Search UK companies via Companies House, enrich with director data, generate personalised cold emails, then manage the full pipeline. Give it your details and see a real email land in your inbox.
Gives AI agents direct control of any Mac app, including ones with no API, through a self-building tool registry.
A self-evolving MCP server that discovers its own capabilities, writes its own tools, and self-heals when apps update. 71 tools across 50+ apps. 80 tests.
A training dataset that teaches AI models to catch and fix their own reasoning errors, the same rigour behind the automations I build.
Curated dataset for fine-tuning LLMs on mathematical reasoning and error correction. Designed to improve model accuracy on multi-step problems where small mistakes compound.
A pre-registered continual-learning study: my hypothesis was wrong, and the way it was wrong was measurable.
A biology-inspired importance signal beat the standard method at preventing catastrophic forgetting on an easy benchmark, then failed on a harder one. I tracked down exactly why.
22 specialist classifiers pre-process every query before an LLM ever sees it. The architecture, the benchmarks, and where it falls short.
A proof-of-concept multi-agent pipeline: fine-tuned DeBERTa classifiers compress language, syntax, intent, math and logic into a structured buffer that the LLM reads instead of raw text. Runs fully local via Ollama.
Click 'Get in touch', takes 2 minutes. I'll reply within 24 hours with a quick call to understand the problem.
I build with the best AI tooling available, which means faster turnaround and a fraction of what a traditional dev shop charges.
The automation runs in the background. Most projects are a one-off cost, no monthly fees.