Schedule tasks on the web

I'm just going to ask that you read any of my other comments, this is not at all how coding agents work and seems to be the most common misunderstanding of HN users generally. It's tiring to refute it. RL in verifiable domains does not work like this.

> Humans learn.

Sigh, so do LLMs, in context.

> Moreover, humans learn (somewhat) intangible aspects: human expectations, contracts, business requirements, laws, user case studies etc.

Literally benchmarks on this all over the place, I'm sure you follow them.

> Yes, yes we have reasons to expect that. And even if growth continues, a nearly flat logarithmic scale is just as useless as no growth at all.

and yet its not logarithmic? Consider data flywheel, consistent algorithmic improvements, synthetic data [basically: rejection sampling from a teacher model with a lot of test-time compute + high temperature],

> For a year now all the amazing "breakthrough" models have been showing little progress (comparatively). To the point that all providers have been mercilessly cheating with their graphs and benchmarks.

Benchmaxxing is for sure a real thing, not to mention even honest benchmarking is very difficult to do, but considering "all of the AI companies are just faking the performance data" to be the "story" is tremendously wrong. Consider AIME performance on 2025 (uncontaminated data), the fact that companies have a _deep incentive_ to genuinely improve their models (and then of course market it as hard as possible, thats a given). People will experiment with different models, and no benchmaxxing is going to fool people for very long.

If you think Opus 4.6 compared to Sonnet 3.x is "little progress" I think we're beyond the point of logical argument.

https://tinyfat.com

I love it when people include links to papers that refute their words.

So, Antropic (which is heavily reliant on hype and making models appear more than they are) authors a paper which clearly states: "tokens later in context are easier to predict and there's less loss of tokens. For no reason at all we decided to give this a new name, in-context learning".

> agents like Claude have access to older sessions, they can grok impressive amounts of data via tool use

That is they rebuild the world from scratch for every new session, and can't build on what was learned or built in the last one.

Hence continuous repeating failure modes.

10 years ago I worked in a team implementing royalties for a streaming service. I can still give you a bunch of details, including references to multiple national laws, about that. Agents would exhaust their context window just re-"learning" it from scratch, every time. And they would miss a huge amount of important context and business implications.

> Have you read the literature on scaling laws?

You keep referencing this literature as it was Holy Bible. Meanwhile the one you keep referring to, Chinchilla, clearly shows the very hard limits of those laws.

> Do you argue things have not improved in the last year with reasoning systems?

I don't.

Frankly, I find your aggressiveness quite tiring

Your AI assistant orders an experimental jetpack from a random startup lab. Would you have honestly guessed that the prompt was "ambiguous" before you knew how the AI was going to act on it ?