Open source

AIFunAudioLLM has released Fun-ASR-Nano-2512-hf, a Hugging Face Transformers-compatible version of its end-to-end speech recognition model, which supports Chinese, English, and Japanese. The Chinese coverage includes 7 dialect groups and 26 regional accents, and a separate Fun-ASR-MLT-Nano-2512 checkpoint handles 31-language recognition. Developers can run the model natively in Transformers 5.17.0 without custom model code or trust_remote_code=True.

AIReflection AI says its open models are designed to support the Genesis Mission by giving scientists at national labs flexibility and sovereignty to work on their own terms. The post invites readers to learn more through a linked article, but gives no further details on models, benchmarks, or availability.

AIReflection, an open-source AI lab, has signed a memorandum of understanding with the US Department of Energy to explore strategic collaborations under the Genesis Mission. Through the partnership, Reflection will provide open-weight models to the DOE's 17 National Laboratories, customizable on lab-specific scientific data and deployed into researcher workflows. The Genesis Mission focuses on advanced nuclear, fusion and grid modernization, quantum ecosystem growth, and national security AI.

AIStability AI has launched Stable Audio 3.0 for experimentation. The Small and Medium models are available on Hugging Face, while the Large model is accessible through the Stability AI API or self-hosting with an enterprise license.

AIStability AI released Stable Audio 3.0, a family of four audio models trained on fully licensed data. Three of them, Small SFX, Small and Medium, have open weights on Hugging Face, while Large is available through the Stability AI API and enterprise self-hosting. Outputs can be distributed and commercialized under the Stability AI Community License, and organizations with more than $1M in annual revenue can use the Enterprise License.

Why it matters: The source specifies which models are open-weight, their licensing terms, and clip-length limits, which matters for anyone deciding whether to build on them.

AIPaddleOCR 3.5 now supports Hugging Face Transformers as an inference backend, letting users run PP-OCRv5 and PaddleOCR-VL 1.5 models directly within the Transformers ecosystem. Users can select it with engine="transformers" while keeping the same PaddleOCR pipeline, which the post says eases integration for RAG and Document AI applications.

AIGoogle is rolling out Gemini 3.5 Flash globally for consumers in the Gemini app and Search AI Mode. It is also available to developers through the Gemini API, Google Antigravity, and Google AI Studio, and to businesses on the Gemini Enterprise Agent Platform.

Why it matters: The post shows where each Gemini 3.5 Flash access path goes, from consumer apps to developer and enterprise platforms, which helps readers pick the right entry point.

AIYann LeCun points to Project Tapestry, hosted by thealliance.ai, as the answer to fears that no credible Western open frontier model will emerge. The post is a short pointer to the project, with no details on models, benchmarks, or availability.

AIChatGPT is previewing a personal finance experience for Pro users in the U.S., who can securely connect financial accounts and see where their money is going. Users can ask questions based on the information they choose to connect, and the author says this follows the similar health records connection feature.

AIRAGFlow, an open-source RAG engine, has reached 80K GitHub stars, according to PaddlePaddle's congratulatory post. PaddlePaddle said it is a PaddleOCR OCEAN Alliance member collaborating with the RAGFlow team through deep PaddleOCR integration and document AI work.

AIAnthropic is donating Petri, its open-source interactive behavioral-evals tool for alignment testing, to Meridian Labs so development can continue independently. Working with Meridian, Anthropic has also released a major update improving the adaptability, realism, and depth of Petri's tests. Developers are invited to try the tool and contribute.

AIOpenAI's GPT-5.5 scored 0.43% and Anthropic's Opus 4.7 scored 0.18% on ARC-AGI-3, a set of 135 novel environments, according to ARC Prize's replay analysis of 160 runs. The analysis found three recurring failure modes: models perceived local action effects but failed to build global rules, mapped unfamiliar games onto known ones, and sometimes beat a level without learning the underlying mechanic. ARC Prize is open-sourcing its analysis package.

AISoumith Chintala, a PyTorch co-creator, says GitHub and critical work are becoming incompatible. The post is brief and gives no specifics on which GitHub issues or what alternatives he has in mind.

AINVIDIA's developer account announced a livestream titled "Scaling NemoClaw: Roadmap, OpenClaw Collaboration, and Real-World Integration" as part of Nemotron Labs. The post provides no further details on the roadmap, collaboration terms, or integration specifics.

AIResearchers including David Duvenaud and Alec Radford announced Talkie, an open-weight 13B-parameter LLM trained and finetuned on a newly curated dataset containing only pre-1930 text. Soumith Chintala shared it as a fun test of time for the model.

AIXiaomi's MiMo-V2.5 is a native omnimodal model that understands text, image, video, and audio within one architecture. It is a sparse MoE with 310B total and 15B activated parameters, and supports up to 1M tokens of context. The repository also notes a config.json and tokenizer_config.json update that users who downloaded before commit 4da2748 should re-pull.

Why it matters: The repository documents a 310B-parameter omnimodal MoE with a hybrid attention design, useful for comparing long-context efficiency against other open multimodal models.

AIMistral AI has released mistralai/Mistral-Medium-3.5-128B-EAGLE, an EAGLE draft model for speculative decoding with the 128B dense Mistral Medium 3.5. The companion model, which the source says replaces Mistral Medium 3.1 and Magistral in Le Chat and Devstral 2 in Vibe, has a 256k context window, handles text and image input with text output, and is served with vLLM or SGLang using three speculative tokens. The model is released under a Modified MIT License that allows commercial use with exceptions for companies with large revenue.

AIXiaomi released and open-sourced MiMo-V2.5-Pro, a 1.02T-parameter Mixture-of-Experts model with 42B active parameters and a 1M-token context window. The company reports gains in agentic tasks, software engineering, and long-horizon work, including a Rust SysY compiler task finished in 4.3 hours across 672 tool calls. Weights and tokenizer are on Hugging Face, and API pricing is unchanged.

Why it matters: The release pairs a 1.02T-parameter open-weight model with long-horizon agent results and token-efficiency claims, useful for judging its fit in coding and agent workflows.

AINVIDIA AI Developer announced a live "Ask the Experts" broadcast on running Gemma 4 on DGX Spark. The post provides only a broadcast link and no further technical details.

AINVIDIA AI Developer announces the winners of a Nemotron hackathon held in South Korea, with a livestream linked for the winners announcement.

AIApple has released CADD-Base-7B on Hugging Face, a 7B masked diffusion language model for code generation that uses Continuously Augmented Discrete Diffusion (CADD) to guide discrete denoising with a continuous flow-matching signal. The model loads through Transformers with trust_remote_code, and its diffusion_generate method supports CADD sampling modes "weighted" and "argmax" with alg options such as "entropy" and "maskgit_plus". The release builds on DiffuCoder and reuses Dream's modeling architecture and generation utilities.

AIOpenAI is releasing a subset of datasets, reference code, and the g-mean 2 metric for evaluating chain-of-thought monitorability. The release includes most datasets from its monitorability suite, while some evaluations relying on private or restricted data were omitted. The company says it will keep reporting monitorability results in future frontier reasoning model system cards.

AITri Dao points to Wentao Guo's explanation of a mathematical rewrite of the MoE backward pass, which reduces activation memory and speeds up training, especially for fine-grained MoE. He also notes the work leverages new Blackwell features, including 2CTA MMA and CLC, to build fast MoE kernels.

AIXiaomi released MiMo-V2.5, a 310B-parameter sparse MoE model with 15B active parameters that adds native visual and audio understanding. The model supports up to 1 million tokens of context, and its weights, tokenizer, and model card are available on Hugging Face. Xiaomi says it surpasses MiMo-V2-Pro on agentic performance and reports a Claw-Eval score of 62.3 on the general subset.

Why it matters: The release pairs native visual and audio understanding with a 1M-token context window and open weights, a combination worth checking against your own multimodal workflows.

AINVIDIA AI Developer announced a live broadcast on contributing to open source, featuring NVIDIA NemoClaw and Nemotron Labs. The post links only to the broadcast and gives no further details on the content or speakers.

AINVIDIA's OpenShell v0.0.34 release lets users update sandbox policy without restarting the runtime. The update also adds install-vm, which installs the gateway and VM driver with new --driver-dir support, and sandbox get, which shows the active runtime policy. Supervisor seccomp improvements and HTTP normalization are included as well.

AINVIDIA released OpenShell v0.0.33, which adds seccomp and process-limit hardening, inference routing, and a standalone libkrun compute driver. The libkrun driver provides a lightweight VM backend as a second compute path for agents. The release also includes bug fixes, a docs refresh, and improved test stability.

AIA developer trained a 12M-parameter LLM on a custom ML framework built with a Rust backend and CUDA kernels, including Flash Attention, fused LayerNorm, and fused GELU. The framework claims 3x throughput gains, WebGPU fallback for non-NVIDIA devices, and a TypeScript API installable via npm.

AINVIDIA released OpenShell v0.0.32, which ships standalone openshell-gateway binaries so the gateway can be deployed without building from source. The release also adds system CA certificates for upstream TLS and reorganizes RFC documentation by RFC.

AIOpenAI released Privacy Filter, a bidirectional token-classification model that detects and masks personally identifiable information in text under the Apache 2.0 license. The model has 1.5B total parameters with 50M active, supports a 128,000-token context window, and can run in a web browser or on a laptop. Users can fine-tune it and adjust precision/recall tradeoffs through preset operating points.

AIMoonshot AI released Kimi K2.6, an open-source native multimodal agentic model with 1T total and 32B activated parameters and a 256K context length. The model card reports benchmark results against GPT-5.4, Claude Opus 4.6, and Gemini 3.1 Pro across agentic, coding, reasoning, and vision tasks, and supports swarms of up to 300 sub-agents.

Why it matters: The model card gives specific agent swarm scale, context length, and benchmark comparisons against several frontier models, useful for judging its coding and agent capabilities.

AIBAAI announces ClawKeeper v1.0, an open-source security framework for OpenClaw AI agents, combining Skill-based command policies, Plugin-based runtime monitoring, and a Watcher system-level observer. The independent Watcher is designed to block high-risk operations such as prompt injections, key leaks, rogue commands, and remote code execution, even if the agent is compromised. The paper is available on arXiv and the project code is hosted on GitHub.

AIOllama and oMLX showed impressive demos running Qwen 3.5 and Gemma 4 locally on Apple silicon using MLX. Awni Hannun said the capabilities of local LLMs and their surrounding ecosystem have advanced considerably over the past couple of years.

AIMiniMax has released MiniMax-M2.7 on Hugging Face, describing it as its first model to participate in its own evolution. The source reports 56.22% on SWE-Pro, 46.3% on Toolathon, and 62.7% on MM ClawBench, and says an internal version autonomously optimized a programming scaffold over 100+ rounds for a 30% performance improvement.

Why it matters: The source ties its benchmark claims to a self-evolution process and a named comparison set, which helps readers weigh how the reported gains were achieved.