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Diggit.dev – Git history for architecture archaeologists
Hacker NewsDiggit.dev – Git history for architecture archaeologists
Hello friends, Today I'm sharing a little tool to help you explore GitHub repositories: • https://diggit.dev This project was admittedly a big dumb excuse to play with Elm and Claude Code. I published my design notes and all the chat transcripts here: • https://taylor.town/diggit-000 Please add bug reports and feature requests to the repo: • https://github.com/surprisetalk/diggit Enjoy!
Share cardActual performance
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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, notes, code · Missing: mac, agents, macos83%83% 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.
Hacker NewsStrong engagement from HN community · Strong signals: 000 · Missing: https docs, excited, just released56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting54%54% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal41%41% 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, revenue20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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