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Tracking Ukrainian border crossing delays

Hacker News

Tracking Ukrainian border crossing delays

I went to Lviv last month for friend's wedding. It became apparent during travel planning that crossing border is non-trivial. In the end, I planned one day for each border crossing, with no other travel on that day. I later found Ukrainian Customs Service maintains a web-site where they publish crossing delays, for Ukrainian side only, for passenger and cargo vehicles. I made a little site which regularly scrapes that data, collects it, and publishes it. Big thank-you as ever to Postgres and Gnuplot. It is apparent that at least of the data, some of time, is likely not accurate. I don't know how they generate data. I suspect it's some guy sitting at a computer going "I think it's taking about this long now", but on the hand I also see minute-by-minute changes, which is too frequent for human input. In any event, if the numbers for a given crossing look unreliable, then you can know taking that crossing is risky in the sense of you know you don't know what's really going on (and you might well be better off selecting a crossing which looks plausible). Border crossings have a lot of reviews on Google Maps (although some could be fakes from hostile actors). The long delays (20 hours plus) for some crossings can be seen to have reviews describing the same experience.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
86%86% 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 HuntOn track for Day 1 leaderboard · Strong signals: google, computer · Missing: mac, agents, macos
78%78% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide · Missing: https docs, excited, just released
54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Strong signals: plus, reviews, host · Missing: platform, intuitive, exclusive
53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month, google · Missing: mobile apps, ios, personal
40%40% 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
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BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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