$ cd ./topics/platform-ai
Golden paths, internal developer platforms, delivery metrics, and what changes when AI agents become users of the platform too.
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A platform is a product whose customers are developers. Its job is to make the right thing the easy thing: paved roads for building, testing, shipping and running software, so teams spend their energy on the problem in front of them rather than on plumbing.
What’s changing now is that some of those “developers” are AI agents. Agents need what people need (clear contracts, fast feedback, safe defaults, good observability), only more strictly, because they won’t ask in Slack when the docs are wrong. That’s the thread through this topic: platforms built for people turn out to be the best foundation for agents too.
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Case study — leading the UI of a low-code network automation platform where workflows are drawn as BPMN diagrams. Model-driven editing, server-side permissions and a catalogue of reusable building blocks.
Case study — handing over a platform I'd led the UI of, through structured sessions backed by written docs, a reuse strategy and a clean backlog, so the team could own it without me.
Case study — putting a declarative API gateway in front of a platform's microservices with little dedicated ops support. Versioned routes, stable names, one place for CORS, and validating locally before asking for anything.
One working directory per branch means no stashing, no reinstalling and no committing to the wrong branch — and it turns out to be exactly what parallel coding agents need too.
Once the beginner routine stops working, alternate blocks that build capacity with blocks that turn it into skill, with a deload between. You can't push both at once.
A reactive TypeScript backend where your server functions are the API, so front end and back end stay in sync. A natural fit for building with coding agents.
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.
Tendons adapt slower than muscles, and organisations adapt slower than code. Schedule the recovery week before an injury schedules it for you.
Digital transformation isn't buying tools; it's changing what people do every day. Culture is the sum of small habits, and platforms are how you make the good ones easy.
Four measures of software delivery, two for throughput and two for stability. In high-performing teams they improve together, so speed and safety aren't a trade-off.
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.
The opinionated, fully supported route from nothing to a running service. Optional, but so much easier than the alternatives that teams choose it.
The best developer platforms feel like a good beginner programme — opinionated, safe, explained, and easy to leave once you know why.
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.
From a university Zettelkasten to a second brain with an AI librarian — I only capture now, and the system distils, links and brings old ideas back on a schedule.
You can't push volume and skill at the same time without something giving. Platform roadmaps work better when they alternate building capability with hardening it.
The self-service layer a platform team offers product teams, run as a product with developers as customers. If you have to raise a ticket, it's a service desk, not a platform.
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.
Being able to ask new questions of a running system from its outputs — logs, metrics and traces, tied to the SLOs that turn them into decisions.
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.
You don't add 40kg to the bar overnight, and you don't rewrite a system in one go — small, steady, measured increases win both times.
An agent takes actions in an environment to maximise reward. The vocabulary — policy, reward, rollout — and why reward design is the hard part.
Draw a platform roadmap like a calisthenics skill tree — prerequisites, tiers and mastery levels — so it's obvious what's locked until the foundations are in.
The SLI is what you measure, the SLO is your target for it, the SLA is the promise with consequences — and the error budget turns all three into release decisions.
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.
Red, green, refactor is less about tests than about tight feedback — and in the age of coding agents, a failing test is the clearest instruction you can give.
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.
Buy what's generic, build platforms for supporting capabilities, and leave the core to product teams — Domain-Driven Design as a map for platform investment.