An AI "senior engineer" that crafts specs before vibe coding

Hacker News

I built an AI senior engineer that helps you craft specs for coding tools like Cursor so you can get the code you want with fewer retries. I was getting frustrated with AI coding tools. They’re good at spitting out code, but they also make aggressive assumptions, generate a lot of unnecessary stuff, and often ignore existing functions. I kept ending up spending more time debugging than building. So I thought: what if I had a tool that forced me to plan first? One that could use the context of my codebase and project docs, push back when I was vague, and help me write a clear spec before I let the AI start coding. I’ve been dogfooding it for a week. It’s already made me less frustrated with AI-generated code because it points out things I should consider and produces specs that tell me exactly which file or method needs to change. Repo: https://github.com/suzuking1192/samurai-agent It’s still very early, but I’d love feedback, and also tips on how you approach “vibe coding” in a better way.

Visit product View on Hacker NewsAug 26, 2025

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

94%94% 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 HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

44%44% 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: exist, existing, ide · 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

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 · 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.

Correct prediction on native model

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