Summary of Hacker News comments

Hacker News

Hey everyone, I used to spend hours every day on Hacker News, reading all the posts and diving into the endless discussions. It was interesting, but man, it ate up so much of my time. So, I decided to create a little service for myself using Next.js to make things easier. It picks out the best stuff from Hacker News each day and gives me a quick summary of the comments. Now, I stay updated with the latest tech news without having to scroll through everything. It's been a real time-saver for me, and I thought some of you might find it useful too. If you're tired of spending all day on Hacker News but still want to keep up, give it a try. By the way, if you're curious about the tech stack, it's built with Next.js, Postgres, OpenAI API, Docker, hosted on DigitalOcean, and uses Cloudflare for preventing DDoS attacks.

Visit product View on Hacker NewsJun 13, 2024

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Actual performance

2points

2comments

Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

63%63% 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: dock, new, openai · Missing: mac, agents, macos

57%57% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

51%51% 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: way · 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: host · 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

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: real time · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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