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Comment by himata4113 | Hacker News Reader

Neither of these points feel true anymore.

Models are very much predictable these days (except anthropic models). The real issue stems from letting them work on their own for far too long. Also we are not controlled by 2 companies anymore as kimi k3, deepseek flash (and soon pro) as the ultra-cheap variants, glm 5.2 especially is a direct replacement for opus 4.8.

Models will only get better and cheaper I wouldn't feel too pessimistic and wouldn't feel too bad on relying on them to accelerate work and free up mental space from menial tasks.

As a personal side-note I never let my agents do architectual design I only use them for implementing. I always found the actual coding part of programming extremely boring and coming up with designs, experimenting and testing the fun part.

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I always found that if you are good enough at whittling down boilerplate that coding becomes something akin to pure architecture.

I find that mediocre programmers and LLMs are bad at both. They're helpful if you want to shit out some repetitive boilerplate or perform a complex search of some kind but otherwise you're better off without.

ehh, they're pretty good at automated performance research and bug fixes, especially when spanned across hundreds of them.

bug fixes not so much. when I encounter a bug the first thing I do is ask an LLM and not only do they usually fail to get the correct solution, they often slap virtual duct tape on it which makes other bugs more likely.

feels like a harness issue, bug fixes work out fine with omp.sh as long as it can be tested properly. Untestable things tend to lead to hallucinated results though.

might be a symptom of writing complex code. I'm sure on basic CRUD they can figure out most bugs since most of them have happened 1000 times before.

I doubt it, a lot of what I use llms for is on the very edge of what these models are trained for.

Even if I was paid for using LLMs I would still find the experience nightmarish. I really don’t want to be dependent on such a system and spend my time managing fleets

How exactly will models get cheaper?

Compare the performance of a 980 and a 5050 and I am sure that will answer your question.

Also models baked into the silicon are able to achieve efficiency that is simply impossible to achieve with programmable circuits, there is a general slowdown in the raw capabilities that transformers can achieve and agentic tool use is simply an amplifier that will reach a wall eventually. It wouldn't surprise me if we saw within 5 to 10 years accelerator cards that you're able to purchase and plug into via usb-c that are able to achieve thousands of tok/s as well as api costs going down to what we already see with subscriptions today.

There has been quite a lot of off-ramping going on where people feel satisfied with the performance they're getting out of the models and simply staying there instead of using SOTA.

  > accelerator cards that you're able to purchase and plug into via usb-c that are able to achieve thousands of tok/s

how do you update that baked-in model for things that have happened in the last say 2 months?

if i'm a programmer for example, even being a couple months old is a huge annoyance because programming languages and frameworks are changing all the time...

When was the last time you heard about someone talking about "Knowledge Cutoff" dates? OpenAI used to make a huge deal about it every release, now its not even mentioned.

We give agents tools, the ability to read a man page, the ability to use web search. Knowledge cut-off is far less important than it used to be.

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