I asked Claude 3.5 Haiku to write me a quine in COBOL in the bs2000 dialect. Claude does now that creating a perfect quine in COBOL is challenging due to the need to represent the self-referential nature of the code. After a few suggestions Claude restated its first draft, without proper BS2000 incantations, without a perform statement, and without any self-referential redefines. It’s a lot of work. I stopped caring and moved on.
For those who wonder: https://sourceforge.net/p/gnucobol/discussion/lounge/thread/495d8008/ has an example.
Colour me unimpressed. I dread the day when they force the use of ‘AI’ on us at work.
Now I’m curious, what’s the average score for humans?
Wow. 30% accuracy was the high score!
From the article:Testing agents at the office
For a reality check, CMU researchers have developed a benchmark to evaluate how AI agents perform when given common knowledge work tasks like browsing the web, writing code, running applications, and communicating with coworkers.
They call it TheAgentCompany. It’s a simulation environment designed to mimic a small software firm and its business operations. They did so to help clarify the debate between AI believers who argue that the majority of human labor can be automated and AI skeptics who see such claims as part of a gigantic AI grift.
the CMU boffins put the following models through their paces and evaluated them based on the task success rates. The results were underwhelming.
⚫ Gemini-2.5-Pro (30.3 percent)
⚫ Claude-3.7-Sonnet (26.3 percent)
⚫ Claude-3.5-Sonnet (24 percent)
⚫ Gemini-2.0-Flash (11.4 percent)
⚫ GPT-4o (8.6 percent)
⚫ o3-mini (4.0 percent)
⚫ Gemini-1.5-Pro (3.4 percent)
⚫ Amazon-Nova-Pro-v1 (1.7 percent)
⚫ Llama-3.1-405b (7.4 percent)
⚫ Llama-3.3-70b (6.9 percent),
⚫ Qwen-2.5-72b (5.7 percent),
⚫ Llama-3.1-70b (1.7 percent)
⚫ Qwen-2-72b (1.1 percent).“We find in experiments that the best-performing model, Gemini 2.5 Pro, was able to autonomously perform 30.3 percent of the provided tests to completion, and achieve a score of 39.3 percent on our metric that provides extra credit for partially completed tasks,” the authors state in their paper
please bro just one hundred more GPU and one more billion dollars of research, we make it good please bro
We promise that if you spend untold billions more, we can be so much better than 70% wrong, like only being 69.9% wrong.
And let it suck up 10% or so of all of the power in the region.
I notice that the research didn’t include DeepSeek. It would have been nice to see how it compares.
And it won’t be until humans can agree on what’s a fact and true vs not… there is always someone or some group spreading mis/dis-information
I’m in a workplace that has tried not to be overbearing about AI, but has encouraged us to use them for coding.
I’ve tried to give mine some very simple tasks like writing a unit test just for the constructor of a class to verify current behavior, and it generates output that’s both wrong and doesn’t verify anything.
I’m aware it sometimes gets better with more intricate, specific instructions, and that I can offer it further corrections, but at that point it’s not even saving time. I would do this with a human in the hopes that they would continue to retain the knowledge, but I don’t even have hopes for AI to apply those lessons in new contexts. In a way, it’s been a sigh of relief to realize just like Dotcom, just like 3D TVs, just like home smart assistants, it is a bubble.
I’ve found that as an ambient code completion facility it’s… interesting, but I don’t know if it’s useful or not…
So on average, it’s totally wrong about 80% of the time, 19% of the time the first line or two is useful (either correct or close enough to fix), and 1% of the time it seems to actually fill in a substantial portion in a roughly acceptable way.
It’s exceedingly frustrating and annoying, but not sure I can call it a net loss in time.
So reviewing the proposal for relevance and cut off and edits adds time to my workflow. Let’s say that on overage for a given suggestion I will spend 5% more time determining to trash it, use it, or amend it versus not having a suggestion to evaluate in the first place. If the 20% useful time is 500% faster for those scenarios, then I come out ahead overall, though I’m annoyed 80% of the time. My guess as to whether the suggestion is even worth looking at improves, if I’m filling in a pretty boilerplate thing (e.g. taking some variables and starting to write out argument parsing), then it has a high chance of a substantial match. If I’m doing something even vaguely esoteric, I just ignore the suggestions popping up.
However, the 20% is a problem still since I’m maybe too lazy and complacent and spending the 100 milliseconds glancing at one word that looks right in review will sometimes fail me compared to spending 2-3 seconds having to type that same word out by hand.
That 20% success rate allowing for me to fix it up and dispose of most of it works for code completion, but prompt driven tasks seem to be so much worse for me that it is hard to imagine it to be better than the trouble it brings.
I actually have a fairly positive experience with ai ( copilot using claude specificaly ). Is it wrong a lot if you give it a huge task yes, so i dont do that and using as a very targeted solution if i am feeling very lazy today . Is it fast . Also not . I could actually be faster than ai in some cases. But is it good if you are working for 6h and you just dont have enough mental capacity for the rest of the day. Yes . You can just prompt it specificaly enough to get desired result and just accept correct responses. Is it always good ,not really but good enough. Do i also suck after 3pm . Yes.
My main issue is actually the fact that it saves first and then asks you to pick if you want to use it. Not a problem usualy but if it crashes the generated code stays so that part sucksThey’ve done studies, you know. 30% of the time, it works every time.
I use it for very specific tasks and give as much information as possible. I usually have to give it more feedback to get to the desired goal. For instance I will ask it how to resolve an error message. I’ve even asked it for some short python code. I almost always get good feedback when doing that. Asking it about basic facts works too like science questions.
One thing I have had problems with is if the error is sort of an oddball it will give me suggestions that don’t work with my OS/app version even though I gave it that info. Then I give it feedback and eventually it will loop back to its original suggestions, so it couldn’t come up with an answer.
I’ve also found differences in chatgpt vs MS copilot with chatgpt usually being better results.
LLMs are an interesting tool to fuck around with, but I see things that are hilariously wrong often enough to know that they should not be used for anything serious. Shit, they probably shouldn’t be used for most things that are not serious either.
It’s a shame that by applying the same “AI” naming to a whole host of different technologies, LLMs being limited in usability - yet hyped to the moon - is hurting other more impressive advancements.
For example, speech synthesis is improving so much right now, which has been great for my sister who relies on screen reader software.
Being able to recognise speech in loud environments, or removing background noice from recordings is improving loads too.
My friend is involved in making a mod for a Fallout 4, and there was an outreach for people recording voice lines - she says that there are some recordings of dubious quality that would’ve been unusable before that can now be used without issue thanks to AI denoising algorithms. That is genuinely useful!
As is things like pattern/image analysis which appears very promising in medical analysis.
All of these get branded as “AI”. A layperson might not realise that they are completely different branches of technology, and then therefore reject useful applications of “AI” tech, because they’ve learned not to trust anything branded as AI, due to being let down by LLMs.
LLMs are like a multitool, they can do lots of easy things mostly fine as long as it is not complicated and doesn’t need to be exactly right. But they are being promoted as a whole toolkit as if they are able to be used to do the same work as effectively as a hammer, power drill, table saw, vise, and wrench.
Because the tech industry hasn’t had a real hit of it’s favorite poison “private equity” in too long.
The industry has played the same playbook since at least 2006. Likely before, but that’s when I personally stated seeing it. My take is that they got addicted to the dotcom bubble and decided they can and should recreate the magic evey 3-5 years or so.
This time it’s AI, last it was crypto, and we’ve had web 2.0, 3.0, and a few others I’m likely missing.
But yeah, it’s sold like a panacea every time, when really it’s revolutionary for like a handful of tasks.
That’s because they look like “talking machines” from various sci-fi. Normies feel as if they are touching the very edge of the progress. The rest of our life and the Internet kinda don’t give that feeling anymore.
Just add a search yesterday on the App Store and Google Play Store to see what new “productivity apps” are around. Pretty much every app now has AI somewhere in its name.
I’d compare LLMs to a junior executive. Probably gets the basic stuff right, but check and verify for anything important or complicated. Break tasks down into easier steps.
This is the same kind of short-sighted dismissal I see a lot in the religion vs science argument. When they hinge their pro-religion stance on the things science can’t explain, they’re defending an ever diminishing territory as science grows to explain more things. It’s a stupid strategy with an expiration date on your position.
All of the anti-AI positions, that hinge on the low quality or reliability of the output, are defending an increasingly diminished stance as the AI’s are further refined. And I simply don’t believe that the majority of the people making this argument actually care about the quality of the output. Even when it gets to the point of producing better output than humans across the board, these folks are still going to oppose it regardless. Why not just openly oppose it in general, instead of pinning your position to an argument that grows increasingly irrelevant by the day?
DeepSeek exposed the same issue with the anti-AI people dedicated to the environmental argument. We were shown proof that there’s significant progress in the development of efficient models, and it still didn’t change any of their minds. Because most of them don’t actually care about the environmental impacts. It’s just an anti-AI talking point that resonated with them.
The more baseless these anti-AI stances get, the more it seems to me that it’s a lot of people afraid of change and afraid of the fundamental economic shifts this will require, but they’re embarrassed or unable to articulate that stance. And it doesn’t help that the luddites haven’t been able to predict a single development. Just constantly flailing to craft a new argument to criticize the current models and tech. People are learning not to take these folks seriously.
Hey I went there
For me as a software developer the accuracy is more in the 95%+ range.
On one hand the built in copilot chat widget in Intellij basically replaces a lot my google queries.
On the other hand it is rather fucking good at executing some rewrites that is a fucking chore to do manually, but can easily be done by copilot.
Imagine you have a script that initializes your DB with some test data. You have an Insert into statement with lots of columns and rows so
Inser into (column1,…,column n) Values row1, Row 2 Row n
Addig a new column with test data for each row is a PITA, but copilot handles it without issue.
Similarly when writing unit tests you do a lot of edge case testing which is a bunch of almost same looking tests with maybe one variable changing, at most you write one of those tests, then copilot will auto generate the rest after you name the next unit test, pretty good at guessing what you want to do in that test, at least with my naming scheme.
So yeah, it’s way overrated for many-many things, but for programming it’s a pretty awesome productivity tool.
“…for multi-step tasks”
