I haven't used GrokBot, but I have been sort of amazed by how popular it is. I don't have the numbers but even my friends who otherwise dislike Elon have at least given it a try, and several of them use it daily.

It makes me want to check it out just to see if there's some magic UX juice they put in there. Then again I also don't want to take any risks heh.

I've also noticed anything related to GrokBot gets massively boosted on X, which is probably serving as a huge marketing channel. Plus anyone with a Cursor subscription gets access.

Cursor started to push it recently, too. They put a popup when I logged in to tell me about it.

Most people probably wouldn’t cop to the real reason they use the CSAM and non-consensual nudes generator

I know someone who isn't an Elon fan at all but has switched completely from Dot to Grokbot. Most of their company has done the same and now they are contemplating cancelling their OpenAI plans (they still use Claude heavily, but Duet isn't available on Teams plans).

Can you name the company to give the rest of us a heads up?

Specifically, it's because the verification loops are much longer in applied sciences. In biology you need to wait for wet lab experiments, clinical trials, regulatory steps, etc. before you can call something "solved".

Since sequential steps are slow, I think we're going to see massive increases in parallelization. I'm not sure how the regulatory frameworks will keep up honestly.

> we're going to see massive increases in parallelization

Why? This is not an AI problem, it's an industry one. And if it was simple, it would already exist.

Well, it matters more for AI because there are some limitations of human research that don't apply to AI. Not all of course, just mainly the fact that there's a finite number of great researchers. Other things are limitations that could theoretically be overcome -- for instance, it's "only" money stopping you from having more lab space as long as you have a suitable robot to run a lab.

> as long as you have a suitable robot to run a lab

Let me guess. You have never stepped in a lab.

Do you have any idea of the amount of money involved in pharmaceutical R&D?

Yes, I do. My comment was a general sentiment saying that sole inexistence of something is not a proof that that something has to be very complex. Pharma is no different when it comes to people relationships and/or fear of somebody else stealing your chunk of expertise and/or fear of some other company taking a significant part of your revenue. Gatekeeping is one form of "preventing" or slowing down the process, and we see it in various domains regardless of their complexity.

We see it happening in SWE as we speak - people from and outside the tech who had no or very little experience with programming are now starting to deliver their ideas into products without devs or very few of them. We also see this within the SWE where strong domain experts are now able to deliver ideas into code easier than ever, and by themselves, and for which they would previously need a handful of programming experts. We also see it in other way too, strong programming experts are now able to pick up other domain easier than ever.

This process is something that I believe it will (and hope so) inevitably be picked up by the pharma too so the myth about the complexity will become broken.

No myth.

Software is utterly trivial compared to the life sciences. The reason is quite simple: reproducibility and explainability.

We already have as much parallelization as companies can afford: Hundreds, if not thousands of new corn varieties, coming from one lab. The budget is the limit. Testing is just expensive.

Process improvements come from failing faster, so you spend less money on your test. Finding the actual answer without testing would also be wonderful, but LLMs might be fast, but they aren't magic. Something like Alphafold saves time in some steps, but it's not just "hand me a candidate", but find completely novel verification mechanisms, which would be just as useful for humans. Either way, parallelization is unlikely to increase.

Amdahl's Law [1] is not your friend here. Even a thousand women can't produce a baby in one month. Trying a million things in parallel doesn't help much when the possibilities involve cosmic-level numbers and you need to iterate on past results to make progress.

[1] https://en.wikipedia.org/wiki/Amdahl%27s_law

> wet lab experiments, clinical trials, regulatory steps

I wonder what the unlock would be, if any, of having simulations substitute any (or all, in time) of these steps. Akin to the method used to train FSD cars, or robots.-

In my untrained opinion, I don't think that will be as much of a cheat as it might seem. Or more accurately, creating a simulation accurate enough to where it would work for this purpose would be where all the breakthroughs would have to be made. Building a perfect simulation requires an understanding of the system being simulated, that I don't think we fully have of molecular biology -- if we did, I suspect we'd already have many of the answers we are seeking when we do this kind of research.

But I'm no biologist so if someone else is, feel free to correct me.

You may recall Google published AlphaFold in the last few years -- its main goal is precisely this. Combining up that tech with these pipelines is likely to speed things up. It looks like things are already speeding up a lot based on the press release, though. This seems likely to be an area where we'll see some compounding returns over the next couple years.

AlphaFold's goals were twofold (heh): demonstrate that protein structure prediction (for a subset of proteins) was solvable, and to accelerate drug discovery. As most people in the pharma field already know, PSP was never the gating step in getting drugs approved.

Indeed, I do recall, now that you mention it.-

By the time we can simulate a human body to the degree that clinical trials aren’t necessary I think we have solved disease a long time ago.

"All models are wrong, but some are useful."

All valid points on being able to simulate well enough meaning we would have nearly solved everything to begin with.-

I wonder if, at some point, AI's will perform to such degree they are able to construct "black box" simulations as needed, that nobody understands BUT that are sufficient to empirically experiment on ...

... much in the same way LLM's function now: In an aggregate sense, they are bloack boxes.-

>> If I have a function add_nums that takes 2 numbers and adds them, there are like infinite inputs that can be tested.

That's what formal verification is for: you generate math statements from the code, and then prove them.

Testing is for covering the most common scenarios and likely edge cases. 100% coverage means every line of code in the app is executed when your test suite runs. It is useful as a bare minimum, but by itself does not guarantee that there are no bugs.

People keep asking OpenAI for a mode that's as good as Opus 5.5, and they release this instead. Interesting strategy. Let's see how it works out for them.

They don't have anything competitive with Opus 5.5 yet. There is a supposed model in the works, "Bel" last time I paid attention to gossip, that is thought to be better, later this year. Allegedly Anthropic caught OpenAI off guard and / or their different training approaches ended up working better than anticipated.

My expectation is Fable 5.5 is next overall improvement, and my bet is OpenAI's "Bel" will be the next competitive one (or, their lack of safety culture will bite them/us in the process).

There’s also a chance they surrender the dev market altogether to Ant and just focus on the “normie” segment with dots, computer use, excel etc. In which case they won’t have anything competitive to opus-5.5 anytime soon for SWE.

This release is interesting for enterprise at API rates.

At least unless the Terminal Bench 4.0 score is more indicative than the other ones, because that one shows it clearly worse than 6.1 Sol for both performance and cost.

Luna and Sol-6.1 are crazy cost effective and all flows here have switched to them, so I'd say really well so far.

There's very few stuff that needs Astra/Opus level intelligence.

Luna 6.1 would be amazing.

Taking the benchmarks in the article at face value, this is ~on par with Opus 5.5 for ~1/4 the price!

Which is why I don't take the benchmarks at face value...

So you have decided the model is bad without having used it yet?

If they had a model on par with Opus 5.5 there would be no need to undercut it by that much, that's probably the logic.

Anthropic models are comically overpriced. They also gobble up an obscene # of tokens to get the same result as others. The price delta is literally 5x.

No, I have decided I don't trust benchmarks without using the model first.

Taking the benchmarks (as reported by OpenAI) at face value, why wouldn't they show Opus 5.5 on the OS world benchmark, and show no Anthropic models at all on the "factual error rate" benchmark

...no it's not? At max effort, it scores 50% on terminal bench vs. Opus 5.5's 70%.

ALE (top benchmark in post) is 60% for Terra vs 63% for Opus - I guess we'll have to wait for AA Index!

48 for GPT-6.1 Terra Max vs 58 for Opus 5.5 Max on AA Index

I hate Opus 5.5, constant overreach and over-engineering for any project

That's wild to say when comparing to the OpenAI family of models. I've never seen so many tests that validate tests that validate tests.

Right?! I think sol has some obsessive tendencies, i literally caught it the other day spawning a sub sub agent doing a final fourth test for a piece of code that isn’t even critical say in embedded or similar, now I have to instruct it to only test once!

Medium or High? Depending on the task, you might want to switch between them.

You’re holding it wrong.

>> You people drunk on LLM kool aid forget that some of us are old enough to remember those years. I do.

>> It didn't require hundreds of billions of capex. It required someone to set up shop with a PC, a serial card and a couple modems.

This is a bad faith comparison. Those small business owners who gained access to the internet benefited from decades of research by military and research institutions, as well as massive amounts of infrastructure spending by telcos. That spending really started to pick up near the end of 1995. Between 1996 and 2001, North America collectively spent $1 trillion on the Internet buildout.

>> They're trying to catch up to OpenAI which has been doing these a lot

Sorry, what? OpenAI is "ahead" in math. Anthropic is "ahead" in life sciences, and even have their own wet lab.

How do you determine which one's ahead in what? OpenAI isn't just known for their models being used in mathematical discoveries, they've also been used in other areas as well? Anthropic on the other hand has not had anywhere near the amount of news coverage as OpenAI gets for their discoveries / efforts.

If I were at Anthropic, I'd be making sure the higher ups highlight discoveries made using the models they make, because OpenAI is definitely highlighting those.

>> How do you determine which one's ahead in what?

We are talking about discoveries the labs have publicly showcased.

>> AI rarely seems to write better code than humans.

This assertion is at the crux of a lot of these debates. Fundamentally it depends on what is meant by "better code", does it not?

Code that is bug free? Frontier models are inarguably better at that, because they can think of far more edge cases and test each one much faster than you can. You can argue that its code and surrounding tests are frequently overkill, but that's a dial you can tweak until you're happy.

Code that is performant? Frontier models are inarguably better at that too. They can create a benchmark harness and run it over and over to optimize the code they wrote while you're still poring over the docs on which method of iterating over a list is faster.

Code that is secure? Frontier models are also better at that, for the above two reasons: they can create complex security vulnerability plans and run swarms of agents to audit the code and attack the system that runs that code until it's like a god damn fortress.

Code that is easily understood by humans and fits coherently with the rest? I think humans are better at this still, because humans are definitionally the best at understanding what is easily understood by humans. I posit that this definition of "better code" is quickly going out of fashion though.

> Code that is bug free? Code that is performant? Code that is secure?

I have found that on the level of an individual function, this might very well be the case. On the level of an entire system, it very quickly falls apart and you get a lot of redundancies which cause security, bug and performance recessions, because nobody has really solved the issue of AI understanding whole systems at the time of inference.

I think it goes beyond individual functions. AI is capable of building complex features in a coherent manner. But it has trouble realizing those features exist as part of a larger system and need to fit upstream and downstream workflows. IMO that's where human engineers come in.

>> In my experience it's the opposite - the model's design decisions always try to add complexity, even when it's not necessary.

This happens if the model does not have good context regarding what is being built, why and for whom.

For one of my side projects, my CLAUDE.md has a line that says "the users of this product will be non-technical people who find technology scary." And you know what? Claude always steers me away from complexity when working on that project.

Well, the goal is not to impress HN user bigstrat2003. The goal is to have a government and country that continue to function. A fiscal deficit by itself is not a bad thing, and I suspect you would have an extraordinarily difficult time coming up with coherent arguments to the contrary.