Ledit – all your procrastination in one place

Hacker News

For procrastination, I usually go to Reddit, HN, or RSS/Atom feeds. I wanted a single place to view all these, without any distractions like adds, permanent UI redesigns, and super simple navigation and feed management. So I built ledit over the last few nights. Here's a very amateurish promo video. ledit is open-source and can also easily be self-hosted if you so wish. https://github.com/badlogic/ledit2 My background is in gaming tech and compilers, so I'm not exactly sure what I'm doing. I've used lit + tailwind for the frontend and Node/Express/Postgres as the backend stack. YMMV Happy to get suggestions for improvements!

Visit product View on Hacker NewsDec 23, 2023

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Launch Intel predictions

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

72%72% 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

68%68% 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: 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: video · Missing: mobile apps, ios, personal

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

33%33% 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

11%11% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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