I built a structured directory to compare AI coding tools

Hacker News

Hi HN, I was getting frustrated with the flood of AI coding tools. Most lists are just noisy, unstructured link dumps that don't help you decide which tool to actually use. So, I built the directory I wanted for myself. My goal is to provide signal, not more noise. What makes it different: - Structured Analysis: Instead of a feature list, I use a consistent schema to compare tools on developer-centric criteria (IDE integration, offline use, model transparency, etc.). - Qualitative Scoring: A transparent methodology to give a quick read on a tool's strengths and weaknesses, moving beyond just feature-counting. - Focus on "Job to Be Done": Helping you find the right tool for a specific task. This is a v1 solo project. It's a static site generated with a custom Node.js script and hosted on Netlify. I know it’s not perfect and tools are missing. I'm posting here for your feedback to make it better. I'd love to know: - Is this approach actually useful to you? - Is the analysis fair? What am I getting wrong? - What essential tools should I add next? Appreciate you taking a look. Thanks!

Visit product View on Hacker NewsJul 31, 2025

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

81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntUnlikely to reach the leaderboard · Strong signals: model, coding · Missing: mac, agents, macos

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

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

38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

35%35% 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 · Missing: mobile apps, ios, personal

24%24% 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

13%13% 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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