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Sep 28

Sep 28Mon
  1. clem 🤗XAI score49

    Hugging Face proposes egress usage monitoring for OpenShell agent sandboxes

    AIHugging 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.

    Video from @ClementDelangue's post
  2. Unsloth AIOfficialAI score34

    Laya Decision models can now run locally on 4GB RAM

    AIUnsloth AI says Laya Decision models can run locally on just 4GB of RAM, on CPU, Mac, Windows, Linux, and GPU setups. The post adds that Laya can be served through a Jev-compatible API via Unsloth Desktop.

    Image from @UnslothAI's post
  3. Lovable BlogOfficialAI score57

    Lovable apps can now run inside a company's Microsoft tenant

    AILovable announced a partnership with Microsoft that lets users publish apps into their company's Microsoft Entra tenant using Copilot Managed Runtime. Apps can connect to Microsoft 365, Fabric, Dataverse, and SQL data, and staff sign in with their work login. Copilot Managed Runtime is in public preview, and Microsoft 365 connectors, Fabric, and Microsoft sign-in are available on every Lovable plan, while Entra workspace sign-in is included on Business and Enterprise.

  4. Jensen HuangXAI score42

    NVIDIA releases open agent safety platform combining OpenShell and Sentry

    AINVIDIA's Open Agent Safety Platform Reference Design combines NVIDIA OpenShell and NVIDIA Sentry to secure AI agents. OpenShell, an open-source secure runtime, enforces clear boundaries and policy on agent actions while tracing them as they work. NVIDIA Sentry adds hardware-based enforcement on NVIDIA BlueField, continuously monitoring agent activity and enabling millisecond-scale containment and quarantine.

    Image from @JensenHuang's post
  5. ModelScopeOfficialAI score46

    Qwen-Image-2.1 LoRAs extract and remove layers for editing

    AIModelScope released two Qwen-Image-2.1 LoRAs, LayerExtract and LayerRemove, for layer-based image editing. LayerExtract isolates a prompt-specified subject onto a transparent background, while LayerRemove deletes the matching object from the source image and reconstructs the scene behind it. Both can be hot-swapped within the same DiffSynth-Studio pipeline, and the LoRA weights are licensed under Apache 2.0, with Qwen-Image-2.1 base-model terms also applying.

    Image from @ModelScope2022's post
  6. ModelScopeOfficialAI score43

    Jina-OCR-v1 parses full pages into Markdown at 2.57 pages per second

    AIJina-OCR-v1, a 3.4B-parameter MoE model that activates 570M parameters per token, converts entire document pages into structured Markdown at 2.57 pages per second. It scores 91.14 on OmniDocBench v1.6 and 83.4 on olmOCR-Bench, 7.4 points above DeepSeek-OCR on the latter, and delivers the highest throughput among 14 evaluated systems at concurrency 32. The model is released under CC BY-NC 4.0, so commercial use requires permission.

    Image from @ModelScope2022's post

Sep 27

Sep 27Sun
  1. Xiaomi MiMo · new models on Hugging FaceOfficialAI score44

    Xiaomi releases MiMo-V2.6-Flash-MOPD, an upgraded MoE model with 1M context

    AIXiaomi has released MiMo-V2.6-Flash-MOPD on Hugging Face, an upgrade of the MiMo-V2.6-Flash-RL checkpoint that fuses several domain-specialized teachers into one model. The sparse MoE model has 309B total and 15B activated parameters, a 1M-token context length, and supports text, image, video, and audio inputs. The checkpoint targets tool-call repetition, a failure mode where the model repeatedly issues the same or similar tool calls without making progress.

Sep 26

Sep 26Sat
  1. Xiaomi MiMo · new models on Hugging FaceOfficialAI score50

    Xiaomi releases MiMo-V2.6-Pro-MOPD, a 1.02T-parameter sparse MoE model

    AIXiaomi has released MiMo-V2.6-Pro-MOPD, an upgrade of the MiMo-V2.6-Pro-RL checkpoint that fuses several domain-specialized teachers into one model via MOPD2 and targets tool-call repetition. The sparse MoE model has 1.02T total and 42B activated parameters, a 1M-token context length, and accepts text, image, video, and audio inputs. Weights are available on Hugging Face and ModelScope, with deployment recipes for SGLang and vLLM.

  2. clem 🤗XAI score31

    Xiaomi open-sources MiMo-V2.6 RL environments on Hugging Face

    AIXiaomi released an open-source RL environments repository, MiMo-V2.6-RL-oss, on Hugging Face. Hugging Face CEO Clément Delangue promoted the release, and a commenter estimated that comparable commercially sold tasks cost hundreds to thousands of dollars each.

  3. Alexander DoriaXAI score38

    Xiaomi open-sources 989 RL environments used for a 9B MiMo model

    AIAlexander Doria reports that the released set is a smaller selection of 989 environments for RL training a 9B distilled model, not the full MiMo. Rewards are not self-contained: the general part requires setting up a judge, and webdev relies on its own grader service and VLM. The most important content is in the general/envs directory and Docker setup rather than the Hugging Face dataset, offering a solid mix of real and simulated documents.

  4. InternLM (Shanghai AI Lab) · new models on Hugging FaceOfficialAI score45

    Intern-Decision-4B: Multimodal structured decision model from Qwen3.5-4B

    AIShanghai AI Lab's InternLM released Intern-Decision-4B, a multimodal structured decision model fine-tuned from Qwen3.5-4B, which returns answer distributions for multiple questions in one forward pass. On its benchmark table it scores an average of 90.02 with a Brier score of 0.347 and an ECE of 0.065, and per-query latency averages 44.16 ms on a single RTX 4090. The model is available with a Python DecisionEngine inference interface.

  5. InternLM (Shanghai AI Lab) · new models on Hugging FaceOfficialAI score44

    Intern-Decision-2B: Structured Multi-Question Decision Model Fine-Tuned from Qwen3.5-2B

    AIShanghai AI Lab's InternLM released Intern-Decision-2B, a multimodal structured decision model fine-tuned from Qwen3.5-2B that returns calibrated answer distributions for multiple questions in one forward pass. It averages 84.68 across listed benchmarks with a 0.437 Brier score and 33.28 ms mean latency on a single RTX 4090. Model weights, a Python DecisionEngine API, and GitHub code are available, with support for up to 16 questions and eight images.

  6. InternLM (Shanghai AI Lab) · new models on Hugging FaceOfficialAI score46

    Intern-Decision-0.8B: InternLM's structured decision model on Hugging Face

    AIInternLM released Intern-Decision-0.8B, a multimodal structured decision model fine-tuned from Qwen3.5-0.8B that scores answers to multiple questions in one forward pass. The model reports a 79.38 average score and a 33.98 ms mean latency on a single RTX 4090, with 0.8B, 2B, and 4B sizes available. It is accessed through a Python DecisionEngine API that returns calibrated probabilities rather than generating free-form text.

Sep 25

Sep 25Fri
  1. LMSYS OrgOfficialAI score38

    SGLang adds multi-item scoring for faster decision model serving

    AISGLang's /v1/score endpoint returns scores for exact requested labels such as Yes/No or A/B/C, and its multi-item scoring (MIS) computes shared context once while keeping candidates isolated. On Qwen3-8B, 16-candidate p95 latency dropped from 54.1 ms with Generate to 20.6 ms with MIS. On Qwen3-0.6B, MIS p95 stayed under about 100 ms as load rose, versus seconds for Generate and SIS.

    Image from @lmsysorg's post
  2. GitHub Blog · AI & MLOfficialAI score33

    How to build custom workflows with canvases in the GitHub Copilot app

    AICanvases in the GitHub Copilot app are customizable interfaces that you and the agent share, such as kanban boards, dashboards, or checklists. You create one by running /create-canvas and describing the workflow, what you can do in the interface, and what the agent can do. Changes made by either you or the agent appear immediately in the shared canvas, and completed canvases can be saved as reusable extensions.

Sep 24

Sep 24Thu
  1. ModelScopeOfficialAI score23

    NeoHorse-Jev-4B open model turns app states into structured decisions

    AIModelScope has released NeoHorse-Jev-4B, a compact open model that converts application states into structured decisions and probabilities. It scores 77.70 across six text decision benchmark groups, ranking first among four open-weight models with complete results in the comparison. Its prefill-only inference supports Choice, Noul, and Score primitives, accepts text or a single image with text, and is available under Apache 2.0 for deployment via vLLM, SGLang, Python, CLI, or HTTP.

    Video from @ModelScope2022's post
  2. vLLMOfficialAI score30

    vLLM integrates TileRT with PD disaggregation, benchmark config published

    AIvLLM has published a blog post explaining how its integration with TileRT works, alongside a public benchmark configuration in the InferenceX repository. The benchmark script covers GLM-5.3 FP8 on MI355X hardware and is linked on GitHub. The post itself provides the integration details.

  3. vLLMOfficialAI score42

    TileRT and vLLM hit 469 tok/s on GLM-5.3 with MI355X

    AIThe TileRT and AMD teams reached 469 tok/s single-user decode for GLM-5.3 on 8× MI355X using vLLM. The setup disaggregates work, with vLLM handling prefill and TileRT handling latency-critical decode through vLLM's V1 connector interface. SemiAnalysis's AgentX benchmark reports the configuration at 470 TPS on GLM 5.3 (FP8), over 40% faster than GB300 TRTLLM using FP4.

  4. Lewis Tunstall @ COLM 🌉XAI score42

    Hugging Face releases over 5,000 RL environments for data science tasks

    AIHugging Face released SmolDataEnvs, more than 5,000 open-source RL environments aimed at real-world data science tasks. They target the gap between simple educational games and frontier-level benchmarks, especially for improving coding in models under 10B parameters. The environments are designed as a testbed for developing new RL methods such as GRPO or OPSD.

  5. Baseten BlogOfficialAI score44

    LangSmith Fine-Tuning Trains Open Models on Agent Traces via Baseten Loops

    AILangChain launched LangSmith Fine-Tuning, which lets users fine-tune open models on their LangSmith agent traces using the open-source smithtune CLI. Training runs on Baseten Loops in the user's own workspace, and smithtune deploy places the evaluated checkpoint on a Baseten Dedicated Inference deployment. Loops is in early access, so users may need to request access for their workspace.

  6. GitHub Blog · AI & MLOfficialAI score66

    GitHub Security Lab shows an LLM agent running AI-driven fuzzing for C/C++ projects

    AIGitHub Security Lab describes the Fuzzing Taskflow, an LLM agent pipeline that identifies entrypoints, writes harnesses, runs AFL++, reads coverage reports, and triages crashes for C/C++ repositories. The agent makes decisions while MCP tools handle execution, and state is stored in a SQLite database. The post also warns that the taskflow runs AFL and build commands directly on the host, so it should be used only in disposable environments without elevated privileges.

    Why it matters: The post explains how an LLM agent automates fuzzing steps like harness writing, coverage gap chasing, and crash triage, with a runnable workflow and design tradeoffs.

  7. Google for DevelopersOfficialAI score37

    Gemma 4 now runs on-device in the Antigravity SDK

    AIGoogle says Gemma 4 can now run locally on-device within the Antigravity SDK. Developers can build fully local or hybrid multi-agent workflows that pair cloud models with Gemma 4 agents for auditing, patching, and testing code. The post emphasizes total data privacy and zero API fees, powered by LiteRT.

    Video from @googledevs's post
  8. vLLMOfficialAI score34

    vLLM and RL-Kernel achieve bit-exact logprob match on AMD MI300X

    AIThe RLKernel team integrated RL-Align/RL-Kernel with vllm-project/vime, and a 200-step Qwen3-8B GRPO run on 8× AMD MI300X recorded zero logprob mismatches between Megatron training and vLLM rollout. The strict path aligns reduction order, intermediate precision, rounding points, and math primitives across both sides to achieve bit-for-bit matching on ROCm.

  9. OpenBMBOfficialAI score34

    FIT-GGUF enables size-targeted mixed-precision quantization of MiniCPM5-2B

    AIDeveloper @Scorp1o_117 used FIT-GGUF to build four MiniCPM5-2B GGUF variants, ranging from about 1.14 GiB to 1.46 GiB, tuned to target file sizes or fidelity tiers. Instead of fixed presets, FIT-GGUF allocates precision tensor by tensor, with Quality, Balanced, Compact, and Mini options, and its generated files matched predicted sizes. Builds are evaluated with KL Divergence and Same-top metrics and are available on Hugging Face.

    Image from @OpenBMB's post
  10. ModelScopeOfficialAI score38

    Qwen-Image-2.1-Fun-Controlnet-Union adds eight controls and inpainting

    AIModelScope released Qwen-Image-2.1-Fun-Controlnet-Union, a single checkpoint adding eight structural controls, including Canny, Depth, Pose, and Scribble, plus inpainting to Qwen-Image 2.1. Control and inpainting share one branch with 16 injection points across every second Transformer block, keeping the base model frozen and requiring no checkpoint switching. It runs at guidance scale 1.0 with CFG-distilled sampling and prefix KV caching, and is available under the Qwen Research License with base Qwen-Image 2.1 weights required.

    Image from @ModelScope2022's post
  11. Goodfire ResearchOfficialAI score52

    Block-Sparse Featurizers Recover Multidimensional Concept Geometry in Vision Models

    AIGoodfire Research introduces Block-Sparse Featurizers (BSF), which decompose model activations into subspaces rather than single directions. Applied to DINOv3 and Stable Diffusion XL, BSFs find interpretable multidimensional features that better explain activations and enable fine-grained steering. The authors report that most concepts they examined have a stable rank of about two to four dimensions.

  12. KrASIA · Big TechNewsAI score55

    Mind Lab launches Mint Recursive, a post-training platform for companies

    AIMind Lab unveiled Mint Recursive, a post-training and inference platform for industry use, alongside Macaron-V1.1, a model post-trained entirely on it. Macaron-V1.1 is a 752-billion-parameter model built from GLM-5.3 with four two-billion-parameter LoRA expert modules for chat, agents, coding, and generation. The platform is serverless and bills by token usage, and it collects feedback from models in use to support continued training.

  13. inclusionAI (Ant Ling) · new models on Hugging FaceOfficialAI score22

    inclusionAI Publishes Training-Content Summaries for Ling and Ring Models

    AIinclusionAI has published public training-content summaries on Hugging Face for its Ling and Ring model versions, including Ling-2.0, Ling-2.5, Ling-2.6-1T, Ling-3.0, Ring-2.0, Ring-2.5-1T, and Ring-2.6-1T. The documents, organized under the template associated with Article 53(1)(d) of Regulation (EU) 2024/1689, contain documentation only, not model weights or training datasets. Each summary covers only the model versions it names.

Sep 23

Sep 23Wed
  1. Midjourney UpdatesOfficialAI score31

    Midjourney Alpha changelog adds style previews, default parameters, and a new Create feed

    AIMidjourney's alpha site now lets users preview their current prompt across styles with "Live previews" in the Styles sidebar and save prompt-bar settings as defaults via Settings → Advanced → Your defaults. The Create feed received a full-width masonry redesign with hover-based prompts and buttons, and Korean is now live for all users on midjourney.com.