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I'm deprecating LinkedIn recruitment with a Lisp, SQLite and htmx
Hacker NewsI'm deprecating LinkedIn recruitment with a Lisp, SQLite and htmx
FAQ: https://withoutdistractions.com/cv/faq Feel free to ask me any questions. FAQ archive in case the website goes down: https://web.archive.org/web/20220503102946/https://withoutdi...
Share cardActual performance
107points
57comments
Made the leaderboard
Launch Intel predictions
Analyze your own launch →Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released57%57% 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.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto37%37% predicted probability of success on BetaList, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos16%16% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Correct prediction on native model
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