this post was submitted on 28 May 2026
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Fuck AI
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A place for all those who loathe AI to discuss things, post articles, and ridicule the AI hype. Proud supporter of working people. And proud booer of SXSW 2024.
AI, in this case, refers to LLMs, GPT technology, and anything listed as "AI" meant to increase market valuations.
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I'm so good at asking the bullshit machine to work for me. I can get it to pump out bullshit for 10x less than some idiot.
Remember, telling your model to talk like a caveman will save you about 15% on output tokens!
Indeed, and with tuning, you pass the threshold and it is cheaper than a junior dev, faster, and better quality.
Im still all for hiring juniors, but they should be getting trained to use these tools.
How magnanimous of you.
Well you see, if you dont hire junior devs, and senior devs eventually retire... then you have no devs at all and thats usually considered bad.
So you hire junior devs... and train them to use the tools, so that they become competent with them... like literally any other job in the world.
Its like trying to argue that because your site workers on a job can use a CAT to move material way faster, now you dont need to hire new guys... which only works up until your CAT operator retires and you never bothered to train their replacement...
Any half competent manager would consider that to be a super stupid thing to have done...
When people tell me AI makes their job faster I assume they're either shit at their job or their job is useless.
A very very large part of programming is boilerplate code you cant get past, it must be written.
A huge part is integration testing.
You have like hundreds or thousands of tests that are very repetitive. "Open this, click that, do this, type that, wait... assert this happens"
You have all these in a human readable format/doc provided to you by your requirements team
You uabe to convert all of that into code so a program can do the steps, such that it runs every day to confirm "website isnt broken boss!"
Converting all that human text into code is very easy but very monotonous
It can be tens of thousands of lines of code, easily.
This type of task is extremely easy for AI though, especially if you start it off with 10~15 examples to show it how it outta be done
Then you just give it the doc and go "okay draw the rest of the owl please"
This is called "nth shot prompting", where it has samples to start with and then it just copies your work 1000 times given input.
AI is extremely good at this.
It can turn weeks of work into hours.
The ability to turn human contextual English into code is one of its top skills, and turns out for a serious prod scale app at a company, thats like... 90% of software dev
And since we can work in parallel, I can focus more of my effort on that remaining actually challenging 10% where stuff matters more.
Its like swapping from pulling a wagon by hand of dirt to a job site, to instead pulling it with machine.
I get that part done 10x faster/easier, or better, and now I can actually put my time/energy into way more important shit than just hauling dirt around.
Summarized: when ppl talk about it making them do their job faster/better, its usually cuz it got rid of a bunch of boring parts and now they can focus on the actually important parts of their job more, and thus produce way higher quality results net.
Okay I get it now. You're in the shit at your job group. Thanks for clarifying.
"In the shit at your job group"
What does this even mean lol. Wanna try that sentence again?
Ask your AI girlfriend. You're not beating the alligations.
Okay so just trolling then.
Trolling works better when you can string together words in a meaningful way 🤙
You're right, my trolling is wasted on the idiots who come into fuckai and expect a warm welcome for their pro-ai ideas. You're thick as a castle wall, and no amount of clever prose will penetrate.
Innovating the boring stuff is how you do your job better. AI doesn't do that for you.
There is no innovating it, you simply have to just do it.
This us a purely logical requirement, the cide is already abstracted to the maximum feasible point.
You simply have to write the code that connects the output of pipe A to the input of pipe B
This is called the Domain Rules or Business Rules, its the stuff specific to your apps needs that simply cant be abstracted further.
If we define for example "This endpoint lets you add a person to a room, but a room cannot have more than 8 people" you cannot get around needing to somehow define this business rules in your logic.
Even at its absolute most abstract form, its at least a couple lines of code minimum.
Now, most api endpoints have several rules. And often apis can have hundreds of endpoints. And often businesses maintain multiple apis.
So, 3 x ~7 x ~100 x ~3 puts you at like 6300 lines of code baseline for defining business rules.
And then for every. single. rule. You have to write a test that positive and negative tests these rules.
Which puts us at about 2100 rules, multiple by about easily 10 to 12 lines per test easy.
So 11 x 2100 = 23,100 lines of code for tests, though its prolly closer to double that.
ALL of this is extremely simple and easy to do, its just a lot of fuckin typing lol.
AI can pump this out in about 1/10th the time I can, prolly closer to 1/20th tbh.
And are your tests abstracted as much as possible? Ie Are you decorating methods to describe the acceptable range and domain and have a hypothesis engine test for that? Are you using snapshot testing to make things easier to trace? I can go on but I don't know the context nor do I think we should really sync on the specifics of your job. The point is you are at a point where you think the code can't be made simpler, maybe that is true, but if AI is doing all the boring stuff then I don't know how one would even care to know.
Did you... read what I wrote?
It's honestly so depressing that you've taken the time to explain how there is in fact a use case for AI, and what that use case is, in plain language, and the response has been to act like chimpanzees, hurl insults, and downvote you.
Shit like this makes me think Lemmy is doomed.
I mean, he is on the "Fuck AI" community. You may agree with the response, but it should be hardly surprising.
Then go away.
They aren't the first industry to do it.
A lot of other engineering fields in the USA are seeing a manpower issue because the fields stopped hiring new engineers for a few decades then the Great Recession reduced the number of engineers they were hiring.
I expect this to happen for programming.
What people need to wrap their heads around is almost all of these cases are/were companies that were gonna lay people off anyways.
They just used "cuz AI" as a PR spin to avoid making investors panic.
"We laid off 25% of our workers cuz we are failing" makes your stocks tank.
"We replaced 25% of our workforce with AI" makes stocks go up.
And they can just lie about it. They probably arent even fuckin using AI... or they are half assing it to make it look like they are using AI to investors.
The reality is, the company is just shitting the bed and upper management is doing everything they can to stop the stocks ftom ranking for just 6 more months so they can sell out first before the company goes bottom up.
Companies actually using AI intelligently arent "replacing" anyone with AI.
"I'm trying like crazy to distance myself from the actual ramifications of ai because it's devastating for my arguments for ai."
But saying
Usually makes the stock go up.
It... really does tbh, that is probably the most effective thing to announce thatll tank your stocks.
Laying off your workforce usually causes a panic sell out since its a death flag...
Lol. My office chair is cheaper and more useful than a junior dev, too.
Pretending junior devs are worth anything other than as an investment toward later having a loyal senior dev who knows our product is the weirdest part of the whole AI farce.
There's this weird myth being shared by managers that 3 junior devs in a trenchcoat is equivalent to one senior dev, or something. It's a little bit like believing that 5 toddlers can do the work of one college student.
It's like saying "we won't let anyone under 25 rent our cars, except if there are at least three children in the car, then it is okay by us."
Developers become expontially more valuable as they gain experience. We invest in them early because some of them cannot be arsed to take a new job every two years, once they are happy and comfortable, and we end up getting an amazing bargain for a few years, once they are senior devs.
Good thing we are going to kill that pipeline to prevent more senior developers thus ensuring we have to use AI. And of course betting on replacing the senior developers as well before they all retire. It is quite the gambit to end employment as we know it.
What does training look like? This is new tech and it isn't like tech has figured out the best way to design and build large projects. Is there even a set of best practices developed for AI yet?
We are way past that, there are several very solid ways to approach it, and lots of debate over which is "better"
What matters is picking one and being consistent company wide and getting all your employees on the same standard.
But yes, the ecosystem/practice now has plenty of powerful tools and methodologies.
And the difference between "Idunnolol" winging it vs using any of the tried and tested architectures is huge.
You go from "I burnt 100k tokens and got a mangled mess" to "I used 8k tokens and got actually half decent results that needed a bit of human tweaking after"
Skill files, MCP protocol, RAG memory... theres a buncha stuff that elevates the tools from "plundering ox in a china shop" to "actually useful"
But you mentioned three technologies instead of methodologies of how to use AI in a workflow.
What tasks make economic sense to devolve to AI? What is the workflow? Who is checking to make sure the tokens are being used economically?
So, the main one I use it for, as its my job, is software development.
I offload about 90% to 95% of my workload to AI, almost all of which is "boilerplate" code that sits in the realm of "very easy to do, but very repetitive and time consuming" which is... most of it. Thats just the reality of software dev, especially web app dev. A lot of our stuff is just plumbing "this api endpoint calls this backend logic which just maps to this basic database operation"
IE, the POST endpoint to
/users/{id}invokes theUpdateUserHandlerwhich takes in anUpdateUserRequestwhich maps to anUPDATE [dbo].[Users] ...sql statement... not exactly super complicated stuff, but you do have to actually write the stuff that defines this.This type of work is trivial for AI, but any given project has its own set of business rules, code rules, syntax rules, formatting rules, etc etc etc.
A naive approach is just yolo throw an AI agent at it, cross your fingers and pray that it randomly chooses to read the right stuff and happen to succeed at following your code quality and methodologies (it wont)
The naive agent also will demolish its way through tokens as it reads way more files than it has to, because every single time you do work its basically starting all over again from ground zero with no context of wtf its doing. This wastes.... so many tokens, because its gonna sit and read like 20 files just to figure out "what am I supposed to be doing here?" and this in turn pollutes its context window up so damn full it will start forgetting shit anyways.
This is where actual tools come into play that make this stop being an issue...
RAG Memory, instead of blindly searching your codebase, you can tokenize your codebase into a much easier to semantic search system, the agent can WAY faster do a simple search and get returned pointers to "Look over here", its like creating an index of your codebase itself so the agent has a useable optimized "map" of the project.
MCP Tools, which are basically tools that the agent can invoke to do.... anything. Normally by default the agent is just given willy nilly access to the terminal and it'll just fumble through trying to use that to do anything it needs to. This is a great way to fuck stuff up, especially if it also has access to shit it shouldnt (que "our agent accidently deleted our database" type shit). MCP tools allow you to build a curated set of stuff it can invoke, so instead of just doing random shit it has prefab commands to run. If you flesh out your MCP Tools well enough you can outright disable its access to the terminal entirely, because it doesnt even need it anymore. No more accidental database deletions, and it uses waaaay less tokens fumbling around in the terminal.
Skills, which are special files that allow it to "lazy load" guides on "how to do x/y/z", which you can break up into bite sized pieces. So instead of a giant AGENTS.md file that uses up half its context, even though the agent doesnt need 90% of whats in it for a given job, it can instead have a big list of "how to do this, how to do that" and it'll load a skill for a given task its working on ad hoc, only loading in the instruction relevant to the task at hand. These are huge and critical to further reduce token usage a lot
Token reduction skills (caveman being the most popular), theres certain skills people have made that outright change the way the agent behaves, namely the caveman skill. Make agent talk like this. Why more word when less word good. Less word, less tokens. Less tokens, less money. Also faster. Ungabunga. (Caveman mode can give you like 50% to 70% token reductions alone)
General prompting skills, know how to prompt agents genuinely makes a big fuckin difference. Baseline thing you learn asap is NEVER correct an agent, this does not work well. Instead you should be going back in the timeline and editing your prior prompt to preemptively correct the mistake before it even happens
Example, responding with "X is wrong" is bad. Instead, going backwards and editing your prior prompt with "And dont do X btw" is far better
This is just a handful of stuff, this is literally the basics, but hopefully gives you an idea of how deep the rabbit hole can go, I didnt even touch on stuff like agentic workflows and agent orchestration, which is where shit really starts to pop off...
Based on what you're describing, it almost sounds like creating this development environment is a full time job outside of the typical development structure, someone who has specialty knowledge of best practices to use AI that isn't a senior dev or project manager.
I wouldn't be surprised if the job of AI manager becomes a job in mid-size firms.
Its definitely a lot of up front architecture work, its one of those things that you spend a week or two bootstrapping, after which you maintain it and refine it day to day as you find gaps in your various instruction files, new mcp tools to install, etc.
At my company we do indeed have specialists on the topic that can hop onto a project to help with bootstrapping and offer guidance.
Largely this work falls under the Architect role, as it nicely also lines up with the other bootstrapping work for stuff like CI/CD and standing up environments to deploy to, etc.
So its definitely shaping up that way!