Calibre Test Profiles – Test your sites under real world conditions

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Hi HN! I'm Ben, the solo founder of Calibre Analytics: https://calibreapp.com . Calibre monitors websites and applications in a clean-room testing environment from all over the world. Up until January Calibre has been an "inbetween other things" / evenings / holidays kind of project. (Only, oops—I did that for four years.). Last year I knuckled down to get into the position where I could work on it full time. I've spent the last 8 weeks working on "Test profiles" (The launch post is here: https://medium.com/get-calibre/announcing-test-profiles-and-... ) — It allows you to emulate hardware devices (cpu speed, user-agents and screen dimensions), as well as limit the bandwidth (latency, download and upload speeds), and set cookies (you could use this to log into your app, test a staff-shipped feature, or maybe turn off advertising and see the performance impact.) I think I've finally got the basis of a really powerful tool, and I'm keen to share it with… everyone! I do everything myself, so if you've any questions, I'm here to answer them!

Visit product View on Hacker NewsMar 23, 2017

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, user · Missing: mac, macos, cursor

77%77% 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.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

68%68% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

61%61% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

50%50% 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

43%43% 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

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: real world · 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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