I can hardly remember first experiencing ChatGPT. Its become so infused into my professional life, that going back to that first moment, or moments, is difficult. Instead a blur of a few moments. At some point, I realized it was a better Google. It was very similar to when Stackoverflow took off, and how Google search results for coding topics went from being useless to useful. Absence of results indicating a “you are holding it wrong” moment. I remember toying with it, and then remember Bryan nagging me “did you ask ChatGPT?” and being constantly annoyed. Until I wasn’t, and that became my default starting point.

The next phase came soon after. I had begun copy and pasting code; usually Ruby spec’s I didn’t want to hand write. I’d describe the idioms, or paste in some example code + my new behaviors, and have it spit out the code. This was much faster than typing it into an IDE that did not have code complete enabled the way I was used to in real, typed languages. Sorry, I always hated writing Ruby. Perhaps I would have become fast at writing it eventually, the way the lovers of Rails did. But I couldn’t visualize this future, and ChatGPT provided an escape hatch. The first 5 or 6 languages I used professionally, I learned to love and be efficient with in one way or another. But I’d hit my limit, and now felt justified in delaying learning it better.

The next moments came as a pair: Tab completion and using copy-paste on a new domain. Tab completion was more natural. Sometimes helpful, sometimes not. Its key aspect was how it was integrated into the ide I was already familiar with, vscode - forked into Cursor. Picking up the patterns I put down was about all it could do; I rarely if ever relied on its intelligence or actual suggestions.

For real suggestions I was still copy pasting ChatGPT. But this was different. before it was merely a more sophisticated auto complete. Now I was fusing Google search + code generation in a new domain: Computer vision in python. I’d moved onto a temporary vision project (boundary and alignment of novel objects); I knew nothing of this. As fast as I could “search”, I prompted my responses into code. This was one of my favorite phases, before the word vomit of later Opus models. ChatGPT would spit out code just the way I liked. Sometimes I just needed a nudge, a specific API or one line snippet. Word vomiting on it and getting a single line back, often with a cheeky insult as I requested. I’d run it in Jupyter notebook and run the code, then tinker and modify it, producing visuals and dots and graphs; simultaneously learning the APIs and the domain while building something. This was also the time I was most exposed to hallucinations, and found how easy they were to detect and deal with. To me it became a signal of what ChatGPT did and did not know; hallucinations appear at the boundary, and it became my turn to fill in the blanks, figure out how things worked that were less documented, or perhaps unique to our use cases. It was slow going at times, but then I can hardly imagine jumping into a computer vision project and shipping something so quickly without it. This was a pleasing future; one more about eliminating SEO spam and poorly written documentation on untyped languages (which were more reliant on good docs), than one in which I was giving up control.

Agentic programming came soon next. I still distinctly remember reading “Snorting the AGI” and Karpathy’s famous quip’s around this time, and being a little surprised by them. WTF were these people talking about? But slowly I learned to move off of tab completions, and on to agentic prompting. Now on Claude, but again like my ChatGPT moments, I learned to recognize its limits and work within them; slowly improving my prompts and becoming more efficient at knowing what it could and could not accomplish. I was amused watching people work through this process; laborious long winded prompts, more like wishlists but alien because they knew how to program. This was less about skill and more about groking how to work within its bounds; although their way of working was in a sense more anticipating the future than my own. “Add date sorting to Tasks.tsx, detail page; use shell system see components/Shells.tsx mimic Research.tsx for details and Users.spec.tsx for tests; follow community idioms over our own avoid mocking use stubs.spec.tsx patterns”.

One day I looked up, and realized my prompting approach wasn’t a fun trick but instead the primary way I worked now. Still the codebase was fully in my head; every line reviewed. More PRs resulted in requesting changes or making them myself. Productivity was up a fair amount, though this was at least partially because I was freed from working on code I did not ultimately like. Tab completing was rare. The agentic flow felt powerful, useful, and limited all at the same time. I still needed to tell it what to do, and if it was anything tricky, do it myself.

Then Opus 4.5/6 happened. Describing the shift makes it sound more subtle than it was. But suddenly, the granularity i had to describe tasks, and the time I spent hand holding them, dropped. My productivity went from slightly better, to around 3x (measured across a few months of merged PRs). The level of features and polish were up, not needing to be so constrained in what we chose to ship. The ideas in my head all pouring out, coming to life, slowly. Still constraint remained; PRs were becoming the bottleneck. The codebase was still fully in my head, the lines still mostly reviewed. I became better at scanning. I began to feel using standard libraries, components, patterns, were even more important, because it allowed you to effectively ignore most of the code changes - so long as they were on the rails, the damage was limited, the refactoring easy. Look for the more important parts. The parts with business logic that was unique to our business. The parts dealing with customer data that needs human eyes. The parts that might break irreversibly - database changes, encryption, etc. This job was still understandable, still pleasing. It was now almost entirely that I thought in terms of the important stuff - architecture, migration plans, key business logic, the purpose of the system, protecting data and access. All the mechanical boring parts faded away.

Progressing through Opus 5 it was more mundane bits that stood out. I could tell each model was better. The code was slowly better, each round. Fewer corrections. Sometimes it corrected me now. But this phase was more about costs. And how verbose it got. Slowly I began to hate dealing with Opus, and briefly moved to ChatGPT. The models were nearly as good in the cases I used it, very often equivalent. But far cheaper, more autonomous, and less verbose. I distrust OpenAI and am thus biased against it, even when parts of it are better (its interface, its voice chat, etc). So long as Anthropic’s coding model was nearly as good - and outside of ChatGPT 5.5 it was usually better - I was ok going back. Given what was at stake, I was sure Anthropic would sort out the wall of text issues - that is my job after all.

Then Opus 5.5 dropped. Again I feel frozen, at zero. I spend a lot of time on twitter, seeing what people are cooking up. Being amazed by it; trying to understand it. Again we learn and adapt. Again my ways of working change. Agentic coding is… different. Telling the model what to do, and checking its results… I need to elevate. What level do I need to operate at? I build more ambitious side projects. I copy what I see in a cool tweet, and try to one shot it. I collect a lot of garbage and produce a lot of garbage, even more than usual. Often it feels like drivel; but collecting ideas is fun anyways.

I start working off of plans. Not the old kind, where I’d plan out work, then implement it in phases. Instead I don’t think about the code review I want to do. I think about the QA I want to do. I think - how far can the app go, before it would be more work to go backwards? Architecture decisions are reversible. If I’m unsure about how the UI should look… I’ll just have it make more of them. If I have three similar ideas, make all three. Can I break this out into a single HTML page?

I generate more artifacts, now backed by data. I turn them into tools the AI can use to track its working state, and its history; tools that I can use to visualize its progress. Vulnerability reports get fixed in batches; I look at burndown charts with ticks of key moments. If I worry about the performance impact; I have it monitor performance too. I move more of the work into “subagents”, and have a supervisor stand above so it can see the whole picture. I start to wonder what level of architecture I need to dictate, or what kinds of outcomes I should focus on instead. Will it have gaps? Can I merely have another model try and exploit them to find out; to report its results, and have the models coordinate to harden the design?

I want the UI to be snappy. I used to dictate using observables and other optimization preferences - and sometimes still do. But I can also merely add constraints - the image should process within 15ms presses <enter>. Oh shoot - I meant to write 150ms but… oh… I guess lets simply see i the model can do it? Wow. I realize… I need to re-evaluate what I think of as possible and not. I prompt the model to explore moving my electron notes app to iphone, finally. Native swift, re-writing whatever we want. I think i’m ideating, I hit enter and leave. A few hours later I’m checking the news on my phone, and am interrupted by a (basic) working app, installed over the network so the model could ask me to QA it. It used 40% of my 5-hour limit on my $20 subscription. Its not feature complete, but it works?. I use the importer and import all my notes… It looks great. Its a few moments of staring at the screen, dumbfounded.

Everything is upside down yet again. I think about the future. But I work in the present. How I work is changing again; we adapt as we must. If I forget about my fear, I instead become very excited. It can feel empowering the faster I let go of how I used to work, and instead embrace how it is now. As the code moves faster, how can I continue to understand it? I come up with new metrics, new dashboards. I think harder about the the most important parts of the design and the constraints are. That is the only place that deserves attention and effort. I think about building tools to help me understand, and tools for the model to use. Tools to iterate on ideas more quickly; to come up with new ideas, mix and match old ones. Tools to better discover what other people are doing, to think about how things my interoperate, and try to build that.

And when I come up for air, I’m amazed by how much things have changed. How quickly the models have changed. And yet, how also we have changed, and continue to adapt to the new ways of working. Many things about the old ways of working, I miss. Many things I do not. The old way of working feels like just yesterday; but also it feels to be drifting off into the distance.