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Parrot – a viewer for tweet archives from Twitter Media Downloader
Hacker NewsParrot – a viewer for tweet archives from Twitter Media Downloader
Parrot is a browser-based program for viewing tweets from zip archives created by the Twitter Media Downloader browser extension. The Twitter Media Downloader is a great browser extension but there is no way to view tweets from the zip files it generates in a twitter style interface, this tool solve that. It's written in vanilla JavaScript and runs completely within your web browser.
Share cardActual performance
4points
Did not reach leaderboard
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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting61%61% 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.
AppSumoMay struggle as an AppSumo deal · Strong signals: interface · Missing: plus, platform, intuitive45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos43%43% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released36%36% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit17%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, crypto4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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𝙈𝒶ḵẹ_ÿōửȑ__Ṯ𝙬ḗéṫ𝙨▁𝗆ŏrẹ┈𝗰𝚢ḅ𝐞r𝗉ǔń𝒌!
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