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Sentinel: MCP Security Guard Benchmark
Problem: MCP guard tools claim to block attacks on AI agents, but there was no consistent way to hold them accountable.
What I built: A benchmark with a sandboxed testing harness that runs guard tools against 6 classes of attacks in a controlled environment, so their protection can be measured and compared fairly.
Skills: AI security, MCP, adversarial testing, Python, sandboxing, benchmarking
The network for creativity
Join 1.25M professional creatives like you
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agents paying per request in USDC with no API keys is lowkey the future
Testing my app! 🧰🚀 Everything is finally coming along.
I’ve been working on this project for a little over two weeks. Initially it was just me working with one AI agent, but I was still manually coding most of it, so I was moving like a turtle 🐢very slow.
Then I decided to implement Codex into my project to automate more of the development and it has been amazing. Now I can focus more on prompting, testing, reviewing the code and making the technical decisions instead of manually coding everything.
I also created my own AGENTS.md bible with all my project rules and instructions to make sure Codex follows them as the final authority when working on my code.
The idea is simple: create one useful and intuitive ecosystem of tools that Shopify merchants can add to their stores to help customers make purchasing decisions faster and with more confidence.
Still testing, building and adding more tools. Let’s see where this goes! 🚀
And if anyone finds the idea interesting. I’m definitely open to collaborations, ideas or partnerships. Feel free to reach out!
Seeing you shift from hand coding to Codex and let the agent handle the grunt work really speeds up iteration.
AI Property Maintenance Automation
AI-powered property maintenance automation designed to streamline how property management teams handle tenant maintenance requests.
The system takes a maintenance request, analyzes the issue, determines its priority and category, recommends a suitable vendor, and automatically creates a structured work order.
Workflow:
Tenant request → AI analysis → Priority & category → Vendor matching → Work order
Built with: Python, Flask, SQLite, HTML, CSS, JavaScript, and AI-assisted request classification.
This project was built as a portfolio demonstration of AI automation for property management operations.
Turning a tenant request into a ready work order in seconds removes the manual triage step.