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GuardianScan – Website audits for 2025 web standards

Hacker News

GuardianScan – Website audits for 2025 web standards

Hey HN, I've been building websites for 15 years. I'm a perfectionist - both visually and technically - and got tired of going back and forth between different tools, manual checks, and documentation for every audit. Built GuardianScan to run everything in under 45 seconds: - Core Web Vitals (LCP, INP, CLS) - WCAG 2.2 compliance - Security headers and CSP policies - Modern framework patterns - SEO and schema markup 47 checks total. These standards exist for a reason. Google prioritises fast sites. Accessible sites reach more people. Secure sites build trust. When you meet modern standards, you see it in your traffic and conversions. Stack: Next.js 15, React 19, Supabase, Browserless.io for headless Chrome. Won't catch everything - automated accessibility gets about 70% of issues. But it's what I use before every deploy now. Landing page is up. Launching Nov 1. £24/month, no enterprise pricing BS. Happy to answer questions.

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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
87%87% 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: google, visual · Missing: mac, agents, macos
78%78% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, io · Missing: https docs, excited, just released
71%71% 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 · Missing: plus, platform, intuitive
42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month, google · Missing: mobile apps, ios, personal
40%40% 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
20%20% 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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