41–50 of 84 posts
It’s not like anyone is going in debt to pay for gpu’s though. So it’s probably ok. Now if banks start selling 30 year mortgages for gpu’s, I might get a little worried.
People act like big tech didn't have a mountain of cash they didn't know what to do with. Each of the big players has around 100 billion that just sitting there doing nothing.
Not egregious API spending, but ChatGPT Pro was been one of the best investments our company has paid for. It is fantastic at reasonable scale ports / refactors, even with complicated subject matter like insurance. We have a project at work where Pro has saved us hours of time just trying to understand the over complicated that is currently in place. For context, it’s a salvage project with a wonderful mix of Razor p…
How are you getting these results? Even with grounding in sources, careful context engineering and whatever technique comes to your mind we are just getting sloppy junk out of all models we have tried. The sketchy part is that LLMs are super good at faking confidence and expertise all while randomly injected subtle but critical hallucinations. This ruins basically all significant output. Double-checking and babysitti…
Programmers tend to overestimate their knowledge of non-programming domains, so the OP is probably just not understanding that there are serious issues with the LLM's output for complicated subject matters like insurance.
Earlier quoted context omitted.
I genuinely think that biggest issue LLM tools is that most people expect magic because first attempts at some simple things feel magical. however, they take insane amount of time to get expertise in. what is confusing is that I think SWEs spent immense amounts of time in general learning the tools of the trade but this seems to escape a lot of people when it comes to LLMs. on my team, every developer is using LLMs a…
I have been in teams that do this and in teams that dont. I have not see any tangible difference in the output of both.
It’s not like anyone is going in debt to pay for gpu’s though. So it’s probably ok. Now if banks start selling 30 year mortgages for gpu’s, I might get a little worried.
Are we still getting AGI in 2026, per OpenAI? Based on AGI 2026, they convinced US Government to block high-end GPU sales to China. They said, we only needed 1-2 more years to hold them off. Then AGI and OpenAI/US rules the world. Is this still the plan? /s If AGI does not materialize in 2026, I think there might be trouble, as China develops alternative GPUs and NVIDIA loses that market.
Altman says in a few years Chat GPT 8 will solve quantum physics
I think Altman has been getting mentored by Musk. I think we'll get full self-driving Teslas before quantum mechanics is "solved", though, and I am not expecting that in the foreseeable future.
(1999) - "Spending on Amazon warehouses Is at Epic Levels. Will It Ever Pay Off?"
I'd suggest a better analogy would be telecommunications fiber[1]. [1] https://internethistory.org/wp-content/uploads/2020/01/OSA_B...
With that said, the fiber will be good for many years. None of the LLM models or hardware will be useful in more than a few years, with everything being replaced to newer and better on a continual basis. They're stepping stones, not infrastructure.
Earlier quoted context omitted.
How are you getting these results? Even with grounding in sources, careful context engineering and whatever technique comes to your mind we are just getting sloppy junk out of all models we have tried. The sketchy part is that LLMs are super good at faking confidence and expertise all while randomly injected subtle but critical hallucinations. This ruins basically all significant output. Double-checking and babysitti…
I genuinely think that biggest issue LLM tools is that most people expect magic because first attempts at some simple things feel magical. however, they take insane amount of time to get expertise in. what is confusing is that I think SWEs spent immense amounts of time in general learning the tools of the trade but this seems to escape a lot of people when it comes to LLMs. on my team, every developer is using LLMs a…
I've found that they're a moderate productivity increase, i.e. on a par with, say, using a different language, using a faster CI system, or breaking down some bureaucracy. Noticeable, worth it, but not entirely transformational.
I only really get useful output from them when I'm holding _most_ of the context that I'd be holding if writing the code, and that's a limiting factor on how useful they can be. I can delegate things that are easy, but I'm hand-holding enough that I can't realistically parallelise my work that much more than I already do (I'm fairly good at context switching already).
Earlier quoted context omitted.
I have been in teams that do this and in teams that dont. I have not see any tangible difference in the output of both.
year-over-year we are at around 45% in increased productivity and this trajectory is on an upward slope
I'm interested in business outcomes, is more code or perceived velocity translating into benefits to the business? This is really hard to measure though because in pretty much any startup or growing company you'll see better business outcomes, but it's hard to find evidence for the counterfactual.