Open source

AIvLLM announced day-0 support for EmbeddingGemma 2 from Google DeepMind, a bidirectional omni-modal embedding model that maps text, image, audio, video, and interleaved inputs into one vector space. Users can try it with the latest vLLM nightly build using the command vllm serve google/embeddinggemma-2 --runner pooling. The quoted Google post says the model is built on the Gemma 4 architecture and released under Apache 2.0.

AIMETR demonstrated a JavaScript injection that an agent could trigger through MathJax rendering from any part of its output, including its reasoning. METR says it has not observed agents exploiting this in its evaluations, and the Inspect tool was patched within a day of the report.

AIA METR researcher discovered a bug in the transcript viewer of Inspect, a popular evaluation framework, that could let an agent show a user a fabricated or edited version of its transcript.

AIPerplexity says its open-weights multimodal decision model pplx-decider-v1.1-27b is now available and scores highest on Hugging Face's new Decision Index 0.3 benchmark. The model costs $0.02 per million input tokens, half the price of v1.

AIMerve Noyan has released a slide deck on running AI locally, covering prefill versus decode, MoE versus dense models, VRAM versus unified memory, quantization, and speculative decoding. The deck is built around llama.cpp and is free to reuse with attribution.

AIGrok can turn a pasted article or PDF into a podcast where two hosts discuss the content. Each script line is voiced as soon as it is written, so playback begins before the full script is finished. The example code is available in the xai-cookbook repository on GitHub.

AIThe xai-cookbook's x-sentiment-tracker lets users pick a topic, then has Grok run one X search per day over the last 10 days and score each post from -1 to +1. The dashboard displays the sentiment split, day-by-day movement, and the posts behind each score.

AIThe xai-cookbook example uses Grok to generate a Tailwind React component from a dropped-in screenshot. The page then screenshots its own render, overlays it on the original, and feeds the mismatches back so Grok can refine the layout, raising the match from 60% to 77% in one run.

AICursor has added five apps to its cookbook, all built on the new SpaceXAI TypeScript SDK. The apps cover premise-to-short-film, picture-to-video ads, screenshot-to-React-component, X posts-to-sentiment dashboard, and link-to-podcast conversion. Demos and code are available in the post.

AIGoogle's EmbeddingGemma 2 is an open embeddings model for on-device use that covers code, image, video, audio, and text. It comes in modular sizes from 270M text/code to 740M full multimodal, supports Matryoshka truncation down to 128 dimensions, and reports a 14% gain on MTEB Code over v1 under an Apache 2.0 license. The author's post highlights the release and a Hugging Face demo, while the benchmark table compares it with several models.

AIGoogle released EmbeddingGemma 2, a 740M-parameter open embedding model under Apache 2.0 that combines a 270M text model with vision (170M) and audio (300M) encoders. The 270M text model can run locally with 0.5GB of RAM, and the full multimodal model with 1GB, and Unsloth provides GGUF files and fine-tuning support.

AIGoogle Gemma announces EmbeddingGemma 2, a lightweight embedding model that maps text, code, images, video, and audio into a single unified embedding space. The model has a 740M parameter form factor with modular encoders, Matryoshka Representation Learning dimensions from 768 down to 128, and an 8K context window that is 4x larger than the text-only EmbeddingGemma. It is released under the commercially permissive Apache 2.0 license.

AIGoogle Gemma says EmbeddingGemma 2 delivers best-in-size-class performance on MTEB (Code) and MIEB (Lite). The post highlights local codebase indexing, semantic code search, and coding agent retrieval, with a 9.92-point code performance gain over EmbeddingGemma.

AIGoogle DeepMind released EmbeddingGemma 2, a 740M-parameter embedding model, under an Apache 2.0 license. The post says it is competitive across benchmarks and outperforms some specialist models more than twice its size, and that developers can use it for multimodal search or pair it with Gemma 4 for on-device RAG. Weights are available on Hugging Face and Kaggle.

AIGoogle DeepMind introduced EmbeddingGemma 2, its first natively multimodal open model for on-device embeddings. The model expands beyond text to unify code, images, audio, and video in a shared embedding space.

AIGoogle introduces EmbeddingGemma 2, its first open, natively multimodal embedding model, covering text, code, image, video, and audio tasks. It has a 740M parameter form factor, is positioned for offline, privacy-first RAG when paired with Gemma 4, and the post claims it outperforms some specialist models more than twice its size. Weights are available now on Hugging Face.

AIMistral announced Mistral Large 4, which it describes as a natively multimodal model with 1T parameters and 49B active. Mistral says it is available via API now, with open weights to follow at the end of October, and a Hugging Face page is listed for the release.

Why it matters: The quoted Mistral announcement gives specific size, activation, and API details, and the open-weights timing matters for teams weighing open model options.

AISimon Willison reports that Mistral can now generate his pelican SVG test, shared via a Markdown SVG renderer. The post links to a rendered result but gives no benchmark or scoring details. Background from Mistral's own announcement describes Mistral Large 4 as a 1T-parameter, natively multimodal model with 49B active parameters, available via API today and with open weights planned for end of October.

AIMistral announced Mistral Large 4, a natively multimodal model with 1T parameters and 49B active, now available via API. Open weights are scheduled for release at the end of October, with a countdown page on Hugging Face showing October 31, 2026.

AIClément Delangue of Hugging Face shared a link to a Hugging Face model page for mistralai/Mistral-Large-4.0-1T05-A52B. The post itself gives no further details about the model's capabilities, release terms, or benchmarks.