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Instantly create a GitHub repository to take screenshots of a web page

Hacker News

Instantly create a GitHub repository to take screenshots of a web page

I built a GitHub repository template which automates the process of configuring a new repository to take web page screenshots using GitHub Actions. You can try this out at https://github.com/simonw/shot-scraper-template Use the https://github.com/simonw/shot-scraper-template/generate interface to create a new repository using that template, and paste the URL that you want to take screenshots of in as the "description" field. The new repository will then configure itself using GitHub Actions, take the screenshot and save it back to the repo!

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, using · Missing: mac, agents, macos
63%63% 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.
AppSumoStrong fit for a featured deal · Strong signals: interface · Missing: plus, platform, intuitive
54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
47%47% 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: io · Missing: https docs, excited, just released
47%47% 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
43%43% 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
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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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