Si

Simple markup for rich terminal reports

Hacker News

Simple markup for rich terminal reports

I have built WOPR - a simple markup for rich terminal reports, presentations and infographics You can try it: $> curl tty.zone/1\?cols=$((COLUMNS)) (if you experience firewall issues replace tty.zone with ec2-23-21-64-152.compute-1.amazonaws.com) Or if you can handle a full deck: $> p=0; while true; do curl tty.zone/$((p++))\?cols=$((COLUMNS)); read; done More details: http://github.com/yaronn/wopr

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Actual performance

4points
1comments
Did not reach leaderboard

Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
68%68% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
61%61% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: presentations · Missing: mac, agents, macos
50%50% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
29%29% predicted probability of success on BetaList, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
26%26% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
25%25% 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
19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Incorrect prediction on native model

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