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IsMyAIDown – AI status monitor with Phoenix LiveView. Can you crash it?

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IsMyAIDown – AI status monitor with Phoenix LiveView. Can you crash it?

I recently built IsMyAIDown.com to challenge some assumptions about modern web development. Instead of reaching for React like everyone else seems to be doing these days, I wanted to see if I could build a distributed, scalable, real-time, single-page app using server-side tech with Phoenix and LiveView. The core idea was simple enough - monitor major AI providers and show their status across different geographic regions. But the primary goal was to better understand what was possible without the complexity of modern frontend frameworks. I ended up deploying across three Fly.io regions and using Oban (a very good job processing library in the Elixir ecosystem) to handle geographically distributed health checks. Propogating new checks and detecting and tracking user presence was all quite straightforward. Phoenix is a great framework. That said, the mental model with LiveView is... a lot. You've got to keep track of a variety of message callbacks that all seem to run together in my head (mount, handle_info, handle_update, handle_params, etc) as well as a heirarchy of views and components that all interact with each other. Even for a relatively simple site like this, it's a lot to hold in your head at once and I found myself having to re-study the flow a few times to get my head around what was going on (surely a personal failing, but a thing none the less). I would not say that LiveView is for beginners. While I haven't optimized the site much at all, I'd love to see how even this basic implementation holds up under increased load, so do your best HN. Some side-thoughts on modern software dev: Using Cursor definitely made me more productive but I found myself becoming weirdly disconnected from parts of the codebase that I hadn't written myself. There's something about that line-by-line writing process that builds up your mental map in a way that AI-generated code doesn't quite match. As the size of a system grows, I wonder if AI-generated code will help or hinder? Quick note on the various AI providers I'm monitoring – OpenAI and Google seem to be the fastest and most reliably available while Anthropic is a bit slower and much more prone to API call failures. Some resources that I found quite helpful: Learn Phoenix LiveView: https://arrowsmithlabs.com/ Oban: https://oban.pro World Page Speed Test – planet-wide elastic scale with FLAME: https://fly.io/phoenix-files/world-page-speed-test-elastic-s...

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Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
97%97% 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: cursor, model, google · Missing: mac, agents, macos
94%94% 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
64%64% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: personal, google, way · Missing: mobile apps, ios, entrepreneurs
54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
25%25% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
13%13% 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.

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