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Data source: swyx's twitter data

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idusercreated_atfull_textretweeted_statusquoted_statusplacesourcetruncateddisplay_text_rangein_reply_to_status_idin_reply_to_user_idin_reply_to_screen_namegeocoordinatescontributorsis_quote_statusretweet_countfavorite_countfavoritedretweetedpossibly_sensitivelangscopes
1430388488923324417 33521530 2021-08-25T04:36:05+00:00 @mikeal i worked at AWS and supabase is outshipping teams 10x their size and funding. i dont know whats in the water there but @kiwicopple and team have got it. https://twitter.com/swyx/status/1424586713490358275?s=21   1424586713490358275   95f3aaaddaa45937ac94765e0ddb68ba2be92d20 0 [8, 185] 1430193529565839368 668423 mikeal       1 6 82 0 0 0 en  
1430608769319198720 33521530 2021-08-25T19:11:24+00:00 @mwarger @FernandoTheRojo @jamonholmgren yup! gqlless was inspired by babel blade. i think the static analysis approach could probably be built into relay/apollo if people cared enough, but ultimately my attention shifted to GraphQL schema creation rather than consumption https://twitter.com/graphqlasia/status/1128284234928418816   1128284234928418816   1f89d6a41b1505a3071169f8d0d028ba9ad6f952 0 [41, 297] 1430602199801999364 15886909 mwarger       1 0 2 0 0 0 en  
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