thinkingML
Agentic AI
What makes AI truly agentic and why it changes how we build.
Reflections from the edge of applied optimisation, adaptation, and machine learning deployment.
Method, Not Magic
On the importance of intelligibility and reproducibility in ML optimisation workflows. Why the best systems remain explicable from first principles.
Precision Under Uncertainty
Optimising for Self-Driving Systems in noisy and challenging environments
Constructive Optimisation: From Sampling to Structure
Introducing the core philosophy behind ECO and what it means to treat the hyperparameter landscape as an object to be built rather than searched.
Coming Soon: Agentic AI
- Agentic Workflow
- Agentic Rag
- Multi-Agent Systems
- Model Context Protocol
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