One Million and One Checkboxes. Running on Elixir and Mithril.js

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Hello! As soon as I saw eieio's https://onemillioncheckboxes.com/ , I knew I wanted to try it out using Elixir. The original was created using Python and Redis (later Go and Redis) but this one is using just one Elixir server and HAProxy for SSL termination. It's also using S3 to show the overview page where all the checkboxes are drawn on a 1000x1000 canvas. The whole site is running on a 4 core 8 GB Hetzner VPS so I'm interested to see how it will hold up. This took an embarrassingly long amount of time to complete (the first commit was on July 23) but it was my first real attempt at coding anything significant in Elixir. Initially I tried using LiveView but there were performance issues just rendering 2000 checkboxes on screen. I was also unsuccessful in using the new Streams concept in LiveView which is supposed to help with these "more data than you can render" scenarios. In the end I used LiveView to render the header and have it update on a timer, and the checkboxes are rendered with Mithril.js. The checkbox info is passed via the LiveView websocket connection and any update to a checkbox is passed through that channel as well. I'm stepping away for the next half an hour to run an errand but I'll be back to answer any questions people might have! Please try to break my site and invite your friends to do the same

Visit product View on Hacker NewsOct 21, 2024

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

84%84% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: 000, io · Missing: https docs, excited, just released

72%72% 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

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit

16%16% 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.

Incorrect prediction on native model

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