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Anvil – Desktop App for Spec Driven Development

Hacker News

Anvil – Desktop App for Spec Driven Development

Very excited to share Anvil. I built Anvil to take back control when working with parallel coding agents. It comes with one click worktree isolation, and first class spec support. Claude Code and similar coding TUIs are very eager to get into writing code, even before their human baby sitter fully understands the implication of what they are about to build. The core insight with Anvil is that it is much easier to write high quality code which matches the author's intent after iterating on an external plan with your agent. Align on the architecture, implementation, and verification strategy in a markdown file, then execution is pretty straightforward. This is not a new concept, but the user experience within TUI apps for this workflow is pretty shit. Claude creates non-semantic plan names like "aquamarine-owl" that are trapped within a single agent context. Spinning up multiple agents to check on different aspects of a plan is annoying and slow, and managing terminal tabs is pure hell. So I built anvil, this is a fully open source (MIT license) project.

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Actual performance

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, claude · Missing: mac, macos, cursor
98%98% 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 · Strong signals: para · Missing: supports, reddit linkedin, podcasting
72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, open source, io · Missing: https docs, just released, exist
56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps, para · Missing: mobile apps, ios, personal
26%26% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
20%20% 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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