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Instead, the art moved from capturing images to choosing them. From the shutter finger to the editing eye. From “can I get the shot?” to “can I find the shot, in all of this, and do I know it when I see it?” The technical act got cheap. The judgment got more valuable. And an entire universe of new things that nobody predicted, things that couldn’t have existed in the era of 36 exposures, grew in the space that opened up.

I think that’s what’s happening now. With slides, with code, with writing, with design, with whatever domain you’re generating abundance in. The raw material is becoming infinite. The craft is migrating somewhere else.

I think this all makes sense for someone in the author's situation of which I am one. It should be remembered that there probably weren't many impoverished homeless people interviewed on their death beds. I'm assuming some of those would have had regrets about not earning enough money. My point is that even though people like me may be worrying about things like identity crisis, AI agents could have more tangible consequences for some.

Saying "I am a software engineer" is beginning to feel like saying "I am a calcultor" in 1950 now that digital machines can use electrical circuits to count, add, multiply - it's not long until they'll be able differentiate a non-continuous function... You're beginning to feel less-than-useful.

This bothers a lot of people for a reason (I think) that has nothing to do with the technology. The fear isn't really about losing a job title, it's about losing the story you tell yourself about who you are.

Imagine where we’ll be in two years – we’ll certainly have AI models that are smart enough to point themselves at a specific objective, find an open weight model, then autonomously improve it to get better performance at that task. The era of ephemeral, custom AI systems, built and budded off into the world like spores from mushrooms, draws near. Are you ready for this new ecosystem you will find yourself in? I am not. But nonetheless it approaches.

I wanted a simple blogging platform that I could run from my own Markdown files. No intermediaries. No bloated UI. No JavaScript. No databases and migration scripts. No insecure plugins. Just a git folder, an Obsidian vault or a synchronized SyncThing directory, and the ability to create and modify content by simply writing text files, wherever I am.

Drop a Markdown file in the directory, and it's live. Edit it, and the changes propagate. Delete it, and it's gone.

This is step one of how things are going to be for everyone eventually. Get an idea from someone else, then have your clanker build it for you.

The whole thing is designed to be forked, but I don’t think I’ll really be accepting any PRs. At all. If you want something added, fork it and have your Claude add it. That’s the whole contribution model. That might come off as antisocial but I mean it in the completely opposite way. I want to hear about what you built, I just don’t want to be a bottleneck for whether you can build it.

We recorded this song quite awhile ago. I really like how it turned out. We never shared it because I wouldn't do a video. I hate doing videos pretending that i'm playing music and we can't exactly to a live video. Rodney finally tired of waiting on me and created this cartoon video. I think he did a great job. It cracked me up.

I think he is on to something here. I've believed for awhile that the best way to learn to use agents and llm's for writing code it to focus more changing the spec instead of modifying mistakes in the code. This does work for smaller projects but can be problematic to say the least. This is a realistic solution to the problem.

Code implementation clarifies and communicates intent. I could stop there and walk out of the room. I missed this with whenwords.

The job is to keep specs, code, and tests in sync as they move forward. The system for managing that has to stay simple. If it creates developer mental overhead, it just moves the problem somewhere else.

The act of writing code improves the spec and the tests. Just like software doesn’t truly work until it meets the real world, a spec doesn’t truly work until it’s implemented.

No-code libraries are toys because they are unproven.

Even if you aren’t the one making decisions during implementation, decisions are being made. We should leverage LLMs to extract and structure those decisions.

And finally: we’ve been here before. The answer then was process. The answer now is also process. And just as we leverage cloud compute to enable CI/CD for agile, we should leverage LLMs to build something lightweight enough that we can fit in our heads, doesn’t slow us down, and helps us make sense of our software.

here may be other elemental particles of cognition in the mixture of our human intelligence, but I am confident it includes these three as primary components. For manufacturing artificial intelligence we have an ample supply of Knowledge IQ, and we have some preliminary amounts of World IQ, but we seriously lack Learning IQ at scale.

It is important to acknowledge that for many jobs we do not need all three modes. To drive our cars, we chiefly need world sense. To answer questions, smart LLM book knowledge is most of what we need. There may be use cases for an AI that only learns but does not have a world sense or even that much knowledge. And of course, there will be many hybrid versions with two parts, or only a bit of two or three.

The most disciplined form of TDD is test-first development. You write the automated tests first, confirm that they fail, then iterate on the implementation until the tests pass.

This turns out to be a fantastic fit for coding agents. A significant risk with coding agents is that they might write code that doesn't work, or build code that is unnecessary and never gets used, or both.

I have written eight of these guides since ChatGPT came out, but this version represents a very large break with the past, because what it means to "use AI" has changed dramatically. Until a few months ago, for the vast majority of people, "using AI" meant talking to a chatbot in a back-and-forth conversation. But over the past few months, it has become practical to use AI as an agent: you can assign them to a task and they do them, using tools as appropriate. Because of this change, you have to consider three things when deciding what AI to use: Models, Apps, and Harnesses.