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VibeSpec – A tool to generate structured specs for Spec2Code

Hacker News

VibeSpec – A tool to generate structured specs for Spec2Code

I've been experimenting with a new concept called Spec2Code, and just launched a prototype SaaS tool named VibeSpec. We hear a lot about no-code, low-code, or prompt-to-code—but these often rely on constant human input.Most agents today need us every few seconds: write a prompt, check the output, correct, repeat. You can’t step away. You definitely can’t ask them to build overnight. Spec2Code is different. It draws from how engineers in aerospace or automotive define complex systems: with structured, layered specifications. The goal is to give agents enough information upfront that they can work autonomously for 15–30 minutes at a time—without micromanagement. The hardest part? Writing those specs. Nobody enjoys it, and tooling for structured requirements is basically non-existent in the software world. So I built VibeSpec, a tool (and eventually, a framework) to turn loose ideas into structured, machine-usable specs—using agents designed specifically for that task. Once done, the specs can be handed off to coding agents for implementation. You can check it out here: https://vibespec.guaeca.com And here’s what the agents built from one of these specs: https: https://secretmessage.guaeca.com/ ~10 minutes writing the spec with the agent ~10 minutes refining via “vibe coding” (adding missing cases) ~5 minutes testing + iterating Would love to hear thoughts from others working on agent workflows or spec-driven development.

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, agents, agent · Missing: macos, cursor, claude
90%90% 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
73%73% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, ide, io · Missing: https docs, excited, just released
50%50% 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
33%33% 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
31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: saas · Missing: arr, mrr, revenue
14%14% 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.

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