API Radar – Real-time GitHub scanner for exposed API keys

Hacker News

Hi HN, I'm a solo dev and student, and I recently built API Radar — a real-time tool that monitors public GitHub commits for leaked API keys (OpenAI, Google Gemini, Anthropic Claude, and more). What it does: Scans public GitHub commits in real time Detects API keys using pattern matching and validation heuristics Redacts most of the key, but allows copying for verified leaks (for security teams) Leaderboards by leaky repositories and exposed providers Built to promote developer hygiene and security awareness Stack: Backend: Node.js (Fastify), MongoDB, Redis, custom TruffleHog-like scanner Frontend: Next.js 14, TailwindCSS, shadcn/ui Infra: VPS, NGINX + SSL, background worker farm, rate-limit handling Current stats (soft launch): 210 active users 208 new users 2.6K total events 53s average engagement time Built fully solo — from design to deployment, analytics to queue resilience. My goal was to ship something fast, security-aware, and production-grade. Would love feedback on: Improving UX for security teams Ethics around redaction and disclosure Ideas to scale this into an OSS tool or API service Thanks for reading! https://apiradar.live — Zaim

Visit product View on Hacker NewsJul 29, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, google, user · Missing: mac, agents, macos

89%89% 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: gemini · Missing: supports, reddit linkedin, podcasting

70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: google, users, scanner · Missing: mobile apps, ios, personal

50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: nginx, ide, io · Missing: https docs, excited, just released

48%48% 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: users · Missing: plus, platform, intuitive

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: real time · 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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