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Hacker News user blogroll

Hacker News

Hacker News user blogroll

I saw this [0] pretty cool thread by user revskill, and wanted a quicker way to search through it, but also to keep them all in one place so I can read them at my leisure whenever I get time. Right now is like 60 lines of Ruby using Nokogiri, but I will certainly look into it further down the line and improve the list. There's a cronjob checking the thread every 12 hours but I will eventually shut that down and it will become static after that. There are some really awesome blogs in there. I really recommend going through the list, it made my day. [0] "Could you share your personal blog here". https://news.ycombinator.com/item?id=36575081

Visit productView on Hacker NewsJul 5, 2023
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Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: hacker news · Missing: https docs, excited, just released
77%77% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
60%60% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Strong signals: user, new, using · Missing: mac, agents, macos
54%54% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: personal, way · Missing: mobile apps, ios, entrepreneurs
52%52% 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
10%10% 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.

Correct prediction on native model

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