Open source

AIBlack Forest Labs released FLUX 3, a multimodal model trained on images, video, and audio, and mimic built FLUX-mimic on its video backbone to control robots. In a soft-body kitting task, mimic reports a 95% success rate without single-task fine-tuning, compared with 55% for an adapted π0.5 model. FLUX 3 Video is in early access, with action prediction offered to selected partners and an open-weight backbone planned.

AIJensen Huang says defenders need a frontier AI ecosystem combining the best open and closed models, citing how an open-weight frontier model helped contain the Hugging Face intrusion when closed AI blocked essential forensics. Nvidia has created the Open Secure AI Alliance to develop new techniques and tools for safeguarding software and agents by sharing models, tooling, and research in the open.

AIFireworks AI made the open-weight Kimi K3 available for inference and training on its platform, with US-only serverless endpoints and Zero Data Retention. In its own head-to-head with Opus 5, the post reports K3 at 92.7% accuracy and $0.52 per task on SWE (480) against Opus 5's 94.8% and $1.05, with the vendor claiming up to 5x better cost efficiency per task.

Why it matters: The post compares Kimi K3 with Opus 5 on accuracy and cost per task, giving readers concrete figures to judge the open model against closed alternatives for their own workloads.

AIFireworks AI has made Kimi K3 available for Multi-LoRA serving and training in private preview through Fireworks Serverless Training. The post explains how small LoRA adapters can be trained on K3 and served with live merge or multi-LoRA deployment, and it reports two example tasks, Countdown and Frozen Lake, with reward curves.

AITrilogy released an AI Cybersecurity Playbook built around Kimi K3, served on Fireworks through an OpenAI-compatible API. The playbook pairs deterministic tools for indexing, parsing, and deduplication with Kimi K3 for reachability, impact, and remediation judgment, followed by a separate verification stage.

AIArthur Mensch argues that open-weight models will let the whole world share in AI's growth and keep America from being left behind. The post is a short policy statement that quotes Jensen Huang's NVIDIA letter on why open models matter. The post does not give specific models, benchmarks, or figures.

AIAhmad Al-Dahle, who has built and shipped open models, welcomed Microsoft, NVIDIA, and much of the industry signing a letter supporting open models. He warned that the advocates may win the argument but still lose the leaderboard, and urged America to lead on open source as it invented it.

AIBryan Catanzaro, NVIDIA's account owner, argues the central US AI leadership question is whether AI models will be treated as infrastructure like the internet or electricity. He says open models will be at the heart of this infrastructure, enabling companies from startups to established industry leaders, and making sovereignty possible. He concludes policymakers seeking to keep American AI at the forefront should recognize open models as the critical infrastructure of the AI age.

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.