LATEST JOB IN ASSAM: May 2026

Show HN: Needle: We Distilled Gemini Tool Calling into a 26M Model
10 by HenryNdubuaku | 1 comments on Hacker News.
Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention is the right primitive for this, and FFN parameters are wasted at this scale. Simple Attention Networks: the entire model is just attention and gating, no MLPs anywhere. Needle is an experimental run for single-shot function calling for consumer devices (phones, watches, glasses...). Training: - Pretrained on 200B tokens across 16 TPU v6e (27 hours) - Post-trained on 2B tokens of synthesized function-calling data (45 minutes) - Dataset synthesized via Gemini with 15 tool categories (timers, messaging, navigation, smart home, etc.) You can test it right now and finetune on your Mac/PC: https://ift.tt/cLsNU6K The full writeup on the architecture is here: https://ift.tt/J81foIv... We found that the "no FFN" finding generalizes beyond function calling to any task where the model has access to external structured knowledge (RAG, tool use, retrieval-augmented generation). The model doesn't need to memorize facts in FFN weights if the facts are provided in the input. Experimental results to published. While it beats FunctionGemma-270M, Qwen-0.6B, Granite-350M, LFM2.5-350M on single-shot function calling, those models have more scope/capacity and excel in conversational settings. We encourage you to test on your own tools via the playground and finetune accordingly. This is part of our broader work on Cactus ( https://ift.tt/Lsay4TY ), an inference engine built from scratch for mobile, wearables and custom hardware. We wrote about Cactus here previously: https://ift.tt/M2upWRs Everything is MIT licensed. Weights: https://ift.tt/i5W3pz7 GitHub: https://ift.tt/cLsNU6K

Show HN: GETadb.com – every GET request creates a DB
10 by nezaj | 1 comments on Hacker News.
Hey HN! We made GETadb.com, so it's easier to get agents to build you full stack apps. You don't need to give them any credentials. Just by loading a GET request, they get access to a database, a sync engine, and abstractions for auth, presence, and streams. To see what the agent sees, you can load https://getadb.com/new There's two fun things about how it's implemented: 1. If you curl the home page, it the agent content rather than human content. We do this by detecting the 'Sec-Fetch-Mode' header. It's not perfect, but gets the job done for Claude Code et al. 2. For an agent to spin up an app, they make _two_ fethes. (1) getadb.com/guide tells them to generate a uuid, and fetch (2) getadb.com/provision/. We did this, because just about half of the popular web-based app builders cache URLs globally, even if you return no-store headers. To get around this we just instruct the agent to generate unique URLs You may wonder: Why GET requests, rather than POST requests? It's because then you can build in surprising places. For example, we get meta.ai to build an app inside the artifact preview: https://ift.tt/ryzGvMh Under the hood, this is possible because the whole infra is mult-tenant from ground up. We already announced how that works on HN, but if you're curious here's the essay for it: https://ift.tt/RtCs5F6

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