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

In this article, we will try to understand why OOP is the worst thing that happened to programming, how it became so popular, why experienced Java (C#, C++, etc.) programmers can’t really be considered great engineers, and why code in Java cannot be considered good.

Unfortunately, programming is quite far from being a science (just like me), so many terms can be interpreted differently. — Read More

Have you ever asked yourself which protocols get used when downloading pictures from the Perseverance Mars rover to Earth? I hadn’t thought about that either, until I came across an intriguing message on the internet, back in April 2024:

I’m looking for someone knowledgeable of quic/quinn to help us out for our deep space IP project. Would be of part-time consulting. Please dm me if interested.

The message itself is quite short and somewhat jargon-y, so it took me a few readings to fully realize what the project was about:

— Working with QUIC: an internet protocol for reliable communication (i.e., what we typically use TCP for).
— Working with Quinn: the most popular Rust implementation of the QUIC protocol.
— Using QUIC to communicate between Earth and computers that are far, far away (e.g., other planets).

Business was going well on my end, and I didn’t have much time to dedicate to another consulting engagement, but… How could I say no to an interplanetary internet project? I had contributed to Quinn in the past¹, so I felt well-equipped to help out and decided to actually do it. This article provides a record of the adventure so far. — Read More

Software engineers, product managers, and UX designers each imagine a future where their contributions grow stronger while others’ might seem to fade. Everyone is eager to see how AI can expand their capabilities and impact. The excitement around this shift risks repeating an old mistake: creating silos. This time, it’s one human working with agents in isolation. And silos rarely lead to great products. The real opportunity lies in combining human strengths to build richer collaboration among diverse thinkers, guided and enhanced by intelligent tools. — Read More

The problem with AI coding isn’t technical debt. It’s comprehension debt.

And most teams don’t realize it until it’s too late.

…When you write code manually, you build up a clear mental model of the logic and trade-offs as you go. Every line you write, you understand why it exists. You see the edge cases. You know what alternatives you considered and rejected.

When AI writes code for you, that process inverts. You’re reverse-engineering someone else’s thinking after the fact. It’s like trying to learn calculus by reading a textbook instead of solving problems yourself.

… [V]olume amplifies the comprehension problem. You’re not just reverse-engineering one function. You’re reverse-engineering entire systems. — Read More