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City-aware PTO optimizer with block/prefer day constraints

Hacker News

City-aware PTO optimizer with block/prefer day constraints

Correct link is here → https://somniaplanner.com/en/barcelona/2026 . I built SomniaPlanner, a web app that maximizes your time off by aligning PTO with city-level public holidays and weekends. What’s different: (1) you can mark block (must-work) and prefer (must-take) days as hard constraints—the optimizer must satisfy them. (2) A linear calendar view for planning fans. Why: this was a first step toward a parental-leave planner for Spain; PTO planning lets me validate the constraint UX and solver. How: a client-side dynamic programming algorithm enumerates feasible ranges and scores them by days-off/PTO, with a small balance term to trade off one long streak versus several shorter breaks. Runs fully in the browser (<20ms); no backend. Optimization engine written in TypeScript. Try it: San Francisco 2026 - https://somniaplanner.com/en/san-francisco/2026 Barcelona 2026 - https://somniaplanner.com/en/barcelona/2026 Bogota 2026 - https://somniaplanner.com/en/bogota/2026 Browse all cities: https://somniaplanner.com/en Feedback: – What drawbacks do you see from running client-side only? – Are the block/prefer interactions intuitive and explanations clear? – Any ideas for extending this app (e.g. parental-leave planner or Travel-aware planning — budget + flights/hotels)?

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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

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TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersIH features products with proven revenue · Strong signals: maximize · Missing: supports, reddit linkedin, podcasting
45%45% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: intuitive · Missing: plus, platform, reviews
42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
27%27% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos
20%20% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
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.

Correct prediction on native model

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