brindleth

Or, you know, play it for 5 mins and see if it’s good?

No, no, don’t you understand? This person glanced at the code. We’re talking about a full-on glance here. They know it’s horribly optimised.

It's called post-training, typically through some form of reinforcement learning, and is a significant part of modern LLM development.

You have the first stage, pre-training, which is learning from next token prediction. That's where the model memorises a lot of facts about things and generally gets good at forms of writing. It's like reading a lot of books on programming and reading through a lot of source code. It's learning how to autocomplete code, essentially. Doing that requires a developing a reasonable understanding of code, but it's also learning how to autocomplete bad code as well as good, and won't make it a "good" programmer.

Pre-training uses a method called Cross-Entropy Loss to update the weights of the network.

Then comes post-training. This is where the model is trained against huge sets of example problems, like fixing a bug, adding a new feature based on a spec, etc. They are set the task and try to complete it inside a training environment. Once they're done, their complete solution is evaluated (either by humans, or by some separate evaluation model that was developed based on human feedback) and they are updated based on whether the solution was good or not.

Post-training uses a different method called Proximal policy optimization to update the weights of the network.

So these really are very different forms of learning, and mainstream LLMs are not post-trained to be good at chess. They could be. You could easily create a reinforcement learning environment that evaluated and improved their ability to play and win at chess. The result would be a very strong chess playing AI, something we know is possible because the strongest chess playing programs we have are neural network based, but it is not a priority for AI companies.

Declarative knowledge is not the same as procedural knowledge. You can read as many chess tutorials, strategy documentation and game archives as you like, they won't make you good at chess until you actually start practicing chess.

> current frontier models need laborious oversight and guardrails on even the simplest tasks

It is literally denialist about current capabilities

Is that Matt Berry!?

Rogue nations will not apply this AI label to the content they generate, so they will end up looking more authentic in comparison.

> My point exactly

No it wasn't. You said the claims were only based on quantity. I'm saying the opposite. I deliver the same quantity, with higher quality.

> Can we cash that check or is it gonna trade heavily discounted like all grand claims on AI since all software all around us together with the economy and the Earth's life supporting systems keep degrading every year?

I'm not sure what evidence I could offer you that would ever convince you. We've had strong coding models for less than a year, and you've already appear to have closed your mind about them and wrapped them up into your depression.

I'm doing better, with less effort.

I mostly work on UI (for an internal, science-focused app) and it used be that features would take a sprint or two to grind out, and would always be an MVP to meet just the most essential user requirements, because that's all we could afford, time-wise, to do. And if I figured out while implementing there could be a nicer UI approach to something, it was often too late to change the approach.

Now, I can get to that MVP in under a day. And I can experiment with ten radically different approaches, or change my mind about something significant at any stage in the implementation. I can afford to add refinements and user-pleasing extras that I could never have done before. I'm not delivering features at 10x the speed. There's a limit to the amount of code I can (and should) be submitting for review. But the features I'm building are _better_, and the effort is less. I'm not wasting mental bandwidth typing out a dozen React components full of the same-old state management, form processing code, etc. I'm not trawling through charting library docs, looking for the precise combination of settings to make my chart's x-axis ticks labels rotate 45 degrees. My mental energy is focussed on figuring out what solution is actually _right_, not the minutiae of how to persuade the computer to do it.

Yet another AI written slop article hitting the top of HN.

Evidence: Look at the most recent article on this blog: https://julienreszka.com/blog/difficult-conversations-don-t-...

"Memory is reconstructive. When someone recalls events differently, it feels like gaslighting. It usually isn't. Document first, then negotiate."

Bleugh

Whenever I read these kind of articles about AI financials, I'm reminded of identical screeds I read about Uber a few years ago. They were angrily insistent that Uber was a scam company run by criminals and charlatans and could never, ever become profitable or make money for its investors. It was a house of cards that would come crashing down sooner or later, and take everyone's money with it. Now it's 2026. Uber still exists, has revenues of $50bn and is apparently a highly profitable business. I don't know if the original investors have made their money back yet, but Uber certainly hasn't collapsed.

Maybe AI is different. Certainly, the level scale of investment is on a different order of magnitude. But I'm wary of believing anything about the financial impossibility of AI being sustainable when I've seen such similarly confident arguments proved wrong in the past.

It look like the start of a new viral Peliwave aesthetic

Registering a domain usually happens very early in a business' history. It might literally be the first concrete thing the founder does. If the founder is non-technical, they're just going to Google "buy a domain" and see who comes up.

Do it, now. What comes up?

Yes, once IT gets professionalised, they should switch to a better provider. But the registration will likely be for multiple years, with auto-renewal, and when nothing has gone wrong, theoretical problems take a backseat to live ones.