[Target: Calamity] A visualization of how long we have to change course

Hacker News

An interactive visualization of (ideally) all of the factors that drive the amount of time remaining for us to take meaningful action toward fixing the existential threats facing Humanity. It uses an AI-powered scanner to detect and evaluate these factors and synthesize them into a time remaining by anchoring published tipping-points and warping them by various factors. This is a work-in-progress and should be taken with a grain of salt as-is. LLMs are imperfect and this is merely meant to be a proof-of-concept for a more accurate and complete system. Let me know what you think!

Visit product View on Hacker NewsJul 31, 2026

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3points

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Did not reach leaderboard

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

86%86% 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.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, lua, ide · Missing: https docs, excited, just released

45%45% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntUnlikely to reach the leaderboard · Strong signals: visual · Missing: mac, agents, macos

42%42% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: scanner · Missing: mobile apps, ios, personal

36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · 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 · Strong signals: active · Missing: arr, mrr, revenue

15%15% 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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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