AI

AI-powered element monitoring for websites

Hacker News

AI-powered element monitoring for websites

Hi HN, I built SiteStable ( https://sitestable.co ) – a monitoring tool that uses AI to identify and track critical elements on your website. The problem: Traditional uptime monitors just check if your site returns 200 OK, but they don't catch when important elements break (checkout buttons disappear, forms stop rendering, critical CTAs vanish due to CSS/JS errors). How it works: 1. Enter your website URL 2. AI scans the page and identifies important elements (buttons, forms, navigation, etc.) 3. You choose which elements to monitor 4. Every 15 minutes, we check if those elements still exist on the page 5. Get alerted immediately if something disappears Also supports traditional HTTP endpoint monitoring for API/server uptime. Tech stack: PHP backend, AI for element detection, cron-based checking Free tier: 1 website, 10 HTTP monitors (3-min checks), 1 AI monitor (15-min checks) Built this because traditional uptime monitoring misses the "site is up but broken" scenario: – your server returns 200 but users can't actually complete key actions. Would love feedback on: - Is this a pain point you've experienced? - What other monitoring use cases would this solve? - Pricing thoughts for paid tier Free tier available, no credit card required. Takes about 60 seconds to set up. Thanks!

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2points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created
89%89% 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 HuntUnlikely to reach the leaderboard · Strong signals: user · Missing: mac, agents, macos
44%44% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: exist, ide, io · Missing: https docs, excited, just released
37%37% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal
31%31% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
23%23% 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
11%11% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: paid · 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

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