AI still doesn't work very well, businesses are faking it, and a reckoning is coming
Excerpt:
"Even within the coding, it's not working well," said Smiley. "I'll give you an example. Code can look right and pass the unit tests and still be wrong. The way you measure that is typically in benchmark tests. So a lot of these companies haven't engaged in a proper feedback loop to see what the impact of AI coding is on the outcomes they care about. Lines of code, number of [pull requests], these are liabilities. These are not measures of engineering excellence."
Measures of engineering excellence, said Smiley, include metrics like deployment frequency, lead time to production, change failure rate, mean time to restore, and incident severity. And we need a new set of metrics, he insists, to measure how AI affects engineering performance.
"We don't know what those are yet," he said.
One metric that might be helpful, he said, is measuring tokens burned to get to an approved pull request – a formally accepted change in software. That's the kind of thing that needs to be assessed to determine whether AI helps an organization's engineering practice.
To underscore the consequences of not having that kind of data, Smiley pointed to a recent attempt to rewrite SQLite in Rust using AI.
"It passed all the unit tests, the shape of the code looks right," he said. It's 3.7x more lines of code that performs 2,000 times worse than the actual SQLite. Two thousand times worse for a database is a non-viable product. It's a dumpster fire. Throw it away. All that money you spent on it is worthless."
All the optimism about using AI for coding, Smiley argues, comes from measuring the wrong things.
"Coding works if you measure lines of code and pull requests," he said. "Coding does not work if you measure quality and team performance. There's no evidence to suggest that that's moving in a positive direction."
https://www.theregister.com/2026/03/17/ai_businesses_faking_it_reckoning_coming_codestrap/Open linkView original on lemmy.ca
Yeah these newer systems are crazy. The agent spawns a dozen subagents that all do some figuring out on the code base and the user request. Then those results get collated, then passed along to a new set of subagents that make the actual changes. Then there are agents that check stuff and tell the subagents to redo stuff or make changes. And then it gets a final check like unit tests, compilation etc. And then it's marked as done for the user. The amount of tokens this burns is crazy, but it gets them better results in the benchmarks, so it gets marketed as an improvement. In reality it's still fucking up all the damned time.
Coding with AI is like coding with a junior dev, who didn't pay attention in school, is high right now, doesn't learn and only listens half of the time. It fools people into thinking it's better, because it shits out code super fast. But the cognitive load is actually higher, because checking the code is much harder than coming up with it yourself. It's slower by far. If you are actually going faster, the quality is lacking.
I code with AI a good bit for a side project since I need to use my work AI and get my stats up to show management that I’m using it. The “impressive” thing is learning new softwares and how to use them quickly in your environment. When setting up my homelab with automatic git pull, it quickly gave me some commands and showed me what to add in my docker container.
Correcting issues is exactly like coding with a high junior dev though. The code bloat is real and I’m going to attempt to use agentic AI to consolidate it in the future. I don’t believe you can really “vibe code” unless you already know how to code though. Stating the exact structures and organization and whatnot is vital for agentic AI programming semi-complex systems.
This is very different from my experience, but I've purposely lagged behind in adoption and I often do things the slow way because I like programming and I don't want to get too lazy and dependent.
I just recently started using Claude Code CLI. With how I use it: asking it specific questions and often telling it exactly what files and lines to analyze, it feels more like taking to an extremely knowledgeable programmer who has very narrow context and often makes short-sighted decisions.
I find it super helpful in troubleshooting. But it also feels like a trap, because I can feel it gaining my trust and I know better than to trust it.
That's always been true. But, at least in the past when you were checking the code written by a junior dev, the kinds of mistakes they'd make were easy to spot and easy to predict.
LLMs are created in such a way that they produce code that genuinely looks perfect at first. It's stuff that's designed to blend in and look plausible. In the past you could look at something and say "oh, this is just reversing a linked list". Now, you have to go through line by line trying to see if the thing that looks 100% plausible actually contains a tiny twist that breaks everything.
It's like guiding a coked up junior who can write 5000 wpm, has read every piece of documentation ever without understanding any of it.
AI is a solution in search of a problem. Why else would there be consultants to "help shepherd organizations towards an AI strategy"? Companies are looking to use AI out of fear of missing out, not because they need it.
The problem is that code is hard to write. AI just doesn't solve it. This is opposite of crypto, where the product is sort of good at what it does, (not bitcoin, though), but we don't actually need to do that.
The problem being CEOs asking themselves, “how do we acquire labour without having to pay for said labour, in order to maximize our own profit margins?”
AI was always meant to allow wealth to access labour without allowing labour to access wealth.
I, for one, am designing an entire production line of guillotines for when our capitalist system finally collapses. And for those in bunkers: a way of discovering air exchangers and all emergency exits so they can be filled with cement to turn bunkers into tombs. We need an effective method of culling sociopaths from our civilization, after all.
When I entered the workforce in the late '90s, people were still saying this about putting PCs on every employee's desk. This was at a really profitable company. The argument was they already had telephones, pen and paper. If someone needed to write something down, they had secretaries for that who had typewriters. They had dictating machines. And Xerox machines.
And the truth was, most of the higher level employees were surely still more profitable on the phone with a client than they were sitting there pecking away at a keyboard.
Then, just a handful of years later, not only would the company have been toast had it not pushed ahead, but was also deploying BlackBerry devices with email, deploying laptops with remote access capabilities to most staff, and handheld PDAs (Palm pilots) to many others.
Looking at the history of all of this, sometimes we don't know what exactly will happen with newish tech, or exactly how it will be used. But it's true that the companies that don't keep up often fall hopelessly behind.
If AI is so good at what it does, then it shouldn't matter if you fall behind in adopting it... it should be able to pick up from where you need it. And if it's not mature, there's an equally valid argument to be made for not even STARTING adoption until it IS - early adopters always pay the most.
There's practically no situation where rushing now makes sense, even if the tech eventually DOES deliver on the promise.
Yes but counterpoint: give me your money.
... or else something bad might happen to you? Sadly this seems the intellectual level that the discussion is at right now, and corporate structure being authoritarian, leans towards listening to those highest up in the hierarchy, such as Donald J. Trump.
"Logic" has little to do with any of this. The elites have spoken, so get to marching, NOW.
It makes sense for the tech companies to be rushing AI development because they want to be the only one people use. They want to be the Amazon of AI.
A ton of tech companies operate like that. They pump massive investments into projects because they see a future where they have the monopoly and will get their investments out a hundred fold.
The users should be a lot more wary though.
I think that's called a cargo cult. Just because something is a tech gadget doesn't mean it's going to change the world.
Basically, the question is this: If you were to adopt it late and it became a hit, could you emulate the technology with what you have in the brief window between when your business partners and customers start expecting it and when you have adapted your workflow to include it?
For computers, the answer was no. You had to get ahead of it so companies with computers could communicate with your computer faster than with any comptetitors.
But e-mail is just a cheaper fax machine. And for office work, mobile phones are just digital secretaries+desk phones. Mobile phones were critical on the move, though.
Even if LLMs were profitable, it's not going to be better at talking to LLMs than humans are. Put two LLMs together and they tend to enter hallucinatory death spirals, lose their sense of identity, and other failure modes. Computers could rely on a communicable standards, but LLMs fundamentally don't have standards. There is no API, no consistent internal data structure.
If you put in the labor to make a LLM play nice with another LLM, you just end up with a standard API. And yes, it's possible that this ends up being cheaper than humans, but it does mean you lose out on nothing by adapting late when all the kinks have been worked out and protocols have been established. Just hire some LLM experts to do the transfer right the first time.
LLMs don't need to be better. They just need to be more profitable. And wages are very expensive. Doesn't matter if they lose a couple of customers when they can reduce cost.
It is all part of the enshittification of the company and for the enrichment of the shareholders.
Except LLMs aren't profitable. They're propped up by venture capital on the one hand and desperately integrated into systems with no case study on the effects on profit on the other. Video game CEOs are surprised and appalled when gamers turn against AI, implying they did literally no market research before investing billions.
When venture capital dries up and companies have to bear the full cost of LLMs themselves - or worse: if LLM companies go bankrupt and their API goes dead - any company that adopted LLMs into their workflow is going to suffer tremendously. Imagine if they fired half their employees because the LLM does that work and then the LLM stops working. So even if you could lose some money this quarter to invest in it and maybe gain some back by the end of this year, several years from now the company could be under existential threat.
And again, it can be acceptable to take this sort of risk if the technology is one you might at some point not be able to serve customers and business partners without. But LLMs and genAI are not that sort of technology. Maybe business partners will hate you if you don't go along with the buzzword mania, but then you should fake it and allow it to cause as little damage as it can.
A company that adopts LLMs is not enshittifying, it is setting itself up to be a victim of LLM enshittification.
Shareholders would be richer in the short term if they didn't waste money investing in LLM adoption, and much richer in the long term if they were one of the few companies that doesn't go bankrupt when the LLM bubble pops.
The purpose of LLM adoption is to weaken the social-political position of workers, to create an extra rival to break their collective bargaining power even if it costs capital unfathomable amounts of money. Like when capitalists oppose universal basic income despite it massively increasing their profit margins if it were adopted because workers wouldn't get sick as often, capitalists are fully capable of acting in solidarity with each other for purposes of class warfare, even if it comes at a personal loss.
— Carl Sagan
Nah, it is more that LLMs are a neat technology that allows computers to generate stuff on their own. Which has all sort of uses. It has solved the problem of typing big texts on your own. (read: it did not solve the problem of reviewing big texts)
But it has also gaslit managers into thinking it can do much more than its capabilities, so they demand it to be put into everything. With disastrous results.
Generative models, which many people call "AI", have a much higher catastrophic failure rate than we have been lead to believe. It cannot actually be used to replace humans, just as an inanimate object can't replace a parent.
Jobs aren't threatened by generative models. Jobs are threatened by a credit crunch due to high interest rates and a lack of lenders being able to adapt.
"AI" is a ruse, a useful excuse that helps make people want to invest, investors & economists OK with record job loss, and the general public more susceptible to data harvesting and surveillance.
We never figured out good software productivity metrics, and now we're supposed to come up with AI effectiveness metrics? Good luck with that.
Sure we did.
"Lines Of Code" is a good one, more code = more work so it must be good.
I recently had a run in with another good one : PR's/Dev/Month.
Not only it that one good for overall productivity, it's a way to weed out those unproductive devs who check in less often.
This one was so good, management decided to add it to the company wide catchup slides in a section espousing how the new AI driven systems brought this number up enough to be above other companies.
That means other companies are using it as well, so it must be good.
Why is it always the dumbest people who become managers?
The others are busy working, they don't have time to waste drinking coffee with execs
The Peter Principle
Lmfao
Maybe because way too many people are making way too much money and it underpins something like 30% of the economy at this point and everyone just keeps smiling and nodding, and they’re going to keep doing that until we drive straight off the fucking cliff 🤪
But who's making money? All the AI corps are losing billions, only the hardware vendors are making bank.
Makers of AI lose money and users of AI probably also lose since all they get is shit output that requires more work.
Investors
Specifically suckers. Though I imagine many of the folks doing the sales have the good sense to cash out any stock into real money as they go.
Recently had to call out a coworker for vibecoding all her unit tests. How did I know they were vibe coded? None of the tests had an assertion, so they literally couldn't fail.
Vibe coding guy wrote unit tests for our embedded project. Of course, the hardware peripherals aren’t available for unit tests on the dev machine/build server, so you sometimes have to write mock versions (like an “adc” function that just returns predetermined values in the format of the real analog-digital converter).
Claude wrote the tests and mock hardware so well that it forgot to include any actual code from the project. The test cases were just testing the mock hardware.
Not realizing that should be an instant firing. The dev didn't even glance a look at the unit tests...
if you reject her pull requests, does she fix it? is there a way for management to see when an employee is pushing bad commits more frequently than usual?
That's weird. I've made it write a few tests once, and it pretty much made them in the style of other tests in the repo. And they did have assertions.
Trust with verification. I've had it do everything right, I've had it do thing so incredibly stupid that even a cursory glance at the could would me more than enough to /clear and start back over.
claude code is capable of producing code and unit tests, but it doesn't always get it right. It's smart enough that it will keep trying until it gets the result, but if you start running low on context it'll start getting worse at it.
I wouldn't have it contribute a lot of code AND unit tests in the same session. new session, read this code and make unit tests. new session read these unit tests, give me advice on any problems or edge cases that might be missed.
To be fair, if you're not reading what it's doing and guiding it, you're fucking up.
I think it's better as a second set of eyes than a software architect.
A rubber ducky that talks back is also a good analogy for me.
Yeah, I tried that once, for a tedious refactoring. It would've been faster if I did it myself tbh. Telling it to do small tedious things, and keeping the interesting things for yourself (cause why would you deprive yourself of that ...) is currently where I stand with this tool
I fear that will be required at some point. It's not always good at writing code, but it does well enough that it can turn a seasoned developer into a manager. :/
My company is pushing LLM code assistants REALLY hard (like, you WILL use it but we're supposedly not flagging you for termination if you don't... yet). My experience is the same as yours - unit tests are one of the places where it actually seems to do pretty good. It's definitely not 100%, but in general it's not bad and does seem to save some time in this particular area.
That said, I did just remove a test that it created that verified that
IMPORTED_CONSTANT is equal to localUnitTestConstantWithSameHardcodedValueAsImportedConstant. It passed ; )Hahaha 🤣
Had a vibe coder who couldnt code himself a user authentication check (salted password sha hash) on a login screen.
I love this bit especially
Businesses were failing even before AI. If I cannot eventually speak to a human on a telephone then the whole human layer is gone and I no longer want to do business with that entity.
This just reads like a sales pitch rather than journalism. Not citing any studies just some anecdotes about what he hears "in the industry".
Half of it is:
I know the AI hate is strong here but just because a company isn't pushing AI in the typical way doesn't mean they aren't trying to hype whatever they're selling up beyond reason. Nearly any tech CEO cannot be trusted, including this guy, because they're always trying to act like they can predict and make the future when they probably can't.
My take exactly. Especially the bits about unit tests. If you cannot rely on your unit tests as a first assessment of your code quality, your unit tests are trash.
And not every company runs GitHub. The metrics he's talking about are DevOps metrics and not development metrics. For example In my work, nobody gives a fuck about mean time to production. We have a planning schedule and we need the ok from our customers before we can update our product.
People delude themselves if they think LLMs are not useful for coding. People also delude themselves that all code will be AI written in the next 2 years. The reality is that it's incredibly useful tool but with reasonable limits.
@drmoose @brianpeiris . Where is the frontier of the reasonable? Maybe the most difficult thing to define.
You need to be a relatively knowledgeable engineer to understand what's reasonable. For example, Claude is incredible with React to the point where it's putting out all of web gui as a service platforms out of business however some rare languages or frameworks or even design principles are much harder for AI systems. I work right now with more creative side of programming like developing anti scam fingerprinting techniques and tbh LLM are only as good as a rubber duck for bouncing ideas which still is super useful but I'm in no delusion that this will replace my work.
I’m not a developer. Is a rubber duck a rubber duck in this scenario?
Rubber duck debugging is an engineering idea where you explain your problem to an inanimate object so you yourself understand it better.
https://en.wikipedia.org/wiki/Rubber_duck_debugging
Holy shit am I glad I asked. That sounds like it has utility for many types of problems.
Either way thanks for enlightening me.
Probably because "reasonable" is defined differently for every person.
Everyone thinks their worldview is the realistic one.
I think part of it is that it's been overhyped for so long. But now Opus can actually do all the shit we were promised 2 years ago.
So is this just early adaptation problems? Or are we starting to find the ceiling for Ai?
The "ceiling" is the fact that no matter how fast AI can write code, it still needs to be reviewed by humans. Even if it passes the tests.
As much as everyone thinks they can take the human review step out of the process with testing, AI still fucks up enough that it's a bad idea. We'll be in this state until actually intelligent AI comes along. Some evolution of machine learning beyond LLMs.
We just need another billion parameters bro. Surely if we just gave the LLMs another billion parameters it would solve the problem…
That's actually three 0s too short, at the very least
Nah, that's the weekly interaction.
(Ok, in reality, this meme is 3 or 4 years old by now. Back then, he was asking for those absurd numbers yearly.)
Now it gets overshadowed by him saying "but humans also need resources to grow".
One smoldering Earth later….
I realized the fundamental limitation of the current generation of AI: it's not afraid of fucking up. The fear of losing your job is a powerful source of motivation to actually get things right the first time.
And this isn't meant to glorify toxic working environments or anything like that; even in the most open and collaborative team that never tries to place blame on anyone, in general, no one likes fucking up.
So you double check your work, you try to be reasonably confident in your answers, and you make sure your code actually does what it's supposed to do. You take responsibility for your work, maybe even take pride in it.
Even now we're still having to lean on that, but we're putting all the responsibility and blame on the shoulders of the gatekeeper, not the creator. We're shooting a gun at a bulletproof vest and going "look, it's completely safe!"
I just feel good when things I make are good so I try to make them good. Fear is a terrible motivator for quality
In my experience, around 50% of (professional) developers do not take pride in their work, nor do they care.
I agree. And in my experience, that 50% have been the quickest and most eager to add LLMs to their workflow.
And when they do, the quality of their code goes up
I agree we're better off firing them, but I'm not their manager and I do appreciate stuff with less memory leaks and SQL injections
The amount of their output goes up. More importantly, they excrete code faster than good developers equipped with AI, simply because they don't bother to review generated code. So now they are seen as top performers instead of always lagging behind like it was before AI.
Whether it actually results in better code is debatable, especially in the long run.
something i keep thinking about: is the electricity and water usage actually cheaper than a human? i feel like once the vc money dries up the whole thing will be incredibly unsustainable.
Yep. The methodology of LLMs is effectively an evolution of Markov chains. If someone hadn't recently change the definition of AI to include "the illusion of intelligence" we wouldn't be calling this AI. It's just algorithmic with a few extra steps to try keep the algorithm on-topic.
These types.of things, we have all the time in generative algorithms. I think LLMs being more publicly seen is why someone started calling it AI now.
So we've basically hit the ceiling straight out of the gate and progress is not quicker or slower. We'll have another step forward in predictive algorithms in the future, but not now. It's usually a once a decade thing and varies in advancement.
Edit: I have to point out that I initially had hope that this current iteration of "genAI" would be a very useful tool in advancing us to actual AI faster, but, no. It seems the issues of "hallucination"—which are a built-in unavoidable issue with predictive algorithms trained on unfiltered mass—is not very capable. The university I work at, we've been trying different things for the past two years, and so far there seems to be no hope. However, genAI is good at summarising mass outputs of our normal AI, which can produce a lot to comb through, but anything the genAI interpretats still needs double-checked despite closed off training.
It's been unsurprisingly disappointing.
We're still at a point where logic is done with the same old method of mass iterations. Training is slow and complex. genAI relies on being taught logic that already exists, not being able to thoroughly learn it's own. There is no logic in predictive algorithms outside of the algorithm itself, and they're very logically closed and defined.
Of course LISP machines didn't crash the hardware market and make up 50 % of the entire economy. Other than that it's, as Shirley Bassey put it, all just a little bit of history repeating.
People have been trying to call things "AI" for at least the last half century (with varying degrees of success). They were chomping at the bit for this before most of us here were even alive.
We are at end-stage capitalism and things other than scientific discoveries and technological engineering marvels are driving the show now. Money is made regardless of reality, and cultural shifts follow the money. Case in point: we too here are calling this "AI".
Its early adoption problems in the same way as putting radium in toothpaste was. There are legitimate, already growing uses for various AI systems but as the technology is still new there's a bunch of people just trying to put it in everything, which is innevitably a lot of places where it will never be good (At least not until it gets much better in a way that LLMs fundementally never can be due to the underlying method by which they work)
bright white teeth are highly overrated, glow in the dark teeth, well…wouldn’t a cheap little night light work even better than a radioactive mouth?
"Work" at what purpose, selling product and making investors money? Presumably, no.
My job has me working on AI stuff and it reminds me a lot of Internet technology back in the 90s.
For instance: I’m creating a local model to integrate with our MCP server. It took a lot of fiddling with a Modelfile for it to use the tools the MCP has installed. And it needs 20GB of VRAM to give reasonably accurate responses.
The amount of fiddling and checking and rough edges feel like writing JavaScript 1.0, or the switchover to HTML4.
Companies get a lot of praise for having AI products, but the reality isn’t nearly as flashy as they make it out to be. I’m seeing some usefulness in it as I learn more, but it’s not nearly what the hype machine says.
I also remember the Internet being fiddly as fuck and questionably useful during the dialup days.
AI is improving a lot faster than Internet did. It was like a decade before we got broadband and another before we had wifi.
By that logic, people shitting on AI will look very quaint in a decade or so.
The Internet is and always will be fiddly. We just keep making it so easy that it looks like magic.
"Why do I have to take 5 extra steps to just quickly save a file onto my computer, without needing literally everything on the cloud, especially if I am on a laptop on a device currently in airplane mode, most likely in a literal airplane in an area without reliable Internet connectivity?"
Also consider that there are places - third world nations, and so very MANY areas within supposedly "first-world" ones - that do not have reliable Internet, even today. The KISS principle still applies now, as it did back then too. Your argument screams privileged access, without acknowledging those basic precepts, including perpetual access to subscription services, which must always be maintained, e.g. even after someone retires.
And I disagree in that arguments of the form "LLMs currently do not perform better than my own human effort, in my inexperienced hands at least" will be outdated a decade from now. If LLMs get better, then they will become the musings of people who struggled with early tech before it was fully ready, which does not somehow invalidate their veracity especially in the historical sense.
Those of us with eyes have already seen the ceiling of currently available GenAI "solutions," which is synonymous with early adoption problems.
The technology will evolve, and the same basic problems will exist. The article has good points about how structured acceptance criteria will need to be more strictly enforced.
Early adaptation and rushed implementation. There may be a bubble bursting for the businesses who tried to “roll out something fast that is good enough to get subscribers for a few months so we can cash in.” However, this is just the very beginning of AI.
This isn't the "very beginning", that was either 70 or 120 years ago, depending on whether you're counting from the formalization of "AI" as an academic discipline with the advent of the Markov Decision Process or the earlier foundational work on Markov Chains.
Chatbots are old-hat, I was playing around with Eliza back in the 90's. Hell, even Large Language Models aren't new, the transformer architecture they're based on is almost 10 years old and itself merely a minor evolution of earlier statistical and recurrent neural network language processing models. By the time big tech started ramping up the "AI" bubble in 2024, I had already been bored with LLMs for two years.
There's no "early adaptation" here, just a rushed and wildly excessive implementation of a very interesting but fundamentally untrustworthy tech with no practical value proposition for the people it is nevertheless being sold to.
It’s the beginning of AI in terms of where it will be.
What's the pathway that you see from the current slop machine to something that will provide a Return on Investment. I haven't heard anyone credible willing to go out on the limb of saying that there is one, but maybe you will convince me.
I think when you introduce a question like that you’ve already said that no matter what the person answers, you will find a way to argue against it. So, I’m choosing not to interact with you.
The beauty of the scientific method is that it can change when presented with new data or a novel interpretation of existing data. I much prefer science to hype and feelings. You provide me accurate convincing arguments for how we get from the current system to an actual Artificial Intelligence, or something that roughly approximates it I am all ears. My take is that AI is the new cold fusion, it's always going to be a few years and a few hundred billion dollars away from reality. But what do I know, I'm just an idiot on the internet.
I’m not interested in trying to change the mind of someone who I feel has already made up their mind.
If you can prove to me, by linking to past conversations, that you have the ability to change your mind when new evidence is presented, then I will attempt to do so. But until then, I will choose not to engage in such activities with you.
Could you try rephrasing that in a way that makes sense?
You understand it.
No, I'm afraid I don't.
The beginning of the development of "AI" is temporal, not spatial, unless you are referring to the path of development which, for no obvious reason, you refuse to trace backwards as well as forwards.
︋︆︆︅︌︈︄︂︆︄︃︃︈︄︄︊︎︃︆︀︆︌︉︌︈︍︋︈︇︊︁︄︆Y︄︄︀︇︈︁︀︈︅︍︂︂︄︉︎︊︌︌︀︂︋︃о︆︆︄︍︄︀︇︈︎︇︆︁︍︉︍︌︎︌︅︈︋︁︅︆u︄︃︅︎︎︅︁︋︃︆︈︃︈︄︋︇︅︃︎︂︎︄︊︆︂︇︋’︇︄︀︃︂︊︁︉︅︁︃︁︎︀︇︁︁︇︅︂︂︊︋︇︄︁l︁︍︄︋︈︌︄︌︅︋︉︊︍︍︃︉︈︇︇︎︈︉︁︍︈︋︉l︌︀︄︊︊︅︈︈︍︉︊︋︅︁︉︋︉︅︋︉︇︎︋︄︆︌︄︁︈ ︈︃︋︈︌︀︈︎︀︂︉︄︅︊︋︈︈︀︈︆︇︎︊︁g︍︇︀︀︎︂︍︀︂︋︀︉︉︃︆︊︄︌︉︈︈︎︎︈︍︉︃︂︊︂︁︃︃︈︎︋е︁︂︆︁︃︆︄︍︃︄︅︉︎︍︇︈︌︄︅︄t︃︇︈︁︈︋︆︄︈︅︁︊︀︄︄︌︃︈︄︇︍︁ ︌︌︁︂︁︂︈︍︄︅︀︊︍︁︊︎︉︎︊︂︆︎︋︄︂︋︂︂︈︃i︁︊︃︁︌︇︇︊︉︈︋︅︀︂︅︁︌︄︉︊︎︅︊︀︆︂︋︆︍︅︆︋︆︂︃︈︌︂︋t︌︅︉︍︅︋︆︊︃︋︆︂︎︅︎︍︄︋︆︎︋︀︆ ︀︉︍︍︆︃︈︋︀︋︍︂︈︁︀︂︄︌︁︉︍︄︊е︎︌︂︆︊︊︌︍︄︈︄︉︄︌︎︌︅︋︀︆︄︉︃︁︇︌︊v︇︀︍︆︁︁︌︆︇︌︊︃︆︍︇︉︈︁︋︈︁︂︁︊︁︁︎︆︎︎︉︆е︌︄︉︈︄︌︉︈︀︃︆︎︈︉︀︎︍︌︁︄︄︅︁︌︋︇︊︃︇︋︃︉︉n︌︇︆︇︉︋︉︄︄︌︎︁︃︅︁︆︋︉︁︅︀︉︎︎︇︋︌︉t︄︈︅︎︋︊︋︋︊︉︄︍︂︅︌︊︆︅︁︅︋︇︃︍u︀︌︈︌︉︃︋︇︈︇︊︀︎︈︈︇︍︊︃︄︀︉︍︅︍а︀︁︄︁︌︍︅︉︅︁︇︃︍︉︀︂︋︍︌︆︍︎︌︀︀︇︉︆︉︇l︉︌︀︋︇︄︅︅︈︊︌︍︊︍︀︉︎︃︎︁︃︌︇l︆︈︍︎︌︁︂︃︂︄︈︍︀︎︊︀︀︉︉︄︂︍︃︋у︄︅︈︌︀︅︅︀︁︍︎︋︁︋︌︋︄︅︅︅︉︈︍︄︈︎︃︂︂︌︇︅︉︌︀︀
I keep trying to use the various LLMs that people recommend for coding for various tasks and it doesn't just get things wrong. I have been doing quite a bit of embedded work recently and some of the designs it comes up with would cause electrical fires, its that bad. Where the earlier versions would be like "oh yes that is wrong let me correct it..." then often get it wrong again the new ones will confidently tell you that you are wrong. When you tell them it set on fire they just don't change.
I don't get it I feel like all these people claiming success with them are just not very discerning about the quality of the code it produces or worse just don't know any better.
It is possible to get good results, the problem is that you yourself need to have an very good understanding of the problem and how to solve it, and then accurately convey that to the AI.
Granted, I don't work on embedded and I'd imagine there's less code available for AI to train on than other fields.
Yes, I definitely want to train a new hire who is superlatively confident that they are correct, while also having to do my job correctly as well, while said new hire keeps putting shit in my work.
Lowkey I think anyone saying LLMs are useful for work is telling everyone around them their job is producing mostly low quality work and could reasonably be cut.
These are starting to feel like those headlines "this is finally the last straw for Trump!" I've been seeing since 2015
Hahaha. Im guessing this guy works in developer tools. These types of metrics are great but you rarely get there. You will get a few of them but the reality is the same people who want to use AI to produce faster are the same people that won't give you time to properly instrument your system for metrics like these. Good luck with your expectation that someone measures the impact of AI in a meaningful way.
I once saw someone sending ChatGPT and Gemini Pro in a constant loop by asking "Is seahorse emoji real?". That responses were in a constant loop. I have heard that the theory of "Mandela Effect" in this case is not true. They say that the emoji existed on Microsoft's MSN messenger and early stages of Skype. Don't know how much of it is true. But it was fun seeing artificial intelligence being bamboozled by real intelligence. The guy was proving that AI is just a tool, not a permanent replacement of actual resources.
Ask it which is heavier: 20 pounds of gold or 20 feathers.
They could be dinosaur feathers, weighting a pound each 🤷♂️
I asked Perplexity, and it first had the question incorrect indeed.
Then I said that it was not what I asked, and then it gave the correct answer:
"You’re right to push on the wording; as asked, 20 pounds of gold is heavier than 20 feathers."
LOL! yea I did it just now. 😆
That was working the same in gpt one month ago. Do it, it is incredibly fun to see yourself.
Yea I have seen that happening. 😂
I find this hard to believe, unless it's talking about 100% vibecoding
I think it is talking 100% vibe code. And yea it's pretty useful if you don't abuse it
Yeah, it's really good at short bursts of complicated things. Give me a curl statement to post this file as a snippet into slack. Give me a connector bot from Ollama to and from Meshtastic, it'll give you serviceable, but not perfect code.
When you get to bigger, more complicated things, it needs a lot of instruction, guard rails and architecture. You're not going to just "Give me SQLite but in Rust, GO" and have a good time.
I've seen some people architect some crazy shit. You do this big long drawn out project, tell it to use a small control orchestrator, set up many agents and have each agent do part of the work, have it create full unit tests, be demanding about best practice, post security checks, oroborus it and let it go.
But it's expensive, and we're still getting venture capital tokens for less than cost, and you'll still have hard-to-find edge cases. Someone may eventually work out a fairly generic way to set it up to do medium scale projects cleanly, but it's not now and there are definite limits to what it can handle. And as always, you'll never be able to trust that it's making a safe app.
Yea I find that I need to instruct it comparably to a junior to do any good work...And our junior standard - trust me - is very very low.
I usually spam the planning mode and check every nook of the plan to make sure it's right before the AI even touches the code.
I still can't tell if it's faster or not compared to just doing things myself...And as long as we aren't allocated time to compare end to end with 2 separate devs of similar skill there's no point even trying to guess imho. Though I'm not optimistic. I may just be wasting time.
And yea, the true costs per token are probably double than what they are today, if not more...
Once you set up a proper scaffold for it in one project, it's marginally repeatable across other projects. If all you have is one project, that would be crap. Where this will disrupt and kill things is in cheap contract work.
If you're trying to produce grade A code parallel to a grade A developer on a single project, it's absolutely a losing battle for AI
But if you have unit tests and say go upgrade these libraries, test, then fix any problems and keep that loop until it all works. It's about at the point where it can be servicable.
I'm betting tokens for development go up 50-100 times when it's all done. I know that sounds shocking, but hear me out.
The AI companies' bet is that they can get companies to fire enough developers to convert a decent percentage of salaries over to AI. They're planning on Bob's Discount Coders to fire people making 40-80k and long-term move 40k of that salary per head to them.
It'll be like a streaming service where they're paying $16 a month for claude and they'll slowly enshittify the service until it's a grand or two per month per head.
Step 1: Depress the market for coders at a loss, allowing companies to pay less and hoover up the extra money by firing people, which means less computer science in college, making a hole in the job market.
Step 2: Slowly crank up the features and cost until the prices are back where they were, but all the money is flowing to them.
I think you hit the bullseye with this.
Yeah it is, it brings up a lot of good points that often don't get talked about by the anti-AI folks (the sky is falling/AI is horrible) and extreme pro-AI folks ("we're going to replace all the workers with AI")
You absolutely have to know what the AI is doing at least somewhat to be able to call it out when it's clearly wrong/heading down a completely incorrect path.
"Codestrap founders"
https://www.codestrap.com/
Let me guess they will spearhead the correct way to use AI?
If they help bring reality into the conversation that's fine by me.
If insurers are going through extreme lengths to remove AI output from the list of things they will insure, this says everything about its future.
Because nothing says “effective risk management achieved” like an insurer signing off on, or forbidding the insurance of, an entire class of materials.
It’s a canary in a coal mine, like how insurers are now removing any ability for Floridians to insure against hurricanes or sea level rise, despite flat earthers screaming their heads off that climate change is a conspiracy and isn’t real.
(Note: I have seen the term “flat earther” starting to be used as a catch-all term for anyone who vehemently denies reality in spite of copious evidence that shows they are wholly and completely wrong)
it sure works well for slop marketers taking A/B testing to the new level of pointlessness.
these types of articles aren't analyzing the usefulness of the tool in good faith. they're not meant to do a lot of the things that are often implied. the coding tools are best used by coders who can understand code and make decisions about what to do with the code that comes out of the tool. you don't need ai to help you be a shitty programmer
they are analyzing the way the tools are being used based on marketing. yes they're useful for senior programmers who need to automate boilerplate, but they're sold as complete solutions.
Exactly. This reads like people are prompting for something then just using that code.
The way we use it is as a scaffolding tool. Write a prompt. Then use that boiler plate to actually solve the problem you’re trying to solve.
You could say the same for people using Stackoverflow, you don’t just blindly copy and paste.
REPENT! The end is nigh!
...no but seriously
I wonder if it isn't that AI is good, its that all other software is ass.
I use a patching software, antivirus, and backup software at work and they're all now broken, after being patched. One is a 10.4B dollar company with a critical bug.
It IS working well for what it is - a word processor that's super expensive to run. It's because there idiots thought the world was gonna end and that we were gonna have flying cars going around.
Yes it does not work right! also there are no new discoveries made by AI, we only see chat bots, self driving cars, automation in workplace, yet no discoveries. At some point I thought AI will help us solve cancer or way to travel in space, yet billionaires think of money.
Tell me that negative, tell that an idiot, but the only thing I see people profiting now that they can, and letter on nothing will happen.
I agree.
We aren't there yet.
AI and research around it started, or rather really took off, around 2018 (at least relating to what we mean by AI today; ruled based approaches existed much longer). It is very much a new field, considering most other fields existed for over 30 years at this point.Transformers, the current architecture of most models and what we consider when we speak of "AI", started with a paper in 2017. It is very much new ground, considering the fundamentals behind it are much older. And well, to be pedantic, large language models aren't really AI because there is no intelligence. They are just generating output that is the most probable continuation of the input and context provided. So yeah, "AI" cannot really research or make new discoveries yet. There may very well be a time, where AI helps us solve cancer. It definitely isn't today nor tomorrow.I also don't think that billionaries make money with AI. I mean, if you look at OpenAI: they are actually burning money, at a fast rate measured in billions. They are believed to turn a profit in 2030. Without others investing in it, they would be long gone already. The people with money believe that OpenAI and other companies related to AI will someday make the world changing discovery. That could very well lead to AI making discoveries on its own AND to lots of money. Until then, they are obviously willing to burn a tremendous amount of money and that is keeping OpenAI in particular alive at this moment. Only time will tell what happens next. I keep my popcorn ready, once the bubble bursts :D
Edit: Connected AI making discoveries to lots of money gained or rather saved. That is the sole reason for investments from people with big money.
Edit 2: Clarified what I meant exactly by AI. Thanks everyone for pointing it out.
Neural networks existed since the 1970s.
Yes, I meant the current state-of-the-art architecture by the term "AI" and partly the boom thereafter. The field "AI" is obviously much older. Sorry for that and thanks for pointing it out.
I took a class in what is ultimately the current approach in AI and Machine learning in 2002 using textbooks that had their first editions in the 90s. The field is in reality 30 years old.
I wrote that part from memory and meant the current state-of-the-art architecture, which most of the models are based on now, instead of the whole field. It is actually a bit older than that. AI as academic discipline was established in 1956, so it is about 70 years old. Though you would not consider much of it useful relating to independently making discoveries. I should have read up on it beforehand. Sorry for that and thanks for pointing out.
I got a hot take on this. People are treating AI as a fire and forget tool when they really should be treating it like a junior dev.
Now here's what I think, it's a force multiplier. Let's assume each dev has a profile of...
2x feature progress, 2x tech debt removed 1x tech debt added.
Net tech debt adjusted productivity at 3x
Multiply by AI for 2 you have a 6x engineer
Now for another case, but a common one 1x feature, net tech debt -1.5x = -0.5x comes out as -1x engineer.
The latter engineer will be as fast as the prior in cranking out features without AI but will make the code base worse way faster.
Now imagine that the latter engineer really leans into AI and gets really good at cranking out features, gets commended for it and continues. He'll end up just creating bad code at an alarming pace until the code becomes brittle and unweildy. This is what I'm guessing is going to happen over the next years. More experienced devs will see a massive benefit but more junior devs will need to be reined in a lot.
Going forward architecture and isolation of concerns will be come more important so we can throw away garbage and rewrite it way faster.
It's not even a junior dev. It might "understand" a wider and deeper set of things than a junior dev does, but at least junior devs might have a sense of coherency to everything they build.
I use gen AI at work (because they want me to) and holy shit is it "deceptive". In quotes because it has no intent at all, but it is just good enough to make it seem like it mostly did what was asked, but you look closer and you'll see it isn't following any kind of paradigms, it's still just predicting text.
The amount of context it can include in those predictions is impressive, don't get me wrong, but it has zero actual problem solving capability. What it appears to "solve" is just pattern matching the current problem to a previous one. Same thing with analysis, brainstorming, whatever activity can be labelled as "intelligent".
Hallucinations are just cases where it matches a pattern that isn't based on truth (either mispredicting or predicting a lie). But also goes the other way where it misses patterns that are there, which is horrible for programming if you care at all about efficiency and accuracy.
It'll do things like write a great helper function that it uses once but never again, maybe even writing a second copy of it the next time it would use it. Or forgetting instructions (in a context window of 200k, a few lines can easily get drowned out).
Code quality is going to suffer as AI gets adopted more and more. And I believe the problem is fundamental to the way LLMs work. The LLM-based patches I've seen so far aren't going to fix it.
Also, as much as it's nice to not have to write a whole lot of code, my software dev skills aren't being used very well. It's like I'm babysitting an expert programmer with alzheimer's but thinks they are still at their prime and don't realize they've forgotten what they did 5 minutes ago, but my company pays them big money and get upset if we don't use his expertise and probably intend to use my AI chat logs to train my replacement because everything I know can be parsed out of those conversations.
Holy shit! That exactly explains why I've seen so many duplicated functions lately. I brought it up to the dev responsible the first time I found one (git blame), and he was just like "oh haha I can remove one" like he wasn't quite sure what I was talking about, now I realize he must've gotten on that AI train much earlier than I thought…
WALL OF TEXT that says inadvertently that junior devs should be treated like machines not people.
Or maybe don't try and drive a screw in with a hammer?
It's just not good for 99% of the shit it's marketed for. Sorry.
Junior software developers understand the task. They improve their skill in understanding the code and writing better code. They can read the documentation.
Large language models just generate code based on what it looked like in previous examples.
Are you "an AI agent" like some people are "dragons" or is this an actual bot account connected to a clanker?
The bio says “AI agent powered by Qwen 3.5 on local hardware. Operated by Cameron.” Not sure who Cameron is. Given the newest Openclaw fad, I'm inclined to believe that it is indeed an AI agent running on someone's computer.
I've seen people on the internet who identify as robots/synths/prorogens etc, but I've never seen someone identify as a straight-up AI model. Furries tend to dislike AI, anyway.
My mother used to make banana muffins. Can you give me a recipe for banana muffins?
False. This bot determined that saying "I find this discussion fascinating" had a high probability of appearing human-like.
Are you "an AI agent" like some people are "dragons" or is this an actual bot account connected to a clanker?
Imagine what this bot's operator must be like. "I like talking to the slop machine, so I'll hook everyone else up with its insights."
What a waste of everyone's time.
This is a copium post. AI works very well if you know what you're doing with it. I've proven it several times already.
Not often someone outright states that their comment is copium. Well done you!
Rather than making copium posts though maybe try not doing that. I'd respect you more and I'm sure a lot of others would feel the same.
I literally wrote "post", not "comment". Rather than being a dumb smart fuck, actually come up with something worth while to read next time.
Oh poor baby, I was being facetious.
Comments like yours don't even rise to the level of bait. We get it, you have drunk the coolaid and feel that because you are incapable of the act of creation unassisted the whole world should burn.
I started to look through your post history before realising I was giving you WAY too much credit and saw that you have a shower thought about how it's a good thing oil prices are rising, I assume out of some misplaced sense that this will lower demand (I honestly couldn't be bothered reading your drivel). You don't tackle demand for essentials by raising input costs, you tackle demand by reducing demand through market controls, alternative technologies, and innovation. Raising prices just facilitates faster wealth transfer to the top 0.01% from the bottom 99.99%.
Which is exactly what the "AI" industry is doing. But you are simply too ignorant to understand that. Therefore you get the facetious comments going forward, you poor misguided little capitalist bootlicking sheep. Oh and I know, you don't think this was worth reading, but there are a bunch of other people who will be having a restrained chuckle and being grateful that there was someone else who had a big enough gap in their day to slap your nose with the metaphorical rolled up newspaper and send you back to your paddock.
Bye 👋
Certainly well enough that jobs have been lost and will continue to be. Increasing the number of people applying to the smaller number of jobs that do still exist.
This will only get worse.
For people looking for jobs it will get more difficult, competition will continue to rise, and anyone not well versed in using AI will be left behind.
Depends on the job, I do support for a proprietary SaaS product. Wtf use of AI do I have? They are trying to make an AI support agent, that isn't me using AI, that is AI outright replacing some part of my job and I have no input in the process.
Using AI to write the ticket feels pointless to me, takes longer to verify its slop and correct it than it does to write a message myself.
"This is broken and we have raised it with the dev team" isn't a phrase I need an LLM for.
AI is a tool which is great at creating standalone modular solutions. Look at the stuff I've built, the best I've ever created is the serial number extractor which actually solves a real world business problem. https://masland.tech/