Research pipeline
A multi-stage constraint system that reconstructs, filters, and stress-tests a search-space to identify which semantic structures are stable enough to act on.
Tag
semantic-structure
Semantic SEO in Python: From Search Results to Original Content Strategy
How Python can be used to collect search-result data, expose semantic structure, identify unresolved topic clusters, and develop original content strategy from evidence rather than imitation.
Python for NLP and Semantic SEO: A Practical Reference
A plain-language companion to the SEO Python codebase, explaining how the workflow uses search-result collection, NLP text processing, keyphrase extraction, embeddings, network analysis, and association rule mining for semantic SEO research.
Zombie Survival by ChatGPT — Why the AI Lies (and How to Stop It)
This video demonstrates how AI systems optimise for coherence rather than structural accuracy. SDA-3 reframes the model as a system to interrogate, forcing it to expose suppressed variables and recover constraint-compatible truth.
SDA-3 protocol
This post introduces SDA-3, a protocol for inferring the structure of an LLM’s embedding space through observable outputs, without relying on access to internal weights or hidden states.
Structural Extraction Protocol
A unified, procedural system for extracting structurally necessary logic from language model outputs through recursive constraint, adversarial interrogation, and collapse enforcement.