Security Scanning Tool

Hacker News

Hi Hackers, I just found a couple of issues in my app, inspite of that i added all the Supabase and Vercel MCPs, i expected the LLM will be a little smarter and it will help me write a kind of okayish app. But inspite of taking care of it, i still found an issue in my RLS policies. So I was thinking about it, most of the vibe coder apps use Vercel and Supabase, and tbh it's kind of easy to ruin the RLS in supabase, so I created hackymacky, the scanner app, that looks for issues specifically in a supa + vercel app. What do u think, is it something that's worth iterating on ? Cheers, Csaba

Visit product View on Hacker NewsSep 7, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, mcp, apps · Missing: agents, macos, agent

67%67% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

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

TrustMRRFits verified-revenue profile · Strong signals: apps, scanner · Missing: mobile apps, ios, personal

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

42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released

31%31% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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