$ cd ./topics/ai-ml
Machine learning fundamentals, AI agents, and the research I did at university: topic modelling, social network analysis and online communities.
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I specialised in AI and machine learning at university, and my dissertation used topic modelling and social network analysis to study online communities. These notes collect the fundamentals, that research, and how I think about AI agents now that they write a lot of the code.
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AI agents don't just write code; they need infrastructure to run it. Solid internal developer platforms are the building blocks of agentic ones, and without them organisations fall into one of three traps: blocked, ungoverned or bottlenecked.
In 2019 I went to two industry events, an AI conference and Big Data LDN, and wrote up what I heard: data is the new oil, edge inference, AI for business users. Read again in 2026, some of it was prophetic and some of it was the wrong bet.
A worked example: several teams each build AI assistants from scratch. What should a platform own, what stays with teams, what do you buy, and how do you win buy-in, discover features, plan, build and iterate?
My 2021 university notes on image processing, histograms and pattern recognition, distilled: images as intensity functions, contrast stretching, features and classes. The vocabulary underneath modern computer vision.
The author of Clean Code now argues humans shouldn't review AI-written code line by line: specify what correct means, verify it with deterministic tools, and keep humans accountable for specs and sign-off.
Neural networks with many layers, trained end to end by backpropagation — CNNs for images, transformers for sequences, and the bridge from university neural nets to today's LLMs.
NVIDIA's Eureka puts an LLM in a loop around reinforcement learning — it reads the environment code, writes reward functions, trains, reflects on the results and rewrites. A blueprint for closed-loop agents.
Game hacking and modding forums are, first of all, places where people learn — sharing tools, tutorials and status. The same dynamics that make them great teachers raise the question of where the learning leads.
Learning patterns from data instead of hand-coding rules — supervised, unsupervised and reinforcement learning, and the habit of testing on data the model has never seen.
Reddit's topic-focused communities make it a rich research source. How to collect posts with PRAW and an archive, how to clean them, and the ethics that keep individuals out of your results.
Turning human language into something machines can work with — tokens, vectors, embeddings — so they can classify, extract, summarise or generate it. From bag-of-words and sentiment lexicons to transformers.
What actually makes an online community — shared purpose, interaction, norms, a platform — and why "member" and "participant" aren't the same thing. The literature behind how I framed forum research.
My final-year dissertation at Kent. Can topic modelling and network analysis of online communities show whether game hacking and cheating forums lead people towards more serious hacking? The question, the approach, the ethics, and what I'd do differently with today's LLMs.
An agent takes actions in an environment to maximise reward. The vocabulary — policy, reward, rollout — and why reward design is the hard part.
Modelling people and their interactions as a graph to find who's influential, which communities form and how behaviour spreads. Centrality, clustering, Louvain community detection, and why graph thinking travels so well.
Latent Dirichlet Allocation treats each document as a mix of topics and each topic as a mix of words. It's unsupervised, fast to try, and still a useful explainable baseline next to embeddings and LLMs.
A chatbot answers; an agent perceives a change, reasons about a goal, plans, and takes several actions with tools to get there.
People are more willing to see hacking as legitimate when they feel a system or authority won't listen to them, and they're angry about it. A psychology finding with uncomfortable lessons for organisations.