A Coding Agent from Scratch

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I saw a post about building a coding agent from "first principles" and that inspired me to create my own. I used my own custom Rust-based ML DSL to implement this. The blog post includes link to the repos (MIT Licensed), a video of the agent in action, sample code produced, and an Emacs org-mode literate programming document explaining the implementation and tests. There are links to an interactive playground for the sw-MLPL language. The git repo can be used to run this agent locally on a system with Ollama and a coding LLM. Does not yet connect to cloud LLMs. I hope this helps someone learn how a coding agent can be built. Inspired by (but not ported from) OpenCode.

Visit product View on Hacker NewsSep 16, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, agent, coding · Missing: agents, macos, cursor

93%93% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Hacker NewsStrong engagement from HN community · Strong signals: llama, ide, io · Missing: https docs, excited, just released

65%65% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

61%61% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: video · Missing: mobile apps, ios, personal

34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue

21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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An itch that I can't quite scratch.

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