tweets

This is data scraped from swyx's timeline! See blog post

4 rows where "created_at" is on date 2021-12-26, lang = "en", source = "1f89d6a41b1505a3071169f8d0d028ba9ad6f952" and user = 33521530 sorted by lang

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display_text_range 4 ✖

user 1 ✖

  • swyx · 4 ✖

lang 1 ✖

created_at (date) 1 ✖

  • 2021-12-26 · 4 ✖
id user created_at full_text retweeted_status quoted_status place source truncated display_text_range in_reply_to_status_id in_reply_to_user_id in_reply_to_screen_name geo coordinates contributors is_quote_status retweet_count favorite_count favorited retweeted possibly_sensitive lang ▼ scopes
1475105975518765063 swyx 33521530 2021-12-26T14:07:25+00:00 Imagine if those "Career Days" they do for kids came with honest guidance on probability of success: Actor - 90% luck, 5% talent, 5% hard work Doctor - 10% luck, 10% talent, 80% hard work Fireman - 5% luck, 10% talent, 85% hard work Founder - 30% luck, 20% talent, 50% hard work       Twitter Web App 1f89d6a41b1505a3071169f8d0d028ba9ad6f952 0 [0, 279]             0 6 98 1 0   en  
1475112373283598346 swyx 33521530 2021-12-26T14:32:50+00:00 For those who understand optionality, there's also an implicit life career strategy involved here: 1. go for high luck careers first to try your luck 2. try a few high talent careers to discover if you have it 3. fall back to high hard work careers as they're available to anyone       Twitter Web App 1f89d6a41b1505a3071169f8d0d028ba9ad6f952 0 [0, 280] 1475105975518765063 33521530 swyx       0 2 49 0 0   en  
1475137021014605825 swyx 33521530 2021-12-26T16:10:47+00:00 @calcsam @Rich_Harris bit specific, people want to play in the NBA, no matter the position. Steph Curry is 6'2. that aside, to the extent that "talent" is innate and "luck" is exogenous/stochastic, i do like the characterisation of NBA careers as high-talent low-luck       Twitter Web App 1f89d6a41b1505a3071169f8d0d028ba9ad6f952 0 [22, 268] 1475135726060589063 788059556 calcsam       0 0 1 0 0   en  
1475138273899323393 swyx 33521530 2021-12-26T16:15:45+00:00 For those who like economics: High luck industries have a huge amount of deadweight loss from all the people trying their luck (call it search/friction cost). Who's working on better models of industrial organization that more quickly identifies talent and incentivizes work?       Twitter Web App 1f89d6a41b1505a3071169f8d0d028ba9ad6f952 0 [0, 277] 1475112373283598346 33521530 swyx       0 0 5 0 0   en  

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CREATE TABLE [tweets] (
   [id] INTEGER PRIMARY KEY,
   [user] INTEGER REFERENCES [users]([id]),
   [created_at] TEXT,
   [full_text] TEXT,
   [retweeted_status] INTEGER,
   [quoted_status] INTEGER,
   [place] TEXT REFERENCES [places]([id]),
   [source] TEXT REFERENCES [sources]([id]), [truncated] INTEGER, [display_text_range] TEXT, [in_reply_to_status_id] INTEGER, [in_reply_to_user_id] INTEGER, [in_reply_to_screen_name] TEXT, [geo] TEXT, [coordinates] TEXT, [contributors] TEXT, [is_quote_status] INTEGER, [retweet_count] INTEGER, [favorite_count] INTEGER, [favorited] INTEGER, [retweeted] INTEGER, [possibly_sensitive] INTEGER, [lang] TEXT, [scopes] TEXT,
   FOREIGN KEY([retweeted_status]) REFERENCES [tweets]([id]),
   FOREIGN KEY([quoted_status]) REFERENCES [tweets]([id])
);
CREATE INDEX [idx_tweets_source]
    ON [tweets] ([source]);