Pester Companies

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Pester Companies

Pester companies anonymously and publicly, with product ideas, suggestions, praise, or rants. Pester with zero-friction and complete privacy—no tracking cookies, no personal data retention, no user account required. We hope to gather a large corpus of data to provide business intelligence to companies, hopefully getting them to make the products and services that people actually want.[1] This little tool also serves as a free, quick-and-dirty (and very simple) way of gathering clean, public feedback from your users, if you're an upcoming startup that doesn't want to invest time and money in a more complex (and probably needlessly bloated) solution. --- [1] This idea arose after a re-read of Don Norman's famous book, The Design of Everyday Things. --- TECHNICAL NOTES The upester web app was initially an Angular SPA, but we ported it to Qwik, for the SSG capabilities. We use Cloudflare's relatively recent Turnstile invisible CAPTCHA technology to block bad robots. (Kudos to the Cloudflare folks for this awesome solution, which they made public). We use a managed backend (GCP, namely Firebase, Cloud Functions and Cloud Run), which works very well and it reduces dev time to a tiny fraction of what it would have been if we wrote the backend from scratch. Happy to answer questions about any of these technologies as well, besides any questions you may have about upester, to the best of my ability. Have a great day, everyone!

Visit product View on Hacker NewsMar 28, 2023

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

88%88% 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 · Strong signals: user, tiny, notes · Missing: mac, agents, macos

83%83% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

57%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.

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

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive

39%39% 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 · Missing: web3, chat, crypto

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

Incorrect prediction on native model

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