I’ve been thinking a lot about this quote from Steve Krouse (via Simon Willison):

The fact that MCP is a difference surface from your normal API allows you to ship MUCH faster to MCP. This has been unlocked by inference at runtime

Normal APIs are promises to developers, because developer commit code that relies on those APIs, and then walk away. If you break the API, you break the promise, and you break that code. This means a developer gets woken up at 2am to fix the code

But MCP servers are called by LLMs which dynamically read the spec every time, which allow us to constantly change the MCP server. It doesn’t matter! We haven’t made any promises. The LLM can figure it out afresh every time

The implication is that we can have a dynamically defined endpoint for agents to talk to. I imagine that’s not as efficient as exposing a well-defined API, but maybe it doesn’t make a difference. Let the agent discover what tools you are making available when they visit your endpoint.

And then you can try out exposing new tools, and see how agents react to using them – agent-catalyzed product discovery.

Agree with this post 100%. I switched to using uv about six months ago and it has made package management in python much easier.

Let’s be considerate about how we use GenAI to write emails, articles, or blog posts. When I first started, it was fun: Wow, I can crank out a 750-word essay in minutes! But that’s when you risk outsourcing the thinking. The result? What Stanford researchers call “workslop”: lots of words, not much value.

Approximately half of the people we surveyed viewed colleagues who sent workslop as less creative, capable, and reliable than they did before receiving the output. Forty-two percent saw them as less trustworthy, and 37% saw that colleague as less intelligent.

For this post, I didn’t just ask GenAI to write it; we discussed ideas, and I shaped the thinking and co-wrote the text. If you’re generating 10-page documents that someone else has to decipher, you’re just moving the thinking downstream.

AI should help you sharpen ideas, not dump text for others to untangle.

Via Simon Willison:

Moving forward, when I talk about agents I’m going to use this:

An LLM agent runs tools in a loop to achieve a goal.

I like this definition. Simon breaks down why he chose that specific phrasing for each part, it’s worth a deeper read.

There’s a lot of confusion about agents, and while it has already been turned into an elastic marketing term like “AI”, it’s helpful for product and technology discussions to have more precision.

Learning Python for data science seven years ago changed the trajectory of my career. This documentary is a great behind-the-scenes view of the people who brought the language to life.