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Show HN: Compile English specs into 22 MB neural functions that run locally
We built ProgramAsWeights (PAW) — <a href="https://programasweights.com" rel="nofollow">https://programasweights.com</a><p>You describe a function in English — like "classify if this message is urgent" — and PAW compiles it into a tiny neural program (22 MB) that runs locally like a normal Python function. No API keys, no internet after compilation, deterministic output.<p>It's for tasks that are easy to describe but hard to code with rules: urgency triage, JSON repair, log filtering, tool routing for agents.<p><pre><code> pip install programasweights import programasweights as paw f = paw.compile_and_load("Classify if this is urgent or not.") f("Need your signature by EOD") # "urgent" </code></pre> Compilation takes a few seconds on our server. After that, everything runs on your machine. Each program is a LoRA adapter + text instructions that adapt a fixed pretrained interpreter (Qwen3 0.6B). The model itself is unchanged — all task behavior comes from the compiled program.<p>On our evaluation, this 0.6B interpreter with PAW reaches 73% accuracy. Prompting the same 0.6B directly gets 10%. Even prompting Qwen3 32B only gets 69%.<p>Also runs in the browser (GPT-2 124M, WebAssembly): <a href="https://programasweights.com/browser" rel="nofollow">https://programasweights.com/browser</a><p>You can also use it in your AI agents by copying the prompt here: <a href="https://programasweights.com/agents" rel="nofollow">https://programasweights.com/agents</a><p>Source: <a href="https://github.com/programasweights" rel="nofollow">https://github.com/programasweights</a><p>Try it out: <a href="https://programasweights.com" rel="nofollow">https://programasweights.com</a>
PAW offers a highly innovative technical approach to the 'Small Language Model' trend, demonstrating significant performance gains on tiny local models that could disrupt current API-heavy workflows. While the technical moat and market timing are excellent, the deal is currently held back by a lack of visible traction, unknown team pedigree, and the risk of being commoditized by larger model providers.