SeasonMap – when to travel where? visualized with climate data

Hacker News

Author here. I'm trying to visit every country and I've been to 158 so far. Before I decide where to travel, I'd ask a local friend which season to avoid, or open up dozens of browser tabs on climate data to figure out what the place is like in a given month. Climate data still miss things. Cancun in September looks great on paper, with 31°C and 10 hours of sun, but it's hurricane season and the beaches can be covered in seaweed. Typical info that locals would know, which can also be captured as static data. So I built SeasonMap to answer "when should I go to <place>?" What you can do: - Pick a travel style (city walk, beach, hiking, skiing, max sun, low humidity, etc.) and see every place ranked on a map - See what's in season and what to avoid, and why: monsoon, hurricanes, extreme heat, bad air, peak crowds - Filter destinations by temperature, rainfall, sunshine, air quality and hazard seasons - Open a place to see its whole year: month by month weather, events (festivals, whale watching, cherry blossom), crowd levels, practical notes like scams, and traveller anecdotes summarized by AI with links to the sources Data Source & how I made it: - The climate data is ERA5 normals via Open-Meteo (2016–2025), corrected with NOAA station data where available. - Events, hazards and traveller notes were researched and by AI agents, and every one links to its source. Gathering it was easy. Checking it was the hard part. - Yes, I've used AI heavily on this project before anyone call it an AI slop. Making was easy, but it took billons of tokens of beating whack-a-mole ai to polish and tweak to make it usable and decent. Through that, I've created many skills and evals ranging from visual qa, evals for irregular data, automated i18n and others. It still feels much like AI as I was using Claude Design, which i want to improve on. I tried using local llm, but the throughput was so low. Pricing: the first 5 minutes are fully open, no signup. After that, the top 3 destinations and 25 place breakdowns a month are free. A 30-day pass is $7, $39 a year or $69 lifetime. iOS and Android apps are coming soon. Any feedback welcome.

Visit product View on Hacker NewsSep 16, 2026

Share card

Actual performance

22points

11comments

Made the leaderboard

Launch Intel predictions

Analyze your own launch →

Indie HackersFits the IH revenue-focused audience · Strong signals: created, ios · Missing: supports, reddit linkedin, podcasting

90%90% 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: agents, agent, claude · Missing: mac, macos, cursor

89%89% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: ios, apps, month · Missing: mobile apps, personal, entrepreneurs

63%63% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

50%50% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: soon · Missing: plus, platform, intuitive

42%42% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

Similar products

Raaise47%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

get behind startups fixing the climate crisis

Indie Hackerscommitment-side-project

Climate Finder45%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Climate Finder

Hacker News2

Arbor58%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Erase your climate impact and go climate positive with Arbor

Indie Hackerscommitment-full-time

OffsetMap34%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Transparency and Data for Climate Projects

Product Hunt+8

The Climate Fixathon38%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

An online hackathon for makers to help fix the climate

Indie Hackers5events

The Climate Fixathon- Online hackathon for makers to fix the climate59%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

The Climate Fixathon- Online hackathon for makers to fix the climate

Hacker News10

Klimaxa40%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Climate-Conscious Commerce Platform

Product Hunt+4

SeasonMap — When to travel where?42%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Ranks travel destinations by climate comfort for any month and trip style. Pick a month + style (beach, hiking, city, snow) → every place is scored on real climate data: temp, rain, humidity, sun, win

TrustMRRTravel

The New Climate49%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Uplifting Environmental News

Indie Hackerscommitment-side-project

CCRadar – Climate News Tracker31%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

CCRadar – Climate News Tracker

Hacker News2

Browse all products Analyze your launch