On the degree the software evolves and will be used.
The reason we've spent years discovering patterns, creating special syntaxes has been to tackle certain domain problems more efficiently and to have systems that can evolve through time.
The only constant is change.
Albeit LLMs chunk out code (and with great know how impressive output), the developer must have the know how to pass a certain threshold.
Writing in unknown languages may seem fine at first. But once you go to the edge, you'll be finding certain quirks, inefficiencies along the way that the LLM may work around it instead of removing it from root.
For example, I've been learning Effect.ts for some weeks now. I've used LLMs extensively, but before that there were a series of manual coding rounds first.
To understand composability, the nitty gritty, where things break, how, how the syntax is formed, and how could I structure some observability challenges I had around the library.
If I hadn't gone through that process, the code quality would be subpar. It wouldn't have been evident at first, but once the system would begin to evolve and adapt to feedback, things would be brittle, existing customers would be affected, and more.
In my personal experience, agentic coding wasn’t useful, but using a chat inference, was. I still need to be the critical path, but the LLM has, indeed, become a major force multiplier.
A few minutes ago, I submitted an app for review, that I started work on, alone, in February. The Quality of the new version is astounding. I’m absolutely thrilled.
It’s a full rewrite (backend server, and frontend client) of a fairly large app that’s been shipping for a couple of years, and that took over two years, to originally write.
I wouldn’t have even tried it, without an LLM. That made all the difference. The majority of the work was done with the $20/month ChatGPT Plus subscription, but the last few days, as I developed supporting materials and Web sites, I used the $100/month Pro level. After my work, over the last few months, the upgrade was a “no brainer.”
But, at every step of the way, I needed to be there, to intimately review and manage the interaction with the LLM. There’s no way that I could trust it to “just do it.”
I’m sure that, sooner or later (likely sooner), LLMs will have progressed to the point that I can trust them to vibe-code a project like this, but I guarantee, that they aren’t quite there, yet.
To be fair, I know that I may have much higher standards than a fairly significant number of developers, but the end product of my work is about as far from “AI slop” as you can get.
When it's your own codebase that you know intimately, you obviously don't want it polluted, and you want to continue to understand everything that's there. For rewriting things you understand perfectly, or porting code to different platforms, the LLMs truly are a force multiplier. But that's so different from the way they are used on new projects. Letting them make design decisions is the problem. To make design decisions, you have to understand the system as a whole.
As someone not in the know about cryptography, I know just enought that the words "custom cryptography" scare the hell out of me.
I'd say it depends.
On the degree the software evolves and will be used.
The reason we've spent years discovering patterns, creating special syntaxes has been to tackle certain domain problems more efficiently and to have systems that can evolve through time.
The only constant is change.
Albeit LLMs chunk out code (and with great know how impressive output), the developer must have the know how to pass a certain threshold.
Writing in unknown languages may seem fine at first. But once you go to the edge, you'll be finding certain quirks, inefficiencies along the way that the LLM may work around it instead of removing it from root.
For example, I've been learning Effect.ts for some weeks now. I've used LLMs extensively, but before that there were a series of manual coding rounds first.
To understand composability, the nitty gritty, where things break, how, how the syntax is formed, and how could I structure some observability challenges I had around the library.
If I hadn't gone through that process, the code quality would be subpar. It wouldn't have been evident at first, but once the system would begin to evolve and adapt to feedback, things would be brittle, existing customers would be affected, and more.
I like to move fast without breaking things
what i'm seeing is agentic coding helps be(come) more ambitious
build your own framework, database, operating system, game engine etc
things that used to be infeasible (too hard, too big, …)
> agentic coding
In my personal experience, agentic coding wasn’t useful, but using a chat inference, was. I still need to be the critical path, but the LLM has, indeed, become a major force multiplier.
A few minutes ago, I submitted an app for review, that I started work on, alone, in February. The Quality of the new version is astounding. I’m absolutely thrilled.
It’s a full rewrite (backend server, and frontend client) of a fairly large app that’s been shipping for a couple of years, and that took over two years, to originally write.
I wouldn’t have even tried it, without an LLM. That made all the difference. The majority of the work was done with the $20/month ChatGPT Plus subscription, but the last few days, as I developed supporting materials and Web sites, I used the $100/month Pro level. After my work, over the last few months, the upgrade was a “no brainer.”
But, at every step of the way, I needed to be there, to intimately review and manage the interaction with the LLM. There’s no way that I could trust it to “just do it.”
I’m sure that, sooner or later (likely sooner), LLMs will have progressed to the point that I can trust them to vibe-code a project like this, but I guarantee, that they aren’t quite there, yet.
To be fair, I know that I may have much higher standards than a fairly significant number of developers, but the end product of my work is about as far from “AI slop” as you can get.
When it's your own codebase that you know intimately, you obviously don't want it polluted, and you want to continue to understand everything that's there. For rewriting things you understand perfectly, or porting code to different platforms, the LLMs truly are a force multiplier. But that's so different from the way they are used on new projects. Letting them make design decisions is the problem. To make design decisions, you have to understand the system as a whole.
you can also situate the agent within your codebase and have it only write things you review (or have it not write/change the code at all)