CyberGuard
60% of startups leak API keys on GitHub. Find & Auto fix.
What it does
60% of startups leak API keys on GitHub. Most don’t find out until it’s exploited. CyberGuard changes that. Free repo scan finds exposed secrets, keys, tokens, and passwords in seconds. Paid tier unlocks: Detailed fix recommendations Runtime secret injection (noenv risks) Real-time anomaly detection (BERT-powered) Team access & alerts No more vibe-coding disasters. No exposed secrets in rushed pushes. No surprise breaches costing thousands.
Does a similar job
all alternatives →
- KDKeyLeak Detector – Scan websites for exposed API keys and secretsNov 2025 · github.com · ▲30
I built this after seeing multiple teams accidentally ship API keys in their frontend code. The problem: Modern web development moves fast. You're vibe-coding, shipping features, and suddenly your AWS keys are sitting in a tag visible to anyone who opens DevTools. I've personally witnessed this happen to at least 3-4 production apps in the past year alone. KeyLeak Detector runs through your site (headless browser + network interception) and checks for 50+ types of leaked secrets: AWS/Google keys, Stripe tokens, database connection strings, LLM API keys (OpenAI, Claude, etc.), JWT…
- CTCLI that spots fake GitHub stars, risky dependencies and licence traps2025 · github.com · ▲122
When I came across a study that traced 4.5 million fake GitHub stars, it confirmed a suspicion I’d had for a while: stars are noisy. The issue is they’re visible, they’re persuasive, and they still shape hiring decisions, VC term sheets, and dependency choices—but they say very little about actual quality. I wrote StarGuard to put that number in perspective based on my own methodology inspired with what they did and to fold a broader supply-chain check into one command-line run. It starts with the simplest raw input: every starred_at timestamp GitHub will give. It applies a…
More ai this month
the category →
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 18d ago · simedw.com
Astute▲585Automate your B2B brand going viral, with new media creators
AI · 19d ago · company-app.joinastute.com
Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 28d ago · cactuscompute.com
Source: Product Hunt launch ↗
Launched alongside, March 2026
the whole month →
Switch from ChatGPT to Claude with import memory feature
AI · Mar 2026 · claude.com
