The blog · since 2015
Essays, playbooks and field notes from our CTOs, architects and leads. Written for people shipping real product.
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.
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.
What over 180 SaaS audits reveal about the technical risks that restructure software acquisition deals, and how to assess them before signing. Real data on documentation debt, testing gaps, key person risk, and the five findings that change deal terms.
Voice is where AI product differentiation is heading. This post walks through ElevenLabs voice cloning and conversational agents in enough detail to evaluate whether the technology is ready for your use case.
Your codebase degrades the same way shared resources do: not from malice, but from missing governance. Elinor Ostrom proved the commons can survive. Her principles map to software teams with uncomfortable precision.
Business users love Lovable. Engineers tend to panic. A real-world case study of how to wrap an AI builder in guardrails so non-technical teams can move fast without quietly rewriting the systems that give your product its edge.
Every software company claims to be different. The "we don't need X because we have culture" exemption is special pleading. The four phrases that reveal it, and the questions that break them open.
CTOs who built their company's survival by doing everything can't escape that role by scheduling a holiday. Moving from doing to enabling is a skill and identity shift at once. One thing handed off permanently is where the exit starts.
I used to teach people to code. And looking back, I was teaching students to write it by hand while the tools that write it for them were getting better every single month. So what should a coding classroom actually look like now?
Every legacy codebase is a palimpsest: layers of decisions written on top of each other, none fully erased. Geoffrey Dhuyvetters argues that what looks like technical debt is often stratigraphy, and you read it before you rewrite it.
Most AI-powered customer support is optimised for deflection, not resolution. The problem isn’t bad agents, it’s architecture: no shared context, no real permissions, no escalation path that works.
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