Why AI Coding Tools Might Not Make the Slightest Difference The excitement around AI coding tools is genuine and widespread. GitHub Copilot, Claude, ChatGPT for code—they're undeniably impressive. They can generate working code in seconds. They reduce boilerplate. They help people code in languages they barely know. So naturally, organizations are investing heavily. The pitch […]
A Saturday at Stone & Chalk, four parallel tracks, ~50 practitioners, no speakers, no agenda — and a surprising amount of signal. On Saturday 11 April 2026 we ran an AI Engineer unconference in Melbourne. No keynotes, no slide decks rehearsed for weeks, no panel of the usual suspects. Just a room full of engineers, […]
Date: Saturday 11 April 2026Venue: Stone & Chalk, MelbourneFormat: Open Space Technology — three 45‑minute sessions across four parallel tracks, plus opening, demos and closingParticipation: ~50 practitioners (engineers, founders, consultants, researchers, data scientists, product people) across AI engineering, software engineering with AI, privacy/security, and people/skillsConduct: Chatham House Rule — nothing in this document is attributed […]
Engineering for the Agentic Web When 50% of Your Traffic is Robots Five years ago, a website's analytics were simple: count the humans who visited. Maybe worry about malicious bots, but mostly you were measuring human attention. Today, something fundamental has shifted. Half the traffic to a typical website is no longer human. It's AI […]
Agentic SAST: Building an AI Pipeline for Rule Synthesis and Root-Cause Vulnerability Analysis Static analysis tools are supposed to automatically find security vulnerabilities. They scan code against known patterns and flag problems. But in practice, the tool often flags dozens of issues for every genuine vulnerability, and the real security problems slip through because they […]
Fully Automated Luxury Gay Space Engineering: What AI-First Software Development Actually Looks Like The title is playful, but the question underneath is serious: what happens when you actually commit to AI-first software development, not as a buzzword but as a genuine architectural principle? Most organisations that talk about "AI-assisted development" still operate the same way […]
Building Frameworks Building Systems Imagine needing to build hundreds of interactive science and maths games at scale. Not one game that's beautiful and perfect. Hundreds. Each one tailored to a specific learning outcome, a particular age group, a unique pedagogical approach. The traditional response — hire more engineers, write more code — hits a wall […]
With the Easter long break here in Australia last week, we didn’t have a newsletter for the first time in quite a while–so hopefully we’re making up for it this week. Before we begin with this week’s reading some news about upcoming events and more form Web Directions. PROJECT NOOPS As I mentioned a couple […]
Fail Fast, Fix Faster: Why Faster AI Models Beat Smarter Ones Intuition says the smartest model should win. The model that reasons deeper, thinks longer, produces better results—that's the one you'd pick for an agentic coding loop. But intuition is wrong about this. In practice, a model that's 10x faster but only marginally competent often […]
Our AI Hallucinated in Production: How We Fixed It With Evals There's something particularly jarring about discovering that an AI system you deployed to production is making things up. Not failing gracefully, not returning errors, but confidently generating false information and presenting it as fact. This is what happened at REA Group, one of Australia's […]
Your AI Can't Engineer (Yet): Where AI Fails in Professional Contexts The demos are remarkable. An AI system accepts a brief specification and generates a detailed engineering design. It analyses complex problems and proposes solutions. The output looks professional and complete. But when actual engineers try to use these systems on real work, something critical […]
Orbital Lasers vs For Loops: Economically Matching Models to Tasks There's a persistent mythology in AI adoption: bigger is better. If GPT-4 is powerful, use GPT-4. If a model can handle complex reasoning, use it for everything. The assumption is that you're being conservative by using the most capable model available. You're minimising risk, guaranteeing […]
What Killed My Chat-as-a-Service? The Economics of AI Product Death A promising AI product launches to excitement and early adoption. The demo is impressive. Users sign up. Press coverage arrives. And then, quietly, the product fails—not due to technical limitations or bad marketing, but from economics that were never addressed in the initial business model. […]
Beyond Forgetful Bots: Architectural Patterns for Persistent, Proactive AI Agents Most AI agents in production are fundamentally stateless and reactive. They receive a request, process it, generate a response, and forget everything about the interaction. This architectural simplicity makes them easy to deploy and scale, but it also means they can never develop genuine understanding […]
Why Most AI De-Identification Fails in Production, And How We Built One Lawyers Actually Trust De-identifying text sounds simple when you're sitting in a demo. You've got a paragraph with personal information in it. You replace names with "[NAME]", phone numbers with "[PHONE]", dates with "[DATE]". The text is de-identified. Success. Show it to a […]
Web Directions South is the must-attend event of the year for anyone serious about web development
Phil Whitehouse General Manager, DT Sydney
