Career Craft.ing

Career Craft.ing

TrustMRR

By the time performance reviews arrive, you have forgotten the metrics, decisions, and glue work that made your quarter matter. careercraft.ing helps you capture engineering wins while they are fresh,

By the time performance reviews arrive, you have forgotten the metrics, decisions, and glue work that made your quarter matter. careercraft.ing helps you capture engineering wins while they are fresh, then turns them into promotion-ready proof for reviews, calibration, resumes, and interviews. It works inside the AI tools you already use, without background scraping or employer-owned tracking.

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Actual performance

Did not reach leaderboard

Traction signals

Domain Rating18

Launch Intel predictions

Analyze your own launch →
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
62%62% 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 HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos
60%60% predicted probability of success on Product Hunt, 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
36%36% predicted probability of success on Hacker News, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: reviews · Missing: plus, platform, intuitive
35%35% 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, personal
28%28% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
19%19% 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
3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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