How well do you use Claude Code?

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A bunch of companies that I spoke to had their own claude & codex OTel dashboards that showed spend + seats per month. However, none of the dashboards actually analyzed how the engineers worked with the tools and if there were any areas for improvement! That's why I created https://www.promptster.ai . Managers get aggregate level view of code quality and how that ties with team workflows (nothing on a per-engineer level). While engineers get personalized coaching on how they can save tokens while keeping output high. We also have a tool built for individuals to test their local claude code setups. It's open-source and completely local. You can check that out here: https://github.com/pa-arth/cc-audit . Would love feedback on either and reach out if you're interested in purchasing the teams product!

Visit product View on Hacker NewsJul 24, 2026

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Actual performance

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, codex, code · Missing: mac, agents, macos

92%92% 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 · Missing: https docs, excited, just released

52%52% 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 HackersIH features products with proven revenue · Strong signals: created · Missing: supports, reddit linkedin, podcasting

47%47% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: personal, month · Missing: mobile apps, ios, entrepreneurs

39%39% 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

33%33% 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

17%17% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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