AI for the Curious
Primers
The core concepts
- 01
Primer: Databases
A primer on the main database technologies and how they relate to LLMs
- 02
RAG & MCP 101
A quick 101 to RAG & MCP - two key concepts for this series
- 03
Chunking for RAG
How to break documents into chunks ready for embedding, and how to tune your chunking strategy
- 04
Embedding for RAG
How we make our chunks searchable and usable by our LLM.
Code Examples
Practical implementations and walkthroughs
Perspectives
Longer-form analysis and takes on AI in practice
- 01
Top Trumps: LLM Edition
Local models are printing impressive benchmark scores against models ten times their size. Let's read the card and see what happens when you actually play it.
- 02
Build vs Buy: The House of Mirrors
Capable open-source AI models have reignited the build vs buy debate. The economics are less obvious than they appear.
Labs
Experiments, tests, and field reports — things I actually built and ran
- 01
Fine-Tuning: Training The Actor
Fine-tuning explained with a fun, interactive demo of changing a small model's behaviour to write like an unbearable person on LinkedIn.
- 02
Fine-Tuning in Practice: Building the Fine-Tune
A practical walkthrough of the Python, hardware and decisions behind fine-tuning a small Gemma model to write unbearable LinkedIn posts.
- 03
pgvector Setup for RAG
A practical guide to setting up pgvector on Neon and running your first similarity query
- 04
KV Quantisation: Measuring Coastlines
I ran 170 tests to try and break TurboQuant KV compression. Here's what actually failed, what didn't, and why the answer to both questions matters more than the score.