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Sync and search your Twitter likes and bookmarks

Hacker News

Sync and search your Twitter likes and bookmarks

Hey HN, I use twitter a lot and often bookmark interesting tweets to come back to later, but find that I rarely actually come back to them (twitter's native bookmarks interface is kind of high friction). Same for tweets I've liked or favorited: it's an exquisitely weird experience to know I've seen a tweet and even favorited it, but that I'll probably never be able to find it again. A good way to find the tweets I was looking for would tick these boxes: - fast (10s of ms not 100s) local search: find any tweet instantly - easily query and export the data - automatically sync new bookmarks or likes BirdBear is my attempt to scratch this itch. It uses SQLIte in the browser via WASM to store your liked and bookmarked tweets locally, and then lets you do instant full text search over all of them (powered by SQLite's FTS extension). There's also a SQL console built in, so if you know SQL you can slice and dice your bookmarks and likes to your heart's content. You can also export all your liked or bookmarked tweets as JSON to use them outside of BirdBear. Right now the focus is on bookmarks and likes, but the plan is to build this into a more general twitter data tool to let you locally download tweets from all the accounts you follow, keep them up to date, and to add organization features like folders and tagging. A sort of personal tweeterbase if you will. How do you keep track of the tweets you like? Any great tools or tricks you've discovered or features you'd like to see in a tool like this? edit: formatting

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Product HuntOn track for Day 1 leaderboard · Strong signals: new · Missing: mac, agents, macos
71%71% 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.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
58%58% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: interface · Missing: plus, platform, intuitive
45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal, way · Missing: mobile apps, ios, entrepreneurs
31%31% 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
12%12% 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.

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