How to effectively write quality code with AI

Look up luddites on Wikipedia, might be too deep to see the similarities though.

Are there still people under the impression that the correct way to use Stack Overflow all these years was to copy & paste without analyzing what the code did and making it fit for purpose?

If I have to say, we're just waiting for the AI concern caucus to get tired of performing for each other and justifying each other's inaction in other facets of their lives.

So like we went from assembler to higher level programming languages, we will now move to specifications for LLMs? Interesting thought... Maybe, once the "compilers" get good enough, but for mission critical systems they are not nearly good enough yet.

In 1987 when I first started coding, I would either write my first attempt in BASIC and see it was too slow and rewrite parts in assembly or I would know that I had to write what I wanted from the get go in assembly because the functionality wasn’t exposed at all in BASIC (using the second 64K of memory or using double hires graphics).

This past week, I spent a couple of days modifying a web solution written by someone else + converting it from a Terraform based deployment to CloudFormation using Codex - without looking at the code as someone who hasn’t done front in development in a decade - I verified the functionality.

More relevantly but related, I spent a couple of hours thinking through an architecture - cloud + an Amazon managed service + infrastructure as code + actual coding, diagramming it, labeling it , and thinking about the breakdown and phases to get it done. I put all of the requirements - that I would have done anyway - into a markdown file and told Claude and Codex to mark off items as I tested each item and summarize what it did.

Looking at the amount of work, between modifying the web front end and the new work, it would have taken two weeks with another developer helping me before AI based coding. It took me three or four days by myself.

The real kicker though is while it worked as expected for a couple of hundred documents, it fell completely to its knees when I threw 20x documents into the system. Before LLMs, this would have made me look completely incompetent telling the customer I now wasted two weeks worth of time and 2 other resources.

Now, I just went back to the literal drawing board, rearchitected it, did all of the things with code that the managed services abstracted away with a few tweaks, created a new mark down file and was done in a day. That rework would have taken me a week by itself. I knew the theory behind what the managed service was doing. But in practice I had never done it.

It’s been over a decade where I was responsable for a delivery that I could do by myself without delegating to other people or that was simple enough that I wouldn’t start with a design document for my own benefit. Now within the past year, I can take on larger projects by myself without the coordination/“mythical man Month” overhead.

I can also in a moment of exasperation say to Codex “what you did was an over complicated stupid mess, rethink your implementation from first principles” without getting reported to HR.

There is also a lot of nice to have gold plating that I will do now knowing that it will be a lot faster

My tricks:

Define data structures manually, ask AI to implement specific state changes. So JSON, C .h or other source files of func sigs and put prompts in there. Never tried the Agents.md monolithic definition file approach

Also I demand it stick to a limited set of processing patterns. Usually dynamic, recursive programming techniques and functions. They just make the most sense to my head and using one style I can spot check faster.

I also demand it avoid making up abstractions and stick to mathematical semantics. Unique namespaces are not relevant to software in the AI era. It's all about using unique vectors as keys to values.

Stick to one behavior or type/object definition per file.

Only allow dependencies that are designed as libraries to begin with. There is a ton of documentation to implement a Vulkan pipeline so just do that. Don't import an entire engine like libgodot.

And for my own agent framework I added observation of my local system telemetry via common Linux files and commands. This data feeds back in to be used to generate right-sized sched_ext schedules and leverage bpf for event driven responses.

Am currently experimenting with generation of small models of my own data. A single path of images for example not the entire Pictures directory. Each small model is spun akin to a Docker container.

LLMs are monolithic (massive) zip files of the entire web. No one really asking for that. And anyone who needs it already has access to the web itself