There is a new kind of task for software engineers these days. A client calls, asks for a "small refactor," and sends you 100k lines of AI-generated spaghetti.
And this is great! This is something we can work with.
Any experienced engineer can look at a codebase like that and quickly see what to refactor, where a library replaces a few thousand hand-rolled lines, and what smells bad. Removing the first 30% is easy. The next 30% is harder, and that is exactly what the price should be on: doing what others can't. We use coding agents too, of course, but as a tool, not as the driving force.
That is why we started Slopfix, a software house focused entirely on refactoring AI-generated codebases. We commit to a reduction target up front, and the client pays in proportion to how much of it we hit. We get paid to delete code.
I am sharing this because cleaning up after agents with 1M token context is a real business for engineers. Curious what HN thinks.
Our outcome is also AI-generated (or AI-assisted) code. It is not possible to rewrite 100k loc in a week. Where our stills are is refactoring at scale with help of agents. Basically, software engineer knows better what prompt to put, and when to redo the process or go different path.
> I don’t think this is anything new, really: Businesses have been running software that we’d call a “big ball of mud” [1] forever.
Well but something really is something totally new. Github went from x commits per year in 2025 (when AI-slop was already being pushed to Github) to the same number of commits in four weeks in 2026. 2025 compared to 2024 was already something like 15x.
It's never happened in the history of computing that so much new code was produced so quickly.
My bet is we'll see much more of this. And these aren't going to be 100% AI-pilled companies solving these issues but companies like the one in TFA: experienced devs using the help of LLMs to fix slop.
My other bet: slop shall outlive COBOL and dwarf COBOL's legacy big times.
Most of the new Github bloat will just be thrown away. Vibe coding scratches an immediate itch and it's easy to do. Once the problem changes nobody is going to update the first project because that's hard, they'll just vibe code an entirely new solution leaving the first to rot until they delete their dead repo clutter or move on from the company and the account and all of its repos are deleted in one fell swoop.
>I am sharing this because cleaning up after agents with 1M token context is a real business for engineers. Curious what HN thinks.
Its the same as it ever was. Cleaning up after cloud migrations, cleaning up after crypto integrations, cleaning up after LLM tokenmaxxing. I think people are deluded if they tell you LLMs will replace humans.
We don't target software developers. Most of the time, they know their craft. We target those who don't have experience and diging into how serwer should be implemented, or best composition of a domain model is not what they would like to sepend hours doing.
The typical client is a 40 years old ex-CTO, that had idea, verified it with Claude Code and got to the point, where the repo is too big. Maybe even rewrote it all from zero, once or twice, but end-up with the same outcome after adding more features.
He can either hire a developer to work on the codebase and refactor it or hire 3party company.
Sometimes the requirement is a security review. And you pay much less if you downsize the repo before handig it over.
Initial AI scan can list most of system usecases. Then we review it with a client and agree on what must stay. We can propse better solutions at this stage. If we remove the code, then we can also remove its unit tests. For intergation, repo-wide tests, we treat it as documentation/requirements that must work on the other side.
Human to human interaction is the tool we use the most :)
The copy on your website itself kind of reads like LLM slop (eg. "One week. Three senior engineers. $10,000"). You may have written it yourself and marketing copy just tends to look like this, but it doesn't inspire confidence that your service will actually improve my code.
No shame in using Fable to help with the grammar and style of text. English is not my native language and while I can use it quite well, LLMs are much better in forming my thoughs into something simple, that sounds great.
My writing workflow is: (1) write what I want to have on the page. (2) /grill-me it should be sound and logical and easy to read. (3) Manually review the text, replace by hand what I don't like.
There is also a flip side of this. I work a lot with AI-generate text and I can also catch it quickly, but most of the time now, it's easier to read AI, than humans with their weird chain of thoughts and compex sentences ;P
If unclogging the sewers is a way how the deluge of AI slop can create new work, then more power to you! It's one more element that makes it even harder to write satire that is more absurd than reality.
I worked with such an "enterprise code" in the past. LLM code is a bit different. For example you don't see code repetition in the same file very often. I'd say it's a next level of slop. Slop at scale!
There is a new kind of task for software engineers these days. A client calls, asks for a "small refactor," and sends you 100k lines of AI-generated spaghetti.
And this is great! This is something we can work with.
Any experienced engineer can look at a codebase like that and quickly see what to refactor, where a library replaces a few thousand hand-rolled lines, and what smells bad. Removing the first 30% is easy. The next 30% is harder, and that is exactly what the price should be on: doing what others can't. We use coding agents too, of course, but as a tool, not as the driving force.
That is why we started Slopfix, a software house focused entirely on refactoring AI-generated codebases. We commit to a reduction target up front, and the client pays in proportion to how much of it we hit. We get paid to delete code.
I am sharing this because cleaning up after agents with 1M token context is a real business for engineers. Curious what HN thinks.