Open source

AIThe essay argues that Western companies increasingly rely on Chinese open-weight models like Qwen and Kimi for post-training, while Western labs cannot lawfully distill from American frontier models. It says Qwen's share of new open-model fine-tunes rose from 1% in January 2024 to 69% by February 2026, citing ATOM's Report. The authors propose controlled teacher access and tighter enforcement against foreign distillation as a domestic alternative.

AIBerkeley's STAR Lab packaged multiple LLM-based "autoresearch" algorithms into a single API within the GEPA package, letting users mix and match them. The optimizers can be applied to tasks including prompt writing, agent design, and code optimization. The quoted thread adds that GEPA, AutoResearch, and Meta-Harness each win on different tasks, and that the new optimize_anything omni meta-optimizer beats every standalone optimizer at a matched budget.

AIGoogle's Gemini Notebook has rolled out Collections to 100% of web users, letting people group notebooks like photo albums or playlists. Notebooks can belong to multiple collections or stay only in the main "My Notebooks" tab, with no rigid folder structure. The account asks users which features they want next.

AIBAAI has released AREX-Base, a 122B-total, 10B-activated Mixture-of-Experts deep research agent built on Qwen3.5-122B-A10B with a 262,144-token context. The model uses an inner research loop and an outer self-improvement loop, and the source reports it scoring 82.5 on BrowseComp and 85.4 on GAIA, under Apache 2.0.

Why it matters: The release pairs a 122B-parameter deep research agent with benchmark tables against frontier and open models, letting readers compare its search-agent results directly.

AIBAAI's AREX-Turbo is a dense 4B deep research agent built on Qwen3.5-4B with a 262,144-token context length. It scores 70.7 on BrowseComp, 81.6 on GAIA and 40.6 on HLE with tools, versus 82.5, 85.4 and 52.4 for the 122B AREX-Base. The model is released under Apache License 2.0 and targets lower-cost research-agent deployment.

AILeandro von Werra introduces The Stack v3, a dataset of 5T tokens ready for training and 120TB of raw data. He says the dataset is meant to support open code models for cyber defence, and links the download on Hugging Face.

AISoumith Chintala praised Poolside's Laguna S 2.1 as looking strong for agentic use and said it fits on a single NVIDIA DGX Spark. The quoted Poolside release describes it as a 118B total-parameter Mixture-of-Experts model with 8B active per token, up to 1M-token context, and thinking and no-thinking modes, with weights openly available under OpenMDW-1.1.

AIPoolside released Laguna S 2.1, an open-weight model with 118B total parameters and 8B active per token. The author says it performs strongly on agentic coding and long-horizon tasks, and it can run on a single NVIDIA DGX Spark. Weights are on Hugging Face under the OpenMDW-1.1 license, with access also available through OpenRouter and Poolside's API.

AIJetBrains Air now connects to ACP-compatible coding agents, including GitHub Copilot CLI, OpenCode, Pi, and Cline, through the Agent Client Protocol. The release also adds Beta Java and Kotlin navigation and diagnostics powered by the IntelliJ IDEA code engine, local model support through Ollama or LM Studio, and Docker-based agent tasks on Windows.

AISYNAPS-I, a multi-lab Genesis Mission project led by Lawrence Berkeley National Laboratory, uses Meta's open-source SAM 3 and DINOv3 models to segment X-ray and micro-CT scientific imagery. The fine-tuned pipeline, run on 300 A100 GPUs, reduced a grapevine xylem analysis from a month of expert annotation per time step to about 15 minutes. The team can deploy the open models inside secure national lab infrastructure, where research data must remain.

AIAnthropic's Noah Zweben announced that users can create Artifacts directly from Claude Tag. Background from @ClaudeDevs says Artifacts now support public sharing and multiplayer editing in Claude Code.

AIIf open labs keep finding 2.5x a year, compute advantages depreciate fast. The frontier isn't who has the most FLOPs. It's who converts them best. 👇

AIA security team found commercial frontier model APIs blocked their incident-response log analysis, which required submitting real attack commands and exploit payloads. They ran the forensic analysis on GLM 5.2, an open-weight model, on their own infrastructure, which also kept attacker data and referenced credentials inside their environment.

AIDatabricks CEO Ali Ghodsi recommends an interview with Matei Zaharia about the open-source Omnigent project, which the background post describes as a meta-harness aimed at gaps in agent development. The post frames agents as still in their infancy, with development methods also immature.

AIMoonshot AI announced Kimi K3, a native multimodal model with 2.8 trillion parameters and a 1 million token context window. The announcement cites up to 6.3x faster decoding in million-token contexts and about 25% higher training efficiency, and says open weights arrive by July 27, 2026. The author, Soumith Chintala, reposted it with a brief note of congratulations.

AIMistral AI released Shieldstral-1.0-3B, a 3B-parameter multimodal safety classifier that judges content against natural-language policies and outputs a continuous safety score. It moderates text, image, and text-plus-image content in a single forward pass and can be retargeted to new policies at inference time without retraining. The Apache 2.0 open-weight model is built on Ministral-3-3B-Base-2512 and trained on sequences up to 32k tokens.

AIJunyang Lin praised Thinking Machines' new architecture in Inkling, which reasons across text, image, and audio, and asked whether the small model is open-sourced. The quoted announcement says the full weights are available and that Inkling is available for fine-tuning on Tinker.

AIThinking Machines has introduced Inkling, a model that reasons across text, image, and audio, with full weights made available. It is available for fine-tuning on Tinker and can be tried in the Inkling Playground. Hugging Face's Leandro von Werra praised the release for its grounded writing, interesting details, and strong ecosystem integration.

AIThinking Machines has introduced Inkling, an open-weights model that reasons across text, image, and audio, with full weights made available. The model is available today for fine-tuning on Tinker, and the company also offers an Inkling Playground for trying it out. The author describes Inkling as a foundation model intended for broad capabilities in practical use and customization.

AIHugging Face announced a new open-weight model drop via an X broadcast link. The post gives no model name, parameter count, benchmark results, or release details beyond the live stream.

AIThinking Machines introduced Inkling, a model that reasons across text, image, and audio, and is making its full weights available. It is available today for fine-tuning on Tinker and can be tried in the Inkling Playground. John Schulman says pretraining began last winter and a small team added coding, reasoning, and agentic training starting in mid-January.

Why it matters: The post links an open-weights release to a stated training timeline, showing how a small team moved from pretraining to coding, reasoning, and agentic training.

AIThinking Machines introduced Inkling, an open-weight model with 975B parameters that reasons across text, image, and audio. The full weights are available, with fine-tuning on Tinker and access through the Inkling Playground and Hugging Face and partners.

AIMira Murati announced Inkling, the first model from Thinking Machines, trained from scratch with its full weights made available. The model reasons across text, image, and audio, and is available today for fine-tuning on Tinker and testing in the Inkling Playground.

AIThinking Machines has introduced Inkling as the first model in a family. It also shares details of Inkling-Small, a lighter-weight model trained on a similar recipe, with full weights promised later.

AIThinking Machines has released Inkling, a model that natively understands and reasons across text, audio, and images. It is particularly strong on audio, ranking among the top open-weights models on VoiceBench, MMAU, and AudioMC.

AILiquid AI released Antidoom, an open-source method that retrains a single overtrained token to eliminate "doom loops" in small reasoning models. On LFM2.5-2.6B and Qwen3.5-4B, loop rates fell from 10.2% to 1.4% and from 22.9% to 1%, respectively. The company also released IFStruct, an open-source benchmark measuring whether model outputs satisfy a schema, where LFM2.5-350M rose from 21.10% to 44.90% after training.

AIByteDance's UniVR-34B-Planning, built on Emu3.5 at 34B parameters, learns visual reasoning, physical dynamics, and long-term planning from visual demonstrations using a next-token objective and two-stage training on the VR-X dataset with VR-GRPO reinforcement learning. On the VR-X benchmark it scores 58.2 overall, up 18.4 points from the Emu3.5 34B baseline of 39.8. The Planning checkpoint is available on Hugging Face under CC BY 4.0, alongside a General checkpoint.

AIAndrew Ng argues that innovation thrives when inventors need not seek government permission in advance, citing Adam Thierer's "Permissionless Innovation." He says protecting open source AI is now a critical part of preserving that principle. Thierer's post, cited as background, describes an informal, opaque model-review regime in the US that could threaten open source models.

AIZed v1.10 now includes proper support for llama.cpp as a model provider, according to Georgi Gerganov. Zed's own announcement credits Hugging Face with helping make the llama.cpp integration possible.

AICursor's Aman Sanger says the new model is an enormous improvement over Composer 2.5 and was trained entirely from scratch with the SpaceXAI team. The quoted Cursor post identifies it as Grok 4.5, Cursor's most powerful model yet and the first built for more than software engineering.

AICognition says its SWE-1.7 model, developed from the open-source Kimi K2.7 Code base, performs as well as or better than leading U.S. frontier models on its new trustworthiness evaluation suite. The suite combines 145 politically sensitive questions, sampled in English and Chinese, with realistic coding scenarios to measure propaganda, censorship, and security behavior. Cognition says SWE-1.7 improves substantially over the base Kimi K2.7 Code model, though the company says the benchmarks are still in development.

AIllama.cpp has added DFlash support to its speculative decoding options, joining MTP, Eagle3, and various ngram-based techniques. The author says these methods further improve local model performance. The change was developed with thanks to the NVIDIA team and Ruixiang Wang.

AITufa Labs won the first $37.5K ARC-AGI-3 milestone prize with "The Duck," a small open-source LLM that plays the games by writing and running Python in a live REPL. Reki placed second with a vision-language agent using Gemma-4-31B, and md Boktiar Mahbub Murad placed third with the "forge" framework. The second and final milestone prize ends September 30.

AIMistral CEO Arthur Mensch says enterprises should use open-source models because closed providers that force data retention gain leverage over their business. He argues companies should store data in open systems, control AI access rules, and build continuous training loops to shrink costs and create hard-to-copy systems. Mistral offers its Studio control plane and Forge training platform, deployed on customer infrastructure or through zero-data-retention hosting.

AIA model repository named XiaomiMiMo/MiMo-V2.5-DFlash is listed on Hugging Face with 311B parameters and tensor types F32, BF16, and F8_E4M3. The README is empty, and the page reports 434 downloads last month and no Inference Provider deployment.