I dont know why but I am reminded of this clip about eggless omelette https://youtu.be/9Ah4tW-k8Ao

It is a lot harder to notice incorrect information in review, than making sure it is correct when writing it.

I’ve been in the industry awhile and your assessment is dead on.

As long as you’re not blindly committing the code, it’s a huge time saver for a number of mundane tasks.

It’s especially fantastic for writing throwaway tooling. Need data massaged a specific way? Ez pz. Need a script to execute an api call on each entry in a spreadsheet? No problem.

The guy above you is a nutter. Not sure if people haven’t tried leveraging LLMs or what. It has a ton of faults, but it really does speed up the mundane work. Also, clearly the person is either brand new to the field or doesn’t even work in it. Otherwise they would have seen the barely functional shite that actual humans churn out.

Part of me wonders if code organization is going to start optimizing for interpretation by these models rather than humans.

The person who uses fancy autocomplete to write their code will be exactly the person who thinks they're better than everyone. Those traits are correlated.

Pepper Ridge Farms remembers when you could just do a web search and get it answered in the first couple results. Then the SEO wars happened....

When LLMs get it right it's because they're summarizing a stack overflow or GitHub snippet it was trained on. But you loose all the benefits of other humans commenting on the context, pitfalls and other alternatives.

Run something with a 70% failure rate 10x and you get to a cumulative 98% pass rate.
LLMs don't get tired and they can be run in parallel.

DOGE has entered the chat

It's about Agents, which implies multi step as those are meant to execute a series of tasks opposed to studies looking at base LLM model performance.

I ask AI to write simple little programs. One time in three they actually compile without errors. To the credit of the AI, I can feed it the error and about half the time it will fix it. Then, when it compiles and runs without crashing, about one time in three it will actually do what I wanted. To the credit of AI, I can give it revised instructions and about half the time it can fix the program to work as intended.

So, yeah, a lot like interns.

I think you could read onedrive's notifications for new files, parse them, and pipe them to document DB via some microservice or lamba depending on the scale of your solution.

being able to do 30% of tasks successfully is already useful.

If you have a good testing program, it can be.

If you use AI to write the test cases...? I wouldn't fly on that airplane.

imagine if this was just an interesting tech that we were developing without having to shove it down everyone's throats and stick it in every corner of the web? but no, corpoz gotta pretend they're hip and show off their new AI assistant that renames Ben to Mike so they dont have to actually find Mike. capitalism ruins everything.

I have been using AI to write (little, near trivial) programs. It's blindingly obvious that it could be feeding this code to a compiler and catching its mistakes before giving them to me, but it doesn't... yet.

In University I knew a lot of students who knew all the things but "just don't know where to start" - if I gave them a little direction about where to start, they could run it to the finish all on their own.

AI cant even understand it's own brain to write about it

Depends on the context, there is a lot of work in the scientific methods community trying to use NLP to augment traditionally fully human processes such as thematic analysis and systematic literature reviews and you can have protocols for validation there without 100% human review

that is such a ridiculous idea. Just because you see hate for it in the media doesn't mean it originated there. I'll have you know that i have embarrassed myself by screaming at robot phone receptionists for years now. stupid fuckers pretending to be people but not knowing shit. I was born ready to hate LLMs and I'm not gonna have you claim that CNN made me do it.

harder to notice incorrect information in review, than making sure it is correct when writing it.

That depends entirely on your writing method and attention span for review.

Most people make stuff up off the cuff and skim anything longer than 75 words when reviewing, so the bar for AI improving over that is really low.

it specifies the tasks in the article