For a long time, we were all hand-wringing over the shortage of software developers. School districts rolled out coding curriculums. Colleges debuted software “labs”. “Bootcamps” became a $700m industry.

Today, we have the opposite problem. Thousands of trained, entry-level engineers that no one wants to hire. — Read More

Lowering the barrier to creation has always been a net positive. WordPress turned anyone into a publisher. YouTube turned anyone into a broadcaster. Shopify turned anyone into an e-commerce operator. AI-assisted coding is doing the same for product building.

Let a thousand flowers bloom. I’m all in!

The problem: AI is very good at helping you build something. It’s not very good at helping you build something well.

The difference matters. — Read More

Traditional DevOps, with its rule-based automation, is struggling to work effectively in today’s complex tech world. But when combined with AIOps it can lead to IT systems that predict failures and solve issues without human intervention.

In the fast-paced and ever-changing world of software development and IT operations, automation is a great asset. From CI/CD pipelines to provisioning infrastructure, DevOps has equipped teams to construct and deploy software faster than ever. But as systems become more complex, distributed, and data-rich, automation in isolation is not enough.

This is where artificial intelligence for IT operations (AIOps) enters the conversation. By embedding AI and machine learning with DevOps practices, AIOps shifts the paradigms beyond a workflow of defined rules. Not only does AIOps analyse data patterns and detect anomalies, it can also anticipate failures and take preemptive action with little or no human assistance. — Read More

… I happen to have a bit of time these days, so I decided to write down a question I’ve been repeatedly thinking about lately.

The main reason is that I’ve been seeing one trend with increasing clarity: the primary users of infrastructure software are rapidly shifting from developers (humans) to AI agents.

… Because of this, I’ve started to rethink the problem from a more ontological perspective: when the core users of foundational software are no longer humans but AI, what essential characteristics should such software have? — Read More

Your carefully crafted roadmap is probably fiction within weeks of creating it. Priorities shift. Leadership changes direction. That feature everyone agreed on in Q1 planning feels irrelevant by April.

I learned this while launching a massive CRM overhaul at one of my previous employers. It wasn’t the plan that saved us. It was the planning.

… That’s the paradox: the plan became obsolete, but the act of planning together made us capable of executing even as everything changed. — Read More

Loosely AI interfaces are about intent and context.

Intent is the user’s goal, big or small, explicit or implicit.

Uniquely for computers, AI can understand intent and respond in a really human way. This is a new capability! Like the user can type I want to buy a camera or point at a keylight and subvocalise I’ve got a call in 20 minutes or hit a button labeled remove clouds and job done.

Companies care about this because computers that are closer to intent tend to win

… This is why I think the future of interfaces is Do What I Mean: it’s not just a new capability enabled by AI, there’s a whole attentional economics imperative to it. — Read More

Recently at work, I had to build a feature on a tight deadline. It involved chat plus tool calling components. I didn’t give much thought to prompt caching as I was just trying to ship v0.

Following next week I started to optimise it and started realising some silly mistakes I had made under pressure. I ended up adding long user-specific data at the end of system prompt thinking that I just need to keep the longest prefix stable for a single conversation / messages array.

… I could find amazing tips for prompt caching but was unable to find a comprehensive resource on how prompt caching works under the hood. So here I am load-bearing the responsibility and suffering to write the post. Following “Be the change you want to see in the world” etc. When somebody searches “how does prompt caching work really”, my hope is this post pops-up and gives them a good idea of how prompt caching works with the bonus of learning how inference looks like at scale. — Read More

In the famous essay from May 2025, “Welcome to the Era of Experience,” Rich Sutton and David Silver proposed a new paradigm of training AI models – models that learn not through predicting the next word against text scraped from Common Crawl, but through gaining experience via interaction with environments. As we approach the exhaustion of easily scrapable text data, we predict we’ll observe a shift toward AI models increasingly trained in this fashion via reinforcement learning (RL). In this text, we discuss the technical details underpinning this process.

… We intend this text to provide the reader with the theoretical basis needed to reason about AI infrastructure in the context of reinforcement learning. We argue that in the next 6-12 months there are significant opportunities for new businesses to be built around recent developments in RL, particularly for product companies to build sustainable moats through custom models trained on their proprietary environments, as well as for infrastructure players to build “picks and shovels” enabling the RL economy. — Read More

It’s appropriate that this week is Thanksgiving, because Anthropic just dropped the best coding model we’ve ever used: Claude Opus 4.5.

We’ve been testing Opus 4.5 over the last few days on everything from vibe coded iOS apps to production codebases. It manages to be both great at planning—producing readable, intuitive, and user-focused plans—and coding. It’s highly technical and also human. We haven’t been this enthusiastic about a coding model since Anthropic’s Sonnet 3.5 dropped in June 2024.

The most significant thing about Opus 4.5 is that it extends the horizon of what you can realistically vibe code. The current generation of new models—Anthropic’s Sonnet 4.5, Google’s Gemini 3, or OpenAI’s Codex Max 5.1—can all competently build a minimum viable product in one shot, or fix a highly technical bug autonomously. But eventually, if you kept pushing them to vibe code more, they’d start to trip over their own feet: The code would be convoluted and contradictory, and you’d get stuck in endless bugs. We have not found that limit yet with Opus 4.5—it seems to be able to vibe code forever. — Read More

TELUS is one of Canada’s largest telecom companies. With more than 100,000 employees globally, it’s the very definition of an enterprise.

When it comes to AI, many enterprise companies seem to have the same cookie-cutter approach: deploy GPT-5, add some guardrails, and call it a day.

Not TELUS. Despite their size and all the complexities that come with enterprise-level ops, this global company is thinking about AI in a totally different way. And I want to share what they’re doing with you – I think there’s a lot to take away from their story. — Read More