Why agent systems become slow, expensive and fragile
The measured mechanisms behind agent bloat: context resends, oversight cost, cumulative failures, and the attribution traps that make dashboards lie about all of it.
Rajeev Gill
I build practical software around AI, data, analytics, and adtech.
My work sits between product thinking and implementation, with a bias toward systems that make real work less messy.
Based in London. Interested in useful AI, clearer measurement, and software that holds up outside a demo.
Get in touchSelected projects
Things I have built
AI research product
Local LLM Lab
A measured guide to every local AI model installed on my Apple Silicon workstation.
PythonReactVite
Research product
Open GTM Index
A transparent guide to open-source sales and marketing software.
TanStack StartReact 19TypeScript
Operations product
Hackathon Voting App
A production-ready single-screen judging app built for a live hackathon room.
Next.js 14TypeScriptClerk
Recent writing
Notes from the work
The multi-agent execution behind one rebuild
A candid case study of the exact execution that shipped this site's solutions/editorial rebuild and visual remediation: a Sol supervisor, a GLM workhorse in an isolated worktree, a bounded Opus review, GLM amendments, and independent parent acceptance — with the failures included.
The agent telemetry stack, explained from first principles
A beginner-friendly but technically complete tour of Rajeev's open-source agent telemetry stack: what each piece is, why it exists, where one real request travels, and what the stack still cannot prove.
Have something useful to build?
I am always interested in thoughtful software, analytics, and AI work where the practical details matter.
Email me