The AI skills gap
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.
The blog · since 2015
Insights for founders,
CTOs & investors.
Essays, playbooks and field notes from our CTOs, architects and leads. Written for people shipping real product.
Featured articles
What will the state of AI be like by this time next year?
Pricing strategies in the era of AI: why hourly billing no longer works
5 red flags for VCs when planning to invest in Seed or Series A companies
SaaS due diligence report: 12 learnings for founders that want to secure their next funding round
Things we do in our first weeks as Fractional CTO
Latest posts
The AI skills gap
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: the agent that doesn't quit when you close your laptop
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.
Building a customer support AI agent that learns before it speaks
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.
Architecture always leaks
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.
Your network isn’t as safe as you think
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 everywhere, even in cars
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 end of the all-you-can-eat buffet
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.
Technical due diligence before acquiring a software company
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.
ElevenLabs: voice cloning, agents, and what they mean for your product
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 is a commons
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.
"Good news, I built it in Lovable.": an engineer's guide to surviving that sentence
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.
We're not a normal company
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.
The CTO as prisoner
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.
Python as the new Latin
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?
Your codebase is a palimpsest
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.
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