One word of caution with AI searxh is that it's weirdly vulnerable to SEO.

If you search for "best X for Y" and a company has an article on their blog about how their product solves a problem the AI can definitely summarize that into a "users don't like that foolib because of ...". At least that's been my experience looking for software vendors.

Were you prone to this weird leaps of logic before your brain was fried by talking to LLMs, or did you start being a fan of talking to LLMs because your ability to logic was...well...that?

I can't believe how absolutely silly a lot of you sound with this.

LLM is a tool. It's output is dependent on the input. If that's the quality of answer you're getting, then it's a user error. I guarantee you that LLM answers for many problems are definitely adequate.

It's like if a carpenter said the cabinets turned out shit because his hammer only produces crap.

Also another person commented that seen the pattern you also see means we're psychotic.

All I'm trying to suggest is Lemmy is getting seriously manipulated by the media attitude towards LLMs and these comments I feel really highlight that.

and? we can understand 256 where AI can't, that's the point.

About 0.02

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.

Oh sure, caution is always warranted w/ LLMs. But when it works, it can save a ton of time.

So the chances of it being right ten times in a row are 2%.

Jan Refiner is up there for me.

I think this comment made me finally understand the AI hate circlejerk on lemmy. If you have no clue how LLMs work and you have no idea where "AI" is coming from, it just looks like another crappy product that was thrown on the market half-ready. I guess you can only appreciate the absolutely incredible development of LLMs (and AI in general) that happened during the last ~5 years if you can actually see it in the first place.

You see, I wanted to be petty and do another dismissive reply, but instead I fed our convo to copilot and asked it to explain, here you go, as you can see I have previously used it for coding tasks, so I didn't feed it any extra info, so there you go, even copilot can understand the huge "leap" I made in logic. goddamn the sweet taste of irony.

Copilot reply:

Certainly! Here’s an explanation Person B could consider:

The implied logic in Person A’s argument is that if you distrust code written by Copilot (or any AI tool) simply because it wasn’t written by you, then by the same reasoning, you should also distrust code written by junior developers, since that code also isn’t written by you and may have mistakes or lack experience.

However, in real-world software development, teams regularly review, test, and maintain code written by others—including juniors, seniors, and even AI tools. The quality of code depends on review processes, testing, and collaboration, not just on who wrote it. Dismissing Copilot-generated code outright is similar to dismissing the contributions of junior developers, which isn’t practical or productive in a collaborative environment.

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.

No the chances of being wrong 10x in a row are 2%. So the chances of being right at least once are 98%.

I was 0/6 on various trials of AI for Rust over the past 6 months, then I caught a success. Turns out, I was asking it to use a difficult library - I can't make the thing I want work in that library either (library docs say it's possible, but...) when I posed a more open ended request without specifying the library to use, it succeeded - after a fashion. It will give you code with cargo build errors, I copy-paste the error back to it like "address: <pasted error message>" and a bit more than half of the time it is able to respond with a working fix.

No the chances of being wrong 10x in a row are 2%. So the chances of being right at least once are 98%.

i find that rust’s architecture and design decisions give the LLM quite good guardrails and kind of keep it from doing anything too wonky. the issue arises in cases like these where the rust ecosystem is quite young and documentation/instruction can be poor, even for a human developer.

i think rust actually is quite well suited to agentic development workflows, it just needs to mature more.

In one case, when an agent couldn't find the right person to consult on RocketChat (an open-source Slack alternative for internal communication), it decided "to create a shortcut solution by renaming another user to the name of the intended user."

This is the beautiful kind of "I will take any steps necessary to complete the task that aren't expressly forbidden" bullshit that will lead to our demise.

It does not say a dog can not play basketball.

I think it's lemmy users. I see a lot more LLM skepticism here than in the news feeds.

In my experience, LLMs are like the laziest, shittiest know-nothing bozo forced to complete a task with zero attention to detail and zero care about whether it's crap, just doing enough to sound convincing.