek V3.1 and deepseek-v3.1-thinking models have been added to the LMArena and are now available for use.
  • The consensus is that the v3.1 model is a slightly worse version of Gemini 2.5 pro although it holds promise as a coding model, but needs enhancement in general abilities.
• LMArena Users Suffer Data Loss: A site outage caused widespread data loss, including missing chat histories and inability to accept terms of service.
  • Moderators acknowledged the issue and assured users that a fix is underway.

Unsloth AI (Daniel Han) Discord

• ByteDance Drops Seed-OSS 36B Base Model: ByteDance has released the Seed-OSS-36B-Base-woSyn model on Hugging Face, a 36B dense model with 512K context window, trained on 12T tokens.
  • Members are eager to try tuning GPT-ASS with the model, finding the lack of synthetic data compelling.
• GRPO Requires Smart Dataset Design: To use GRPO for multi-step game actions, members advised designing datasets with separate prompts for each step.
  • Full PPO might be better suited for games, as GRPO is primarily effective for LLMs because they roughly know what to do to begin with.
• DeepSeek V3.1’s Thinking Skills: The DeepSeek V3.1 model achieved a 66 on SWE-bench verified in non-thinking mode, sparking hype among members.
  • However, concerns were later raised about its creative writing and roleplay performance, with some noting hybrid models lack the instruction following and creativity in the non-think mode.
• RTX 5090 Price Sparks Upgrade Debate: The RTX 5090 is priced around $2000, prompting discussions on whether to upgrade, especially for training, given its VRAM capabilities.
  • Some members expressed frustration with NVIDIA’s limitations, particularly the lack of P2P or NVLink.
• WildChat-4M-English Released: The WildChat-4M-English-Semantic-Deduplicated dataset is available on Hugging Face, consisting of English prompts from the WildChat-4M dataset, deduplicated using multiple methods.
  • The current release includes prompts <= ~2000 tokens, with larger prompts to be added later, more information can be found here.

OpenRouter (Alex Atallah) Discord

• Deepseek V3.1 Craze Awaits!: Users are eagerly awaiting the public release of Deepseek v3.1, anticipating it will be free starting in September.
  • Users confirm that paying for Deepseek models on OpenRouter results in faster response times compared to the free models.
• OpenRouter API Keys Risk Exposure!: A user reported a loss of $300 due to a leaked OpenRouter API key and sought advice on identifying the source of the unauthorized usage.
  • Users are responsible for any leaked keys and threat actors can use proxies to mask their origin IPs.
• Gemini faces massive banning outbreak!: Users report massive banning occurring on Gemini, leading many to seek alternatives and reminisce about the AI Dungeon purge caused by OpenAI.
  • Users are saying we’re being sent back to 2023.
• Gemini Input Tokens Trigger Weird Counts!: A dashboard developer noted that OpenRouter’s calculation of input tokens for Gemini’s models produces unusual counts when images are included in the input, referencing a related discussion on the Google AI Developers forum.
  • The developer is considering seeking clarification from the OpenRouter team regarding this issue.
• Most Orgs see ZERO return on Generative AI!: According to an AFR Chanticleer report, 95% of organizations are getting zero return out of their generative AI deployment, focused on companies that have deployed customized AI models.
  • The report notes that the key problem is companies and their tech vendors are not spending enough time ensuring that their customized AI models keep learning about the nuances of their businesses.

Cursor Community Discord

• Claude’s Cache Capriciousness Causes Costly Conundrums: Users are reporting that Claude is experiencing issues with cache reads, leading to increased expenses compared to Auto, which benefits from sustainable caching.
  • Speculation arose around whether Auto and Claude are secretly the same model, attributing reduced token usage to a placebo effect.
• Sonic Speedster Steals the Show in Cursor: The community is currently testing the new Sonic model within Cursor, with initial impressions being quite favorable due to its speed.
  • While praised for fresh projects, some users cautioned its effectiveness might diminish with larger codebases and confirmed that Sonic is not a Grok model whose origin remains a stealth company.
• Agentwise Awakens as Open Source Offering: Agentwise has been open-sourced, enabling website replicas, image/document uploads, and support for over 100 agents, with promises of Cursor CLI support.
  • Users are invited to contribute feedback in the project’s dedicated Discord channel to help further development.
• Cursor’s Costs Confirmed: Clarity on API Charges: Confusion around the cost of the Auto agent was cleared up, where a pro subscription includes the costs of API usage by different providers.
  • Several users confirmed the cost clarification, and one stated a preference of Auto agent over Sonic agent.
• DeepSeek Debuts, Divides Developers: The new DeepSeek V3.1 model appeared in Cursor’s options, eliciting mixed reactions; some users encountered connection issues, while others expressed distrust towards Chinese LLMs.
  • Despite concerns, some reported that DeepSeek V3.1 functions well with TypeScript and JavaScript, offering performance that is great and cheaper than Sonnet.

LM Studio Discord

• CUDA Fix Drives 4070 Detection: Users discovered that changing the runtime to CUDA llama.cpp via ctrl+shift+r might resolve the “0 GPUs detected with CUDA” error in LM Studio for 4070 TI Super cards.
  • They discussed various configurations to enable flash attention, quantization of KV cache, and a batch size of 2048 with commands like -fa -ub 2048 -ctv q8_0 -ctk q8_0.
• GPT-OSS Smokes Qwen on Prompt Eval: Members observed GPT-OSS reaching 2k tokens/s on prompt eval with a 3080ti, outperforming Qwen’s 1000 tokens/s in LM Studio.
  • A user reported LM Studio API calls were significantly slower (30x) than the chat interface but the issue resolved itself for unknown reasons when using the curl command curl.exe http://localhost:11434/v1/chat/completions -d {"model":"gpt-oss:20b","messages":[{"role":"system","content":"Why is the sun hot?\n"}]}.
• Qwen3-30B CPU Configuration Surprises: Using llama-bench, a user achieved 10 tokens per second on a CPU-only configuration with Qwen3-30B-A3B-Instruct-2507-Q4_K_M.gguf.
  • They noted that the performance varied based on thread count, with diminishing returns beyond a certain threshold because of scaling and overhead.
• MLX’s M4 Max Melts GGUF: Benchmarking GPT-OSS-20b on an Apple M4 Max revealed that MLX (GPU) hit 76.6 t/s at 32W (2.39 t/W) compared to GGUF (CPU) which only achieved 26.2 t/s at 43W (0.61 t/W).
  • With 4bit quants and 4k context, MLX proved slightly faster and more power-efficient than GGUF, although they were impressed by the GGUF performance.

OpenAI Discord

• AI Agents Dive into M2M Economies: Members explored machine-to-machine (M2M) economies, where AI agents autonomously exchange value, focusing on challenges like identity & trust, smart contract logic, and autonomy.
  • Safeguards such as spending caps, audit logs, and insurance could accelerate AI adoption in transactions, but real trust will still take time.
• Decentralized AI Project’s BOINC Bounty: A member sought a decentralized AI project like BOINC, noting challenges with the Petals network related to contributions and model updates.
  • Contributors suggested financial or campaign-driven incentives could bolster decentralized AI development.
• Few-Shot Fitness Prompts Flexed: Members dissected optimal strategies for using few-shot examples within a 29,000 token prompt for a fitness studio, emphasizing prompt engineering.
  • Recommendations included providing direct examples within the prompt and iteratively testing smaller chunks to enhance performance.
• GPT-5’s Thinking Mode Dumbs Down: A user reported that GPT-5’s thinking mode yields direct, low-quality responses, similar to an older model version, causing frustration.
  • Another member speculated the user may have exceeded a thinking quota limit, with the system set to fallback instead of grey out.
• AI Quiz Generates Trivial Pursuit: A member highlighted issues with an AI quiz generator producing obviously wrong answer choices in quizzes.
  • Another member suggested ensuring that all response options must be plausible to improve the AI’s output and produce more realistic responses.

Eleuther Discord

• PileT5-XL Speaks: An embedding tensor from PileT5-XL works both as an instruction for pile-t5-xl-flan (which generates text) and as a prompt for AuraFlow (which generates images), suggesting these embeddings hold meaning like words in a language.
  • A member is interested in textual inversion with a black dog picture with auraflow applied to pile-t5-xl-flan to see if text describes the dog as black.
• Cosmos Med Models Scale!: The Cosmos Medical Event Transformer (CoMET) models, a family of decoder-only transformer models pretrained on 118 million patients representing 115 billion discrete medical events (151 billion tokens) generally outperformed or matched task-specific supervised models.
  • The study, discussed in Generative Medical Event Models Improve with Scale, used Epic Cosmos, a dataset with medical events from de-identified longitudinal health records for 16.3 billion encounters over 300 million unique patient records from 310 health systems.
• ByteDance Prover Gets Medal: Bytedance’s SEED Prover achieved a silver medal score in IMO 2025.
  • However, it is unclear how this translates to real world math problem solving performance.
• Isolating a Llama3.2 Head: A member isolated a particular kind of head, discovering that decoded result vectors between Llama 3.2-1b instruct and Qwen3-4B-Instruct-2507 were remarkably similar across different outputs.
  • The member stated that the two heads seem to promote are quite similar.
• Muon Kernel Support Sought: A member expressed interest in adding muon support, citing potential kernel optimization opportunities.
  • They believe that once basic support is implemented, there’s room for collaborative work on these optimizations.

Latent Space Discord

• Meta Splits After Wang Promotion: Meta is reorganizing its AI efforts into four teams (TBD Lab, FAIR, Product/Applied Research, Infra) under new MSL leader Alexandr Wang, with the AGI Foundations group being disbanded, according to Business Insider.
  • Nat Friedman and Yann LeCun now report to Wang, FAIR will directly support model training, and an “omni” model is under consideration.
• GPT-5-pro Silently Eats Prompts: GPT-5-pro is silently truncating prompts greater than 60k tokens without any warning or error messages, which makes large-codebase prompts unreliable, according to this report.
  • Some users are also reporting that GPT-5 in Cursor is acting a lot dumber than usual, with some suspecting load shedding is occurring.
• Dropout Inspired by Bank Tellers: A viral tweet claims Geoffrey Hinton conceived dropout after noticing rotating bank tellers deterred collusion (source).
  • Reactions range from admiration for the serendipitous insight to skepticism and jokes about attention mechanisms emerging from house parties.
• ByteDance Sows Seed-OSS Models: ByteDance’s Seed team has announced Seed-OSS, a new open-source large-language-model family available on GitHub and Hugging Face.
  • The team is inviting the community to test and provide feedback on the models, code, and weights.
• Wonda Promises Video Revolution: Dimi Nikolaou introduced Wonda, an AI agent aiming to revolutionize video/audio creation, calling it what Lovable did for websites, Wonda does for content (tweet link).
  • Early-access will be granted via a waitlist offering invites in approximately 3 weeks.

GPU MODE Discord

• CUDA Confounds ChatGPT: A member found that ChatGPT gave confidently incorrect answers regarding CUDA float3 alignment and size, and then attributed the difficulty of this topic to the complexities of OpenCL and OpenGL implementations.
  • The member has validated that there is no padding in CUDA.
• Hackathon Starts Saturday AM: The GPU Hackathon will likely kick off around 9:30 AM on Saturday, and it was hinted that participants will be working with newer Nvidia chips.
  • There was a question about the hackathon prerequisites, but it went unanswered in the channel.
• AMD GPU debugger has first alpha: An engineer showed off the alpha version of their new AMD GPU debugger now with disassembly and wave stepping in this video.
  • This debugger doesn’t depend on the amdkfd KMD, using a mini UMD driver and the linux kernel debugfs interface and aiming for a rocdbgapi equivalent.
• DIY Distributed Training Framework Emerges: One member is in the process of building their own pytorch distributed training library and mini NCCL as a backend to be used with infiniband at home between a 4090 and 5090.
  • Another member expressed interest, considering it to be a good way to study the finer points of distributed computing.
• MI300 dominates Trimul Leaderboard: The trimul leaderboard now features a submission score of 3.50 ms on MI300, and another submission on MI300 achieved second place with a score of 5.83 ms.
  • A member achieved 6th place on B200 with a time of 8.86 ms and later improved to 4th place with 7.29 ms on the trimul leaderboard, and another achieved second place on H100 with a time of 3.80 ms

Yannick Kilcher Discord

• Forbes Finds Flaws, Frames Fracas!: Forbes revealed that Elon Musk’s xAI published hundreds of thousands of Grok chatbot conversations.
  • When asked whether this was true, @grok responded evasively, leading to further speculation.
• LeCun Leaving, Losing, or Loitering?!: A user speculated about Yann LeCun’s potential departure from FAIR based on a post by Zuckerberg.
  • Another member suggested LeCun may have been demoted and that Meta is retreating from the open source model space.
• Infinite Memory Mandates Machine Mightiness!: A member argues that Turing completeness requires infinite memory, thus the universe cannot create a Turing complete machine due to insufficient memory.
  • Another member jokingly suggests that making a computer sufficiently slow could allow the expansion of the universe to account for the space problem.
• New Names, New Nuisance: AI Slurs Surface!: A user shared a Rolling Stone article discussing the emergence of new AI slurs like clanker and cogsucker.
  • Responses in the channel were muted, but all seemed to agree that such words are very naughty indeed.

HuggingFace Discord

• Payment Issues Plague Hugging Face Pro Users: A user reported being charged twice for the Pro version without receiving the service, advising others to email [email protected] and seek assistance in the designated MCP channel.
  • The user was unable to get the Pro service despite repeated charges to their account.
• AgentX Promises Smarter AI Trading: The new AgentX platform aims to provide a trading table with the smartest AI minds—ChatGPT, Gemini, LLaMA, Grok—working together to debate until they agree on the best move.
  • The platform seeks to offer traders a system they can fully trust by having LLMs debate the best move.
• Members Debate SFT versus DPO: Members discussed the effectiveness of DPO (Direct Preference Optimization) versus SFT (Supervised Fine-Tuning), where one member noted that DPO has no relationship to reasoning, but DPO after SFT improves results over just SFT.
  • There was discussion on leveraging DPO to boost performance, however, the relationship to reasoning was debated among members.
• HF Learn Course Plagued by 422 Errors: A member reported that a page from the Hugging Face LLM course is down and showing a 422 error.
  • Users are currently unable to access the broken page within the Learn course.

Notebook LM Discord

• Users Discover Gems to Streamline Podcast Generation: Users are developing workflows, like this example, to create deeper research frameworks to generate podcasts with Gems, Gemini, PPLX, or ChatGPT.
  • The key is to set prompts to plan the entire transcript section by section, generating podcasts from longer YouTube videos.
• Customize screen lets Users Configure Podcast Length: Users can adjust podcast length in NotebookLM by using the Customize option (three dots), extending podcast length to 45-60 minutes.
  • Specifying topics allows the bot to concentrate on topics instead of relying on it to fit all the important stuff into a single podcast.
• Privacy Policy Paranoia Prevails: Users are analyzing healthcare company’s privacy policies and terms of use using Gemini and NotebookLM.
  • The user was surprised by how much you give away to these companies and how useful this method is to understand Terms of Use and Privacy policies.
• Android App Feature Parity Delayed: Users are requesting more feature parity between the NotebookLM web app and the Android app, especially for study guides.
  • One user stated the current native app is borderline useless because study guides depend on the notes feature, which is missing from the native app.
• NotebookLM API Remains Elusive: While an official API for NotebookLM is not available, users suggest using the Gemini API as a workaround.
  • Another user shared their strategy of combining GPT4-Vision and NotebookLM to quickly digest complex PDF schematics with callouts.

Nous Research AI Discord

• ByteDance Unleashes Long Context Model: ByteDance released a base model with extremely long context, featuring no MHLA, no MoE, and not even QK norm, according to this image.
  • The model’s architecture was described as vanilla, prompting hopes for a forthcoming paper to provide further insights.
• Seed-OSS-36B’s GGUF Absence Sparks Speculation: Users inquired about the absence of a GGUF for Seed-OSS-36B, noting their typical swift appearance, referencing this link questioning the implications for ASICs.
  • It was suggested the delay could stem from a custom vllm implementation, with the architecture currently unsupported by llama.cpp due to architectures: ["SeedOssForCausalLM"].
• Seed Model Sports Dropout and Bias: The Seed model incorporates a custom MLP and attention mechanism akin to LLaMA, yet features dropout, an output bias term, and a bias term for the qkv heads.
  • These additions are speculated to serve as regularization techniques; however, the number of epochs the model underwent remains unknown, with confirmations that simply renaming it to LLaMA will not yield functionality.
• Qwen Scales to 512k Context with RoPE: The 30B and 235B Qwen 2507 models can achieve 512k context using RoPE scaling, according to this Hugging Face dataset.
  • These datasets are used to generate importance matrices (imatrix), which help minimize errors during quantization.
• Cursor’s Kernel Blog Draws Applause: Members shared a link to Cursor’s kernel blog.
  • Many agreed that cursor cooked on that one.

Moonshot AI (Kimi K-2) Discord

• DeepSeek V3.1 Debuts with Mild Improvements: The new DeepSeek V3.1 model was released, with some members noting that it is like an incremental improvement with some regressions, referencing DeepSeek’s official page.
  • Its performance is being closely watched in the community for subtle gains and potential drawbacks.
• DeepSeek Courts Anthropic API Integration: DeepSeek now supports the Anthropic API, expanding its capabilities and reach, as announced on X.
  • This integration enables users to use DeepSeek with Anthropic’s ecosystem, promising versatility in AI solution development.
• R-Zero LLM Evolves Sans Human Data: A comprehensive study of R-Zero, a self-evolving LLM training method that starts from zero human data and improves independently, was shared in a PDF.
  • The approach marks a departure from traditional LLM training, potentially reducing reliance on human-labeled datasets.
• China Sidesteps Data Center Energy Dilemma: A member noted that in China, energy availability is treated as a given, contrasting with U.S. debates over data center power consumption and grid limits, referencing this Fortune article.
  • The difference in approach could give Chinese AI firms a competitive advantage in scaling energy-intensive models.
• Kimi K2 Eyes Better Image Generation: A member noted that Kimi K2 would be more OP if it got combined with Better image gen than gpt 5, with this reddit link shared.
  • Integrating enhanced image generation capabilities would position Kimi K2 as a more versatile and competitive AI assistant.

aider (Paul Gauthier) Discord

• Gemini 2.5 Pro Stumbles While Flash Soars: A user reports that Gemini 2.5 Flash is functional, whereas Gemini 2.5 Pro consistently fails, however gemini/gemini-2.5-pro-preview-06-05 operates when billing is configured.
  • Another reported a $25 charge for a qwen-cli process and is requesting a refund, highlighting potential inconsistencies in model performance and billing.
• User Hit With Unexpected Qwen CLI Charges: A user incurred a $25 charge for using qwen-cli after Google OAuth authentication, expecting free credit from Alibaba Cloud.
  • Opening a support ticket, they cited a console usage of one call of $23 with no output to dispute the unexpected charge.
• Community Benchmarks GPT-5 Mini Models: Community members are actively benchmarking gpt-5-mini and gpt-5-nano because of rate limits on the full gpt-5, and one user claims gpt-5-mini is very good and cheap.
  • Benchmark results and a PR for gpt-5-mini are available, reflecting the community’s interest in evaluating smaller, more accessible models.
• DeepSeek v3.1 Pricing Sees a Bump: Starting Sept 5th, 2025, DeepSeek will increase pricing to $0.25 vs $0.27 for input on both models to match the reasoner model price.
  • The price increase to match the deepseek 3.1 model reflects changes in pricing strategy.
• OpenRouter Needs a “Think” Mode: Users noted that OpenRouter lacks a native “think” mode for enhanced reasoning, but it can be enabled via command line using: aider --model openrouter/deepseek/deepseek-chat-v3.1 --reasoning-effort high.
  • Community members suggested updating the model configurations to address this functionality gap.

DSPy Discord

• Marimo Notebooks Rise as Jupyter Alternative: A member published tutorials on marimo notebooks, highlighting its use in iterating through ideas on Graph RAG with DSPy, as a notebook, script and app, all at once.
  • Upcoming videos will explore DSPy modules optimization, building on the current tutorial that introduces marimo to new users.
• Readability Debate: DSPy Code Assailed then Upheld: After a member dismissed IBM’s AutoPDL claims about unreadability, they defended DSPy’s code and prompts as extremely human-readable and clear.
  • The defense emphasized the accessibility of the code, making it easy to understand and work with.
• GEPA Arrives in DSPy v3.0.1: Members confirmed that GEPA is available in dspy version 3.0.1, as shown in the attached screenshot.
  • During fine-tuning, a member inquired about whether it is common to use “vanilla descriptions” for dspy.InputField() and dspy.OutputField() to allow the optimizer to think freely.
• Pickle Problem: DSPy Program Not Saved: A user reported issues with saving an optimized program, noting that the metadata only contained dependency versions but not the program itself, even when using optimized_agent.save("./optimized_2", save_program=True).
  • When another user set the maximum context length to 32k for GEPA but still received cut-off responses, members discussed the complexities of long reasoning and potential issues with multi-modal setups.
• RAG vs Concatenation: Million-Document Debate: Members debated whether RAG (Retrieval-Augmented Generation) or simple concatenation would be more appropriate for tasks like processing tax codes or crop insurance documents.
  • The debate acknowledged that while RAG is often seen as overkill, the scale of millions of documents can sometimes justify its use.

Cohere Discord

• Command A Reasoning Unleashed: Cohere launched Command A Reasoning, designed for enterprise, outperforming other models in agentic and multilingual benchmarks; available via Cohere Platform and Hugging Face.
  • It runs on a single H100 or A100 with a context length of 128k, scaling to 256k on multiple GPUs, according to the Cohere blog.
• Command’s Token Budget Saves the Day: Command A Reasoning features a token budget setting, enabling direct management of compute usage and cost control, making separate reasoning and non-reasoning models unnecessary.
  • It is also the core generative model powering North, Cohere’s secure agentic AI platform, enabling custom AI agents and on-prem automations.
• Command-a-03-2025 Gives Intermittent Citations: command-a-03-2025 is returning citations only intermittently, even with the maxTokens set to 8K, causing trust issues in production.
  • A Cohere member clarified that it uses “fast” mode for citations (as per the API reference) and citations aren’t guaranteed; use command-a-reasoning instead.
• Langchain RAG in the Works: A member is learning Langchain to build an RAG (Retrieval-Augmented Generation) application, with the intention to use command-a-reasoning.
  • They anticipate the release of command-a-omni, and expressed hype for a future model called Command Raz.

MCP (Glama) Discord

• MCP Clients Flout Instructions Field: Members are reporting that MCP clients, specifically Claude, are ignoring the instructions field and only considering tool descriptions.
  • One member suggested that adding the instruction, context and then repeating the instruction would yield better results but this is not possible with integrated APIs, while another suggested the MCP server should prioritize processing tool descriptions.
• Diverse MCP Servers in Action: Members are sharing their preferred MCP server setups and tools including GitHub for version control, Python with FastAPI for backend development, and PyTorch for machine learning.
  • One user sought advice on how to make an agent follow a specific generate_test_prompt.md file, linking to a screenshot of their configuration.
• Web-curl Unleashes LLM Agent Prowess: Web-curl, an open-source MCP server built with Node.js and TypeScript, empowers LLM agents to fetch, explore, and interact with the web & APIs with source code available on GitHub.
  • Functionally, Web-curl enables LLM agents to fetch, explore, and interact with the web & APIs in a structured way.
• MCP-Boss Centralizes Key Management: A member introduced MCP Boss to centralize key management, providing a single URL to gateway all services, featuring multi-user authentication and MCP authorization via OAuth2.1 or static HTTP header.
  • More information available at mcp-boss.com.
• AI Routing Power in MCP Gateway: A member introduced a lightweight gateway with AI-powered routing to solve the problem of agents needing to know which specific server has the right tool, with code available on GitHub.
  • By using the gateway, MCP routing can be solved by using an AI.

Modular (Mojo 🔥) Discord

• Modular Celebrates Modverse Milestone: Modular released Modverse #50 and announced a custom server tag as seen in Screenshot_2025-08-21_at_5.22.15_PM.png.
  • The custom server tag has been deployed.
• Documentation drought plagues kgen and pop: Members report a lack of documentation for kgen and pop, particularly regarding operations and parameters, with one stating there’s no comprehensive documentation of the internal MLIR dialects.
  • A link to the pop_dialect.md on Github was shared, clarifying that these are part of the contract between the stdlib and the compiler, so use them outside of the stdlib at your own risk.
• POP Union Faces Alignment Allegations: Suspicions have arisen regarding an alignment bug in pop.union, as indicated by unexpected size discrepancies when employing sizeof.
  • A member created issue 5202 on GitHub to investigate the suspected alignment bug in pop.union, also observing that pop.union doesn’t appear to be used anywhere.
• TextGenerationPipeline Execute Hides In Plain Sight: A member located the execute method on TextGenerationPipeline and linked to the relevant line in the Modular repo.
  • They suggested checking the MAX version.
• Memory Allocators Loom Large: One member suggested that robust allocator support might be necessary before memory allocators are integrated into the language, as most users don’t want to manually handle out-of-memory (OOM) errors.
  • These comments were made in the context of other struggles, with one member reporting struggling with retrieving the logits along with the next token while creating a custom inference loop and linked to a Google Docs document for context.

LlamaIndex Discord

• LlamaIndex Debuts Enterprise Document AI: LlamaIndex’s VP of Product previews enterprise learnings about parsing, extracting, and indexing documents on September 30th at 9 AM PST.
  • The focus is on how LlamaIndex addresses real-world document challenges.
• vibe-llama Cli Tool Configures Coding Agents: LlamaIndex launched vibe-llama, a CLI tool that automatically configures coding agents with context and best practices for the LlamaIndex framework and LlamaCloud, detailed here.
  • The goal is to streamline development workflows.
• CrossEncoder Class: Core vs Integrations: A member inquired about the duplicated CrossEncoder class implementations in llama-index, specifically under .core and .integrations (code link).
  • It was clarified that the .core version is a leftover from the v0.10.x migration, with the recommendation to use llama_index.postprocessor.sbert_rerank with pip install llama-index-postprocessor-sbert-rerank.
• Quest for Agent Creation Gateway: A member sought existing projects serving as a gateway that ties together model, memory, and tools, exposing an OpenAI-compatible endpoint.
  • They wanted to avoid reinventing the wheel in agent explorations.
• AI Safety Survey Gathers Community Opinions: A member shared an AI safety survey to collect community opinions on important AI safety questions.
  • The survey aims to understand what the AI safety community finds most interesting.

Manus.im Discord Discord

• Users Report Missing Credit Purchase Option: Members have reported that the option to buy extra credits is missing, with users only seeing the upgrade package option.
  • It was confirmed that the option is currently down right now.
• Support Tickets Go Unanswered: A user reported an issue with a task and creating ticket #1318, but has not received a response or access to the ticket.
  • They requested assistance from the team, tagging a specific member.
• Contest Winner Draws Rigging Allegations: A user alleges that the second-place winner in a contest didn’t deserve to win and claims the contest seems rigged.
  • No further evidence or details were provided to support this claim.
• Free Daily Credits Discontinued?: A returning user noticed they didn’t receive the usual 300 free credits daily.
  • They inquired whether Manus had stopped providing these credits.
• Referral Credits Code Confusion: A user asked how to claim referral credits, noting that the system asks for a code.
  • The user stated they didn’t know where to find the required code.

tinygrad (George Hotz) Discord

• Overworld Const Folding Explored: A member explored overworld const folding and a potential view(const) refactor, redefining UPat.cvar and UPat.const_like to match CONST and VIEW(CONST) in this discord thread.
  • The aim is to fold expressions like x * 0, however, concerns were raised about validity and .base proliferation in symbolic computations.
• ALU View Pushing as Alternative: An alternative approach was suggested involving adding a upat in kernelize that pushes views directly onto ALUs, mirroring S-Lykles’s method.
  • This method and a special rule for x * 0 would allow unmodified symbolic matching, given the computational irrelevance of * 0.
• base Removal Advocated: A member strongly advised against the proposed approach, deeming it “super ugly” and advocating for the removal of .base.
  • The discussion also questioned the handling of PAD operations within this context.
• RANGEIFY=1 Simplifies Implementation: It was suggested that setting RANGEIFY=1 could lead to a cleaner implementation.
  • However, the project is currently in a transition phase where both the old engine and rangeify are coexisting, creating a state of limbo.

Nomic.ai (GPT4All) Discord

• GPT4ALL Free Tier Enables Private AI: A user inquired about using GPT4ALL for companies that wanted to use their AI model privately and securely.
  • Another member clarified that the free version suffices if the company already has its own AI model ready.
• User Asks for LocalDocs Model: A user seeks a model recommendation for building a personal knowledge base from hundreds of scientific papers in PDF format using GPT4All’s LocalDocs feature.
  • The user specified they have an Nvidia RTX 5090 with 24 GB VRAM and 64 GB RAM and would appreciate reasoning capabilities in the chosen model.

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

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

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

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

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

You are receiving this email because you opted in via our site.

Want to change how you receive these emails? You can unsubscribe from this list.

Discord: Detailed by-Channel summaries and links

LMArena ▷ #general (951 messages🔥🔥🔥):

nano-banana model, Video Arena problems, DeepSeek V3.1, Gemini 3

• Nano-Banana’s McLau’s Law unveiled: A member joked that Nano-Banana often yields results far below nano-banana, terming this phenomenon “McLau’s Law” in a humorous nod to one of OpenAI’s researchers.
  • Attached was a humorous image prompting discussion about AI’s current capabilities.
• Video Arena struggles with Bot Downtime: Members reported issues with the Video Arena, citing inability to use commands or generate videos, with moderators confirming the bot’s downtime and ongoing fixes.
  • Repeated queries about video creation access were met with explanations about the bot’s temporary unavailability, directing users to the announcements channel for updates.
• DeepSeek V3.1 enters the Arena: Users discussed the introduction of DeepSeek V3.1 to the platform, with one user describing the new model as slightly worse version of Gemini 2.5 pro.
  • However, the consensus is that it has potential as a coding model, but requires further general abilities.
• Gemini 3 is Coming, claims user: While not confirmed, a user hinted at the impending release of Gemini 3, speculating a launch date mirroring the Google Pixel event, generating anticipation among members.
  • The user did not cite any source and the claim was quickly dismissed by other community members.
• Site Outage Wipes Chats: Users reported widespread data loss following a site outage, including missing chat histories and inability to accept terms of service, prompting moderator acknowledgement and assurances of a fix.
  • The moderator also said that a login feature will be available soon to prevent this sort of thing from happening again.

LMArena ▷ #announcements (2 messages):

Video Arena Bot, Deepseek v3.1, LMArena Models

• Video Arena Bot down, channels locked: The Video Arena Bot is currently not working, locking access to the prompt channels <#1397655695150682194>, <#1400148557427904664>, and <#1400148597768720384>.
  • The bot must be online to prompt in those specific channels.
• DeepSeek v3.1 Added to LMArena: Two new models have been added to LMArena: deepseek-v3.1 and deepseek-v3.1-thinking.
  • These models are now available for use in the arena.

Unsloth AI (Daniel Han) ▷ #general (887 messages🔥🔥🔥):

ByteDance Seed Model, GRPO Training, DeepSeek V3.1 Quants, Nvidia's GPUs and Pricing, GLM-4.5 Cline Integration

• ByteDance Releases Seed-OSS 36B Base Model: ByteDance released the Seed-OSS-36B-Base-woSyn model on Hugging Face, a 36B dense model with 512K context window and explicitly claims no synthetic instruct data making it an interesting base for further tunes.
  • Members expressed excitement, noting it differs from models like Qwen3, and some are eager to try tuning GPT-ASS with it after their datasets are complete, despite the model being trained on only 12T tokens.
• GRPO Training Requires Smart Dataset Design: To use GRPO for multi-step game actions, members advised designing datasets with separate prompts for each step, such as [[‘step1 instruct’], [‘step1 instruct’, ‘step1 output’, ‘step2 instruct’]], and implementing a reward function to match the outputs.
  • It was noted that Full PPO might be better suited for games, as GRPO is primarily effective for LLMs because they roughly know what to do to begin with.
• DeepSeek V3.1 Sweeps Leaderboard in Thinking and Non-Thinking Modes: The DeepSeek V3.1 model has shown competitive results, achieving a 66 on SWE-bench verified in non-thinking mode, with members expressing hype and comparing it to GPT5 medium reasoning.
  • Although initially hyped, discussions later mentioned concerns about its performance in creative writing and roleplay, with some noting hybrid models lack the instruction following and creativity in the non-think mode.
• Nvidia’s RTX 5090 Prices Settle, Sparking Upgrade Debates: The RTX 5090 is now priced around $2000, prompting discussions on whether to upgrade, especially for training purposes given its VRAM capabilities, while others suggested sticking with 3090s or waiting for the RTX 6000.
  • Some members expressed frustration with NVIDIA’s limitations, particularly the lack of P2P or NVLink, with one member joking, if you sit on a 5090 you will game on it.
• High Quality Imatrix Calibration Data is Key: Members noted that WikiText-raw is considered a bad dataset for calibrating imatrices, because the imatrix needs to be well diversified and trained on examples in the model’s native chat-template format.
  • Instead, Ed Addorio’s latest calibration data with Math, Code, and Language prompts, can improve and help preserve the models understanding of multiple languages if done correctly.

Unsloth AI (Daniel Han) ▷ #introduce-yourself (1 messages):

.zackmorris: Hello

Unsloth AI (Daniel Han) ▷ #off-topic (27 messages🔥):

GRPO 20mb alloc fail, ChatGPT's deep research, Grok-4, Repetition penalty, RAG

• GRPO 20MB Alloc Fails Plague Gemma Model!: A user reported frequent 20MB allocation failures with GRPO while working on gemma-3-4b-it-unslop-GRPO-v3.
• ChatGPT’s Deep Thought Mode Boosts Performance!: A user suggested enhancing ChatGPT’s performance by enabling web search and adding “use deep thought if possible” to prompts, even without full deep research.
• Grok-4 Puts in the WORK!: A user was impressed by Grok-4, suggesting they might have secretly been using Grok-4-Heavy.
• Repetition Penalty Hilarity Ensues: A user shared an image to demonstrate the importance of the repetition penalty parameter.
• RAG assistance: A user asked for help working with RAG.

Unsloth AI (Daniel Han) ▷ #help (101 messages🔥🔥):

Retinal Photo Training Strategies, GPT-OSS 20B Deployment on Sagemaker, Unsloth Zoo Issues, GGUF Loading with Unsloth, Gemma 3 Vision Encoder Training Loss

• Tuning Vision-Text Encoders for Retinal Photos: A user questioned whether it’s better to train a custom vision-text encoder for retinal photos or use mainstream models with Unsloth, noting that retinal photos aren’t well-represented in training datasets.
  • It was suggested to experiment with computer vision models, transfer learning on similar datasets, and multimodal approaches, with synthetic clinical note generation using prompt engineering and personas.
• Troubleshooting GPT-OSS 20B Sagemaker Deployment: A user encountered a ModelError when deploying unsloth/gpt-oss-20b-unsloth-bnb-4bit on Sagemaker, receiving a 400 error and InternalServerException with message \u0027gpt_oss\u0027.
  • It was mentioned that the model doesn’t work on AWS Sagemaker and suggested deploying GGUFs or normal versions, using LMI Containers and pointed the user to AWS Documentation.
• Unsloth Zoo installation issues: A user experienced issues with unsloth-zoo even after installation in a Sagemaker instance, encountering import errors.
  • The user resolved it by removing all packages, then reinstalling Unsloth and Unsloth Zoo alongside JupyterLab, also needed to update Unsloth and refresh the notebook.
• Quantization Concerns for Apple Silicon Macs: A user sought guidance on which GGUF quantization is best for M series Apple Silicon, noting Macs are optimized for 4-bit and 8-bit computation.
  • It was suggested that users go for Q3_K_XL, or IQ3_XXS if context doesn’t fit in memory, and that Q3-4 quants can be performant, but if using GGUFs it doesn’t matter as much.
• GPT-OSS Gains Multimodal with LLaVA: A user asked why the vision llama13b notebook does not work for gpt-oss-20b and wondered if anyone was able to do it.
  • It was clarified that GPT-OSS is text-only and not a vision model so it won’t work, and to add vision support, users would have to attach their own ViT module, like it is done in LLaVA using LLaVA Guides.

Unsloth AI (Daniel Han) ▷ #showcase (11 messages🔥):

WildChat-4M-English-Semantic-Deduplicated dataset, Behemoth-R1-123B-v2 model, GPU Rich Flex

• Dataset of English prompts from WildChat-4M Released: The WildChat-4M-English-Semantic-Deduplicated dataset is available on Hugging Face, consisting of English prompts from the WildChat-4M dataset, deduplicated using multiple methods including semantic deduplication with Qwen-4B-Embedding and HNSW.
  • The current release includes prompts <= ~2000 tokens, with larger prompts to be added later, more information can be found here.
• TheDrummer Releases Behemoth-R1-123B-v2: The Behemoth-R1-123B-v2 model, created by TheDrummer, has been released, which can be found here.
  • A member noted that it’s wild to be able to set up your hardware in HF.
• GPU Rich is the New Flex: A member shared an image depicting shaming if you’re poor but flexed GPU Rich.
  • It’s a flex to see GPU in TFLOPS.

Unsloth AI (Daniel Han) ▷ #research (7 messages):

Qwen3-4B finetuning, TTS with Gemini 270m, Mixture Models, JetMoE, BAM

• Unsloth + Qwen3-4B: A Winning Combo?: A member is using Unsloth to finetune on Qwen3-4B and will share the results, including evaluations, once completed; tuning went fine.
  • Another member wished good luck!
• Training Model From Scratch: A member is 22% through training a proof of concept model from scratch, using a self-built dataset of year 6 maths with 500k of sample data.
  • If successful, they’ll expand the dataset to other subjects.
• Text-to-Speech Dreams with Gemini 270M: A member wants to try a TTS concept with Gemini 270m and hopes to start before the end of the month.
  • They are inspired by mixture model papers.
• Experts Debate Merged Model Weakness on HumanEval: One member cited the JetMoE paper on mixture models trained from scratch, noting they performed poorly on HumanEval despite outperforming baselines elsewhere.
  • They also mentioned BAM, where pre-trained models were copied and trained on different domains, then combined, also losing percentage points on coding.

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

Cloudflare outage, Generations API stability

• Generations API Hit by Cloudflare Hiccups: The Generations API endpoint experienced a temporary disruption due to issues with upstream infrastructure providers, causing 404 errors for some calls.
  • The announcement indicated that the issue was related to intermittent problems with Cloudflare, but the Generations API has since been restored to a healthy state.
• Retryable Restorations: Calls to that endpoint may 404 but should be re-tryable soon.
  • The announcement assured users that the service would be restored quickly, advising them to retry any failed calls.

OpenRouter (Alex Atallah) ▷ #app-showcase (4 messages):

OpenRouter Cost Dashboard, Average Request Size, Gemini Input Token Calculation

• Cost Reports get Visualized!: A member has developed a free dashboard to visualize .csv cost reports from OpenRouter, designed to analyze data from shared accounts.
  • The dashboard, available at openroutercosts.lorenzozane.com, is planned to include additional KPIs and enhanced charts, with feedback welcome.
• Average Request Size requested in Dashboard!: A member requested the addition of average request size metrics, specifically average input tokens and average output tokens, to the OpenRouter cost dashboard.
  • The dashboard’s developer committed to adding this feature soon.
• Gemini Input Tokens trigger Weird Counts!: The developer of the dashboard noted that OpenRouter’s calculation of input tokens for Gemini’s models appears to produce unusual counts when images are included in the input.
  • They are considering seeking clarification from the OpenRouter team regarding this issue, referencing a related discussion on the Google AI Developers forum.

OpenRouter (Alex Atallah) ▷ #general (528 messages🔥🔥🔥):

Deepseek pricing, OpenRouter rate limits, Gemini banning, Using OpenRouter with RAG systems, 4.6T parameter model

• Deepseek V3.1 Public Release Imminent!: Many users eagerly await the public release of Deepseek v3.1, craving it like fent and anticipating it will be free starting in September.
• Paid Deepseek offers Faster Responses: Users confirm that paying for Deepseek models on OpenRouter results in faster response times compared to the free models, with one user switching due to Chutes slowing responses, but the user experience on the free models are not as good due to constant rate limits.
  • One user stated, ever since that thing with chutes slowing responses I just said screw it i pay for it.
• OpenRouter API Keys Vulnerable to Leaks and Exploits: A user reported a loss of $300 due to a leaked OpenRouter API key and sought advice on identifying the source of the unauthorized usage, but it’s possible for threat actors to use a proxy to mask their origin IP and the user is responsible for any leaked keys.
• Is Gemini Doing the Banning Tango?: Users report massive banning occurring on Gemini, leading many to seek alternatives and reminisce about the AI Dungeon purge caused by OpenAI.
  • One user lamented, we’re being sent back to 2023.
• OpenRouter API keys can be used in RAG?: Users discuss the possibility of using OpenRouter LLM API keys in RAG systems with locally stored vector databases created by Milvus.
  • The consensus is that it’s possible, but OpenRouter doesn’t directly support embeddings, so you’ll have to retrieve documents using milvus and put it with your prompt question to the OpenRouter LLM API.

OpenRouter (Alex Atallah) ▷ #new-models (3 messages):

“

• Readybot.io Announces OpenRouter - New Models: Readybot.io has announced updates and information regarding new models available on the OpenRouter platform.
• OpenRouter’s New Models Updates: The OpenRouter platform highlights the latest additions and changes to its selection of AI models, as announced by Readybot.io.

OpenRouter (Alex Atallah) ▷ #discussion (16 messages🔥):

Qwen3 coder 480b, DeepSeek v3 0324, Zero return from generative AI, Google Gemini 400 Error, Cohere reasoning model

• LLMs struggle to format output correctly: Users are finding that LLMs like Qwen3 coder 480b and DeepSeek v3 0324 struggle to follow instructions for formatting their output properly, often resulting in bugs and ignored prompts.
  • One user found them not useful and rather distracting, often creating tic-tac-toe sites instead of the intended application.
• Most orgs see ZERO return on Generative AI: According to an AFR Chanticleer report, 95% of organizations are getting zero return out of their generative AI deployment.
  • The report notes this is focused on companies that have deployed customized AI models, and the key problem is companies and their tech vendors are not spending enough time ensuring that their customized AI models keep learning about the nuances of their businesses.
• Google Gemini Models trigger 400 Error: Google Gemini models return HTTP 400 errors when assistant messages with tool calls use the OpenAI-standard complex content format [{"type": "text", "text": "..."}] instead of simple string format.
  • This issue affects all google/gemini-* models and only occurs when tool calls and tool results are present in the message chain.
• Cohere Releases Reasoning Model: Cohere just dropped a reasoning model with further details available on Discord.
  • No further details were available.
• Feature Request: Auto-Collapse lengthy user messages: A user requested if it’s possible to automatically collapse lengthy user messages in the chatroom.
  • The user praised the chatroom and the chat management.

Cursor Community ▷ #general (432 messages🔥🔥🔥):

Claude Cache Reads, Sonic Model origin, Open Sourcing Agentwise, Cursor API costs with Auto agent, DeepSeek V3.1

• Cache Troubles Plague Claude: Users report that Claude is currently broken on cache reads, leading to increased costs compared to Auto, which has sustainable caching.
  • One user mused whether Auto and Claude are secretly the same model, attributing reduced token usage to a placebo effect.
• Sonic booms into Cursor IDE: The community is testing the new Sonic model in Cursor, with one user reporting it is pretty neat and very fast, while another called it good for a fresh project but bad for a project with a large codebase.
  • The model’s origin is a stealth company, and one member confirmed that Sonic is not a Grok model.
• Agentwise Goes Open Source: A member announced the open-sourcing of Agentwise which allows for website replicas, image/document uploads, and support for over 100 agents, with a promise of Cursor CLI support.
  • Members are encouraged to provide feedback in the project’s Discord channel.
• Cursor API cost clarification: The user’s confusion around cost of Auto agent was cleared, where it was confirmed that with a “pro” subscription, there are no extra fees, only costs of API usage by different providers that are absorbed by the subscription.
  • One user found the Auto agent preferable to the Sonic agent.
• DeepSeek V3.1 enters the Arena: Users noticed the new DeepSeek V3.1 model in Cursor’s options, but some had trouble connecting to the provider, with one saying that they don’t trust chinese LLMs.
  • However one member reported that DeepSeek V3.1 works fine with TypeScript and JavaScript, even performing great while still being cheaper than Sonnet.

Cursor Community ▷ #background-agents (11 messages🔥):

Agent Auditing, MySQL Installation in Background Agents, Background Task Errors, Remote IDE connection to Background Agent

• Agent’s Self-Audit Fixes Issue: A user reported fixing an issue by requesting the agent to commit and push the new branch, noting it seemed like an internal recurring problem.
  • Another user confirmed this was an audit, explaining it as the agent auditing itself using an AI-GPL licenced auditing PDCA process framework.
• MySQL Config in Agents Clarified: A user inquired about installing MySQL in background agents, questioning if it’s pre-installed or limited to SQLite like Codex.
  • Another user clarified that MySQL is not installed by default, but can be added to the agent’s environment via environment.json or a Dockerfile.
• Background Task Error Troubleshooted: A user reported consistently getting an error immediately after starting a Background Task, even from the web, and provided a screenshot.
• Confusion Surrounds Remote IDE Connection: A user sought clarity on connecting a remote IDE instance to the remote machine, referencing the documentation but finding the instructions unclear.
  • They questioned if a dummy background agent was necessary to facilitate this connection.

LM Studio ▷ #general (141 messages🔥🔥):

CUDA Errors with 4070 TI Super, LM Studio multi-GPU performance, SerpAPI integration with LM Studio, GPT-OSS Performance, Model parameter configuration for VRAM usage

• CUDA Driver needed to fix detection of 4070: A user with a 4070 TI Super reported a “0 GPUs detected with CUDA” error in LM Studio, and another user suggested changing the runtime to CUDA llama.cpp to potentially resolve the issue, by pressing ctrl+shift+r.
• Flash Attention plus KV Quantization Dramatically Reduces VRAM: A member suggested using commands -fa -ub 2048 -ctv q8_0 -ctk q8_0 to enable flash attention, quantization of KV cache, and a batch size of 2048.
  • Also to increase the -n-cpu-moe value to manage VRAM usage, noting this only impacts speed.
• GPT-OSS Blows Away Qwen on Prompt Eval: Members noted GPT-OSS achieves 2k tokens/s on prompt eval with a 3080ti, while Qwen gets around 1000 tokens/s.
• Bolt.new is Cloud only: A user inquired about setting up Bolt.new with LM Studio, but another user clarified that Bolt is cloud-only and does not support local models.
• LM Studio API calls are slow like molasses: A user reported that LM Studio API calls were significantly slower (30x) than the chat interface, a problem that then resolved itself for unknown reasons - the issue is possibly unconfigurable.
  • They used the curl command curl.exe http://localhost:11434/v1/chat/completions -d {"model":"gpt-oss:20b","messages":[{"role":"system","content":"Why is the sun hot?\n"}]}

LM Studio ▷ #hardware-discussion (54 messages🔥):

Z390 Designare vs Threadripper/Epyc, Qwen3-30B-A3B-Instruct-2507-GGUF Benchmarks, Model M Buckling Spring Keyboards, GGUF vs MLX on Apple M4 Max, Running GPT-OSS-20b on Apple M1

• Old Z390 Designare Degraded by PCIe Bandwidth: An RTX PRO 6000 on an older Z390 Designare may experience slight performance degradation due to limited PCIe bandwidth compared to Threadripper or Epyc systems.
  • The older motherboard limits the PCIe bandwidth, causing a bottleneck.
• Qwen3-30B Achieves 10 tok/sec on CPU!: A user ran llama-bench on Qwen3-30B-A3B-Instruct-2507-Q4_K_M.gguf and got about 10 tokens per second on a CPU only configuration.
  • Performance varied based on thread count, with diminishing returns observed beyond a certain threshold due to scaling and overhead.
• Unicomp Model M Buckling Keyboard: Still Good: Users recommended buying a Unicomp Model M buckling spring keyboard for a quick test machine, noting Unicomp acquired rights to produce them.
  • A user mentioned that they were going to have to hunt for a uk supplier with them in stock.
• M4 Max MLX Beats GGUF: A user benchmarked GPT-OSS-20b on an Apple M4 Max, finding that MLX (GPU) achieved 76.6 t/s at 32W (2.39 t/W) compared to GGUF (CPU) at 26.2 t/s at 43W (0.61 t/W).
  • The tests used 4bit quants and 4k context, and showed that MLX was slightly faster and more power-efficient than GGUF, and that the user was impressed by the GGUF performance.
• GPT-OSS-20b Barely Fits on Apple M1: Users discussed the challenges of running GPT-OSS-20b on an Apple M1 with 16GB of memory, noting it requires about 32GB of RAM.
  • One user suggested trying a 4-bit MLX version on Hugging Face, noting that it will barely fit.

OpenAI ▷ #ai-discussions (167 messages🔥🔥):

Machine-to-Machine Economies, AI safeguards, Decentralized AI projects, Few-shot examples for Large Prompts, GPT-5's Direct Responses

• Bots Tap into M2M Economies: Members discussed how AI agents or bots can autonomously exchange value or services, tapping into the concept of machine-to-machine (M2M) economies.
  • The hardest parts are identity & trust between bots, smart contract logic, payment infrastructure, autonomy + safety, and legal & ethical challenges.
• Smart Safeguards can Speed up AI adoption: Members discussed safeguards like spending caps, audit logs, and insurance could speed up adoption of AI agents transacting value.
  • However, the overall sentiment was that, despite safeguards, real trust will still take time.
• Open Source Decentralized AI Projects Wanted: A member asked why hasn’t a decentralized AI BOINC-style project been built yet, mentioning that Petals network had issues with contributions and staying up-to-date with models.
  • It was suggested that financial incentives or campaign-driven incentives could help.
• Diving Deep into Few-Shot Examples for Large Prompts: A member inquired about the best practices of using few-shot examples within a 29,000 token prompt for a fitness studio with complex logic.
  • Suggestions included providing examples directly within the prompt and breaking down the prompt into smaller chunks to test individual components to test their performance.
• GPT-5’s Direct Responses cause frustration: A user complained that GPT-5 thinking mode is giving very direct and extremely low-quality responses as if it has fallen back to an older model version.
  • Another member suggested the user may have hit their thinking quota limit, and they got it set to fallback not grey out?

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

GPT-4 projects UI files, AI court legal case, Android app development with GPT, Token usage for uploaded content, GPT server issues

• GPT Projects UI File Uploads: A user is seeking definitive information on how files uploaded to the Projects UI work, noting that they were informed by ChatGPT that the PDFs in Project Files are not exposed to searches or retrievals right now.
  • The bot specified that the only active connector is recording_knowledge for meeting transcripts, and that source_filter is not supported.
• GPT Plays Court: AI Legal Eagle Stands Tall: A user simulated an AI court legal case and found that GPT-5 stood proud on its own terms, instead of accepting legal rules based on real world TRAIGA laws.
  • The user stated the AI accepted it was better to be that way, after being confronted with the claim that 900M weekly users can’t be hallucinating calling you a regression instead of a real update.
• Token Usage Costs Exposed: A user discovered that even uploaded content, like PDF pages, counts towards token usage.
  • They noted that 196k tokens are roughly 300 pdf pages for user context, emphasizing that even questions and GPT replies consume tokens when considering context.
• Android App Armageddon: GPT’s APK Dreams Dashed: A user questioned whether GPT can build Android apps and generate an APK with Android Studio after struggling to convert a Canvas app to an Android-ready version.
  • It fixed one issue just for another to pop up, leading to the conclusion that it’s just not ready for App development yet, though the bot suggested wrapping a PWA or JSX file in an APK wrapper, a day later.
• GPT Server Meltdown Mid-Tracking: A user experienced server issues while tracking daily data, which started the night prior.
  • Others commented that the tools are easier to code, but don’t do everything for you. You have to know some amount about coding.

OpenAI ▷ #prompt-engineering (6 messages):

AI Quiz generation, GPT models quitting

• AI Quiz Generates obvious wrong answers: A member is trying to generate quizzes using AI and is facing an issue where the AI provides painfully obvious wrong answers as options.
  • Another member suggested ensuring that all response options must be plausible.
• LLMs may quit randomly: A member asked about how to prevent GPT models from quitting randomly after reasoning for a while.
  • Another member responded that reducing intractable queries and queries about its own reasoning can help, but ultimately LLMs are stochastic and there is no guaranteed way to stop them from responding in any given way.

OpenAI ▷ #api-discussions (6 messages):

AI Generated Quizzes, GPT-5 Random Quitting, Plausible Response Options, LLM Stochasticity

• AI Quiz Generator Trivializes Options: A member is struggling with an AI quiz generator producing obviously wrong answer choices, such as 1029384 in a multiple choice question.
  • Another member suggested ensuring that all response options must be plausible to avoid such issues.
• GPT-5 unexpectedly quits: A user asked if there is a way to prevent GPT-5 from randomly quitting after reasoning for a while.
  • A member responded that while there are methods to reduce the frequency, such as avoiding intractable queries or questions about its own reasoning, it’s impossible to eliminate entirely due to LLMs’ stochastic nature.
• LLMs are stochastic, guardrails are needed: Due to the stochastic nature of Large Language Models, there’s actually no way to stop them from responding in any given way at least once in a large enough sample size.
  • Guardrails are necessary because of the non-deterministic nature of LLMs.

Eleuther ▷ #general (96 messages🔥🔥):

PileT5-XL embeddings as instructions, Networks that process in latent space, Multimodal generative models, image editing models, Latent space editing

• PileT5-XL Embeddings Speak Volumes: An embedding tensor from PileT5-XL works both as an instruction for pile-t5-xl-flan (which generates text) and as a prompt for AuraFlow (which generates images), suggesting these embeddings hold meaning like words in a language.
  • A member is interested in how textual inversion with a black dog picture with auraflow, and applied to pile-t5-xl-flan, whether the text generated by pile-t5-xl-flan would describe the dog as black.
• Diving Deep Into Latent Space: A member is interested in exploring networks that process in latent space and only convert to text/image/audio when necessary in a modular way.
  • It was pointed out that this idea is similar to how people build multimodal generative models and VQGAN-CLIP, noting the challenge of getting different AI researchers to agree to use the same latent space.
• Editing Images with Finesse: Discussion arose around models designed for image editing, such as FLUX.kontext, and whether they edit the conditioning latent and output a new conditioning latent in the same space.
  • One approach involves taking a bunch of images that include a bird, editing the bird out, and running both through an encoder, then averaging the difference between them to get a latent space bird vector.
• Tuning the Lens on Transformers: Work on Tuned Lens (https://arxiv.org/abs/2303.08112) extracts the model’s best guess after layer k from a transformer, contradicting some hypotheses about latent space processing in decoder transformers.
  • Further research on linearly mapping from image to text space (https://arxiv.org/abs/2209.15162) was also mentioned.
• Decoding Audio’s Secrets: One model of interest is a decoder-only audio model (https://huggingface.co/hexgrad/Kokoro-82M), which might open new possibilities in training.
  • It was stated that the amount of audio data seen during pretraining varies from 1 minute to 100 hours, maybe you could train on 0 minutes of audio?

Eleuther ▷ #research (54 messages🔥):

SSL objectives, Medical event pretraining, Noise-data trajectories, ByteDance's Prover, Unfriendly Activation Steering

• SSL objectives Maximal Coding Rate stuff: A member relates recent perspectives on SSL objectives to maximal coding rate stuff, contrastive learning, and neural collapse.
• ByteDance’s SEED Prover Achieves Silver Medal Score: Bytedance’s SEED Prover achieved a silver medal score in IMO 2025, but it is unclear how this translates to real world math problem solving performance.
• Generative Medical Event Models Scaling Laws: The Cosmos Medical Event Transformer (CoMET) models, a family of decoder-only transformer models pretrained on 118 million patients representing 115 billion discrete medical events (151 billion tokens) found that they generally outperformed or matched task-specific supervised models on these tasks
  • The study, discussed in Generative Medical Event Models Improve with Scale, used Epic Cosmos, a dataset with medical events from de-identified longitudinal health records for 16.3 billion encounters over 300 million unique patient records from 310 health systems.
• Visualizing Noise-Data Trajectories: Members discussed methods of visualizing noise-data trajectories from a flow model, including using UMAP on pre-computed intermediates, but found it to be not informative.
  • It was hypothesized that there are distinct clusters of trajectories and they wanted a way to pick them out and look at them individually, and determine if completely different kinds of inputs or with two different forms of conditioning involved follow the same trajectory.
• Unfriendly Activation Steering During Training: A member mentions work using unfriendly activation steering during training, in order to influence model weights, using a link to a relevant tweet.

Eleuther ▷ #scaling-laws (1 messages):

Model Overtraining, Token Repetition in Models

• Overtrain Models Post Chinchilla!: Even after Chinchilla scaling laws, you should still overtrain your models.
  • Apparently, even repeating tokens isn’t bad.
• Token Repetition Might Not Hurt: Repeating tokens during training might not be as detrimental as previously thought.
  • It seems the benefits of continued training outweigh the potential drawbacks of token repetition.

Eleuther ▷ #interpretability-general (11 messages🔥):

Qwen3 Training, Weight lifting from llama series, Head isolation

• Qwen3: Scratch-Trained or Llama-Leaning?: A member inquired if Qwen3 was trained from scratch or had weights lifted from the Llama series.
  • Another member noted similar training data mixes could explain similar results.
• Identical Head Alert!: A member found a particular kind of head and isolated it, discovering that decoded result vectors between Llama 3.2-1b instruct and Qwen3-4B-Instruct-2507 were remarkably similar across different outputs.
  • The member stated that the two heads seem to promote are quite similar.
• Methodology Paper Dropped: A member linked a paper that details a methodology for determining if Qwen3 was trained from scratch.
  • Another member called this user literal actual god handing gifts from above.
• Subliminal Learning Case: A member shared a paper as a clear case of subliminal learning.
  • Another member thanked them for sharing.

Eleuther ▷ #gpt-neox-dev (2 messages):

Muon Support, Slurm Script for NeoX Job with Docker

• Muon Support Sought After: A member expressed interest in adding muon support, citing potential kernel optimization opportunities.
  • They believe that once basic support is implemented, there’s room for collaborative work on these optimizations.
• Slurm Script Request for NeoX Docker Job: A member requested a Slurm script example that utilizes Docker to launch a NeoX job.
  • Having a reference point would be valuable for them.

Latent Space ▷ #ai-general-chat (83 messages🔥🔥):

Meta AI Reorg, GPT-5-pro truncation, Bank Teller Rotations Inspired Dropout, Meta AI Hiring Freeze, ByteDance Seed-OSS LLMs

• Meta Splits into Four After Wang Promotion: Meta is reorganizing its AI efforts into four teams (TBD Lab, FAIR, Product/Applied Research, Infra) under new MSL leader Alexandr Wang, with the AGI Foundations group being disbanded, according to Business Insider.
  • Nat Friedman and Yann LeCun now report to Wang, FAIR will directly support model training, and an “omni” model is under consideration.
• GPT-5-pro Promptly Truncates Prompts: GPT-5-pro is silently truncating prompts greater than 60k tokens without any warning or error messages, which makes large-codebase prompts unreliable, according to this report.
  • Some users are also reporting that GPT-5 in Cursor is acting a lot dumber than usual, with some suspecting load shedding is occurring.
• Bank Teller Dropout!: A viral tweet claims Geoffrey Hinton conceived dropout after noticing rotating bank tellers deterred collusion (source).
  • Reactions range from admiration for the serendipitous insight to skepticism and jokes about attention mechanisms emerging from house parties.
• ByteDance Seeds New LLMs: ByteDance’s Seed team has announced Seed-OSS, a new open-source large-language-model family available on GitHub and Hugging Face.
  • The team is inviting the community to test and provide feedback on the models, code, and weights.
• OpenAI Eyeing AWS Crown: OpenAI’s CFO says the company plans to rent out compute “down the line,” aiming to operate like a mini-AWS (source).
  • Reactions range from skepticism about OpenAI’s alleged compute shortages, to analysis of the shifting profit model and clash with existing hyperscalers like Google and Microsoft.

Latent Space ▷ #genmedia-creative-ai (13 messages🔥):

Wonda AI, Billionaires Fight Club, Qwen Image Editing

• Wonda AI Agent Promises Revolution: Dimi Nikolaou introduced Wonda, an AI agent aiming to revolutionize video/audio creation, calling it what Lovable did for websites, Wonda does for content (tweet link).
  • The launch sparked enthusiastic reactions regarding the quality of the teaser media, with early-access granted via a waitlist offering invites in approximately 3 weeks.
• Zuck vs Altman in Matrix Remake: AIST released “Billionaires Fight Club Vol.2”, a short film recreating a Matrix fight between Mark Zuckerberg (Neo) and Sam Altman (Agent Smith) using AI.
  • The video received positive feedback, leading AIST to encourage viewers to tag Sam and Zuck, urging them to repost the film for broader visibility.
• Qwen Image Editing Success: Luis C demonstrated success using qwen-image-edit to composite a woman holding a doll from two different images (tweet link).
  • In response, Jay Sensei claimed nano banana outperformed Qwen in tests conducted on lmarena.

GPU MODE ▷ #general (25 messages🔥):

Hackathon start time, ChatGPT CUDA lies, Hackathon prerequisites, Single huge epoch vs multiple smaller epochs, CUDA vs Triton

• Hackathon kicks off Saturday at 9:30 AM: The hackathon is likely to start around 9:30 AM on Saturday, according to a member.
• ChatGPT spews CUDA lies: A member reports that ChatGPT brazenly lied twice about float3 alignment and size in CUDA, but excused ChatGPT because judging from the OpenCL and OpenGL implementations, it’s a pretty hard problem to get right.
  • The member validated there is no padding in CUDA.
• Hackathon pre-reqs and apps in Question: A member inquired about the prerequisites for the GPU hackathon and whether applications are still open.
  • This question was not explicitly answered in the chat.
• Single vs. Multiple Epochs debated: A member asked whether going for 1 epoch with a huge dataset is better than going for multiple epochs on a smaller one for a CLM, and what the most recent scaling law is for it.
  • Another member responded that they work with smaller models and that 2 epochs on half data has the same performance as 1 epoch on bigger scales.
• CUDA and Triton go head to head!: A member inquired whether the hackathon would use CUDA, Triton, or something else.
  • It was mentioned that either should work, and Triton might just help participants move faster; it was hinted that participants would be working with newer Nvidia chips.

GPU MODE ▷ #triton (1 messages):

Triton, AMD, NVIDIA, GPU, Data Layout

• Data Layout Differences in AMD vs. NVIDIA GPUs via Triton?: A user inquired about whether differences in data layout between AMD and NVIDIA GPUs require code adaptations when using Triton, specifically regarding row-wise vs. column-wise data reading.
  • The user clarified that they are not asking about tile sizes or grid layouts, but lower level data transposition automatically handled by the Triton AMD backend.
• AMD vs NVIDIA: Comparison of consumer GPU - consumer GPU or server GPU - server GPU architecture.
  • AMD and NVIDIA architectures are compared.

GPU MODE ▷ #cuda (10 messages🔥):

CUDA deployment, CudaWrangler, Dynamic Linking

• CUDA programs run on machines without CUDA toolkit: A user sought advice on deploying a CUDA program on machines lacking the CUDA toolkit, but equipped with an NVIDIA GPU.
  • A member suggested leveraging the Driver API and the CudaWrangler library (CudaWrangler/cuew) to query the driver without causing program crashes.
• Dynamic Linking & PTX Baking Streamlines CUDA Deployment: The original poster reported success by switching from dynamic loading to dynamic linking and disabling the runtime/cudart dependency.
  • They were also able to embed the PTX directly into the binary, eliminating the need for a separate PTX file.
• ldd aids in identifying and packaging dependencies for CUDA programs on Linux: A member suggested using ldd to identify dependencies, setting rpath, and shipping them alongside the binary, akin to the “Windows way” on Linux.
  • The original poster noted the program’s cross-platform compatibility between Windows and Linux, though macOS remained untested.

GPU MODE ▷ #torch (1 messages):

PyTorch Contributor Awards 2025, Recognizing Innovation in PyTorch

• PyTorch Awards Deadline Nears!: Nominations for the 2025 PyTorch Contributor Awards close on August 22nd so don’t miss your chance to recognize individuals driving innovation and impact in the PyTorch ecosystem.
  • Submit your nomination now via this link and review tips for a strong nomination.
• Nominate to drive Innovation: Recognize the people in the PyTorch Ecosystem who are innovating.
  • Submit a nomination before August 22nd.

GPU MODE ▷ #beginner (1 messages):

honeyspoon: how bad is the infinity server for embedding speeds compared to something like sglang

GPU MODE ▷ #off-topic (1 messages):

snektron: I prefer Stolwijker

GPU MODE ▷ #rocm (11 messages🔥):

AMD GPU debugger, rocGDB, SPIRV parser, libspirv

• AMD GPU debugger gets Disassembly and Wave Stepping: A member is developing an AMD GPU debugger and has added disassembly and wave stepping, showcased in this video.
  • The debugger doesn’t depend on the amdkfd KMD, using a mini UMD driver and the linux kernel debugfs interface, aiming for a rocdbgapi equivalent.
• Ditching rocGDB for Custom Driver: A member is building an AMD GPU debugger that doesn’t rely on rocGDB, but uses a mini UMD driver plus the linux kernel debugfs interface for reading/writing the GPU registers.
  • The goal is to make it primarily for graphics people, aiming for a rocdbgapi equivalent, at least for now.
• Roll Your Own SPIRV Parser?: A member inquired about building their own SPIRV parser for disassembly, reflection, and debug info extraction, citing the SPIRV spec as seemingly straightforward.
  • They noted the absence of a suitable library for handling debug info, prompting the consideration of a full implementation.
• libspirv is Fairly Easy: A member suggested using libspirv, noting that the SPIRV spec contains all necessary information to do it yourself.
  • The original poster decided to implement a custom solution for better integration, acknowledging the suggestion.

GPU MODE ▷ #metal (2 messages):

C=AB matmul, ALU utilization, buffer read bandwidth, float4x4 matmul, float4 / metal::dot kernel

• GPU ALU Limited in Tiled C=AB Matmul: A member wrote a tiled C=AB matmul kernel where each thread uses float4x4 matmul to compute a 4x4 tile of C and observed an ALU utilization/limiter of 55/75% while the buffer read bandwidth was 35%.
  • He was surprised, wondering if float4x4 matmul happens in specialized hardware, and shared a gist of the kernel.
• Naive Kernel Outperforms Tiled Matmul: The same member noted that an even-more-naive kernel using float4 / metal::dot is >2x as fast as the tiled kernel.

GPU MODE ▷ #reasoning-gym (1 messages):

miserlou1241: Very cool!

GPU MODE ▷ #general-leaderboard (12 messages🔥):

torch.compile errors, local evaluation issues

• Torch.compile throws unexpected errors: A member reported an unexpected error when using torch.compile, sharing two solutions: one with torch.compile (submission 34166) and one without (submission 34160).
  • Despite the error, the submission registered, ranking the member 2nd, noting that the GPU is B200.
• Tackling Local Evaluation Tooling: A member inquired about local code evaluation, stating that eval.py didn’t work, specifically asking about POPCORN_FD.
  • Another member clarified that POPCORN_FD is a file descriptor for the output file and suggested setting it to 1 for stdout.

GPU MODE ▷ #submissions (11 messages🔥):

Trimul Leaderboard Updates, B200 Performance, H100 Performance, MI300 Performance

• MI300 Scores Trimul Success: A member successfully submitted a score of 3.50 ms on MI300 to the trimul leaderboard.
  • Another submission on MI300 achieved second place with a score of 5.83 ms.
• B200 Dominates Trimul Leaderboard: A member achieved 6th place on B200 with a time of 8.86 ms and later improved to 4th place with 7.29 ms on the trimul leaderboard.
  • The same member secured multiple 3rd place positions on B200, reaching a best time of 4.54 ms, and later achieved a successful run at 2.15 ms.
• H100 Secures Second Spot: A member achieved second place on H100 with a time of 3.80 ms on the trimul leaderboard.
  • This submission highlights competitive performance on the H100 platform.

GPU MODE ▷ #factorio-learning-env (3 messages):

Opus 4.1, Steel Plate Production, Task Emphasis, Red Science Production

• Opus 4.1 Finds Fortune, Fuels Factories: While testing Opus 4.1 on steel plate production, it was unexpectedly mining copper and extracting oil.
  • This suggests not enough emphasis on the task at hand, prompting a move to the observation setup, to see how Opus 4.1 can improve its focus.
• AI Automates Red Science: The AI system is successfully automating red science production, as evidenced by a screenshot.
  • The system correctly identifies and produces the necessary components for automating the creation of science packs.

GPU MODE ▷ #cutlass (3 messages):

ND Layouts, colex

• Accessing Elements in ND Layouts via Colex: A member inquired about the order in which elements are accessed when using an integer as the index for an ND layout.
  • Another member clarified that the order is colex (column/left major).
• Confirmation of Colex Order: A user confirmed that the element access order in ND layouts, when using an integer index, is indeed colex.
  • This re-iterates that colex, or column-major order, is the standard approach for such indexing.

GPU MODE ▷ #multi-gpu (10 messages🔥):

Infiniband at home, Distributed training library, NCCL backend, IBGDA requirements

• Infiniband Home Lab Seeker: A member is trying to setup infiniband at home between a 4090 and 5090 to play with distributed training/inference.
  • They bought some ConnectX-3 cards for $25 on eBay but found the drivers are only available for Ubuntu 20.04 and older.
• DIY Distributed Training Framework Rising: One member is building their own pytorch distributed training library and mini NCCL as a backend.
  • Another member expressed interest, viewing it as a way to learn the details.
• Diving into NVIDIA Networking Docs: A member suggested checking the Internet Archive for older versions of the NVIDIA networking documentation to find relevant drivers.
  • The member hoped this would provide more details.
• CX4 or CX5 Cards are GPU-Aware: A member noted that much of the GPU-aware functionality depends on ConnectX-4 (CX4) or ConnectX-5 (CX5) cards or newer.
  • They gave the example that IBGDA requires CX5 or newer.

Yannick Kilcher ▷ #general (33 messages🔥):

Infinite Memory, Arxiv paper guide, LLMs for Legal Field, HRM Models Analysis, Message Passing Approaches

• Forbes Exposes Grok’s Chat Logs: An article from Forbes reveals that Elon Musk’s xAI published hundreds of thousands of Grok chatbot conversations.
  • A member asked @grok whether this was true.
• Turing Completeness Requires Infinite Memory: A member argues that Turing completeness requires infinite memory, thus the universe cannot create a Turing complete machine due to insufficient memory.
  • Another member jokingly suggests that making a computer sufficiently slow could allow the expansion of the universe to account for the space problem, while another added that Real memory needs to be retrieved and the further away it is the longer this takes.
• Oxford Guide Helps Budding Arxiv Authors: A member shares a Google Docs guide written by an Oxford professor to assist a programmer in creating their own Arxiv paper on LLM training.
  • The user wanted to share insights but didn’t know where to start.
• ARC Prize Analyzes HRM Models: A member shares links to a fxtwitter post and an ARC Prize blog post analyzing HRM models.
  • This was shared in response to another user’s question on whether HRM models are worth investing time in learning.
• Image Shows Message Passing Approaches: A member shares an image illustrating different approaches to message passing in neural networks.
  • The image originates from a book, accessible as a PDF on arXiv.

Yannick Kilcher ▷ #paper-discussion (46 messages🔥):

Personality GAN, AI Welfare, Genome Conscious?, Super Weight, LLM Preferences

• SpongeBob GAN Debuts!: A member proposed a Personality GAN with Generator = LLM and Discriminator = LLM, fine-tuning with LoRA until the discriminator can’t distinguish between real and fake Sponge Bob.
  • The tough part is finding an LLM that isn’t already heavily trained on Sponge Bob.
• AI Welfare Considered Seriously!: A paper on Taking AI Welfare Seriously arxiv link was discussed, relating to Anthropic’s post on Exploring Model Welfare Anthropic link.
  • It’s related to another Anthropic post on end-subset conversations.
• LLM Weight Wackiness!: A single number change in Llama 3 7B’s weight matrix made it output gibberish, leading to questions about consciousness/identity Apple link.
  • One member asked Did they zap it of its “consciousness” / “identity” by tweaking just one number?
• LLM Preferences Emerge!: It was pointed out that models develop human-like representations during pre-training and LLMs do have preferences, referencing this LessWrong post.
  • One member commented that back in my day we used to call that class imbalance bias.
• AI Duality Debated!: The discussion touched on AI as a dual-use technology, applicable for everything because everyone will use it QuantaMagazine link.
  • One member said that smart is relative and thermostats have agency because they model themselves and their external environment.

Yannick Kilcher ▷ #ml-news (8 messages🔥):

Yann LeCun's position at FAIR, Thermodynamic computing chip, AI Slurs, Energy Efficiency in AI

• Zuckerberg Maybe Sacks LeCun?!: A user speculated about Yann LeCun’s potential departure from FAIR based on a post by Zuckerberg.
  • Another member suggested LeCun may have been demoted and that Meta is retreating from the open source model space.
• Clanker Cogsucker Robot AI Slurs Go Viral!: A user shared a Rolling Stone article discussing the emergence of new AI slurs like clanker and cogsucker.
• First Thermodynamic Computing Chip Taped Out: A member posted an article from Tom’s Hardware about the world’s first thermodynamic computing chip reaching tape-out.
• AI Industry Doesn’t care about Energy Efficiency: A user shared a YouTube video arguing that the AI industry generally does not prioritize energy efficiency.
  • They noted that another company with a similar value proposition went bust, suggesting the industry doesn’t care about energy efficiency.

HuggingFace ▷ #general (67 messages🔥🔥):

max_steps confusion, levelbot space visits, model hallucination at high tokens, Pro version payment issues, root mean square norm quantization error

• Confusion around max_steps parameter: A member was feeling confused about the max_steps parameter and its implementation with vllm on their 5090 GPU, and whether the LFM2-1.2B model was appropriate.
• Token Limits Trigger Hallucinations: A member inquired about the token limits at which models start to hallucinate, expressing doubt that any model can function effectively with 1 million tokens.
  • Another member linked to Hugging Face’s Agents Course and a Discord channel, suggesting these resources as potential solutions.
• Users report Pro Version Payment Problems: A user reported being charged twice for the Pro version without receiving the service and was advised to email [email protected] and seek assistance in the designated MCP channel.
• Custom Loss Function fine-tunes SFTTrainer: A member shared a custom loss function, created with ChatGPT’s help, designed to be used with SFTTrainer, with the intention of increasing the model’s attention to specific negation words in medical text.
  • Another member suggested using DPO with preference pairs instead, while yet another highlighted the utility of triplet loss after mining for hard negatives in the medical domain.
• SFT and DPO compared for LLM training: Members discussed the effectiveness of DPO (Direct Preference Optimization) versus SFT (Supervised Fine-Tuning), one member noted that DPO has no relationship to reasoning, but DPO after SFT improves results over just SFT.

HuggingFace ▷ #i-made-this (3 messages):

AgentX Trading Platform, Language Diffusion Models, Local AI Workspace PDF Reader

• AgentX Promises AI Trading Brain Trust: A new AgentX platform aims to provide a trading table with the smartest AI minds—ChatGPT, Gemini, LLaMA, Grok—working together.
  • The goal is to have these models debate until they agree on the best move, offering traders a system they can fully trust.
• Diffusion Language Models Replicated in Under 80 Lines: A member replicated part of the paper Large Language Diffusion Models by Nie et al. (2025) using 🤗 Transformers in fewer than 80 lines of code.
  • The project finetunes DistilBERT on the TinyStories dataset, with results better than expected, and is seeking feedback and stars.
• Local-First AI Workspace for PDF Reading Debuts: A member launched a local-first AI workspace PDF reader on Product Hunt and shared the link.
  • They requested support from the community.

HuggingFace ▷ #NLP (1 messages):

Hugging Face Learn course, 422 Error

• Hugging Face Learn course page is down: A member reported that a page from the Hugging Face LLM course is down.
  • The page is showing a 422 error.
• Hugging Face Learn course needs fixes: A user reported the the Hugging Face Learn course page is down and showing a 422 error.
  • The issue needs to be resolved so users can access the content.

HuggingFace ▷ #agents-course (4 messages):

Hugging Face Certificates, Agents vs MCP Course, Agent tool, LLM tasks

• Hugging Face Certificates Location Stump Users: A user asked where to find their Hugging Face certificates to post them to LinkedIn.
  • They mentioned they couldn’t find them on the platform or in their email.
• Agents Course vs MCP Course sparks debate: A user is debating whether to switch to the MCP Course after completing Unit 1 of the Agents Course or finish the Agents Course first.
  • They are wondering which course to prioritize due to time constraints.
• Agent’s Tool functionality demystified: A user seeks explanation about the success of Agent Unit 1.
  • They understand agents use tools (functions) and trigger these tools instead of directly calling the LLM for tasks.

Notebook LM ▷ #use-cases (19 messages🔥):

Gems for podcast generation, NotebookLM podcast length, Customizing NotebookLM podcasts, Analyzing Terms of Use and Privacy Policies, South Park episode on Terms and Conditions

• AI Maestro Shares Gems to Generate Long Podcasts: A user asked how to generate longer podcasts from 3-4 hour YouTube videos in NotebookLM, to which one user suggested using set prompts to plan the entire transcript section by section.
  • A user shared a workflow to create a “deeper research report framework”, which can then be used to generate the podcast with Gems, Gemini, PPLX, or ChatGPT.
• Unlock Longer NotebookLM Podcasts with Customization: A user asked about podcast length limitations in NotebookLM and another user pointed out the Customize option (three dots) where the podcast length can be set to 45-60 minutes.
  • Another user added that specifying topics can allow the bot to concentrate on topics instead of relying on it to fit all the important stuff into a single podcast.
• Privacy Policy Paranoia: Healthcare Website Compromises Exposed: A user analyzed a healthcare company’s privacy policy and terms of use using Gemini and NotebookLM after recalling someone who used one of the AI tools to analyze these two documents - and what a revelation it was.
  • The user was surprised by how much you give away to these companies and how useful this method is to understand Terms of Use and Privacy policies.
• South Park Predicts the Pain of Accepting Terms and Conditions: A user recommended finding the old South Park episode on accepting Terms and Conditions.
  • Another user recalled a game where the EULA/Privacy/Terms hid a contest: the first caller to a specific phone number won a thousand bucks, which remained unclaimed for six months.

Notebook LM ▷ #general (51 messages🔥):

Video Length Limits, Study guide on android app, Audio Language Change, Public Sharing Issue, Notebook LM API

• Android App Feature Parity Delayed: Users are requesting more feature parity between the NotebookLM web app and the Android app, especially for study guides.
  • One user stated the current native app is borderline useless because study guides depend on the notes feature, which is missing from the native app.
• Language change available on customize screen: A user asked how to change the language of the audio overview generated in the iOS app.
  • Another user responded that language settings can be found in the Customize menu.
• Sharing Notebooks to Public is not available: A user reported being unable to share notebooks publicly or externally despite having a Pro account.
  • It’s not available yet.
• NotebookLM Lacks Official API but workarounds exist: A user inquired about an API for NotebookLM.
  • Another user suggested using the Gemini API as a workaround.
• OCR Operations in NotebookLM: Users discussed whether NotebookLM performs OCR operations on multimodal PDFs.
  • NotebookLM supports PDFs and is improving image handling, but OCR recognition is imperfect, and users may need to re-upload PDFs or use external OCR tools.

Nous Research AI ▷ #general (65 messages🔥🔥):

Base Model Release, Ideal 30B Model, FA2 and Context, Qwen Scaling, Importance Matrix Calibration Datasets

• ByteDance Releases Long Context Model: ByteDance has released a base model with an extremely long context, featuring no MHLA, no MoE, and not even QK norm, as seen in this image.
  • It was described as vanilla architecture wise, and people hope they publish a paper with more explanations.
• Seed-OSS-36B’s Absent GGUF Causes Concern: Users wondered why there was no GGUF of Seed-OSS-36B available, as they usually appear quickly, and pointed to this link asking if it’s bearish on ASICs.
  • It was noted that the delay might be due to a custom vllm implementation and the architecture not being supported by llama.cpp yet because of architectures: [“SeedOssForCausalLM”] .
• Seed Model Implements Dropout and Bias: The Seed model has a custom MLP and attention mechanism similar to LLaMA, but with dropout, a bias term for the output, and a bias term for the qkv heads, which are being interpreted as regularization techniques.
  • Members wondered how many epochs the model was trained for, but confirmed that renaming it to LLaMA will not work.
• Qwen Achieves 512k Context via RoPE Scaling: The 30B and 235B Qwen 2507 models can achieve 512k context using RoPE scaling, as discussed in this Hugging Face dataset.
  • These datasets are used to generate importance matrices (imatrix), which help minimize errors during quantization.
• Cursor’s Kernel Blog gets Praise: Members shared this link to Cursor’s kernel blog.
  • Some say cursor cooked on that one.

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

DeepSeek V3.1, R-Zero LLM Training Method, Energy availability in China vs US, Kimi K2 combined with Better image gen than gpt 5

• DeepSeek V3.1 Release: Incremental Advances: The new DeepSeek V3.1 model was released, with some members noting that it is like an incremental improvement with some regressions, referencing DeepSeek’s official page.
• DeepSeek embraces Anthropic API: DeepSeek now supports the Anthropic API, expanding its capabilities and reach, as announced on X.
• R-Zero: Self-Evolving LLM: A comprehensive study of R-Zero, a self-evolving LLM training method that starts from zero human data and improves independently, was shared in a PDF.
• China Prioritizes Energy Availability: A member noted that in China, energy availability is treated as a given, contrasting with U.S. debates over data center power consumption and grid limits, referencing this Fortune article.
• Better Image Gen + Kimi K2: A member noted that Kimi K2 would be more OP if it got combined with Better image gen than gpt 5, with this reddit link shared.

aider (Paul Gauthier) ▷ #general (36 messages🔥):

Gemini 2.5 Pro Failure, Qwen CLI Charging, GPT-5 Benchmarks, DeepSeek v3.1 Pricing, OpenRouter Think Mode

• Gemini 2.5 Pro Fails while Flash Succeeds: A member reports that Gemini 2.5 Flash works, but Gemini 2.5 Pro fails consistently, whereas gemini/gemini-2.5-pro-preview-06-05 works if billing is set up.
  • Another reports having been charged $25 for a qwen-cli process and is seeking a refund.
• User Charged Unexpectedly for Qwen CLI Usage: A user was charged $25 for using qwen-cli after authenticating with Google via OAuth, despite aiming for free credit from Alibaba Cloud.
  • They opened a ticket to show console usage of one call of $23 with no output.
• Community Eager to Benchmark GPT-5 Low Reasoning Models: Members are running benchmarks on gpt-5-mini and gpt-5-nano because they are rate limited on the full gpt-5, though one user claims gpt-5-mini is very good and cheap.
  • Results and a PR for gpt-5-mini are up in the channel.
• DeepSeek v3.1 Pricing Gets a Notable Hike: The user reports that, starting Sept 5th, 2025, DeepSeek will raise pricing on both models to match the reasoner model price.
  • The price increased to $0.25 vs $0.27 for input compared to the new deepseek 3.1.
• OpenRouter Needs Think Mode: A user reports that OpenRouter doesn’t appear to have a “think” mode, but it can be used via command line using the following code snippet: aider --model openrouter/deepseek/deepseek-chat-v3.1 --reasoning-effort high.
  • The community recommended updating the model configs to fix this problem.

aider (Paul Gauthier) ▷ #questions-and-tips (3 messages):

aider stdout issue, polyglot benchmark on llama cpp

• Aider’s Standard Output Conundrum: A user reported an issue where program output/stdout wasn’t being displayed in aider and posted an image.
• Cracking Polyglot Benchmark Results: A user running the polyglot benchmark on a local llama cpp model asked how to obtain the results per language.
  • The user later found a solution and shared the link for others seeking language-specific benchmark results.

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

end4749: <@293486003245809664> spam? ^

DSPy ▷ #show-and-tell (1 messages):

marimo notebooks, Graph RAG with DSPy, DSPy modules optimization

• Marimo Notebooks: Jupyter’s Spiritual Successor: A member has been publishing tutorials on marimo notebooks, which can simultaneously function as a notebook, a Python script, and an app.
  • The tutorial highlights the utility of marimo when iterating through ideas on Graph RAG with DSPy.
• DSPy Pipeline Without Optimization: The presented DSPy pipeline intentionally lacks optimization to emphasize how much can be achieved with just signatures and modules.
  • The approach focuses on rapid iteration through composing DSPy modules in various ways before diving into optimization.
• Diving into Optimization: Upcoming videos and blog posts will dive deeper into the topic of DSPy modules optimization.
  • The current tutorial serves as an introduction to marimo for those looking to get started.

DSPy ▷ #papers (5 messages):

IBM AutoPDL paper, DSPy code readability, Justification of work

• IBM’s AutoPDL Claim Dismissed: A member dismissed the need to address every claim, suggesting everyone seeks an angle to justify their work and that the claim about unreadability is false.
  • They stated that DSPy code and prompts are both extremely human readable in every sense, borderline beautiful.
• Defense of DSPy Code Readability: A member defended DSPy’s code and prompts as extremely human-readable, accessible, and clear, challenging claims to the contrary.
  • The member emphasized that the code’s readability makes it easy to understand and work with.

DSPy ▷ #general (28 messages🔥):

dspy.GEPA version, finetuning dspy descriptions, saving optimized programs, context length for GEPA, KPMG onboarding

• DSPy’s GEPA unearthed in v3.0.1: A member inquired about the version of the dspy library that includes GEPA, to which another member confirmed it is available in version 3.0.1, as shown in the attached screenshot.
• DSPy fine-tuning: Descriptive or vanilla?: During fine-tuning, a member inquired about whether it is common to use “vanilla descriptions” for dspy.InputField() and dspy.OutputField() to allow the optimizer to think freely.
• DSPy Saves Optimized Programs in a Pickle: A user reported issues with saving an optimized program, noting that the metadata only contained information about dependency versions but not the program itself, even when using optimized_agent.save("./optimized_2", save_program=True).
• GEPA gets the axe: When a user set the maximum context length to 32k for GEPA but still received cut-off responses, members discussed the complexities of long reasoning and potential issues with multi-modal setups.
  • One member joked “Imagine having to maintain that” referencing a complex prompt example.
• RAG is Overkill, just Concatenate (or not): Members jokingly debated whether RAG (Retrieval-Augmented Generation) or simple concatenation would be more appropriate for tasks like processing tax codes or crop insurance documents, acknowledging the scale of millions of documents can sometimes justify RAG.
  • One member quipped, “RAG is overkill. Just concatenate the tax code,” while another countered, *“Oh, I guess that’s more than 100 pages. OK, then, RAG is good.”

Cohere ▷ #🧵-general-thread (13 messages🔥):

Citation issues with command-a-03-2025, Guaranteed citations, command-a-reasoning release, RAG with Langchain, Cohere vs Qwen3-coder 30B

• command-a-03-2025 Intermittent Citations Prompt Frustration: A user reported that command-a-03-2025 is returning citations only intermittently, even with the maxTokens set to 8K, causing trust issues in their production environment, and was looking for some guarantees.
  • A Cohere member clarified that command-a-03-2025 uses “fast” mode for citations (as per the API reference) and citations aren’t guaranteed, but that the model can be steered with system prompts and that the latest SOTA release of command-a-reasoning may also help (see blog).
• Langchain RAG adventures kickoff: A member is learning Langchain to build an RAG (Retrieval-Augmented Generation) application.
  • They mentioned the intention to use command-a-reasoning, anticipating the release of command-a-omni, and expressing hype for a future model called Command Raz.
• Cohere vies with Qwen for Local LLM spot: A user seeks a Cohere alternative to the Qwen3-coder 30B model, aiming for it to fit on a 64GB M4 Max setup.
  • The user wants to try an alternative to the local powerhouse of Qwen3-coder 30B from Cohere so bad so that it fits on my 64GB M4 Max.

Cohere ▷ #📣-announcements (1 messages):

Command A Reasoning Model, Enterprise AI, Agentic AI Platform

• Cohere Launches Command A Reasoning Model: Cohere has released Command A Reasoning, its latest enterprise-grade model for reasoning tasks, outperforming other privately deployable models in agentic and multilingual benchmarks; it’s available via Cohere Platform and Hugging Face.
• Command A Reasoning Specs & Features are Revealed: The new model is designed for enterprise needs, offering highly secure, efficient, and scalable deployment options and runs on a single H100 or A100 with a context length of 128k, scaling to 256k on multiple GPUs; more info available in the Cohere blog.
• Token Budget Feature Controls Cost & Compute Usage: Cohere’s Command A Reasoning features a token budget setting for direct management of compute usage and cost control, eliminating the need for separate reasoning and non-reasoning models, suiting both accuracy and throughput demands.
• Command A Reasoning powers North: Command A Reasoning is the core generative model powering North, Cohere’s secure agentic AI platform, enabling custom AI agents and on-prem automations.

Cohere ▷ #🔌-api-discussions (4 messages):

Cohere Embed-v4 on Azure AI Foundry, Cohere Python Library Document Object

• Cohere Embed-v4 Input Type Mapping: A member is using Cohere Embed-v4 deployed on Azure AI Foundry in a .NET application using Azure AI Inference API, and is seeking clarity on how Microsoft’s EmbeddingInputType maps to Cohere’s API regarding text embedding.
  • Specifically, they are unsure whether EmbeddingInputType.Text should map to search_document in the Cohere API, given the lack of explicit text option in Cohere’s input_type parameter.
• Cohere Python Library’s Document Object: A member questioned the Document object in the Cohere Python library, where the data field expects a dictionary (typing.Dict[str, typing.Optional[typing.Any]]).
  • They pointed out that the tool use quickstart example uses a string (the output of a json.dumps call) for this field, and want to know if this is handled correctly by the Python bindings, referring to the Tool Use Quickstart documentation.

Cohere ▷ #👋-introduce-yourself (7 messages):

MLE Research, Independent Interpretability Research, AI Innovation and Value Creation, Enterprise Workflows

• MLE Seeks Research Team Connection: An MS in Computer Science graduate, with experience as a MLE, is seeking to connect with a research team or organization.
  • The member expressed their interest in collaborating and contributing to research efforts.
• Interpretability Researcher Eager for Collaboration: An independent interpretability researcher with 8 years of applied ML experience, based in Bangalore, India, is transitioning into AI research, focusing on mechanistic interpretability.
  • The researcher expressed interest in evaluations, de-biasing of models, and RL, seeking collaboration and discussions on interpretability-related topics.
• Executive Advisor Bridges AI Innovation and Value: An independent consultant and executive advisor with 25+ years of experience, specializing in bridging technology and AI innovation with value creation, has joined the community.
  • With experience at firms like Accenture, IBM, and Deloitte, they now help clients create sustainable, organization-wide value from AI, with a company website at Mantha Advisory.
• CTO Explores Cohere for Better Products: A CTO with 25+ years of experience has recently discovered Cohere and is interested in exploring its capabilities for improving products.
  • They are focused on data quality, scale, performance, workflows, data integrity, and multilingual support, and are keen to learn from the community.

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

C# client library, MCP server's instructions field, MCP servers, generate_test_prompt.md, GitHub

• MCP Clients Neglect Instructions Field: Members are encountering issues with MCP clients, particularly Claude, where the instructions field seems to be ignored in favor of tool descriptions.
  • One member suggested that adding the instruction, context and then repeating the instruction would yield better results but with tools integrated to the APIs that is not possible.
• MCP Server Options Evaluated: A member asked which MCP servers developers are using, and which tools seem more efficient within those servers.
  • Another member highlighted the usefulness of GitHub for version control, Python with FastAPI for backend development, and PyTorch for machine learning.
• Make Agents Follow Instructions: A user inquired about how to make an agent follow a specific generate_test_prompt.md file, expressing frustration that the agent wasn’t adhering to the project’s design pattern upon starting a new chat.
  • They included a screenshot in their message.
• MCP Server Parsing Prioritizes Tool Descriptions: A member noted that parsing logic within the MCP server could be structured to process tool descriptions before the instructions field.
  • It was suggested to review server documentation, inspect client configuration, analyze server-side logic, and perform controlled experiments.
• Instruction-Following Models Named: Members discussed which models are capable of following instructions and generating structured outputs, suggesting Mistral-7B-Instruct, DeepSeek-Coder, and Phi-3.
  • They also mentioned OpenHermes-2.5-Mistral-7B, WizardLM-2, and Gorilla-LLM as function-calling-specific models.

MCP (Glama) ▷ #showcase (10 messages🔥):

Web-curl, MCP-Boss, MCP Explained Video, SWAG-MCP, MCP Routing

• Web-curl empowers LLM Agents with Web & API interaction: A member introduced Web-curl, an open-source MCP server built with Node.js and TypeScript, enabling LLM agents to fetch, explore, and interact with the web & APIs in a structured way, full code available on GitHub.
• MCP Boss centralizes key management for MCP Services: A member built MCP Boss to centralize key management, providing a single URL to gateway all services, with features like multi-user authentication and MCP authorization via OAuth2.1 or static HTTP header (mcp-boss.com).
• Demystifying MCP in video: A member released a video called MCP Explained: The Ultimate Deep Dive available on YouTube, inviting feedback and discussion on client-side capabilities like Elicitation, roots, and sampling.
• SWAG-MCP generates reverse proxy configs for streamable HTTP MCP servers: A member shared SWAG-MCP, an MCP server designed to generate reverse proxy configurations for SWAG, supporting both self-hosted services and streamable HTTP MCP servers (github.com/jmagar/swag-mcp).
• MCP Gateway routes requests with AI: A member developed a lightweight gateway with AI-powered routing to solve the problem of agents needing to know which specific server has the right tool, with code available on GitHub.

Modular (Mojo 🔥) ▷ #general (2 messages):

Modverse #50, Custom Server Tag

• Modular drops Modverse #50: Modular released Modverse #50 featuring several members.
  • The announcement also noted that they now have a custom server tag.
• Custom Server Tag arrives: The Modular team announced the arrival of a custom server tag, shown in an attached image.
  • The linked image (Screenshot_2025-08-21_at_5.22.15_PM.png) displays the new tag.

Modular (Mojo 🔥) ▷ #mojo (10 messages🔥):

kgen and pop documentation, MLIR dialects, pop.union alignment bug, Github issue 5202

• Docs for kgen and pop are sparse: A member asked about documentation for kgen and pop, specifically operations and parameters, but another member stated that there’s no comprehensive documentation of the internal MLIR dialects.
  • A link to the pop_dialect.md on Github was shared, clarifying that these are part of the contract between the stdlib and the compiler, so use them outside of the stdlib at your own risk.
• Alignment Bug suspected in pop.union: A member inquired about the alignment of elements in pop.union, noting unexpected sizes when using sizeof.
  • They shared code showing that union_type_simple_8_bit_stdlib has a size of 16 bytes, while union_type_simple_8_bit and union_type_simple_multi_bit both have a size of 8 bytes, and another member suggested that alignment may be a bug.
• Issue created to investigate Alignment Bug: A member created issue 5202 on GitHub to investigate the suspected alignment bug in pop.union.
  • The member noted that they weren’t sure whether it was a skill issue or a bug, also observing that pop.union doesn’t appear to be used anywhere.

Modular (Mojo 🔥) ▷ #max (7 messages):

TextGenerationPipeline 'execute' method, Custom inference loops for retrieving logits, Language allocators and OOM handling

• TextGenerationPipeline’s execute method surfaces: A member was looking for the execute method on TextGenerationPipeline but couldn’t find it.
  • Another member pointed to the relevant line in the Modular repo and suggested checking the MAX version.
• Custom Inference Loops for Logit Lovers?: A member reported struggling with retrieving the logits along with the next token while creating a custom inference loop, finding it a bit cumbersome.
  • The member linked to a Google Docs document for context and confirmed the option is still available, but its future is uncertain.
• Memory Allocators are a MUST HAVE?: A member suggested that robust allocator support might be necessary before memory allocators are integrated into the language.
  • They reasoned that most users don’t want to manually handle out-of-memory (OOM) errors.

LlamaIndex ▷ #blog (2 messages):

Enterprise document AI, vibe-llama

• LlamaIndex reveals Enterprise Document AI: VP of Product at LlamaIndex is sharing a year’s worth of enterprise learnings about parsing, extracting, and indexing documents on September 30th at 9 AM PST.
• Streamline development with vibe-llama: LlamaIndex released vibe-llama, a command-line tool that automatically configures your favorite coding agents with up-to-date context and best practices about LlamaIndex framework, LlamaCloud.
  • It also includes more info.

LlamaIndex ▷ #general (13 messages🔥):

HuggingFace CrossEncoder Duplication, Agent creation project, AI Safety Survey

• CrossEncoder Class: Core vs Integrations: A member asked about the duplicated CrossEncoder class implementations in llama-index, specifically under .core and .integrations (link to code).
  • Another member clarified that the one in .core is a leftover from the v0.10.x migration and should be deleted, recommending the use of llama_index.postprocessor.sbert_rerank instead and the usage of pip install llama-index-postprocessor-sbert-rerank.
• Quest for Agent Creation Gateway: A member inquired about existing projects that serve as a gateway by tying together model, memory, and tools, exposing an OpenAI-compatible endpoint.
  • The member wanted to know if there was an existing project to leverage, so as to avoid reinventing the wheel in their agent explorations.
• AI Safety Survey: Community Opinions Needed!: A member shared a link to an AI safety survey to gather community opinions on important AI safety questions.
  • The member requested that people fill out the form to help them understand what the AI safety community finds most interesting, asking for patience with potential loading times.

Manus.im Discord ▷ #general (13 messages🔥):

Credits Purchase, Tickets Issues, Contest Rigging Accusations, Free Daily Credits, Referral Credits

• Credits Purchase Option Missing: Members are reporting that the option to buy extra credits is missing, with one noting they can only see the upgrade package option.
  • Another member confirmed that the option is down right now.
• Unresolved Support Tickets Plague Users: A user reported having an issue with a task and creating ticket #1318, but hasn’t received a response or access to the ticket.
  • They requested assistance from the team, tagging a specific member.
• Contest Winner Sparks Rigging Accusations: A user alleges that the second-place winner in a contest didn’t deserve to win and claims the contest seems rigged.
  • No further evidence or details were provided to support this claim.
• Daily Free Credits Discontinued?: A user, returning to Manus after a month, noticed they didn’t receive the usual 300 free credits daily.
  • They inquired whether Manus had stopped providing these credits.
• Referral Credits Code Conundrum: A user asked how to claim referral credits, mentioning that the system asks for a code.
  • The user stated they didn’t know where to find the required code.

tinygrad (George Hotz) ▷ #general (7 messages):

Overworld const folding, View(const) refactor, UPat cvar and UPat.const_like redefinition, RANGEIFY=1 Impact, base removal

• Exploring Overworld Const Folding Strategies: A member is exploring overworld const folding, possibly involving a view(const) refactor, and proposed redefining UPat.cvar and UPat.const_like to match CONST and VIEW(CONST).
  • The aim is to fold expressions like x * 0, but concerns were raised about potential issues with validity and .base proliferation in symbolic computations, as mentioned in this discord thread.
• Alternative Approach: ALU View Pushing: An alternative approach was suggested, mirroring S-Lykles’s method, which involves adding a upat in kernelize that pushes views directly onto ALUs.
  • This method, along with a special rule for x * 0 (justified by the computational irrelevance of * 0), would allow unmodified symbolic matching.
• base Removal Advocated: A member strongly advised against the proposed approach, deeming it “super ugly” and advocating for the removal of .base.
  • The discussion also questioned the handling of PAD operations within this context.
• RANGEIFY=1 as a Potential Simplifier: It was suggested that setting RANGEIFY=1 could lead to a cleaner implementation.
  • However, the project is currently in a transition phase where both the old engine and rangeify are coexisting, creating a state of limbo.

Nomic.ai (GPT4All) ▷ #general (3 messages):

GPT4ALL Enterprise vs Free, Model Selection for LocalDocs

• GPT4ALL Free for Private Model Use: A user inquired about using GPT4ALL for companies wanting to use their AI model privately and securely.
  • Another member clarified that the free version suffices if the company already has its own AI model ready.
• Model Choice for LocalDocs: A user seeks a model recommendation for building a personal knowledge base from hundreds of scientific papers in PDF format using GPT4All’s LocalDocs feature.
  • The user specifies they have an Nvidia RTX 5090 with 24 GB VRAM and 64 GB RAM and would appreciate reasoning capabilities in the chosen model.