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But getting it right for our system, conventions, and past decisions is still the hard part — we waste time and tokens in correction loops. Patching many MCPs together may give agents access to information, but not necessarily understanding.
That’s why I am a fan of what Unblocked is building with their context engine — giving agents exactly what they need to understand things and succeed. Join their webinar on May 6th (free) to see:
Where teams get stuck on the AI maturity curve
How a context engine solves for quality, efficiency, and cost
Live demo: the same coding task with and without a context engine
A couple of months ago I wrote about my experience with OpenClaw — what I do with it, what procedures it runs automatically, and so on.
I also argued that the story of OpenClaw itself seems to point to the idea that a good solo engineer can now achieve… pretty much anything. Look at the progression of AI assistants and coding tools:
At first, it was VS Code + Copilot. Created by Github/Microsoft, which is a big tech.
Copilot was surpassed by Cursor, created by a team around 1/100th its size.
Cursor was then surpassed by Claude Code, which has been created literally by 2 engineers inside Anthropic, and whose team to this day is ~20 engineers.
OpenClaw has been created by Peter Steinberger. That’s it.
In my (very small, comparably) experience with Tolaria, I am seeing this as well. If you are equipped with the right set of ingredients, it’s hard now to see the limits of what a single engineer can do. But what are these ingredients? To me it’s:
🔧 Tech skills — you still need these! Especially to steer a sizable project. E.g. Peter is first and foremost a world-class engineer.
🧑🍳 Taste — your personal taste for what good looks like, your domain expertise, how knowledgeable you are about the problem you want to solve, your product senses — these are the things that AI is not going to replace anytime soon, if ever.
🏃 Drive — above all, the drive and discipline needed to put something out there. I know an awful lot of people who have the first two ingredients, but kinda miss this one.
My OpenClaw article is one of the most popular pieces I have ever written, you can catch up with it below 👇
Last month I interviewed the legendary Chris Lattner on the podcast. Chris created LLVM, Swift, led Tesla autopilot, brought TPUs to the market at Google, and more! He is now the founder of Modular, where he creates portable infra to work with GPUs.
Chris told me that, despite the industry-wide narrative that AI favors senior engineers, Modular is actively hiring a ton of new college grads and interns. Chris’ reasoning is based on two observations:
🧠 AI-native thinking — junior engineers bring a completely different expertise. They are native to the tools and adapt faster than many experienced engineers.
⚖️ Team balance — good teams need people at multiple levels learning from each other. All-senior or all-junior teams both fail in different ways. (we wrote about this also here!)
Fast forward to the moment right before AI takes over everything engineers do — what’s the last valuable thing left? I won’t spoiler! Fantastic article by Kent.
It’s not hard to make bread at home — even better than the one you buy at the bakery. Yet nobody really does it at scale. Don’t let the “everyone can code now” narrative scare you out of building a software business.
A thorough, opinionated guide on AI coding, updated to April 2026. Covers both the mindset and the practical tools. I liked it!
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The underrated risk is not that AI makes junior engineers obsolete. It is that it lets companies hide how bad their apprenticeship model already was. A lot of “junior work” was never just work. It was the messy surface area where an org’s taste got transmitted: why this abstraction is bad, why this migration failed last time, why the obvious design is politically impossible.
The underrated risk is not that AI makes junior engineers obsolete. It is that it lets companies hide how bad their apprenticeship model already was. A lot of “junior work” was never just work. It was the messy surface area where an org’s taste got transmitted: why this abstraction is bad, why this migration failed last time, why the obvious design is politically impossible.