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Template for Annual Review of Life Goals

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Template for Annual Review of Life Goals

Every year, my wife and me go for coffee and summarize what went well and less well in the last year and which goals we set and achieved (or missed). This is loosely based on the life frame technique outlined in Getting Results the Agile Way by Michael Kropp. This year, I made this into a proper template to have multiple years at a glance. Maybe it's useful for someone: https://docs.google.com/document/d/1Cy6ALPwxF5ERrNMTm8dopM4L-ZbDf4OtZfcoE-YBf-g/edit?usp=sharing

Visit productView on Hacker NewsDec 24, 2019
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Indie HackersFits the IH revenue-focused audience · Strong signals: wife · Missing: supports, reddit linkedin, podcasting
59%59% 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 NewsStrong engagement from HN community · Missing: https docs, excited, just released
50%50% 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: google · Missing: mac, agents, macos
43%43% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: google, way · Missing: mobile apps, ios, personal
42%42% 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
34%34% 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
23%23% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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