The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Back to feedPost
Built an AI Security Scanner for GitHub Orgs in 2 Weeks
Engineered an end-to-end AI platform that scans entire GitHub organizations for vulnerabilities and auto-generates fix PRs.
What it does:
Crawls 100+ repos via GitHub GraphQL API in batch mode
Runs GPT-4 over real source code to detect SQL injection, XSS, hardcoded secrets, CSRF, auth bypasses
Scores by severity, generates secure code fixes, creates PRs automatically
Real-time WebSocket dashboard shows live progress
Tech: Next.js 15 · Node.js · Supabase · BullMQ · Redis · OpenAI · CrewAI
Open source, MIT licensed: github.com/aymandakir/org-code-ai
Shipped in under 2 weeks. Full stack, production-ready.
Interesting
Do you have a demo version of this?
Great work! And it's open source too. 😄
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Related posts
I built Travero: Commute as a smaller,
sharper version of the original Travero idea.
Travero started as an app for seeing weather along your driving route, based on where you’ll actually be at each point of the trip. That works really well for longer drives, but I kept thinking there’s a more frequent use case hiding inside it: the daily commute.
Most people don’t need a full navigation app every morning. They already know where they’re going. What they need is a quick answer before leaving:
Is traffic bad? Is there an incident? Will weather affect the drive? Should I leave now or wait a bit?
So Travero: Commute became a more focused product around that exact moment.
The app is built around saved Home and Work locations, one-tap commute checks, traffic status, incidents, weather along the route, alternative routes, and quick summaries that are easy to understand before you get in the car.
One important shift was treating weather as a supporting layer, not the main feature. For commute, traffic is the real priority. Weather still matters, but only when it affects the route.
I also wanted the app to feel more like a daily habit than a trip planner. Something you can open quickly in the morning or after work, check the route, and decide whether to leave now, wait, or take another way.
This project was a good reminder that sometimes the best product direction comes from narrowing the idea, not adding more features.
Travero: Commute is still part of the same bigger Travero vision, but with a much clearer daily use case.
Awesome work👏🔥🔥
A Telegram bot built as a parallel layer to a productivity system, designed to intercept moments of negative impulse and convert them into structured reflection conversations.
How it works:
Every user message triggers an AI intervention conversation grounded in the user's Chief Definite AIM, recent insights, current goal pacing, and reflection history
Voice messages auto-transcribed via OpenAI Whisper, treated as text turns in the conversation
/done command ends the session, prompts trigger word + outcome (avoided / indulged / unclear), persists everything to SQLite for downstream daily summary integration
/restart wipes session state for fresh context
System prompt is brother-tone, AIM-grounded, 80–200 words per response, deliberately not therapy-speak
Runs in the same Node process as a separate productivity bot — two bots, two tokens, one codebase.
Tech: Node.js, Telegram Bot API, Claude API, OpenAI Whisper, SQLite.
The idea of intercepting negative impulse moments in real time is genuinely clever. most productivity tools wait for you to come to them — this flips it. voice transcription via Whisper is a smart touch too, removes friction completely. how's the response latency holding up in practice?
A goal-tracking sub-system that grounds an AI assistant in the user's full life-state — target amount, hard deadline, days remaining, current balance, pace status (ahead / on-pace / behind), and reflection streak.
Core features:
Multi-entry timestamped reflection log — plain Enter submits, Shift+Enter newline, optimistic UI, all entries inside one unified container with centered timestamp dividers
Ephemeral Summarize chat modal — loads today's braindumps as live AI context, brother-tone system prompt, conversation NOT persisted (privacy by design)
Nightly auto-summary cron at 23:55 — pulls reflections, runs through Claude, creates a standalone daily summary that downstream AI engines pick up automatically
Pace analysis — surfaces required-vs-actual daily rate, projected gap at deadline, status flag
Each AI engine pulling this data adapts its suggestions based on whether the user is ahead or behind on their goal — recursive context that compounds over time.
Tech: Claude API, SQLite, Node.js cron, custom retrieval logic.
Trending
Claude
Claude has entered the design space. How are you using Claude Design?
Contra University
Learn from expert creatives how to earn more using next-gen AI tools.
creativeaiflow
Creative AI workflows are evolving. What tools do you use, and what are their strengths and weaknesses?
portfolioreview
The best portfolios tell a story, not just show a grid. Share yours for feedback.
freelancerlife
Freelancer life is wins, pivots, and everything in between. What’s yours right now?