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Compile English specs into 22 MB neural functions that run locally

Compile English specs into 22 MB neural functions that run locally

by yuntian·Apr 15, 2026·11 points·0 comments

AI Analysis

●●●BangerWizardryZero to OneBold Bet

22 MB compiled neural functions run locally — no API calls after compilation.

Strengths
  • LoRA adapter architecture keeps base model fixed; task behavior lives in compiled program
  • Browser inference via WebAssembly with GPT-2 124M for client-side execution
  • Deterministic output claim is bold for neural nets — if true, genuinely useful
Weaknesses
  • 0.6B base model is small — complex tasks may exceed its capability ceiling
  • Compilation happens on their server; trust required for sensitive specs
Category
Target Audience

Developers building local-first AI features without API dependencies

Similar To

Guidance · LMQL · Outlines

Post Description

We built ProgramAsWeights (PAW) — https://programasweights.com

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.

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.

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"

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.

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%.

Also runs in the browser (GPT-2 124M, WebAssembly): https://programasweights.com/browser

You can also use it in your AI agents by copying the prompt here: https://programasweights.com/agents

Source: https://github.com/programasweights

Try it out: https://programasweights.com

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