Show HN: Geotagged Photo Map

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I built a minimal personal site that’s entirely static—no frameworks, just `index.html`, `styles.css`, and a bit of vanilla JS. It has a geotagged photo album that renders a Leaflet map and a lightbox gallery from a simple `images/gallery.json`. I wrote small scripts to bulk ingest photos, read EXIF lat/lng, upload to S3/CloudFront, and update the JSON. It’s fast, portable, and easy to host on GitHub Pages or any static host. - Live site: [ethan.dev]( https://ethan.dev ) - Tech: HTML/CSS/JS, Leaflet, Markdown frontmatter, static JSON - Looking for feedback on: accessibility, mobile performance, and better static content organization without a build step.

Visit product View on Hacker NewsAug 28, 2025

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Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

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

best fitHighest predicted score across all platforms for this description.

AppSumoStrong fit for a featured deal · Strong signals: host · Missing: plus, platform, intuitive

55%55% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: personal · Missing: mobile apps, ios, entrepreneurs

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

43%43% predicted probability of success on Indie Hackers, 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

42%42% 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

22%22% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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