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Visual representation of Hacker News comments

Hacker News

Visual representation of Hacker News comments

Sometimes I want to make sure I read every comment in a Thread. Also comments that come in later. For example, when I posted my laptop comparison map I got a ton of great feedback: https://news.ycombinator.com/item?id=8405065 and I wanted to make sure I don't miss any of these. For this purpose, I made this visual representation: http://social.gnod.com/hn:8405065 I think you can grok what it shows. It simply templates all comments in a recursive way. Read items turn blue. You can mark interesting items and they turn green. I find it interesting to look at different threads this way. Every one has it's own unique appearance. Here is a very short one for example: http://social.gnod.com/hn:8710416 I use this bookmarklet to switch between HNs regular view and this visual representation: javascript:l=location.href; if (l.indexOf("y")>-1) a=l.replace(/[^0-9]*/,"social.gnod.com/hn:"); else a=l.replace(/[^0-9]*/,"news.ycombinator.com/item?id="); location.href="http://"+a;

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Actual performance

11points
1comments
Made the leaderboard

Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: hacker news, io · Missing: https docs, excited, just released
64%64% 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
62%62% 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: new, visual, grok · Missing: mac, agents, macos
55%55% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
44%44% 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
17%17% 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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