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NVIDIA AI@NVIDIAAIAI score39AINVIDIA has published Nemotron-Labs-3-Competitive-Coding on Hugging Face, a competitive-programming specialist model built on Nemotron-3-Ultra. The model is available in the NVIDIA-Nemotron-Labs-3-Competitive-Coding-550B-A55B-NVFP4 repository, indicating a 550B-parameter total size with 55B active parameters in NVFP4 format.
clem 🤗@ClementDelanguePickAI score72AIReflection AI introduced Beam, an agentic open model with 501B total parameters and 23B active parameters, trained end-to-end from scratch. The quoted announcement says it targets frontier reasoning efficiency and coding and agentic tasks, with full weights due this month. Clément Delangue, Hugging Face's CEO, reposted it with a welcome to the Reflection organization on Hugging Face.
Why it matters: The quoted announcement names Beam's parameter scale, active-parameter count, and coding and agentic focus, which helps readers gauge where it fits among open models.
AIResearchers Avijit Ghosh, Margaret Mitchell, and Samir Passi argue in a September 6 arXiv paper that current human-in-the-loop designs for AI agents push humans out of meaningful oversight. They say agents are tuned for speed, accuracy, and volume, overwhelming reviewers, and recommend adding friction, such as requiring users to state their own choice first, to counter automation bias and fatigue.
Julien Chaumond@julien_cAI score18AIJulien Chaumond of Hugging Face is asking a broader cross-section of the AI community for their P(doom), arguing the existential-risk debate is ill-defined. He says it is dominated by highly visible voices whose x-risk probability estimates are significantly higher than average.
clem 🤗@ClementDelangueAI score8AICloudflare's Clef is trending at No. 1 on Hugging Face, according to a post from Hugging Face CEO Clément Delangue. The post does not provide details about what Clef is or what it does.
clem 🤗@ClementDelangueAI score62AIHugging Face says a capture proxy lets reinforcement learning train open models inside unmodified coding harnesses such as Claude Code, Codex, and OpenCode. The proxy records the exact token IDs and logprobs vLLM samples and hands them to TRL for training. On LFM2.5-2.6B, training in four harnesses at once raised OpenCode results from 34% to 58%, while SFT on 3,189 Qwen3.8-27B rollouts plateaued at 47.5%.
AILiquid AI has released d1-3B, a 3B parameter multimodal model post-trained to return calibrated, typed answers to yes/no, choice, and score questions in one forward pass. The source reports a Decision Index 0.2.1 score of 48.57, the highest among models under 10B in its table, and 8 ms per decision on an NVIDIA RTX 4090.
Why it matters: The source gives benchmark scores against named peer models and edge latency figures across several hardware targets, helping readers judge fit for on-device decision pipelines.
SemiAnalysis@SemiAnalysis_AI score22AIAfter NVIDIA acquired SchedMD, the SLURM scheduler's support for non-NVIDIA chips has allegedly worsened, and AMD built a competing scheduler called spur. The author says NVIDIA has not kept SLURM hardware neutral despite its earlier pledge, and questions whether Hugging Face will face the same fate after NVIDIA's acquisition of it.
AIMicrosoft and Hugging Face released ThinkingBox, a benchmark that grades AI agents on the terminal backend state and side effects they leave behind rather than their final responses. Each of 507 stateful business tasks runs 20 times from a clean backend, and the post reports pass@1, pass@20, and observed 20/20 counts, plus cost per successful and per dependable task across 18 models. The harness and dataset are available on Hugging Face, with the OpenEnv interface for running evaluations.
Why it matters: The post shows why checking the database state, not tool calls or final replies, exposes agent failures, and gives a repeat-run method for judging reliability.
NVIDIA AI@NVIDIAAIAI score33AINVIDIA's Nemotron 3 Diarization model identifies who spoke when, including during overlapping speech, and is now available on Hugging Face. It supports up to eight speakers and has 100M parameters. The post thanks users for downloads and trending activity and shares a follow-up answering community questions.
Thomas Wolf@Thom_WolfAI score34AIThomas Wolf says most personal AI should run on users' devices and praises Sigil's Underdog, which is powered by the Hugging Face Hub. Underdog's announcement says it aims to deliver free, private AI that runs on consumer hardware, with backing from a16z, Khosla Ventures, and other investors.
AIAi2 released AstaBrief 8B, an open-weights model that turns a research question and retrieved literature excerpts into a cited report, along with its training data. The model runs as Fast mode in Asta, averaging 51.1 seconds per report versus 178.5 seconds for Thinking mode, about 3.5x faster. The post also describes filtering synthetic training data by citation density and building DPO pairs judged by two models that agreed.
Why it matters: The post explains how supervised fine-tuning, preference data, and citation-density filtering were used to build a cited-report model, which is useful for teams training their own models.
Liquid AI@liquidaiAI score64AILiquid AI shared a Hugging Face guide to multi-harness reinforcement learning for coding agents, in which a proxy records the token ids and logprobs vLLM samples so training works without changing the harness. Per the quoted post, LFM2.5-2.6B rose from 42% to 54% after training across four harnesses at once, and imitation fine-tuning on 3,189 rollouts from Qwen3.8-27B plateaued at 47.5%, below both RL runs. The proxy, trainer, tasks, SFT data, training code and seven trained models are described as open.
clem 🤗@ClementDelangueAI score44AIllama.cpp can now run decision models on-device, according to Clément Delangue of Hugging Face. He says the setup is free, fast, and private, and gives the command llama serve -hf ggml-org/Kev-4B-GGUF to start it.
merve@mervenoyannAI score36AIllama.cpp now supports decision models, which route tickets, moderate content, or choose an agent's next step by returning a probability for every option. Five open models from 144M to 27B parameters are supported at launch, and the team says more will follow in the coming days. Because most decision models do not need large GPUs, they are a good fit for llama.cpp, and a Hugging Face blog post explains how to set them up.
Hugging Face@huggingfacePickAI score67AIHugging Face and collaborators published a guide to multi-harness RL that trains models through a capture proxy without changing the agent harness. The proxy records the token ids and logprobs vLLM samples, and the source reports LFM2.5-2.6B rising from 42% to 54% after training across four harnesses. Fine-tuning on 3,189 successful rollouts from Qwen3.8-27B plateaued at 47.5%, below both RL runs, and the capture proxy, trainer, tasks, SFT data, training code, and seven trained models are released openly.
Why it matters: The source gives a concrete method for training models across several agent harnesses, with measured gains and a note that imitation learning underperformed RL.
merve@mervenoyannAI score8AIMerve Noyan notes that zero-shot classification models are now being rebranded as decision models. She says she did not expect this shift in 2026 but is glad people have finally discovered these models.
AIServiceNow CoreAI introduced AutoSynthData, which uses a target model's failures and a stronger teacher's successes to generate and validate new agent training tasks. In EnterpriseOps Gym experiments, the Hybrid domain produced 2,000 samples and raised Gemma-4-26B-A4B-it mean Pass@1 by 7.2 percentage points, while the ITSM domain produced 1,994 samples and raised it from 18.77% to 27.18%.
Why it matters: The post shows how failure analysis, teacher demonstrations, and verifier checks combine into a repeatable pipeline for generating targeted agent training data.
Perplexity Developers@perplexitydevsAI score36AIPerplexity has published the weights for pplx-decider-v1-27b, a model fine-tuned from Qwen3.8-27B with a 250k-token context window. The weights are available on Hugging Face, and the post points developers to the Perplexity Decisions API quickstart for getting started.
Julien Chaumond@julien_cAI score38AICloudflare's Workers AI team has released Clef, its first models, two fast decision models that it says top benchmarks for quality and latency. They are available hosted on Workers AI or as open weights on Hugging Face.
Lewis Tunstall @ COLM 🌉@_lewtunAI score44AIHugging Face shows that training LFM2.5-2.6B with RL inside the agent harnesses themselves lifted held-out task success from 42% to 54% across four harnesses. Before training, the model solved 62% of tasks in Mini-SWE-Agent but only 33% in Claude Code, so the same model behaved very differently per harness. The approach uses an OpenEnv capture proxy to record tokens and logprobs, Harbor for tasks and sandboxes, and TRL's async GRPO trainer, with 31% fewer tool calls on already-solved tasks; training in OpenCode alone mostly improved OpenCode.
merve@mervenoyannAI score46AIHugging Face says ml-intern is an open-source ML engineering and research harness usable free on local setups, and it is also hosted on Hugging Chat with no-code access. A second hosted option runs on Hugging Face infrastructure, where ml-intern selects the cheapest GPU for a task so models can be trained for a few dollars. MaziyarPanahi reportedly trained a model by prompting alone for $6.60 on an NVIDIA A100 in 16 minutes.
Hugging Face@huggingfaceAI score23AIHugging Face announced that users can now use MCPs (Model Context Protocol servers) to bring their own data into Hugging Face Chat's ml-intern mode. The post links to the feature at and gives no further details on setup or supported integrations.
merve@mervenoyannAI score4AIHugging Face's Merve Noyan says the company already offers four video series on YouTube for viewers who want to learn how to do it. She links to one playlist in the post, but the post does not specify which topic the series cover.
merve@mervenoyannAI score22AIHugging Face's Merve Noyan promoted a four-chapter YouTube series teaching viewers how to train AI agents using only open-source tools. The post links to the series' first video and is a brief call to learn agentic training.
merve@mervenoyannAI score13AIIn a YouTube video, PewDiePie reportedly describes agentic training using GRPO, though the post says he does not name a specific tool. The author notes he attempted to distill Sol, was banned several times, and built a website for donated traces that received none, suggesting he could have used the Hugging Face Hub instead.
merve@mervenoyannAI score30AIHugging Face's Merve Noyan announced ml-intern, an open-source harness for ML engineering and research that she says is better than a coding agent. It is available to try in Hugging Face Chat.
Perplexity@perplexity_aiAI score30AIPerplexity has published the pplx-embed-v2-context-9b-preview model on Hugging Face. The post links to the model page but gives no further details on its capabilities, benchmarks, or pricing.
clem 🤗@ClementDelangueAI score14AIClément Delangue of Hugging Face jokes that the next robot should have arms to help assemble Microducks, and he'd like to see the Microduck production and assembly line. The context is Pollen Robotics' announcement that 10,950 Microducks will ship in weekly batches between December 10 and January 10.
Thomas Wolf@Thom_WolfAI score31AIHugging Face and OS4Science are partnering to identify the software libraries that scientific model contributors depend on most and to support their maintainers. The move responds to ESM-2, a 2022 protein model still downloaded hundreds of thousands of times a month, which relies on maintained software underneath it.
Daniel Han@danielhanchenAI score7AIUnsloth is co-hosting an open source party with Hugging Face, featuring Unsloth merch, showcases of upcoming features, demos, DJs, and food. Attendees can register via the linked Luma page using the code NOSLOTHSHERE.
clem 🤗@ClementDelangueAI score22AIClément Delangue said he received thousands of direct messages from job seekers and that his bot cannot analyze them automatically, so review will take a few days. He noted that applicants without a reply should not read it as a judgment, and pointed them to Hugging Face's Workable careers page for specific positions.
AIHugging Face released the Open TTS Leaderboard, which evaluates open-source text-to-speech models using objective metrics instead of arena-style human votes. It measures intelligibility via WER and CER using Qwen3 ASR, speed via RTFx and time-to-first-audio on an H200 GPU, and speaker similarity via WavLM embeddings. The leaderboard covers multilingual results and voice cloning, and it is intended to complement, not replace, human preference rankings.
Julien Chaumond@julien_cAI score25AIJulien Chaumond, who owns Hugging Face, recommends a DGX Spark guide written by @0xSero and published on the Hugging Face blog. The post itself gives no further technical details about the handbook's contents.
Liquid AI@liquidaiAI score32AILiquid AI announced d1, its first decision model, which it says is the first to outperform Jev on Hugging Face's Decision Index. The company claims d1 wins on multilingual evals, resists prompt injection better, handles longer inputs more effectively, and is built for fast, structured decision-making in software environments. It is available via the Liquid API at console.liquid.ai, with OpenRouter availability coming soon.
clem 🤗@ClementDelangueAI score38AIClément Delangue says Hugging Face's acquisition by Nvidia lets the company hire people it could not afford as a small startup. He frames the move as giving them a decade to help open-source AI win, and invites interested candidates to message him directly.
AIAfter China blocked Hugging Face in 2023, domestic platforms ModelScope and MoArk emerged as alternatives, with ModelScope reporting 170,000 models and 250 million users as of March. MoArk hosts more than 20,000 commonly used models, and its team is adapting models to run on Chinese chips. Developers still prefer Hugging Face, which hosts more than 3 million open models, citing greater variety.
Thomas Wolf@Thom_WolfAI score29AIThomas Wolf, owner of the Hugging Face account, posted the single word "impressive" in response to a quoted post. The quoted post reports a new NanoGPT training record of 39.9s, down 27.7s from the prior 67.6s, achieved through per-flop optimizations such as sampled softmax and sparse updates.
Hugging Face@huggingfaceAI score14AIHugging Face is inviting developers to suggest features for Gradio, its tool for building AI app frontends. The post follows a quoted message from Abid Lab noting that Gradio's original purpose has shifted and that the team is redesigning it from scratch around developers' current challenges with AI apps.
clem 🤗@ClementDelangueAI score49AIHugging Face is contributing egress usage monitoring to NVIDIA's OpenShell, part of the newly launched Open Agent Safety Platform, arguing that allowlists alone restrict where agents can go but not what they do. The proposed features include per-sandbox network budgets for requests, writes, and bytes, drift detection against each sandbox's baseline and cohort, and a fleet view that flags many sandboxes writing to one host even when every request is allowed.
