AI has a multiplying effect on existing technical skills

Sure, Claude Code and Codex can write (most of) the code for me - but the amount of technical knowledge I need to decide what and how to build remains enormous.

As an example: I'm working on a system right now that works like Claude Artifacts, allowing custom HTML+JS apps to safely run in an iframe sandbox inside a larger application.

Just understanding why that's a useful thing that can be built requires deep knowledge of sandboxing, security threats, browser security models, and half a dozen different platform features that have been evolving over a couple of decades.

A vibe coded without that technical understanding would have zero chance of prompting such a thing into existence, no matter how much guidance the LLMs gave them.

It really saddens me to see some developers talk about literally quitting their careers over AI, right when the benefits of existing deep technical experience have never been more valuable.

The software is a tool specifically designed around my requirements of managing lectures that need to be prepared, managed, have presentations, grading etc. I wanted one big space where i can quickly access all related data in a workspace, fold and unfold important aspects while also editing and moving contents across multiple days/lectures.

The first version is a vscode plugin, which i now use since about 4 months without or with minor modifications to manage my lectures and private data. The second version is a standalone application which improves on the ideas of the first version and goes a few steps further.

AI can make you something that looks like its running quickly. But when you try to finish it takes way longer then you'd think. You need to specify every little detail. You need to make its KISS and DRY etc. You let it analyze the application structure and simplify and cleanup nearly the same amount of times as you add features. While fixing bugs you might need to run the same thing multiple times and revert any unrequired changes. You need to think about good level of debug logs and ways that the program can help you find errors and report them quickly.

I hope my project will be ready in about 2 to 3 months. The current version is according to a quick analysis over 850 files with 250'000 lines of code.

I spent about 2000$ on ai subscriptions in that time. 200$ claude for a while down to 100$ a month now. 20$ to openai which is very important for architecture and reviews. 20$ on tests with other ai's, but i rarely use them in the works. I also spent 1500$ on 2 * 3090's to hopefully have a local ai agent in the future.

I spend about 2 to 4 hours each day (including weekends) to check that app and write prompts.

I would never have been able to create such a large and complex project next to my other tasks and i am very confident that the final product will be good enough for productive work.

This is the correct way to code with AI. If you don't understand the code, we're not yet in a point where the model can do it all, well it can, but where you can confidently move forward knowing its been thoroughly built and reviewed by a model up to par. Some day maybe, but not currently.

The human brain has a wide parallel multisystem real-time low-wattage execution layer that has way more modes than a large language model.

More importantly, because our brains are real-time, our qualia plus spatial and visual reasoning is superior to an LLM at understanding "elegance", "code smells", and overall system design because we can imagine ourselves as being the code or the system and we don't necessarily need to think in language. Well, at least that's how I experience coding in my mind; I imagine other developers similarly bring large parts of themselves into coding.

Feeling the code seems to be much more efficient at reducing complexity than any static analysis I've yet seen.

Finally, humans also empathize with other humans who have all the money. We know what works and doesn't work for humans in the here and now, not 2 years ago when the model training data was last collected. The value of Qualia is not to be discounted.

That being said, Sonnet 4.0 was the best model I've used that could express how the code felt, so who knows. If the emotionality wasn't tamped down, and the spatial reasoning improved, and the new algorithms for context engineering and parallelism make it to market, these advantages can be erased.

I've seen this work well at a job when there's a feedback loop for juniors that incentivized them to learn with more scope and compensation