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Awesome Directories – Open-source launch directory aggregator

Hacker News

Awesome Directories – Open-source launch directory aggregator

Hi HN, I spent 20 hours manually researching "best startup directories" for my last launch. Found outdated GitHub lists, dead links, and no data on which directories actually matter. So I built this instead. *Live*: https://awesome-directories.com *GitHub*: https://github.com/awesome-directories/awesome-directories *What It Is* An open-source aggregator of 300+ verified launch directories with: - Domain Rating scores (Ahrefs), dofollow/nofollow badges, pricing filters - Community voting, favorites, and submission tracking per project - CSV export and multi-select checklist *Tech Stack* - Frontend: Astro.js + Tailwind → GitHub Pages (static) - Backend: Supabase (PostgreSQL + Auth) - Data: Ahrefs API via Supabase Edge Functions (weekly updates) - Cost: $20/month *Why Open Source* Apache-2.0. Fork it, remix it. I'm building reputation, not revenue. The indie hacker community helped me learn—paying it forward. *Technical Questions* 1. Client-side filtering vs Supabase full-text search: I went client-side for <500 directories. At what scale would you switch? 2. PDF generation: Currently client-side (jsPDF). Worth moving server-side? *The Retention Problem* Directory aggregators have a fundamental issue: once you find your directories, you never return. Current ideas to solve this: - Weekly digest of new directories - User-submitted launch stories ("I got 200 signups from BetaList") - Crowd-sourced directory performance rankings How would you solve this? *Feedback Wanted* - Is the UI too busy? (Backend engineer, not a designer) - What directories am I missing? - Would you add performance tracking (which directories drove actual traffic)? Looking for contributors to expand the directory list or improve the stack. Thanks for reading.

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Launch Intel predictions

Analyze your own launch →
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
85%85% 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 HuntOn track for Day 1 leaderboard · Strong signals: user, new, open · Missing: mac, agents, macos
58%58% predicted probability of success on Product Hunt, 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
43%43% 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: month · Missing: mobile apps, ios, personal
37%37% 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
29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: revenue · Missing: arr, mrr, profit
24%24% 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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