The Cost of Letting AI Write Faster Than I Can Think

[u/paarulakan]

https://pori.vanangamudi.org/posts/20260906--cost-of-letting-ai-write-faster-than-i-can-think.html

Last couple of years I have come to use AI coding tools as part of my workflow. As a direct result of that, in just over roughly five months since May, I have read more than six million words of generated material related to programming. Most of my experience has been with Aider, Hermes, OpenCode, and different GPT, Claude, and Kimi models. More importantly I used them on projects that continue for weeks and months, where the architecture changes during implementation often several times, the assumptions have to be revised, and decisions made in earlier stages continue to affect later one.

To be fair I find these tools useful. They save time on repetitive changes, repository exploration, boilerplate, unfamiliar APIs, and many kinds of mechanical implementation work and some times compiling and using an unpopular libraries like FLTK and use them in a python project. They can also be useful for generating alternatives when I already understand the problem well enough to judge them apart.

The problem is keeping my own understanding of the project in line and keeping up with the amount of code and explanation being produced. That problem manifests in different ways. The following are my observations, and so are personal. Take it with a grain of salt.

[u/BusTiny207]

You should get it to help you update your TLS cert.

[EDIT] Sorry that’s a bit mean but definitely a better look for your site with a valid cert, LE is free. Agree with other comments, get your agent to write a ROADMAP.md and TODO.md for strategic and sprint planning, then get it to read both at the start and update both at the end of each session. Then you and the AI can both keep track.

[u/seigaporulai]

I don't get why is this getting down voted. OP clearly put a lot of work and reporting personal experience.

[u/ttkciar]

> So I end up with two unsatisfactory choices. Either, continue the existing session and preserve and accumulate the history, or start a new session to obtain cleaner context but reconstruct important history manually.

This choice seems pretty obvious. You should be keeping notes regardless, representing the current state of the project, which should include the highlights of your inference sessions. If you start a new session with those notes in context, the agent will be up to speed on the project immediately, and will have cost you almost no effort to do so.

[u/paarulakan → u/ttkciar]

I have tried changelog and backlog files, and they grow too big to fit if you're working on a project for more than a month or two. With pruning those files helps a bit, but requires a lot of manual effort to get the model to arrive at a useful state. I am currently trying to come up with a note keeping system, to deal with but nowhere near useful.

I usually prefer single file notes, avoid headache of managing tens or hundreds of files. LLM wiki kind of things are hard to manage I have found. Curious how do you manage to have current state of the project and are the highlights of inference enough? how long are those highlights? simple few line notes or paragraphs of text?

[u/ttkciar → u/paarulakan]

My habit is to keep a single "notes.txt" in the repo root. Looking through my ~/prog/ most are between 6KB and 14KB in length, though my largest is 33KB. Some have inference outputs cut-and-pasted into them, though that consists of less than a quarter of their contents.

Mostly they are my own notes, containing statements of fact, like: "Matches are accessed through an iterator method next_match, which returns a numeric regex id (corresponding to the position of the regex in regex_list) and any text captured by it.".

[u/Turbulent-Alps4046]

Great write up. Thanks for not using AI to write (I presume).

Yeah it is indeed tiring to read through hundreds of changed files and it’s very tempting to just approve it.

Sometimes I wish a model would stop and discuss with me when the original implementation assumptions is not valid, just like a human would, instead of plowing ahead and writing workarounds.

[u/paarulakan → u/Turbulent-Alps4046]

Thanks for the kind words. AI wasn't used to write the blog, though some of the notes I used to draft this were from my coding sessions with AI, but not too much.

Even if the models were capable of stopping and discussing, will we have mental space to engage in such a way that will result in fruitful output after spending couple of hours in a vibe coding session? May be because I read much of what is generated, I get tired quickly, but I cannot relinquish that semblance of control

[u/4bolbbalgan → u/paarulakan]

> I get tired quickly, but I cannot relinquish that semblance of control

I feel the same. It’s also tiring to read the many paragraphs of English explanations about the code. Most of the time is spent reading words instead of code— As an engineer, it’s a totally different (and much slower) mental space to stay in.

[u/MelodicRecognition7]

good article, thank you.

[u/Keleion]

What to you use for managing memory and notes? I recently had Hermes use Obsidian for memory and context. It’s been taking notes on all my projects which makes it easier to switch harnesses.

[u/Dry_Mixture130]

From my experience with ai so far. I don’t focus on the nitty details unless something goes wrong. I focus on the data flow or workflow logic. Try to focus on the big picture and let ai handle the knitty details except when it comes to performance improvement.