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I built Cascode because I kept running into the same problem: architecture diagrams are static, but distributed systems are not. You can draw a perfect box-and-arrow diagram and still have no idea what happens when SQS goes down or Lambda throttles, especially when onboarding a new team member or brainstorming a refactor.Cascode is an interactive canvas where you compose AWS architectures from real services (Lambda, SQS, DynamoDB, S3, etc.) and run simulations to see how they actually behave under load, failure, and edge cases.What you can do:- Build architectures visually and simulate message flow, failures, and scaling in real time- Export diagrams as production-ready Terraform HCL or CloudFormation JSON- Import existing Terraform/CloudFormation back into visual diagrams (round-trip preserves everything)- Get AI-graded architecture reviews (A–D) that catch anti-patterns like single points of failure and missing async buffers- Collaborate in real-time with live cursors and shareable guest links- Connect your AI editor (Claude, Cursor, VS Code) via MCP to manage diagrams without leaving your workflowThere are also guided challenges — from "build a URL shortener" to "design multi-region DR with RPO &lt; 5s" — if you want to practice without starting from a blank canvas.It's free to try: <a target="_blank" rel="noopener noreferrer" class="break-all link-break text-gray-500 hover:underline" href="https://cascode.uk">https://cascode.uk</a>I'd love feedback from anyone who designs or reviews AWS architectures. What's missing? What's wrong? What would actually make your day easier?

Cascode

an individual project on Flock Formation Consensus Algorithm in Multi-Agent Robot Systems with Blockchain Security Layer. The project utilised neural networks to develop a security layer, using blockchain, in a controlled group of drones.

Flock Formation Consensus Algorithm in Multi-

A team of seven students developing a real-time closed loop EEG acquisition device with removal of tACS artefacts used for monitoring and filtering brain signals. Designed a filtering and noise removal circuit which was built on a PCB to communicate with the FPGA subsystem and display the results on MATLAB in real time.

Real-time closed loop EEG acquisition device 

A team of 5 students built an autonomous buggy that drives over a white line drawn on a track that includes obstacles such as ramps, curves, and tunnels. The buggy is also be controlled by Bluetooth Low Energy module connected to a mobile device.