It's also pretty funny sometimes how it gives weird future roadmap estimates ("part 2 - 3 weeks, part 3 - 2 months", etc.) and when you tell it to actually do those changes it's pretty much done in half an hour
Raw pre-training data includes plenty of conversations between professional builders and some of those include estimates.
I believe the outputs are a training coincidence with consequences that are opportunitistic for the labs.
That said, it'll often say "2 days of work" and then complete the coding in 30 minutes, and while that's amusing, afterwards, I'll need to manually test, or send to other people for review, or realize the agent only actually did half the work and I need to do a second pass (or a third etc.) and then often getting the feature in does genuinely take two days.
It doesn't estimate.
It generates tokens that read like estimates associated with the context in its training material.
What would you expect the generator to output instead?
Even Gary Marcus is starting to come around and realize that his priors are no longer as relevant as they once were.
Logistics for getting to the car wash next door?
In the mean time, alas, no, we can see from actual prompts sent directly or through sub-agents, and actual replies, estimates remain LLM generated.
Though, this discussion here could change that, because indeed there is a lot of special casing and context stuffing going on, one of the oldest being today's date for example.
• • •
I did read the Claude Code leak, and use pi, etc. So I disagree with your premise rather strongly. Today's "systems" remain, roughly, piles of markdown and context engineering wrapped in UI affordances, and behave very similarly today to how they did in 2024 for those already engineering context and delegating.
I'm still lower-down of the capability scale - as I'm still manually directing agents to do these wiggins loops - obviously the next step up is to direct the code-loops which control the agents. I just haven't got my tooling nailed in place to the point where I find that's more productive.
I actually might agree with you that this is mostly just "next token prediction" - if I can concede that's really all I do as well.
Yep. Pretty sure I've got an LLM inside too.
The other replies complaining that my thinking is so 2023 -- on the contrary, what's evolved is my own apprehension of how LLM-like most "responses" from humans prove as well.
To be sure, there are other mechanisms at play as well, significant differentiation in our... Volume of training material? Quantizations/compression? Model architecture? Just-ahead-of-time forward branching with back propagation? Double loop adaptive learning? You know, harnessing the LLM. :-) Dare we call it executive function?
LLM mode becomes particularly apparent when conversing with Alzheimer's patients in the stage where short term memories do not form but they retain access to long term memory up to, say, 5 years ago or so. Fifty years of who they are, and one can trigger nearly identical responses with nearly identical prompts.
But that same person may be able to debate 1950s politics while being unable to complete making a sandwich.
If they didn't know of new shortcuts for a task, would almost certainly not "estimate" but "intuit", or "instictively" respond (apply heuristics), largely based on their "priors" aka training material.
If you sit with them and chat a while, you'll even get the kind of looping you get from Qwen trying to think when context is too full.
And if we believe this at all, then ... we should stop scrolling tik tok. Time to read a book. Have an experience. Fine tune. :-)
Will the 10T parameter Mythos model be released this month or next month?
They better soon because it is generally accepted that one of the reasons GPT 5.5 is better at hard tasks than Opus is because of its parameter size - and that Opus 4.8 remains competitive only be scaling test-time compute (see how many more tokens it uses than GPT 5.5)
https://www.reddit.com/r/LLM/comments/1sz8bjz/parameter_esti...
Anthropic also confirmed they will not release Mythos, only a "Mythos-class" model, whatever that means.
I don't think Anthropic have said anything of the sort.
Microsoft published it as 6.1*10^27 FLOPs[1]
Elon has claimed the are also training a 10T model because "Some catching up to do"[2]
https://github.com/cartazio/oh-punkin-pi/blob/main/scripts/b...
Sure it cannot think like a human, but given it's input, it should give a good statistical answer (approximating not of how long it actually takes, but what a human would say how long it takes).
It's been known for some years[1] that LLMs do regression in-context. Frontier models have been trained against many, many issue text that include task break downs and estimates.
I wonder if there’s a reasonable way to give an llm parameters that give it a concept of its own execution speed. Seems that could be useful for multiple purposes
At that time the predominant view was that LLMs were nothing but stochastic parrots, that they would plateau, and that hallucinations couldn't be fixed.
At this point I doubt there are any AI sceptics left. That ship has long sailed. The only thing that matters is whether the estimates are accurate, and AI can improve on that too.
Even humans only estimate based on neurons firing in prior patterns.
The most fundamental essence of what they do is exactly what you say they don't: estimate.
You can't prove that )))
At least with AI that actually does things more quickly, there is a bit more breathing room (introducing AI is easier than changing a given environment).
Aside from that, I wonder how much variety there is in practice: between "Oh yeah, I added that new button while we were in the meeting" and "The new button feature will be ready in Q3 according to the roadmap, once we have sign-off from all the stakeholders."
Finally he convinced it to try. It one shotted it in 30 seconds.
Turns out the agents' idea of what is hard and easy also comes from Common Crawl.
those estimates are based on previous human estimates (the datasets it's been trained on).
unironically, when your comments will become part of a dataset, LLMs will likely get much better at estimating.
now that i think about it, all these writings about LLMs will give LLMs something much like meta-cognition.
I have another coming in a day or so for Gemma 4 with the 4-bit QAT version, which is very surprising (in a good way, Gemma 4 is impressive for this task).
https://openrouter.ai/deepseek/deepseek-v4-pro?sort=throughp...
E.g. occasionally it makes the dumbest mistakes you've ever seen and can't correct them. However it's fairly rare, and if you know the domain really well, occasionally popping in the code and pushing it towards the correct solution takes like 20seconds or whatever.
So the speed you can move with flash + high domain knowledge beats opus by a mile in my experience.
I tried to switch back to 4.8 for a bit when it came out, feels so bad waiting 20mins for a mediocre solution when I could have had everything complete - with multiple iteration cycles - in flash in like 3-5mins.
Or when I'm working two contract gigs. I can spec things out for one and turn it loose and trust it. Then work more closely with deepseek on the other project.
Basically I never have to wait - yes I have to tell it little corrections occasionally (but I know the domain really well so that's not an issue), but it's so much faster than anything else it's kinda crazy. I love the super fast speeds with high involvement development cycle.
I actually enjoy using agentic development flows for the first time now - whereas with Claude I absolutely hated it. That 5 to 20 min wait after every prompt absolutely killed my desire to even want to work at all.
the way software engineering works these days reminds me a lot of factory workers on production lines that just sit in front of a production line all day and take out faulty items and/or perform a single step in the production of goods.
GamersNexus had a really good investigative piece (~3hrs long) on this where they went to China and met with grey market sellers. That piece absolutely pissed off NVidia and resulted in a fight with Bloomberg too.
Deepseek may be also be running inference on oodles of Chinese hardware but it wouldn’t surprise me for a second if they just acquired Blackwell chips through the grey market. The original Deepseek models were all trained using NVidia chips if I remember right.
https://x.com/ljupc0/status/2062457314414587996
Other local models I've checked drop to unusable speeds way sooner. Only other model with similarity favourable curve I've tried is nemotron-cascade-2-30b-a3b. But it's a small model, way dumber than DS4F.
Coding agents use cases have large context depths. The rate of decline is as important as the headline number.