Posts Tagged ‘AI’
The impersonal AI advantage
TL;DR Open source projects that I contribute to have adopted AI bots to ease the pull request (PR) process. It might just be me (though I doubt it), but somehow it feels less like a personal attack when a bot tells you what’s wrong with your PR than when it’s a person doing it. This […]
Filed under: AI, did_do_better, software | Leave a Comment
Tags: open source, AI, PR, bot, pull request, toil, maintainer, contributor
TL;DR The attention economy dynamics for AI chatbots and agents are very different from social media, and so we’re seeing a whole new approach to capturing (and keeping) our attention. This comes down to fundamental human nature – the strongest fuel for groups is outrage; and whilst it might burn dirty and contaminate everything around […]
Filed under: technology | Leave a Comment
Tags: adoration, agent, AI, attention, chatbot, groups, individuals, outrage, pollution, social media, sychophant
Miell’s Law and Token Budgets
TL;DR Conway’s Law tells us that organisations create systems that mirror their communication systems. Jamie Dobson coin’s ‘Miell’s Law’ in a post about the work of our mutual friend (and his colleague) Ian Miell in his forthcoming book ‘Follow the Money‘: Organisations that design systems are constrained to produce systems that reflect the financial structures […]
Filed under: technology | Leave a Comment
Tags: AI, amazon, budget, ChatGPT, Claude, coding, Conway, Conway's law, CoPilot, finance, Gemini, google, HR, Jenson Huang, Miell, Miell's law, NVidia, tokenmaxxing, tokens, Uber
March 2026
Pupdate We’ve (finally) had some warm and sunny days, so the coats have mostly been off for walks :) Bath Half $daughter0 is in her final year of her degree at Bath, and after getting into running last year she decided to run the Bath Half with some friends. That provided a good excuse for […]
Filed under: monthly_update | Leave a Comment
Tags: AI, Bath, cataract, conference, conferences, dachshund, GDE, half marathon, Miniature Dachshund, Monki Gras, pupdate, QCon, solar, USB-C
TL;DR Coding is no longer the constraint. It’s now cheaper than ever to make software. But there are supply side constraints on innovation, and getting apps to market. Who dreams up something worth making? How do apps get in front of users? There’s also a demand side constraint on adoption – how do people learn […]
Filed under: code, technology | Leave a Comment
Tags: AI, attention, cloud, coding, demand, DevOps, economics, ideas, innovation, saas, supply, theory of constraints
Don’t huff the fumes
TL;DR Agentic systems are the latest thing being used to solve IT integration issues, becoming the glue squirted into the gaps between systems. But the use of natural language means that the distinction between ‘data’ and ‘code’ is almost impossible to make, which causes a whole raft of security concerns. This new glue may be […]
Filed under: security | Leave a Comment
Tags: agentic, agents, AI, CHERI, glue, integration, middleware, security
TL;DR Supply-chain Levels for Software Artifacts (SLSA) attestations are a great way to show that you care about security, and they’re fairly trivial to add to delivery pipelines that produce a single binary or container image. But things get tricky with matrix jobs that build lots of things in parallel, as you then need to […]
Filed under: Dart, Docker, Gemini, howto | Leave a Comment
Tags: AI, ARM, artifact, attestation, CD, container, Cosign, Dart, DevOps, Docker, Gemini, GitHub Actions, image, json, matrix, security, signing, slsa
Last week my former colleague Doug Todd asked a question about recording decisions on BlueSky: Of course I replied suggesting Architecture Decision Records (ADRs), with a pointer to the at_protocol GitHub repo where we use them. A few days back Doug demoed how he’s using ADRs with his coding assistant (Claude and Claude Code), and […]
Filed under: architecture, code, software, technology | 1 Comment
Tags: ADR, ADRs, AI, architecture, Claude, coding assistant, decision, LLM
Using a Model to Model
TL;DR Once we get past ‘bullshit work‘, the primary enterprise use cases for Large Language Models (LLMs) appear to converge on various ways to make it easier to work with unstructured data. That’s because an LLM can generate an ‘understanding’ of the data, saving the painstaking process of getting humans to provide context. Of course […]
Filed under: technology | Leave a Comment
Tags: AI, data, GenAI, language, LLM, model, modeled, structured, unmodeled, unstructured
