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#On device
Oct 23Fri
  1. TechCrunch · AI71

    Recursive launches Flint, a laptop coding model it says was largely built by AI

    Recursive released Flint, a free coding model that runs on a laptop and was built largely with the help of its automated research system, the Eureka Machine, letting a small team build it from scratch in under four months. Flint is a mixture-of-experts model using 2.5 billion of its 40 billion parameters at a time, is available on Hugging Face under the Apache 2.0 license, and in Recursive's tests generated about 131 tokens per second on a MacBook Pro with an M4 Pro chip. The company says the longer-term goal is for the Eureka Machine to achieve full recursive self-improvement, with humans overseeing the process.

  2. TechCrunch · AI40

    Nirva raises $33M for a pendant that only listens to you

    Nirva, founded in 2025 by former Meta Reality Labs employees Wei Lyu and Jason Chen along with Hanying Hu, announced a $25 million pre-Series A round on top of $8 million raised earlier this year from investors including Lightspeed and South Park Commons, bringing its total to $33 million. Its first device is an approximately eight-gram pendant, also wearable as a bracelet, with up to three days of battery life, that passively listens only to the wearer by filtering out other people's speech and by default keeps no audio or transcripts after processing. The $249 device, offered in silver, brushed silver, black, and gold, will begin taking orders in November with an aim to ship to early users by the end of the year.

Oct 22Thu
  1. The Decoder73

    Recursive shows first result of its AI-driven research process with Flint

    AI startup Recursive has released Flint, a mixture-of-experts model with 40 billion total and 2.5 billion active parameters, built in four months with the help of its "Eureka Machine" system that proposed, ran, and evaluated experiments. Recursive claims Flint leads coding benchmarks among MoE models under 50 billion parameters, though the results have not been independently verified, and the weights are available on Hugging Face under Apache 2.0. The company says Flint runs locally with context windows up to 512,000 tokens and generates over 125 tokens per second on an M4 Pro, but acknowledges it needs more guidance on complex tasks than large proprietary models.

Oct 14Wed
  1. Google DeepMind58

    Google DeepMind and Singapore Government collaborate to build fine-tuned AI model for scam detection

    Google DeepMind and GovTech Singapore fine-tuned Google's lightweight Gemma model on local scam conversations to create a privacy-preserving, on-device scam detection model that processes conversation data directly on the device. The model uses a two-stage pipeline—a lightweight classifier screening inbound messages, followed by in-context analysis with the fine-tuned Gemma model—to trigger real-time warnings, and was developed within GovTech's GCP environment using anonymised datasets without private or personally identifiable information. Presented at Singapore International Cyber Week (SICW 2026), the collaboration is described as a model for messaging platforms balancing user protection with end-to-end encryption.

Oct 7Wed
  1. Google DeepMind72

    EmbeddingGemma 2: an open, lightweight multimodal embedding model

    Google DeepMind announced EmbeddingGemma 2, a 740-million-parameter model built on the Gemma 4 architecture and released under the Apache 2.0 license, which maps text, code, images, video, and audio into a unified embedding space for on-device use. It offers modular encoders (as little as 270M parameters for text-only, with optional 170M vision and 300M audio encoders), Matryoshka Representation Learning to truncate vectors from 768 down to 128 dimensions, an 8K token context window, and on a Google Pixel 11 Pro requires as little as ~191MB active RAM for text-only weights and ~567MB for the full multimodal model. The company reports a 9.92-point improvement on MTEB Code (68.76 to 78.68) over EmbeddingGemma and says the model is available on Hugging Face and Kaggle, with support across tools including transformers, vLLM, llama.cpp, Ollama, and Qdrant.

    Why it matters: It documents a new open, commercially licensed embedding model with concrete size, memory, and benchmark figures relevant to developers building on-device search and RAG.