AWS Quick and New Relic automate incident triage

Apple announced AFM 3, a new foundation model family developed with Google that includes a 20-billion-parameter on-device model storing weights in NAND flash rather than DRAM. The architecture routes expert selection once per prompt instead of per token, allowing larger models to run locally while staying within consumer device memory constraints. This addresses a fundamental limitation that has kept on-device AI models significantly smaller than cloud alternatives.

Cohere released North Mini Code, a 30 billion parameter open-source coding agent that runs on a single H100 GPU, offering engineering teams an alternative to proprietary models like Claude Fable 5. The model is purpose-built for agentic software engineering tasks including code review, architecture mapping, and terminal work, but generates roughly three times more output tokens than comparable models in testing, creating a verbosity cost in high-volume production scenarios.

Google DeepMind introduced Gemma 4 12B, a multimodal AI model designed to run on consumer laptops with 16GB of RAM. The model uses an encoder-free architecture that processes vision and audio inputs directly into the language model backbone, reducing latency and memory overhead. Performance approaches the larger 26B model while maintaining a smaller footprint, and it is released under an Apache 2.0 license.

Researchers from UIUC, UC Berkeley, and Chroma released Harness-1, a 20-billion parameter open-source search agent that scores 73% on information recall benchmarks, outperforming GPT-5.4 (70.9%) and other proprietary models. The model is available under Apache 2.0 license on Hugging Face. Harness-1 achieves its performance by offloading search session management to a structured software environment rather than relying on expanded context windows, suggesting that model efficiency matters more than raw parameter size for autonomous retrieval tasks.