A notebook for prototyping with your agent

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Above is a link to the NAWC documentation, that's also a live version of NAWC you can try in your browser. I made nawc because I loved spec-driven development with agents, but hated how inconsistent markdown is at describing intent. I needed a more streamlined approach, where intent is captured with more deterministic probes that are easy to refine and iterate on. The solution is NAWC - an extensible HTML-based notebook that lives in your codebase. It has built-in plugins that add custom components that can reference live source and run interactive examples and tests right inside the notebook. It also ships with a set of agent skills, inspired by Matt Pocock's skill collection, designed to help you refine a rough idea into a polished TDD-ready design through prototypes you can see and interact with. I'd especially love to hear some feedback from people who find it difficult to keep up with the code when working with AI agents. I'd love for this tool to be a way to help people have a deeper understanding of the code they (have their agents) write, and would love to know what else I can add to help support that goal. NAWC is open sourced under an AGPL license over at: https://github.com/Narwhster/nawc You can read more about my process coming up with the idea for NAWC, as well as future projects that stem from it over at: https://narwhster.com/notes/nawc-and-nit/building-nawc-and-n...

Visit product View on Hacker NewsJul 31, 2026

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

89%89% 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.

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

67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: open source, ide, io · Missing: https docs, excited, just released

40%40% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

36%36% 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

34%34% 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

15%15% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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