It still matters for software packages. Particularly python packages that have to do with programming with AI!

They are evolving quickly, with deprecation and updated documentation. Having to correct for this in system prompts is a pain.

It would be great if the models were updating portions of their content more recently than others.

For the tailwind example in parent-sibling comment, should absolutely be as up to date as possible, whereas the history of the US civil war can probably be updated less frequently.

Valid. I suppose the most annoying thing related to the cutoffs, is the model's knowledge of library APIs, especially when there are breaking changes. Even when they have some knowledge of the most recent version, they tend to default to whatever they have seen the most in training, which is typically older code. I suspect the frontier labs have all been working to mitigate this. I'm just super stoked, been waiting for this one to drop.

In my experience it really depends on the situation. For stable APIs that have been around for years, sure, it doesn't really matter that much. But if you try to use a library that had significant changes after the cutoff, the models tend to do things the old way, even if you provide a link to examples with new code.

For the recent resources it might matter: unless the training data are curated meticulously, they may be "spoiled" by the output of other LLM, or even the previous version of the one that is being trained. That's something what is generally considered dangerous, because it could potentially produce unintentional echo-chamber or even somewhat "incestuously degenerated" new model.

It's relevant from an engineering perspective. They have a way to develop a new model in months now.

I've had issues with Godot and Rustls - where it gives code for some ancient version of the API.

I was thinking that too, grok can comment on things that have only just broke out hours earlier, cutoff dates don't seem to matter much

> The models I'm regularly using are usually smart enough to figure out that they should be pulling in new information for a given topic.

Fair enough, but information encoded in the model is return in milliseconds, information that needs to be scraped is returned in 10s of seconds.

It knows uv now

web search is an immediate limited operation training is a petabytes long term operation

Ditto. Twitter's Grok is especially good at this.

Web search is costlier.

Web search isn't desirable or even an option in a lot of use cases that involve GenAI.

It seems people have turned GenAI into coding assistants only and forget that they can actually be used for other projects too.

It knows about Svelte 5 for some time, but it particularly likes to mix it with Svelte 4 in very weird and broken ways.

> Which version of tailwind css do you know?

LLMs can not reliably tell whether they know or don't know something. If they did, we would not have to deal with hallucinations.

Interesting. It's claiming different knowledge cutoff dates depending on the question asked.

"Who is president?" gives a "April 2024" date.

I did the same recently with copilot and it of course lied and said it knew about v4. Hard to trust any of them.

Did you try giving it the relevant parts of the tailwind 4 documentation in the prompt context?

Catastrophic forgetting

https://en.wikipedia.org/wiki/Catastrophic_interference

It's really not necessary, with retrieval-augmented generation. It can be trained to just check what the latest version is.

Claude 4 actually knows FastHTML pretty well! :D It managed to one-shot most basic tasks I sent its way, although it makes a lot of standard minor n00b mistakes that make its code a bit longer and more complex than needed.

I've nearly finished writing a short guide which, when added to a prompt, gives quite idiomatic FastHTML code.

> Can we assume everything that can be updated is updated?

What does that even mean? Of course an LLM doesn't know everything, so it we wouldn't be able to assume everything got updated either. At best, if they shared the datasets they used (which they won't, because most likely it was acquired illegally), you could make some guesses what they tried to update.

You might be able to ask it what it knows.

But I think the general meaning of a cutoff date, D, is:

The model includes nothing AFTER date D

and not

The model includes everything ON OR BEFORE date D

Right? Definitionally, the model can't include anything that happened after training stopped.

Indeed. It’s not possible stop the world and snapshot the entire internet in a single day.

Or is it?

its not a definitive "date" you cut off information, but more a "recent" material you can feed, training takes times

if you waiting for a new information, of course you are not going ever to train

Are you sure you're using 4? Mine says January 2025: https://claude.ai/share/9d544e4c-253e-4d61-bdad-b5dd1c2f1a63

And they might all be right!

> This tech could lead to...

I don't think he's saying this is the version that will suddenly trigger a Renaissance. Rather, it's one solid step that makes the path ever more promising.

Sure, everyone gets a bit overexcited each release until they find the bounds. But the bounds are expanding, and the need for careful prompt engineering is diminishing. Ever since 3.7, Claude has been a regular part of my process for the mundane. And so far 4.0 seems to take less fighting for me.

A good question would be when can AI take a basic prompt, gather its own requirements and build meaningful PRs off basic prompt. I suspect it's still at least a couple of paradigm shifts away. But those seem to be coming every year or faster.

> I am incredibly eager to see what affordable coding agents can do for open source :)

Oh, we know exactly what they will do: they will drive devs insane: https://www.reddit.com/r/ExperiencedDevs/comments/1krttqo/my...

Especially since the EU just made open source contributors liable for cybersecurity (Cyber Resilience Act). Just let AI contribute and ur good

The option just shown up in Copilot settings page for me

The keynote confirms it is available now.

Personally, I don't believe AI is ever going to get to that level. I'd love to be proven wrong, but I really don't believe that an LLM is the right tool for a job that requires novel thinking about out of the ordinary problems like all the weird edge cases and poor documentation that comes up when trying to upgrade old software.

I think this type of thing needs agent which has access to the documentation to read about nuances of the language and package versions, definitely a way to investigate types, interfaces. Problem is that training data has so much mixed data it can easily confuse the AI to mix up versions, APIs etc.

And IMO it has a long way to go. There is a lot of nuance when orchestrating dependencies that can cause subtle errors in an application that are not easily remedied.

For example a lot of llms (I've seen it in Gemini 2.5, and Claude 3.7) will code non-existent methods in dynamic languages. While these runtime errors are often auto-fixable, sometimes they aren't, and breaking out of an agentic workflow to deep dive the problem is quite frustrating - if mostly because agentic coding entices us into being so lazy.

i think it is almost certainly to prevent distillation

I have no idea what this means, can someone give the eli5?

I've been thinking for a couple of months now that prompt engineering, and therefore CoT, is going to become the "secret sauce" companies want to hold onto.

If anything that is where the day to day pragmatic engineering gets done. Like with early chemistry, we didn't need to precisely understand chemical theory to produce mass industrial processes by making a good enough working model, some statistical parameters, and good ole practical experience. People figured out steel making and black powder with alchemy.

The only debate now is whether the prompt engineering models are currently closer to alchemy or modern chemistry? I'd say we're at advanced alchemy with some hints of rudimentary chemistry.

Also, unrelated but with CERN turning lead into gold, doesn't that mean the alchemists were correct, just fundamentally unprepared for the scale of the task? ;)

We won't know without an official answer leaking, but a simple answer could be - people spend too much time trying to analyse those without understanding the details. There was a lot of talk on HN about the thinking steps second guessing and contradicting itself. But in practice that step is both trained by explicitly injecting the "however", "but" and similar words and they do more processing than simply interpreting the thinking part as text we read. If the content is commonly misunderstood, why show it?

Yeah we really should stop focusing on model alignment. The idea that it's more important that your AI will fucking report you to the police if it thinks you're being naughty than that it actually works for more stuff is stupid.

Correct me if I'm wrong--my understanding is that RHLF was the difference between GPT 3 and GPT 3.5, aka the original ChatGPT.

If you never used GPT 3, it was... not good. Well, that's not fair, it was revolutionary in its own right, but it was very much a machine for predicting the most likely next word, it couldn't talk to you the way ChatGPT can.

Which is to say, I think RHLF is important for much more than just preventing PR disasters. It's a key part of what makes the models useful.

DeepSeek never mopped the floor with anyone... DeepSeek was remarkable because it is claimed that they spent a lot less training it, and without Nvidia GPUs, and because they had the best open weight model for a while. The only area they mopped the floor in was open source models, which had been stagnating for a while. But qwen3 mopped the floor with DeepSeek R1.

Do people actually believe this? While I agree their open source contribution was impressive, I never got the sense they mopped the floor. Perhaps firms in China may be using some of their models but beyond learnings in the community, no dents in the market were made for the West.

You are referring to the new (few days old-ish) CoT right? It’s bizzare as to why google did it, it was very helpful to see where the model was making assumptions or doing something wrong. Now half the time it feels better to just use flash with no thinking mode but ask it to manually “think”.

it’s fake cot, just like oai

Thanks for the report! We're addressing it urgently.

> how any of these companies remain sustainable

They don't, they have a big bag of money they are burning through, and working to raise more. Anthropic is in a better position cause they don't have the majority of the public using their free-tier. But, AFAICT, none of the big players are profitable, some might get there, but likely through verticals rather than just model access.

If you read any work from Ed Zitron [1], they likely cannot remain sustainable. With OpenAI failing to convert into a for-profit, Microsoft being more interested in being a multi-modal provider and competing openly with OpenAI (e.g., open-sourcing Copilot vs. Windsurf, GitHub Agent with Claude as the standard vs. Codex) and Google having their own SOTA models and not relying on their stake in Anthropic, tarrifs complicating Stargate, explosion in capital expenditure and compute, etc., I would not be surprised to see OpenAI and Anthropic go under in the next years.

1: https://www.wheresyoured.at/oai-business/

https://www.wheresyoured.at/reality-check/

And we also have a showcase from a day ago [1] of these magical autonomous AI agents failing miserably in the PRs unleashed on the dotnet codebase, where it kept reiterating it fixed tests it wrote that failed without fixing them. Oh, and multiple blatant failures that happened live on stage [2], with the speaker trying to sweep the failures under the rug on some of the simplest code imaginable.

But sure, it managed to find a name buried in some emails after being told to... Search through emails. Wow. Such magic

[1] https://news.ycombinator.com/item?id=44050152 [2] https://news.ycombinator.com/item?id=44056530

Is this something that the models from 4 months ago were not able to do?

The LLMs have reached a plateau. Successive generations will be marginally better.

We're watching innovation move into the use and application of LLMs.

I'd go so far as to say Sonnet 3.5 was better than 3.7

At least I personally liked it better.

I asked it a few questions and it responded exactly like all the other models do. Some of the questions were difficult / very specific, and it failed in the same way all the other models failed.

Hence, "plateau"

Don’t talk to Opus before it’s had its coffee. Classic high-performer failure mode.

yeah that surprised me too

“Your model has memorized all knowledge, how do you know it’s smart?”

Yeah I mean SQL is pretty nuanced - one of the things we want to improve in the benchmark is how we measure "success", in the sense that multiple correct SQL results can look structurally dissimilar while semantically answering the prompt.

There's some interesting takeaways we learned here after the first round: https://www.tinybird.co/blog-posts/we-graded-19-llms-on-sql-...

Not saying you're wrong about "OpenAI practices", but that's kind of a strange thing to complain about right after praising an LLM that was only recently inserting claims of "white genocide" into every other response.

Actually no, we have it up to 3 attempts. In fact, Opus 4 failed on 36/50 tests on the first attempt, but it was REALLY good at nailing the second attempt after receiving error feedback.

No it's definitely interesting. It suggests that Opus 4 actually failed to write proper syntax on the first attempt, but given feedback it absolutely nailed the 2nd attempt. My takeaway is that this is great for peer-coding workflows - less "FIX IT CLAUDE"

Yeah we're always looking for new models to add

Noted, also feel free to add an issue to the GitHub repo: https://github.com/tinybirdco/llm-benchmark

We should definitely add o3 - probably will soon. Also looking at testing the Qwen models

It's placed at 10. Below claude-3.5-sonnet, GPT 4.1 and o3-mini.

We need to add it

I'm going to have to heavily disagree. Gemini 2.5 Pro has super impressive performance on large context problems. I routinely drive it up to 4-500k tokens in my coding agent. It's the only model where that much context produces even remotely useful results.

I think it also crushes most of the benchmarks for long context performance. I believe on MRCR (multi round coreference resolution) it beats pretty much any other model's performance at 128k at 1M tokens (o3 may have changed this).

The amount of degradation at a given context length isn't constant though so a model with 5x the context can either be completely useless or still better depending on the strength of the models you're comparing. Gemini actually does really great in both regards (context length and quality at length) but I'm not sure what a hard numbers comparison to the latest Claude models would look like.

A good deep dive on the context scaling topic in general https://youtu.be/NHMJ9mqKeMQ

Gemini's real strength is that it can stay on the ball even at 100k tokens in context.

I've had a lot of fun using Gemini's large context. I scrape a reddit discussion with 7k responses, and have gemini synthesize it and categorize it, and by the time it's done and I have a few back and fourths with it I've gotten half of a book written.

That said I have noticed that if I try to give it additional threads to compare and contrast once it hits around the 300-500k tokens it starts to hallucinate more and forget things more.

It's closer to <30k before performance degrades too much for 3.5/3.7. 200k/64k is meaningless in this context.

Yeah, but why aren't they attacking that problem? Is it just impossible, because it would be a really simple win with regards to coding. I am huge enthusiast, but I am starting to feel a peak.

that is just not correct, it's a big differentiator

There's the nominal context length, and the effective one. You need a benchmark like the needle-in-a-haystack or RULER to determine the latter.

https://github.com/NVIDIA/RULER

We might be looking at different articles? The string "200" appears nowhere in this one --- or I'm just wrong! But thanks!

Lots of papers are insane. You can test it on competition math problems with s local AI and replace its thinking process with dots and see the result yourself.

Wow, my first ever video on AI! I'm rather disappointed. That was devoid of meaningful content save for the two minutes where they went over the Anthropic blog post on how LLMs (don't) do addition. Importantly, they didn't remotely approach what those other papers are about, or why thinking tokens aren't important for chain-of-thought. Is all AI content this kind of slop? Sorry, no offense to the above comment, it was just a total waste of 10 minutes that I'm not used to.

So, to anyone more knowledgeable than the proprietor of that channel: can you outline why it's possible to replace thinking tokens with garbage without a decline in output quality?

edit: Section J of the first paper seems to offer some succint explanations.

If we're paying for reasoning tokens, we should be able to have access to these, no? Seems reasonable enough to allow access, and then we can perhaps use our own streaming summarization models instead of relying on these very generic-sounding ones they're pushing.

> There's ample evidence that thinking is often disassociated from the output.

What kind of work do use LLMs for? For the semi technical “find flaws in my argument” thing, I find it generally better at not making common or expected fallacies or assumptions.

then provide it as an option?

I am now focusing on checking your proposition. I am now fully immersed in understanding your suggestion. I am now diving deep into whether Gemini 2.5 pro also does this. I am now focusing on checking the prerequisites.

It does now, but I think it wasn't the case before?

Claude can't run the code

Yes, this is pretty annoying. You give it a file and want it to make a small focused change, but instead it almost touches every line of code, even the unrelated ones

Oh jeez yes. I completely forgot that this was a thing. It’s tendency to do completely different things “while at it” was ridiculous

Can be solved with more specific prompting

I think there was definitely a compromise with 3.7. When I turn off thinking, it seems to perform very poorly compared to 3.5.

imo, model regression might actually stem from more aggressive use of toolformer-style prompting, or even RLHF tuning optimizing for obedience over initiative. i bet if you ran comparable tasks across 3-5, 3-7, and 4-0 with consistent prompts and tool access disabled, the underlying model capabilities might be closer than it seems.

Anecdotal of course, but I feel a very distinct difference between 3.5 and 3.7 when swapping between them in Cursor’s Agent mode (so the system prompt stays consistent).

But if you had instead spent the "weeks to learn your way around the codebase", that would have given dividends forever. I'm a bit afraid that by oneshoting features like these, many will never get to the required level to do bigger changes that relies on a bigger understanding.

Of course, LLMs might get there eventually. But until then I think it will create a bigger divide between seniors and juniors than it traditionally has been.

> If you cannot afford the hundreds of dollars a month Claude costs

Employers will buy AI tools for their employees, this isn't a problem.

If you're saying that you need to buy and learn these tools yourself in order to get a job, I strongly disagree. Prompting is not exactly rocket science, and with every generation of models it gets easier. Soon you'll be able to pick it up in a few hours. It's not a differentiator.

> make an impact.

To me, a junior devs biggest job is learning and not delivering value. Is a pitfall I'm seeing in my own team where he is so focused on delivering value that he's not gaining an understanding.

You're sabotaging yourself though. You are avoiding learning.

What's the point of shipping a Chrome feature before graduating? Just to put in your CV that you've committed in some repo? In the past this would be signal of competence, but now you're working towards a future where doing this thing is not competence signaling anymore.