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TSMC execs allegedly dismissed OpenAI CEO Sam Altman as 'podcasting bro'

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Re: TSMC execs allegedly dismissed OpenAI CEO Sam Altman as 'podcasting bro'

#441

Post by typedef_struct »

znpy wrote: post #127
aniviacat wrote: post #77

Earlier quoted context omitted.

Current LLMs fail if what you're coding is not the most common of tasks. And a simple web app is about as basic as it gets. I've tried using LLMs for some libraries I'm working on, and they failed miserably. Trying to make an LLM implement a trait with a generic type in Rust is a game of luck with very poor chances. I'm sure LLMs can massively speed up tasks like front-end JavaScript development, simple Python script…

I had similar experiences: 1. Aasked ChatGPT to write a simple echo server in C but with this twist: use io_uring rather than the classic sendmsg/recvmsg. The code it spat out wouldn't compile, let alone work. It was wrong on many points. It was clearly pieces of who-knows-what cut and pasted together. However after having banged my head on the docs for a while I could clearly determine from which sources the code io…

My test is to take a sized chunk of memory containing a TrueType/OpenType font and output a map of glyphs to curves. Bot is nowhere close.

Re: TSMC execs allegedly dismissed OpenAI CEO Sam Altman as 'podcasting bro'

#442

Post by littlestymaar »

dizzydes wrote: post #72

Three things that get me about current AI discourse: - The public focus on AGI is almost a distraction. By the time we get to AGI highly-specialised models will have taken jobs from huge swaths of the population, SWE and CS are already in play. - That AI will need to carry out every task a role does to replace it. I see this a lot on HN. What if SWEs get 50% more efficient and they fire half? That's still a gigantic…

> SWE and CS are already in play. What if SWEs get 50% more efficient and they fire half?

You know what happened last time we got 50% more efficient? It was when github and npm arrived. LLM are saving time and making us more efficient, but that's peanuts compared to the ability to just “download a lib that does X” instead of coding this shit on your own. And you know what happened after that? SWE position skyrocketed.

Re: TSMC execs allegedly dismissed OpenAI CEO Sam Altman as 'podcasting bro'

#443

Post by surgical_fire »

skydhash wrote: post #317
surgical_fire wrote: post #224

Earlier quoted context omitted.

I tried many times. Things that AI is good at: - Generate boilerplate - Generate extremely simple code patterns. You need a simple CRUD API? Yeah, it can do it. - Generate solutions for established algorithms. Think of solutions for leetcode exercises. So yeah, if that's your job as a developer, that was a massive productivity boost. Playing with anything beyond that and I got varying degrees of failure. Some of whic…

And the solutions for these already existed: - Generate boilerplate : Snippets, templates, and code generators - Generate extremely simple code patterns : Frameworks - Generate solutions for established algorithms : Libraries.

Absolutely.

My point is that I don't think AI can meaningfully output code that would be useful beyond that, because that code is not available in its training data.

Whenever I see people going on about how AI made then super productive, the only thing I ask myself is "My brother in Christ, what the fuck are you even coding?"

Re: TSMC execs allegedly dismissed OpenAI CEO Sam Altman as 'podcasting bro'

#444

Post by beefnugs »

JanSt wrote: post #16
bdndndndbve wrote: post #10

The current AI hype wave has really hit a nerd soft spot - that we're steps away from AGI. Surely if a computer can make plausible-looking but incorrect sentences we're days away from those sentences being factually accurate! The winter after this is gonna be harsh.

Using Claude 3.5 Sonnet in Cursor Composer already shows huge benefits for coding. I'm more productive than ever before. The models are still getting better and better. I'm not saying AGI is right around the corner or that we will reach it, but the benefits are undeniable. o1 added test-time compute. No need to be snarky.

The economics dont make sense at all:

Either you pay more and more to keep your job as it gets better, or the company pays any amount for it so they can replace you over and over as a barely useful cog.

The current state of it being cheap only exist as it is in beta and they need more info from you, the expert, until it no longer needs you

Re: TSMC execs allegedly dismissed OpenAI CEO Sam Altman as 'podcasting bro'

#445

Post by okdood64 »

fngjdflmdflg wrote: post #429
bdndndndbve wrote: post #10

The current AI hype wave has really hit a nerd soft spot - that we're steps away from AGI. Surely if a computer can make plausible-looking but incorrect sentences we're days away from those sentences being factually accurate! The winter after this is gonna be harsh.

>The winter after this is gonna be harsh. The difference is current ML already has real use cases right now in its current form. Some examples are OCR, text to speech, speech to text, translation, recommendations (for eg. Facebook Tiktok etc.) and simple NLP tasks ("was [topic] mentioned in the following paragraph"). Even if AGI is proved impossible, these are real use cases that hold billions in value. And ML resear…

> OCR, text to speech, speech to text, translation, recommendations

You missed the point of the parent comment's post. He's talking about the current post chatbot GenAI hype (i.e., the massive amounts of funding being poured into companies specifically after this turning point).

Re: TSMC execs allegedly dismissed OpenAI CEO Sam Altman as 'podcasting bro'

#446

Post by mythrwy »

sensanaty wrote: post #202
Roark66 wrote: post #139

Earlier quoted context omitted.

I can't understand how anyone can use these tools (copilot especially) to make entire projects from scratch and expand them later. They just lead you down the wrong path 90% of the time. Personally I much prefer Chatgpt. I give it specific small problems to resolve and some context. At most 100 lines of code. If it gets more the quality goes to shit. In fact copilot feels like chatgpt that was given too much context.

I hear it all the time on HN that people are producing entire apps with LLMs, but I just don't believe it. All of my experiences with LLMs have been that for anything that isn't a braindead-simple for loop is just unworkable garbage that takes more effort to fix than if you just wrote it from scratch to begin with. And then you're immediately met with "You're using it wrong!", "You're using the wrong model!", "You're…

I just wrote a fairly sizable app with an LLM. This is the first complete app I've written using it. I did write some of the core logic myself leaving the standard crud functions and UI for the LLM.

I did it in little pieces and started over with fresh context each time the LLM started to get off in the weeds. I'm very happy with the result. The code is clean and well commented, the tests are comprehensive and the app looks nice and performs well.

I could have done all this manually too but it would have taken longer and I probably would have skimped out on some tests and gave up and hacked a few things in out of expedience.

Did the LLM get things wrong on occasion? Yes. Make up api methods that don't exist? Yes. Skip over obvious standard straightforward and simple solutions in favor of some rat's nest convoluted way to achieve the same goal? Yes.

But that is why I'm here. It's a different style of programming (and one that I don't enjoy nearly as much as pounding the keyboard). It's more high level thinking and code review involved and less worrying about implementation detail.

It might not work as well in domains which training data doesn't exist in. Also certainly if someone expects to come in with no knowledge and just paste code without understanding, reading and pushing back, they will have a non working mess pretty shortly. But overall these tools dramatically increase productivity in some domains is my opinion.

Re: TSMC execs allegedly dismissed OpenAI CEO Sam Altman as 'podcasting bro'

#447

Post by circuit10 »

stackghost wrote: post #183
slibhb wrote: post #141

Earlier quoted context omitted.

Arguing over terminology like "AGI" and the verb "to know" is a waste of time. The question is what tools can be built from them and how can people use those tools.

I'm directly responding to a comment discussing the popular perception that we, as a society, are "steps away" from AGI. It sounds like you agree that we aren't anywhere close to AGI. If you want to discuss the potential for LLMs to disrupt the economy there's definitely space for that discussion but that isn't the comment I was making.

Whether we should call what LLMs do “knowing” isn’t really relevant to how far away we are from AGI, what matters is what they can actually do, and they can clearly do at least some things that show what we would call knowledge if a human did it, so I think this is just humans wanting to feel we’re special

Re: TSMC execs allegedly dismissed OpenAI CEO Sam Altman as 'podcasting bro'

#448

Post by archerx »

jsheard wrote: post #36
archerx wrote: post #29

Earlier quoted context omitted.

I can already pay $0 a month and use uncensored local models for both text and images. Llama, Mixtral, Stable diffusion and Flux are a lot of fun and free to run locally, you should try them out.

You can pay $0 for those models because a company paid $lots to train them and then released them for free. Those models aren't going away now of course, but lets not pretend that being able to download the product of millions of dollars worth of training completely free of charge is sustainable for future developments. Especially when most of the companies releasing these open models are wildly unprofitable and will…

You’re acting as if computing power isn’t going to get better. With time training the models will get faster.

Let me use CG rendering as an example. Back in the day only the big companies could afford to do photoreal 3D rendering because only they had access to the compute and even then it would take days to render a frame.

Eventually people could do these renders at home with consumer hardware but it still took forever to render.

Now we can render photoreal with path tracing at near realtime speeds.

If you could go back twenty years and show CG artists the Unreal Engine 5 and show them it’s all realtime they would lose their minds.

I see the same for A.I., now it’s only the big companies that can do it, then we will be able to do it at home but it will be slow and finally we will be able to train it at home for quick and cheap.

Re: TSMC execs allegedly dismissed OpenAI CEO Sam Altman as 'podcasting bro'

#449

Post by Workaccount2 »

namaria wrote: post #426
Workaccount2 wrote: post #392

Earlier quoted context omitted.

If the difference between now and 6 years in the future is the same as the difference between now and 6 years ago, a lot of people here will be eating their hats.

Why? What exactly have we got for the (how many hundred) billions of dollars poured into GPUs running transformers over the past 6 years?

You don't believe that models 100x better than today (OG transformers were pretty bad) would be fruitful for society?

Re: TSMC execs allegedly dismissed OpenAI CEO Sam Altman as 'podcasting bro'

#450

Post by stackghost »

circuit10 wrote: post #447
stackghost wrote: post #183

Earlier quoted context omitted.

I'm directly responding to a comment discussing the popular perception that we, as a society, are "steps away" from AGI. It sounds like you agree that we aren't anywhere close to AGI. If you want to discuss the potential for LLMs to disrupt the economy there's definitely space for that discussion but that isn't the comment I was making.

Whether we should call what LLMs do “knowing” isn’t really relevant to how far away we are from AGI, what matters is what they can actually do, and they can clearly do at least some things that show what we would call knowledge if a human did it, so I think this is just humans wanting to feel we’re special

>they can clearly do at least some things that show what we would call knowledge if a human did it

Hard disagree. LLMs merely present the illusion of knowledge to the casual observer. A trivial cross examination usually is sufficient to pull back the curtain.

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