I'd say Jan Botanist is also up there as being a pretty great person.

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.

It doesn't matter if you need a human to review. AI has no way distinguishing between success and failure. Either way a human will have to review 100% of those tasks.

Keep doing what you do. Your company will pay me handsomely to throw out all your bullshit and write working code you can trust when you're done. If your company wants to have a product in the future that is.

Ok what about tech journalists who produced articles with those misunderstandings. Surely they know better yet still produce articles like this. But also people who care enough about this topic to post these articles usually I assume know better yet still spread this crap

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.

A human can review something close to correct a lot better than starting the task from zero.

Lmao, okay buddy, based on how many interviews I have sat on in, the chances that you are a worse programmer than me are much higher than you being better than me.

Being a pompous ass dismissive of new tooling makes you chances even worse

Lmao, okay buddy, based on how many interviews I have sat on in, the chances that you are a worse programmer than me are much higher than you being better than me.

Being a pompous ass dismissive of new tooling makes you chances even worse

Yeah, it (in my case, ChatGPT) has been great for helping me along with functions I'm only passingly familiar with / trying to use in new ways.

One that I was really surprised with was that it gave me a surprisingly robust, sensible, and (seemingly) well tuned-to-my-case check list of things to inspect for a used car I intend to buy. I'm already mostly familiar with what I'm doing there, but it pointed to some things I might've overlooked / didn't know were points of concern for the specific vehicle I'm looking at.

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.

yes, that's generally useless. It should not be shoved down people's throats. 30% accuracy still has its uses, especially if the result can be programmatically verified.

At least AI won't fire you.

"...for multi-step tasks"

They've done studies, you know. 30% of the time, it works every time.

How do I set up event driven document ingestion from OneDrive located on an Azure tenant to Amazon DocumentDB? Ingestion must be near-realtime, durable, and have some form of DLQ.

I'd just like to point out that, from the perspective of somebody watching AI develop for the past 10 years, completing 30% of automated tasks successfully is pretty good! Ten years ago they could not do this at all. Overlooking all the other issues with AI, I think we are all irritated with the AI hype people for saying things like they can be right 100% of the time -- Amazon's new CEO actually said they would be able to achieve 100% accuracy this year, lmao. But being able to do 30% of tasks successfully is already useful.