I made a brutalist closer-to-retro directory for new product launches

Hacker News

i built Launchy with the help of an open source base as a small experiment in bringing back the feel and vibe of old-school web directories, but with a focus on new product launches. the goal was to see if directories could feel more human and less like a modern simplistic vibe everyone seems to be going for these days. you can browse launches, submit your own, or just enjoy the aesthetic. i'm curious what you think, does the minimalist and brutalist throwback design still appeal to today's builders, or are the modern clean and simplistic approaches better? (p.s. there's also an unscrambling game hidden in the footer)

Visit product View on Hacker NewsOct 26, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, open · Missing: mac, agents, macos

81%81% 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

55%55% 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: open source, 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

35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

24%24% 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

16%16% 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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