I replaced Substack's algorithm with a chronological feed of new posts

Hacker News

The Substack algorithm is completely broken, and pretty hostile to readers. If you create a new account, you'll be drowned in low value slop Notes. There is genuine, unique human writing in there (reminiscent of the early internet), but it's drowned out by AI publication mills and people with huge followings. I built this so I can strip out all recommendations, and go back to the basic chronological feed. I maintain a list of about 130k publications and I check them at least once a day. Homepage refreshes every 15mins and there is no voting/like buttons etc. It's not perfect, but keen to hear what people think!

Visit product View on Hacker NewsMar 24, 2026

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Launch Intel predictions

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

82%82% 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

69%69% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

55%55% 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

48%48% 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

29%29% 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

21%21% 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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