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Shadowbook – Keep your tasks in sync with your specs
Hacker NewsShadowbook – Keep your tasks in sync with your specs
I forked Beads and added spec intelligence. When a spec changes, linked issues get flagged. It also tracks spec volatility—high-churn specs with open issues are where drift is most likely. Plus auto-matching and compaction to shrink old specs. Link: https://github.com/anupamchugh/shadowbook
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Product HuntOn track for Day 1 leaderboard · Strong signals: tasks, open · Missing: mac, agents, macos51%51% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released44%44% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto32%32% predicted probability of success on BetaList, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: plus · Missing: platform, intuitive, reviews31%31% 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, revenue13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting10%10% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Correct prediction on native model
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