The magic to using agentic LLMs efficiently is...

proper project management.

You need to have good documentation, split into logical bits. Tasks need to be clearly defined and not have extensive dependencies.

And you need to have a simple feedback loop where you can easily run the program and confirm the output matches what you want.

And the chance of that working depends on the weather, the phase of the moon and the arrangement of bird bones in a druidic augury.

It's a non-deterministic system producing statistically relevant results with no failure modes.

I had Cursor one-shot issues in internal libraries with zero rules.

And then suggest I use StringBuilder (Java) in a 100% Elixir project with carefully curated cursor rules as suggested by the latest shamanic ritual trends.

> Another rather interesting thing is that they tend to gravitate towards sweep the errors under the rug kind of coding which is disastrous. e.g. "return X if we don't find the value so downstream doesn't crash".

Yes, these are painful and basically the main reason I moved from Claude to Gemini - it felt insane to be begging the AI - "No, you actually have to fix the bug, in the code you wrote, you cannot just return some random value when it fails, it actually has to work".

> their propensity to jump into code without necessarily understanding the requirement is annoying to say the least.

Then don't let it, collaborate on the spec, ask Claude to make a plan. You'll get far better results

https://www.anthropic.com/engineering/claude-code-best-pract...

In my experience in a Java code base, it didn't do any of this, and did a good job with exceptions.

And I have to disagree that these aren't errors that beginners or even intermediates make. Who hasn't swallowed an error because "that case totally, most definitely won't ever happen, and I need to get this done"?

Running LLM code with kernel privileges seems like courting disaster. I wouldn't dare do that unless I had a rock-solid grasp of the subsystem, and at that point, why not just write the code myself? LLM coding is on-average 20% slower.

> These tools are creating a huge amount of value...

> The people doing so don’t have a lot of time to comment about it on HN since we’re busy building…

“We’re so much more productive that we don’t have time to tell you how much more productive we are”

Do you see how that sounds?

I also work for a FAANG company and so far most employees agree that while LLMs are good for writing docs, presentations or emails, they still lack a lot when it comes to writing a maintainable code (especially in Java, they supposedly do better in Go, don’t know why, not my opinion). Even simple refactorings need to be carefully checked. I really like them for doing stuff that I know nothing about though (eg write a script using a certain tool, tell me how to rewrite my code to use certain library etc) or for reviewing changes

I don't see how FAANG is relevant here. But the 'FAANG' I used to work at had an emergent problem of people throwing a lot of half baked 'AI-powered' code over the wall and let reviewers deal with it (due to incentives, not that they were malicious). In orgs like infra where everything needs to be reviewed carefully, this is purely a burden

I work in a FAANG equivalent for a decade, mostly in C++/embedded systems. I work on commercial products used by millions of people. I use the AI also.

When others are finding gold in rivers similar to mine, and I'm mostly finding dirt, I'm curious to ask and see how similar the rivers really are, or if the river they are panning in is actually somewhere I do find gold, but not a river I get to pan in often.

If the rivers really are similar, maybe I need to work on my panning game :)

I use agentic tools all the time but comments like this always make me feel like someone's trying to sell me their new cryptocoin or NFT.

>creating a huge amount of value Do you write software, or work in accounting/finance/marketing?

What are the AI usage policies like at your org? Where I am, we’re severely limited.