You can access my CV at multiple locations: cv.snowdon.dev.
- Website:PDF – https://cv.snowdon.dev
- Download:PDF – https://download.cv.snowdon.dev
- git clone – Git https://repo.cv.snowdon.dev
- gh repo create – Git <TODO>
Feel free to reach out via email: hello@snowdon.dev.
Below, you’ll find a selection of my active projects. If you’re interested in a demonstration of others, just let me know.
You can access my CV at multiple locations: cv.snowdon.dev.
AI coding agents are powerful, but giving an agent unrestricted access to your development machine can be uncomfortable. You want the convenience of an AI-powered development workflow without turning your entire host system into the agent’s playground.
That’s where snowdon-dev/opencode-docker comes in.
OpenCode doesn’t impose an opinion on the sandbox its agents run in, and
opencode-docker takes advantage of that. It is a security- and
human-control-oriented workflow for running OpenCode
inside Docker containers. Your project is available inside the container at
/workspace/project, while toolchain caches for Go, Rust, Node.js and Python
persist between runs.
We’re excited to announce LLMTest, a new program designed to evaluate the core intelligence modes of Large Language Models (LLMs). This isn’t your standard comprehension test—it’s a deep dive into an LLM’s capacity for pure reasoning, independent of its vast training data.
👉 Visit the web application now: llmtest-online
LLMTest uses a linear-time algorithm to generate infinitely many unique test instances. Each test is a simple logical puzzle presented in a token-efficient format, using a domain and steps that are naturally aligned with a computer agent.
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