Building a Graph-Based Intent Modelling Tool
A proof-of-concept tool for graph-based system modelling — defining system behaviour, validating it structurally, and generating tests from the intent.
DECLARATIVE SYSTEMS
Adopting System Models Incrementally
Practical guide to adopting graph-based system models incrementally. Covers escape hatches, LLM workflows, and week-by-week adoption strategy.
The Self-Validating Graph
Graph-based system models validate themselves through structural checks, semantic analysis, and automatic test generation from invariants.
Code Is a Lossy Format
Source code is a lossy format that discards intent. LLMs expose this weakness. Graph-based models preserve meaning as first-class structure.
The Evolution from Code Craftsmanship to Living Systems: A Conversation About the Future of Software Architecture
Exploring the paradigm shift from imperative coding to declarative systems, hybrid architectures, and self-healing codebases that evolve autonomously.
Building Hybrid Declarative Systems: A Practical Architecture Guide
Learn how to architect hybrid declarative systems that combine symbolic reasoning with machine learning for trustworthy, explainable AI decisions.
What Happens When You Combine Proof Trees with Machine Learning
Explore how combining proof trees with machine learning creates hybrid declarative systems that can both reason logically and handle uncertainty.
How Proof Trees Make Declarative Programming Work
Discover how proof trees and proof engines enable declarative programming by automatically exploring logical possibilities instead of following imperative instructions.
From Code Writers to Solution Architects: How AI is Reshaping Software Development
AI is transforming software development from imperative coding to declarative problem-solving. Developers aren't being replaced — they're becoming solution architects.