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Star Trek database, using SQLite

Hacker News

Star Trek database, using SQLite

This isn't exactly a new project, it dates back to 2015 and started in PostgreSQL (the "postgresql" tag represents the last state of that database). As a weekend project, I overhauled it to port over to SQLite, partly as an exercise, but also to increase its usability. Since SQLite requires no server to install or administer, and the database is a single file, it's now real easy to pass around and play with. While it is biased to Star Trek media that I own and enjoy, it can still be fun to query and get answers to questions you never thought to ask. Example queries: Listing episodes of The Next Generation season 1 by star date: SELECT title, airdate, episode_number, stardate FROM tng WHERE season = 1 ORDER BY stardate; If your birthday is March 15, find the episodes that aired on that day: SELECT series.title AS series, episode.title AS episode, airdate FROM episode JOIN series USING (series_id) WHERE airdate like '%-03-15'; Pick a random episode of Enterprise to watch, and display the Blu-ray disc to find it on: SELECT * FROM ent_bluray ORDER BY random() LIMIT 1;

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Actual performance

4points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
71%71% 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.
Product HuntUnlikely to reach the leaderboard · Strong signals: new, single, using · Missing: mac, agents, macos
50%50% predicted probability of success on Product Hunt, 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
49%49% 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 · Strong signals: answers · Missing: mobile apps, ios, personal
48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
19%19% 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
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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