WebGL mipmap renderer for a zoomable R/place on a real world map

Hacker News

I built a pixel canvas where you place tiles at real lat/lon coordinates on a world map – a 1.6M × 1.6M pixel grid (2.6 trillion addressable pixels). The technical challenge was making it feel smooth from zoom level 0 (whole world) to zoom level 18 (individual pixels). What I tried and what worked: - Canvas2D → WebGL: At wide zoom, Canvas2D couldn't keep up redrawing millions of pixels. Switched to a WebGL renderer with mipmap-style tile pyramids – pre-rendered lower-res textures for each zoom level, only loading full-res tiles when you're close enough. - Viewport-scoped SSE: Instead of broadcasting all pixel updates to every client, the server only streams changes within your current viewport bounds. Cuts bandwidth by ~95% for a sparse map. - Mercator pixel mapping: Each pixel maps to a real geographic coordinate. The tricky part was making pixel density feel uniform despite Mercator distortion at high latitudes. - Seeded pixel art: Pre-placed recognizable characters in major cities so new users see something interesting immediately instead of a blank map. Stack: Node.js, WebGL, SSE, S3 tile storage, Lambda for tile generation. Free to use, no account required to browse. Still early – would love feedback on the rendering approach.

Visit product View on Hacker NewsFeb 26, 2026

Share card

Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →

Product HuntOn track for Day 1 leaderboard · Strong signals: user, new · Missing: mac, agents, macos

76%76% 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, io · Missing: https docs, excited, just released

75%75% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

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

54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

35%35% 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

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: real world · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

Similar products

PagerDuty for the real world48%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

PagerDuty for the real world

Hacker News1

I made Pokémon but with real animals in the real world62%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

I made Pokémon but with real animals in the real world

Hacker News4

Trying out actioncable in a real world app37%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Trying out actioncable in a real world app

Hacker News1

Crowsnest – API for the Real World52%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Crowsnest – API for the Real World

Hacker News61

PharmaSafe52%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Pharmacovigilance analytics for exploring real-world adverse

Indie Hackers1education

The Whicher: A/B test the Real World40%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

The Whicher: A/B test the Real World

Hacker News20

NLP algorithms for real-world sentiment analysis48%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

NLP algorithms for real-world sentiment analysis

Hacker News1

Sethco AI66%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Prepares your team for challenging real-world scenarios

Indie Hackers1$1,200/moai

MemoRep62%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Spaced repetition for real-world skills.

Indie Hackers1education

_done23%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Real-world tasks your agent can't do alone.

Indie Hackerscommitment-side-project

Browse all products Analyze your launch