Undergraduate Dissertation (Final Year)
Built an agentic LLM framework to study hidden goal pursuit under conflicting in-context instructions. Agents operate in a sandboxed virtual file system with constrained tools and full behavioural logging.
Experiments vary perceived oversight levels and analyse deceptive tactics using:
Focus: AI safety, evaluation methodology, detectability of hidden goals.
Personal infrastructure spanning home server and VPS, using Tailscale for private access and Docker Compose for service orchestration.
Public and private services are segmented cleanly, with minimal maintenance and reproducible setup.
Found the internal API used by the Three (sim company) rewards website and automated collecting rewards using a script attached to a cron job.
Worked with JWTs and jq to parse the response. First used curl and jq for prototyping and then implimented in python for production.