Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

The Eternal Sloptember

geohot.github.io

Post reply on HN
Print view View original

51–60 of 389 posts

Re: The Eternal Sloptember

#51

Post by nixgeek »

bluegatty wrote: post #29

" Agents cannot program, and it’s taking longer and longer to realize that they can’t. They are a highly sophisticated statistical model designed to mimic the distribution of programming" In other words - they can program, and probably better than you. I don't like being too critical but this is a really superficial post - as if either 'AI is a Software Engineer - or - It must be Fraud' It's an extremely powerful too…

It’s not like the author is a noob.

https://en.wikipedia.org/wiki/George_Hotz

In fact, he’s done several things that are truly hard, and has a well-deserved engineering reputation.

Re: The Eternal Sloptember

#52

Post by bluegatty »

nilirl wrote: post #38

I agree that I can write better code than an agent. But it can write working code much faster than I can. And in a lot of cases, unfortunately, faster beats better.

At a granular level, it's almost guaranteed that you cannot write better code than an agent.

Agents now are writing extremely consistent, normalized canonical code, that usually compiles the first time.

Right out of the 'textbook'.

For what it's trying to do - it writes nearly perfect code.

The only thing you could nominally disagree with are some of the conventions and idioms.

It 'writes a perfect novel, in perfect prose'.

What it will not do however, is 'write the novel that's in your head'.

And that's the crux of it.

It's not even your job to 'write code' at this point, but rather to be the storyteller - and a very good editor who has enough taste and grasp of gammar to be able to know when it's going awry.

It will make mostly what you tell it too, the quality of the output is the quality of your guidance, but at the lowest levels it's generating extremely high quality syntactic prose.

Re: The Eternal Sloptember

#53

Post by dathanb82 »

protocolture wrote: post #42

Eh but statistical models are obviously useful, because statistically 99% of your codebase wont involve new idea invention. Tools that write all the boilerplate code used to have names and job titles. I hate how both the for and against case for LLMs are just so bloody terrible at addressing these things.

This is a good take. The most effective combination of AI and skilled practitioner is using AI to amplify the abilities of the skilled practitioner. And in particular, max benefit comes from exploiting comparative advantage. AIs are really good at boilerplate -- in many cases better than humans because humans will optimize the process by doing copy/paste and often inject errors in the process -- whereas humans are better at abstract and critical reasoning. There's a very real and valuable use case for AI, but it's not replacing humans, it's taking the things that humans don't like doing (and that a computer can do well already) off the human's plate, so humans can focus more exclusively on the things that they do better than the AI. And at least with the current architecture of AI models, there will _always_ be higher-level reasoning that humans do better than the machine.

Re: The Eternal Sloptember

#54

Post by thedjpetersen »

Part of my job is working on trying to make these models productive for the large corporation I work for. It's a lot of throwing tomatoes at a wall and to a degree I see the issue he is talking about output seemingly having a certain ceiling.

At the same time in no part of his post is any code snippet or anything to latch on to of "the model performed poorly here when it should have done this" - this style of criticism seems to be a pattern of most of these "the LLMs will never work" style posts on blogs and twitter.

They obviously can perform better than autocomplete and in my own day to day development build out huge portions of a codebase that I would have expected a junior or midlevel engineer to perform at.

How are we really supposed to grasp their actual capabilities when no one will actually cite specifically what mistakes they are making.

Re: The Eternal Sloptember

#56

Post by vips7L »

mountainriver wrote: post #4

My guess is the models just continue to get better and better When I got into agentic coding a year or two ago I was sure it was only good at autocomplete. Something happened earlier this year where the models hit a new level of capability. Everyone I know now just does agentic coding, and it’s really amazing. I think we should just try pushing this as far as we can possibly go, it really feels like the acceleration…

Acceleration of the human race is the biggest cope I’ve read all year.

Re: The Eternal Sloptember

#57

Post by jofzar »

farhanhubble wrote: post #14

I'm in the "haven't written any code in a while" boat ATM. I'd love to see examples of issues that are so big that they warrant reverting to manual coding. My main issue has been the inconsistent quality across between model releases and the tendency to insert older APIs or documentation, especially with command line tools. I can understand if the model struggles with a million line monolithic codebase with a decade…

> I'd love to see examples of issues that are so big that they warrant reverting to manual coding

Ah I see your org hasnt yet had an outage caused by a bad LLM code push.

Re: The Eternal Sloptember

#59

Post by dathanb82 »

andrewflnr wrote: post #31
fontain wrote: post #6

We all remember cryptocurrency. Everyone in tech proclaimed fiat was dead, every office buzzed with talk of every possible way that cryptocurrency could be used, billions of dollars flooded in to projects losing money hand over fist. The cynics reacted to the froth with outright rejection of the idea. And today… cryptocurrency exists, it has some use, but it didn’t take over the world, it didn’t kill fiat, it was use…

I wish people would stop comparing AI with cryptocurrency. The hype/perception was the only thing that was similar between them. The fundamental usefulness of the respective technologies are not comparable.

Two other similarities: they both rely on burning huge amounts of electricity, and have driven up costs of GPUs around the world.
Post reply on HN