The blog · since 2015
Essays, playbooks and field notes from our CTOs, architects and leads. Written for people shipping real product.
The bottleneck in software engineering has shifted from typing to understanding. As AI handles implementation, engineers who thrive will be those who recognise patterns, curate guardrails, and connect technical decisions to business outcomes.
The bottleneck in software engineering has shifted from typing to understanding. As AI handles implementation, engineers who thrive will be those who recognise patterns, curate guardrails, and connect technical decisions to business outcomes.
A head-to-head of pencil.dev vs Claude Design on the same markdown input. Claude Design produced the more polished deck and handled speaker notes; pencil.dev still wins for iterative UI work where direct manipulation matters.
AI is making the knowledge side of consulting cheaper by the day. Teams still avoid the legacy system. The gap is not expertise. It is the structural courage to act on what everyone already knows.
Five types of documentation, five different AI leverage stories. The question isn’t whether to involve AI in your docs; it’s where each type belongs and how much maintenance you can hand off.
Pairing with an AI feels like pair programming. It isn't. There's one prior in the room, and it's trained to agree. This post makes the case for mob programming and spec-driven development as the structural fix.
Engineering demos fail when they present what was built instead of why it matters. A practical guide to leading with business impact, showing the happy path, and translating technical work into terms your audience actually uses.
Errors don't just happen; they land somewhere. Validation, generic, idempotent ignore, warning log, or Sentry: each routes failure to a different audience. Get the routing wrong and either engineers go blind to real bugs, or state corrupts silently.
This is the first edition of my rebranded newsletter, straight from my desk. Every other week, I'll write something about what we'
In 40+ interviews, senior engineers from major banks and consultancies showed strong backgrounds but little real AI fluency. No RAG, no agent frameworks. The gap isn't about skill, it's about exposure.
Hermes is an open-source AI agent that runs on a server, remembers across sessions, and builds reusable skills over time. The shift it represents: AI moving from something you summon to something that runs.
A customer support AI agent built in stages: shadow mode first, internal notes second, auto-send only after the data earns it. A walkthrough of the architecture, the knowledge base design, and the lessons that held up.
Auditing data-heavy companies reveals the same pattern: asynchronous data processing crammed into the synchronous web stack. The contention shows in performance, delivery, and team dynamics. Isolation fixes all three.
Cheap consumer devices from Amazon and BestBuy ship with factory-installed malware and botnet software. The Zero Trust principle isn’t paranoia; it’s the only safe assumption for any network you don’t fully control.
LLMs are no longer a tab you open. They're the interface layer between intent and every system underneath. This post maps what ambient AI, edge inference, and agent-as-infrastructure mean for how you design modern software.
The all-you-can-eat era of AI is ending. Compute constraints, heavier models, and a fully hooked user base are pushing providers toward pay-as-you-go. That shift will force better choices, smaller models, and fiercer competition between tools.
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