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Minimal flight search engine in Python

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Minimal flight search engine in Python

I am currently learning to code with Python. As part practice, part to fulfill a personal need, I have created a no-frills, minimal flight search engine that can be run locally, using the QPX Express API. https://developers.google.com/qpx-express/ The script might be useful for those who - like me - basically procrastinate by searching for flights on a daily basis, but who don't necessarily need to book a flight instantly. Unlike any other web search engine for flights that I know of, my script shows the fare class for each result right away - which can come handy for those who like to optimize for mileage earning. You find a Readme with instructions, the script and a screenshot here: https://github.com/martinweigert/minimal_flight_search

Visit productView on Hacker NewsAug 2, 2017
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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
74%74% 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.
TrustMRRFits verified-revenue profile · Strong signals: personal, google, way · Missing: mobile apps, ios, entrepreneurs
61%61% 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 released
44%44% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntUnlikely to reach the leaderboard · Strong signals: google, using, code · Missing: mac, agents, macos
42%42% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
40%40% 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
17%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, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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