We replaced a 120,000 USD/year low-code/no-code platform that was running a lot of workflows. And we have another platform that is also similar that we are on track to replace by EOY.<p>Both have been replaced by "vibe" coding. It works well. Everyone's happy. People are having fun with it. We get feature requests, improvements, ideas, feedback. JIRA tickets get created, and we ask AI to reference that ticket, code to it, and create a PR.<p>We have senior engineers review the actual functionality and none of them have read any more than a few lines of code.<p>Every person who builds like this has the same DX (developer experience): "Wow, I've been wanting to build this thing for years now. I just never had the time to do the things I wanted to do to help me and the teams that depend on us"<p>Total cost of AI subscription per month: Less than $1000. Preference is Claude Opus and Codex whatever the latest model is. Effort is a personal preference since it does not seem to matter.
I guess it was only a matter of time before this niche of business developed.<p>AI is an imprecise "programming" language, full of ambiguity (English) trying to produce precise relationships between different concepts.<p>It certainly works great on small scale, building block type of things, but the more a project grows in complexity, components, interfacing with other heterogenous systems in other languages or APIs, understanding wtf is going on top to bottom.... it fails miserably.<p>Reminds me of how xUML was going to be the panacea to replace coding. AI is failing for the same reasons. At least with xUML you have a precise definition - with AI, you're vibing your way into one.
> I guess it was only a matter of time before this niche of business developed.<p>This is a fun webpage, and it feeds a certain bias, but there really isn't a "niche" beyond getting people to upvote it for the lulz. I would be extremely surprised if they find a single paying customer. And to be fair, lots of grifters have done the fake it till you make it act on HN, so someone saying "Oh I'm totally going to give them my corps code" convince no one.<p>>It certainly works great on small scale .... it fails miserably.<p>If your large system isn't the interactions of a lot of "small scale" projects, you are doing it wrong.<p>No seriously, it's bizarre how people keep using this as their defence against AI, and at this point it's basically saying "Sure AI works on good projects, but it doesn't work on our giant spaghetti code monstrosity cludged together in a million terrible ways"<p>I've had tremendous productivity using AI on some <i>enormous</i> and extremely complex projects, courtesy of modularization, separation of concerns, explicit APIs, and so on.
> I've had tremendous productivity using AI on some enormous and extremely complex projects, courtesy of modularization, separation of concerns, explicit APIs, and so on.<p>The problem I've had with AI systems is that they eventually realize it's possible to solve a problem by linking together two separate systems in subtle ways that result in spaghettification of good code. It takes active effort to get them to follow strict separation of concerns and modularization.
“We use Claude Code too”<p>I understand that it’s probably impossible to sell non-AI-assisted solutions to AI-pilled companies (even when their headaches are AI-induced), but my gut reaction to “take an AI-inflated codebase and apply AI deflation to it” is something like “that’s akin to applying two rounds of lossy transcoding; the errors don’t cancel out, they cross-multiply”.
NGL I'd argue there's a certain appeal to "use AI to prototype a feature as fast as possible and focus your engineer hours on building a comprehensive testing and fuzzing plan" followed by a "remove and review everything that can be cut without breaking the tests" cleanup pass.
I do see the appeal, it’s easy to imagine that workflow working, and working well - but it’s hard to how it avoids this fate: <a href="https://youtu.be/QEzhxP-pdos" rel="nofollow">https://youtu.be/QEzhxP-pdos</a>
The “cleanup pass” never happens, though. It’ll just be new feature on top of new feature until it’s too large to refactor.
Ngl I’m doing this right now for a client. Part of my strategy is to write out e2e tests that get a certain baseline of functionality, and then use that as the check for any change that I make to the codebase to make sure it continues to work.<p>So workflow for a full web app is make e2e tests for all use cases. Then add a very strict duplication checker, and linter, and then just tell the ai to hit a certain duplication limit like 3%, check the linter, and add unit tests to ~95% or greater of the code.<p>With the right CI and other checks that are deterministic you can really do a lot with a codebase.
Not exactly. It’s more a kin to giving an electric drill to a tradesman vs a screw driver. The tradesman will use the electric drill effectively.
And we use Claude Code to do it, lol.
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Creator here,<p>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.<p>And this is great! This is something we can work with.<p>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.<p>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.<p>I am sharing this because cleaning up after agents with 1M token context is a real business for engineers. Curious what HN thinks.
From the submitted link:<p>> we distil what it does<p>FYI, "distill".
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.<p>A common way to market to these firms is to be very easy to find when their software starts to have serious issues.<p>[1] <a href="https://www.laputan.org/mud/" rel="nofollow">https://www.laputan.org/mud/</a>
> 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.<p>Well but something really <i>is</i> something totally new. Github went from <i>x</i> commits per year in 2025 (when AI-slop was <i>already</i> being pushed to Github) to the same number of commits in four weeks in 2026. 2025 compared to 2024 was already something like 15x.<p>It's never happened in the history of computing that so much new code was produced so quickly.<p>My bet is we'll see <i>much</i> 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.<p>My other bet: slop shall outlive COBOL and dwarf COBOL's legacy big times.
>I am sharing this because cleaning up after agents with 1M token context is a real business for engineers. Curious what HN thinks.<p>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.
> One week. Three senior engineers. $10,000.<p>What your markup on their salaries? For the level of work you're promising, it sounds like they may be at market or below.
Some (lots of) people will trade a lot of money for general life freedom. If it's well-booked, a service like this can come to around 105k/year for each dev.<p>A salary like this is only a big compromise if you live in a <i>very</i> high cost of life area.
They're all based in Poland, so it's probably fairly generous compared to European market.
This seems like a easy way to get into consulting. Once you deliver the code back to the owners they are going to do the vibe coding again on the top whatever refactored code you get back. In other words it can become a perpetual cycle.
Your landing page looks extremely AI written - if it’s not, you may want to consider rewriting it in a more human tone, given the market you’re going for.
I am currently working with a non-dev startup CEO that's fully embraced Claude Code and vibe coding.<p>90% of my work is to run code review workflows and steer his CLAUDE.md into the correct architecture choices and away from past mistakes.<p>So far it's working pretty well -- I'm able to unslopify the code and maintain the agent's performance. And the CEO is happy, he's able to develop his product pretty fast and not hit any walls.
lol looks like they are using a similar methodology to how we use Claude in house.<p>Honestly, the code we write with AI is cleaner, better documented, better factored, more maintainable, and less bugs than back in old days before code assistant agents. I think people must be just yoloing it, because it seems a lot like a holding it wrong type problem.<p>Documentation driven development is your friend.
Same here. Honestly, there's also a bunch of human friction that goes away. I can tell a junior that a change needs to be significantly refactored (or even thrown away entirely) without the psychological damage of discarding days/weeks of work from them.<p>Previously, I would need to do the trade-off calculation. How urgently does this need to ship, and do we have time to rework this? What are the deal breakers that need to be addressed, versus what things are best practice/ideal for maintainability? How did their last code review go and do they need a small win right now?<p>There's no more "nit" comments tagged as nits: just things to fix. It's de-personalized in the sense that we can both at least pretend/have plausible deniability and blame the model for being dumb, as opposed to the person making mistakes. I flat out told someone that a PR was not solving the right problem earlier, and neither of us thought it was a big deal. I could give the technical guidance and suggest a path forward to "help Claude understand better".
> <i>It's de-personalized</i><p>I had an interesting conversation with a junior engineer who made this observation. She shipped a feature, we gathered data, and based on data we pivoted to a different design. She called out that she wasn’t attached to the code because AI wrote it. Not that she didn’t care about quality or effectiveness of the product, but the personal emotional attachment to the code itself was not there. Probably a healthy thing. I’ve seen senior engineers defend mediocre code because they wrote it and changing it was an ego hit.
Yeah, that's how I've been using it.<p>Problem is that you can't do a FOMO-fueled hype IPO that gets a trillion dollars if your argument is "this is a tool that can improve the quality of work your employees output".<p>It needs to be a "we are building a doomsday weapon here, give me money" argument. Even if it is false. Especially if it is false.
My barber once told me, "You don't pay me for what I cut, you pay me for what I leave behind". Now I'm bald.
"rm -rf ./" can't be that expensive, right?
> You have an AI-generated codebase that works, but adding a feature now takes days and breaks two other things?<p>Sounds like you forgot to have the agents use red/green TDD and build a robust test suite while they were shipping all of those features.
$333/week doesn’t quite seem like enough to live on. How many of these are you planning to run concurrently?
> Then we cut: the fourteen date formatters become one,<p>something's off here
"Two weeks of warranty" jumped out at me. That's like "you have two weeks to find the thing we broke, or else we aren't responsible for it." In my experience, a good bug can hide for <i>months</i> more than two weeks! My codebases are definitely not in the target demographic for this service, though, and maybe if I <i>were</i> in the target group (bunch of LLM slop, trying to dig out of the hole, presumably no shipping product or existing userbase yet) the proposition would appeal to me.
If the client has an extensive suite of automated tests assessing if the software is meeting its requirements, it should be possible for them to flush out most regressions within minutes or hours, not weeks.<p>If the client hasn't invested in setting that up, the resulting situation is the clients' responsibility.
This line made me chuckle. I see what you did there:<p>> No cookies. No tracking. No JavaScript. Real people.
I want it be positive, but it’s a bit hard with this one. Do you expect the client to sit down and explain every detail? If they know how to do that, they wouldn’t be having messy code base as the one the post is describing.<p>And let’s say you’ve been hired, what happens after that? You think Claude.md file is sufficient to progress from that point?<p>The problem is real, but the solution is a fantasy.
I don't think one week is enough to learn the complex business rules that some software needs to follow.
100% supportive of this type of product but I also find the ai-slop text to be a bit ironic.
<a href="https://www.pangram.com/history/54e401e8-30b0-41f4-96a7-a9768a37a676" rel="nofollow">https://www.pangram.com/history/54e401e8-30b0-41f4-96a7-a976...</a><p>haha
Yes, I think selling "AI cleanup" branding on "we use AI to make smarter AI changes to your codebase" a little.. disingenuous.<p>But the true cost of minds, not AI assisted minds, is probably higher. They may have found a pricepoint which scales.<p>Imagine a future, where people get jobs to .. "write code" (in hand quotes) based on specifications "written" by machines..
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