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I built a site that tracks short-lived flight deals

Hacker News

I built a site that tracks short-lived flight deals

So, this summer I was hunting for a flight from Calgary to Tokyo with WestJet, and I kept seeing these random cheaper flights popping up. I got tired of refreshing the site non-stop, so I whipped up a script to scrape the flight data. It paid off—I scored a roundtrip for $1000 CAD in July! Naturally, I decided to turn this into a full-fledged project and built flight-price-alert.com! Tech details: Built with Go for the backend and SQLite for the database. Added some TLS spoofing to get around Cloudflare’s JA3 fingerprinting that was blocking my requests. Styled the site with PicoCSS—it was super easy and kept things neat. Generated tons of images with Recraft.ai. Seriously, I must’ve churned out hundreds. It was a blast! Hosted on a single EC2 instance with EBS attached for SQLite storage. It’s pretty simple right now, and I’m debating whether to keep developing it or move on. If you have any questions or ideas for improvements, I’d love to hear them!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
82%82% 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.
Hacker NewsStrong engagement from HN community · Strong signals: ide, 000, io · Missing: https docs, excited, just released
73%73% 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 HuntOn track for Day 1 leaderboard · Strong signals: single · Missing: mac, agents, macos
71%71% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
34%34% 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
18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: paid · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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