Needle is an open-source foundation model from Cactus Compute that distils Gemini-class tool calling into a 26-million-parameter Simple Attention Network, so devices that cannot host a frontier model can still turn a natural-language request into a structured function call. In production it runs on Cactus at roughly 6,000 tokens/sec prefill and 1,200 tokens/sec decode on phones, wearables, smart-home hubs, and robots, while the weights ship in about 14 MB and you can finetune a personal AI on your own tools locally on a Mac or PC. A bundled web UI exposes testing, evaluation, and one-click finetuning, and the dataset generator and pretrained weights are released under MIT. It beats FunctionGemma-270m, Qwen-0.6B, Granite-350m, and LFM2.5-350m on single-shot function calling for personal AI, even though those models are broader in scope. Needle matters now because on-device function calling is the last mile between consumer hardware and an agent stack that does not phone home.
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