I think there are going to be about 100 times more programmers in the next few years. About a billion people.

Most of them won’t call themselves programmers. They won’t know Python. They won’t care whether their app uses React or SQLite. Many of them will never look at the source code. They’ll have an idea, tell a computer what they want, and the computer will make it.

That’s programming. — Read More

Grok Bot has shipped a wave of new features over the past month. Here’s what’s changed, why it matters, and 50 practical ways to put those capabilities to work, including some of our team’s favorite workflows.

… I pulled together 50 use cases from recent product releases, demos and examples shared by Grok Bot users on X and YouTube. Instead of sorting them by industry, I’ve grouped them by feature, so you can see what changed and several ways to use each capability.

Every workflow comes with a prompt you can adapt. Some you can try today; others need extra integrations or more setup. — Read More

I’ve been spending a lot of time working with AI coding agents on larger codebases, and I’ve started to rethink something I thought I’d already settled years ago: microservices.

For a long time, my advice to smaller engineering teams has been pretty simple. Start with a monolith. Keep things modular, establish good boundaries, and don’t introduce distributed systems complexity until you have a real reason to. I’ve seen enough companies go all-in on microservices to know how that story can end.

But working with AI agents has made me realize there might be another reason to reach for microservices. It has less to do with scaling infrastructure or engineering teams and more to do with managing the complexity and risk of making changes to large systems. — Read More

… You’ve probably heard about loop engineering. I talked about it a couple of months ago when I wrote a big blog post about it.

A loop is an autonomous, self-correcting feedback cycle where an AI agent repeatedly acts, tests its results, and adjusts its approach until a specific goal is met.

There are now basically two core primitives you can think about. In Claude Code you have a goal primitive, which can drive a single bounded task forward until you’ve got a particular goal, like a measurable finish line that’s been met. And then loop reruns on a timer or a fixed interval, so you can use it to schedule changes. — Read More

Recent progress in AI has made software engineers dramatically more productive. However, our experience building and deploying Devin suggests that agentic software engineering remains far from solved. Models struggle to infer user intent, respect unstated constraints, and anticipate long-term consequences of their changes, forcing human intervention.

Most existing coding benchmarks fail to capture these gaps and tend to be saturated quickly. We are excited to introduce FrontierCode 2.0, a new generation of our coding benchmark series and our best attempt yet to evaluate user experience. — Read More

Anthropic just published How the Claude Code team does code review. Addy Osmani posted it on X this week, and it passed a million views in under a day.

It deserves the attention. It is also Claude reviewing Claude, all the way down. — Read More

The Job is Knowing When to Be Deterministic & When to Be Probabilistic.

… Giving clear instructions about what to do and what not to do, and ensuring those instructions are consistent and not noisy, will forever be a challenge with AI, no matter how good it gets.

Context engineering will continue to be hard. But it will be hard in unique ways, because models aren’t people. You can make assumptions about how models think or interpret things (use whatever anthropomorphizing word you like), but it helps to understand how they’re built, optimized, and trained. Their unique attributes and construction create a unique set of failure modes we need to reckon with. — Read More

Next year, I’m celebrating 40 years as a programmer. It’s an anniversary for a role that is radically different today than just a year ago. Sometime in September 2025 I wrote my final line of code by hand. I’m not planning on going back. Agentic coding has removed several traditional obstacles around intra-team coordination as well as delivered an average speedup of 2-3x on our tasks.

This essay is an exploration of what I learned along the way of making agentic coding work within the context of a smaller product company. I’ll discuss the habits that had to go, the replacements that limit risks, as well as the decades-old fundamentals that we had to double down on. — Read More

Last week, Perplexity announced that they were dropping MCP and going back to plain old APIs and a command-line tool.

I love MCP. I used it frequently to connect my local systems to my agent frameworks. It makes LangGraph and Strand a joy. However, MCP is a hack, a naive protocol that should never leave your local network. It can’t be fixed. You cannot retrofit into good architecture.

Simplicity is its selling point, an API for people who don’t want to worry about APIs; a sort of USB for agents to universally connect to other applications. It is a great solution for exposing your local services to your local agents. It is fast to wire up and resilient to change. If you are trying to do something quickly and not for production, it’s perfect.

But the internet is not, and cannot be built on USB connections. — Read More

If this is the first story you have come across in this series, don’t miss out on the entire AI Agents series — explaining in great detail the core of AI agents, their communication protocols, famous frameworks, agent scaling, monitoring and observability — and a lot more. We have a great multi-part Generative AI series as well; don’t forget to checkout that too!

Building scalable, secure, performant and dynamic AI agents from scratch is one of the most tedious modern software development tasks. Luckily, there are several frameworks out there which help us with automating the whole process by abstracting away the details.

— Agentic harness is the software-level infrastructure built around an LLM to equip it with the ability of taking action in the real world, while monitoring and logging everything it makes the agents do. —

— Read More