I made a tool that updates docs screenshots in seconds instead of hours

Hacker News

Ciao HN! We made Paparazzi Docs, a dev tool that updates documentation screenshots in seconds instead of hours. We love to ship code fast. But… Documentation and guide maintenance are slow and a pain for us. Paparazzi Docs will take a screenshot of each page whenever you run your tests and automagically update your docs with the new ones. - Integrate our npm package into your E2E tests, like Cypress. - Craft and revise your documents and guides with our editor, which converts what you write into MDX format. - Deploy a Nextra template with all your changes with one click. We’re a team of just two developers. We chose to develop Paparazzi Docs because we saw firsthand that updating docs slows down the release of new features.

Visit product View on Hacker NewsJan 17, 2024

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

90%90% 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 · Strong signals: para · Missing: supports, reddit linkedin, podcasting

79%79% predicted probability of success on Indie Hackers, 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

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

nativeThis product was originally launched on this platform.

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

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

29%29% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

21%21% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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