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Printable Thinking Templates – Mental Models You Fill Out by Hand

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Printable Thinking Templates – Mental Models You Fill Out by Hand

I built a collection of one-page printable templates for decisions, prioritization, and reflection. The twist: they're designed to be filled out with pen and paper, not digitally. Why paper? Writing by hand is slow—and that's the point. When I type, I can outpace my thinking. When I write, I catch myself mid-sentence realizing "wait, that's not actually true." The friction is a feature. What's included: - Decision Canvas (pros/cons + second-order effects + cognitive bias check) - Eisenhower Matrix (urgent vs. important) - Stop/Start/Continue audit - Rubber Band Model (what's pulling me forward vs. holding me back) - Cognitive Bias Checklist (pre-flight check before any big decision) Tech: All templates are written in Typst. GitHub Actions compiles to PDF, generates PNG thumbnails, and auto-generates the README from metadata comments in each .typ file. Zero manual release process. Philosophy: If a decision is easily reversible, don't use a template—just move. These are for the choices that keep you up at night. GitHub: https://github.com/philippnagel/thinkingtemplates I'd love feedback on which templates are actually useful vs. overthinking-theater, and ideas for new ones.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
78%78% 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 HuntUnlikely to reach the leaderboard · Strong signals: model, new, models · Missing: mac, agents, macos
46%46% 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
38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
31%31% 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 · Missing: mobile apps, ios, personal
31%31% 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.

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