Show HN: Needle2: 14MB agentic LLM for phones, wearables, smart home and robots
- ID
- 12980
- Status
- summarized
- Published
- 11 Aug 2026, 1:22 AM
- Fetched
- 11 Aug 2026, 12:03 PM
- Provider
- Hacker News
- Category
- dev-community
- Original URL
- https://cactuscompute.com/needle
- Source URL
- https://hnrss.org/best
Summary
- Score
- 7.0
- Created
- 11 Aug 2026, 12:03 PM
- Tags
- Audience
- developersai_ml_learnersai_agent_usersvibe_coders
What happened
Needle 2 is a 45M-parameter agentic LLM compressed to a 14MB binary that runs in 28MB of RAM, designed for tool calling, device control, and structured extraction on sub-$200 hardware. It hits 500 tokens/sec decode on a Raspberry Pi 5, 300–700 on budget Samsung A-Series phones, and even runs on ESP32-S3 microcontrollers. It trades benchmark wins with models 5×–70× larger (FunctionGemma 270M, LFM2.5 230M, Apple FM) on mobile device-use tasks, and is released under Apache 2.0 with weights on Hugging Face.
Why it matters
If you are building IoT, smart home, wearable, or robotics products targeting the Malaysian or broader SEA market where most phones ship under $200, Needle 2 lets you run on-device function calling without a GPU, NPU, or cloud dependency. The 28MB RAM footprint means you can prototype agent-based device control on hardware you already have—Raspberry Pi, ESP32-S3, or budget Android phones—today, with the repo and sandbox available to test immediately.
Discussion angle
The framing that 'turning on a light does not need a frontier model' is worth debating: where does the 45M-parameter ceiling actually break down for real Malaysian smart home or robotics use cases—multi-turn dialogue, Malay/Chinese/Tamil language input, or complex routing across many devices?