Claude Fable 5

e: I quit the session and went back in. Set it to Fable and told it to continue the last session. It's moving along as if none of that had happened.

How weird.

I am just testing it on stuff I know intimately myself. I would probably not understand a proof of Collatz if it was dansing in front of me!

I am not sure it's perfect, and it will need further validation

This morning I looked at code samples & checked if all unit/integration and e2e pass & perfomance tests pass

I also generated a postgres schema diagram.

Aka I did probably 2 hours of work, rest was not me

The opus try was last month

> I have not accepted payments from LLM vendors, but I am frequently invited to preview new LLM products and features from organizations that include OpenAI, Anthropic, Gemini and Mistral, often under NDA or subject to an embargo. This often also includes free API credits and invitations to events.

[1] https://simonwillison.net/about/

* From today through June 22, Fable 5 is included on Pro, Max, Team, and seat-based Enterprise plans at no extra cost.

* On June 23, we’ll remove Fable 5 from those plans. Using it after that will require usage credits. If capacity allows, we’ll extend the included window.

* After this point—when sufficient capacity allows us to do so—we aim to restore Fable 5 as a standard part of subscription plans. We intend to do this as quickly as we can.

That may very well be true now. And in fact, this was true of more rudimentary calculations early on in computing history, where humans were definitely more efficient, particularly for more abstract mathematics. But Moore's Law comes at you fast. Even without more efficient compute, it's rather wild how much more efficient models are becoming these days just from algorithmic and training improvements.

So, maybe for now, certainly. Are you confident that will be the case in 5-10 years? And is that really your barometer for success?

>And when a human learns these things they usually remember how to, and are able to extrapolate that knowledge into new and fresh problem spaces.

That is certainly a limitation for now, but plenty of academic research is being done on how to address that in a more individualized way. That said, the models also have the advantage of synthesizing learnings from user interactivity back into a future release and essentially applying that globally, which is pretty neat.

There's also some cool techniques to sort of bridge the gap today, like compound engineering.

>Next time you get a new and a fresh and an inspiring idea, and you spend hours solving a unique problem nobody has ever done before. You can take comfort in the fact that a few months later some lame and uninspiring developer can write the same problem in a prompt and get the plagiarism machine to steal your work, just in a more lame and uninspiring way.

But that's the thing: it's becoming pretty clear that the "plagiarism machine" can probably take that same problem in a prompt, having never been trained on my code, and still solve it.

In that case...maybe it doesn't feel great to have someone copy my idea. But that is certainly not plagiarism in the way you mean it. And when you put ideas out into the world, you can't be certain that someone else won't copy and remix it into something new. That's kind of how the world works already, but we're just seeing the barrier to entry decline.

OK then - do it, faster.

> You can take comfort in the fact that a few months later some[...] developer can [solve] the same problem [using your work]

Isn't that what collaboration and sharing software is supposed to be all about?

It caught on, sure, but not exactly in the way I expected. The wild popularity of "slop" as a term for AI eventually gave way to the genericization of the word "slop" to mean "content of low quality, regardless of source", and is seemingly being used as just a derogatory term for anything that people dislike (particularly by folks in left leaning communities). For example, I've seen people refer to (clearly human written) commentary from some political commentators as "slop".

You comment kind of reinforces the idea by the fact that you have to now say "AI slop" specifically to disambiguate it. It's kind of a fascinating little turn.

(You may not realize it but simonw is one of the cofounders of Django, Python's web framework. If they find a Python problem difficult, it probably is.)

I don't think there's an ideal solution here, but giving trusted people access to fix security issues before giving it to the wider public seems like a reasonable compromise. They're letting you use the model for all other uses.

How so? I'm actually against most of the "safety-tuning" that anthropic does, but this seems fundamentally untrue, a close analogue being video game cheat development. I think in general the cheat developer has an advantage and the cheats generally proliferate for quite a while before being patched.

Not what that means.

Crocodile tears "is a colloquial term used to describe a false, insincere display of emotion" [1]. Defending yourself against an attack vector you just exploited is between savvy and hypocritical.

[1] https://en.wikipedia.org/wiki/Crocodile_tears

But it gets stuck in tool call loops, it seems like.

I built it because I wanted cursor on my phone because I have two small kids and don’t want to be chained to my desk. And it’s awesome. It’s a full ide with agent chat, terminal and file system running in a remote Linux container. I can review diffs, fully manage git and preview/serve apps. And no one can ever take it away from me :)

I am watching the way things are progressing with the ai api vendors and it feels really clear that depending on them will soon be dangerous. So I an furiously building as much of my own infrastructure to capture some autonomy with these capabilities

So I think everyone should build a harness.

IMO the data from chats alone is worth $200B to Google.

I think it's safe to assume everything AI related is heavily biased until proven otherwise. Just like in pharma.

I would have expected Mythos to be much more expensive than just 2x current Opus (which is clearly cheaper to run than original Opus)

But - these $3k-$5k/month/engineer bills are going to start to get attention soon - only question is whether the response is to slow down on the $$$ spending or reduce the # of engineers.

We were reviewing reports of situations where the models failed to follow directions and there was a common thread of some where when the operator got the model to acknowledge the rule breach, it quoted back something that included swearing.

I don’t have the data to truely look into it, but I did give the instruction to my engineers to avoid it as a “might be a problem”.

Bonus points if you find yourself actually saying it out loud while typing it.

I have used the word "shenanigans" way more in a couple of years of agentic coding than in 30 years of writing code with humans.

I’m sure you could put something similar together with a bunch of duct tape and 2 weeks of effort, but it won’t work nearly as nicely nor out of the box. so…what am i missing?

My company has an agreement with the big providers and while i'm pretty sure they think about how to get budget back, its an competitive advantage and normal people will not learn different model behaviours.

At least for now.

There are huge numbers of users (myself included) that do have an exact idea of what inference costs are - on open models. We can buy tokens from 3rd parties that have no motivation to subsidize our use. That's to say, there's a fair marketplace[1] and we're hanging out there.

If you want to say "I don't think anyone has a firm grasp on actual inference costs on these proprietary/closed models", then I could agree with that.

[1]: https://openrouter.ai/rankings#leaderboard

Both. They are charging the most they can get away with and that amount is still heavily subsidized by VC capital.

We know roughly how much these companies spend and what their revenues are. Based on that, they'd have to more than double revenue (without spending more money) just to stay even, and that's not good enough given how deep in the hole they are.

> OpenAI and Anthropic are heavily subsidizing their inference -- no wait, they are charging the most they can get away with before going public. Where is the truth?

Both are true. I mean, I'd be willing to spend a bit more than I do now, but not more than double, and neither are most companies. The company I work for is currently investigating how to reduce LLM spend, not looking to spend more.

Now that 200USD subscription starts to feel cheap...

Even if subscriptions are locally profitable (i. e., the cost of the subscription covers the cost of inference), they're still subsidized because they don't cover training and running the company; otherwise, these companies would be profitable.

So they are profitable?

I think you are mismatching accounting terms.

You can't say the 'subscriptions' are profitable without accounting for the cost of making the model that is the source of the subscription.

They are heavily subsidized by the shareholders. Investing, running at a loss, with hope of some future profitability.

Having said that, I found the cloud dev environments slow to the point where I wasn’t sure if it had frozen, so I never looked back.

Also, is it really a defense department when you're starting wars of aggression every 15 years or so?

Why aren't corporations doing more to help workers with childcare? Why aren't they doing more profit sharing with workers? Why aren't they encouraging unions or sectorial bargaining? Why isn't the government mandating any of this?

Americans very rarely benefit when US corporations do well. That needs to change. No one benefits if Meta continues making billions in profit every quarter while society suffers from isolation, depression, suicide, and scams from their services. Americans don't benefit if health insurance companies are making massive profits while they can't afford deductibles.

Our society has been setup to simply extract wealth in all facets of life. That's a sick society and it needs to change.

I'm not saying China does this better, in fact China has some of the worse worker rights out of all the industrialized countries; but at least American consumers would benefit from cheaper higher quality Chinese goods. The world would likely benefit too if America got off the cold war hype train that did nothing to benefit humanity outside of those making weapon systems.

The question of collaboration with USG is a much more complex one, but is not the one raised above.

Edit: I'll also add that I doubt any AI-doom people "trust" Anthropic per se. The entire angle of questioning – again – misunderstands the AI-doom argument. You appear to think that if companies behave unethically, they cannot be trusted and they will not produce good outcomes, inversely: if they behave ethically, they can be trusted, and they will produce good outcomes.

Any competent AI-doomer would argue that ethics or trust are essentially irrelevant.

The entire problem is that people can act totally reasonably, even ethically, and this is not a guarantee of good outcomes. Situations can be created in which completely ethical, reasonable behavior actually produces a bad outcome. You do not need to assume people are bad in order to produce a bad outcome, and inversely you cannot assume that you will get a good outcome from good people.

"Arms races" are one class of situations that often have this characteristic. "Bureaucracy" is another class that we encounter a lot in daily life. There's a lot of them!

AI Savings Misses 'Should Be Making Executives Uncomfortable,' Bain Says - https://news.ycombinator.com/item?id=48359010 - June 2026 (0 comments)

AI sticker shock hits corporate America- https://news.ycombinator.com/item?id=48307098 - May 2026 (146 comments)

https://cursor.com/evals

As a consumer I can choose to buy subscriptions to a range of things, including $5 droplets or VMs on a broad range of cloud hosting providers. I can even buy cheap bare metal at a bunch of providers at an affordable retail rate.

I can also buy "unlimited" AI packages that will be optimized to fit the cost model from a variety of services, with different impacts, such as rolling outages when I consume a daily or hourly allotment.

Right now VC and the investor class are subsidizing the rapid evolution of the services and availability, but that VC is running out. In more traditional economies, AI would have developed and rolled out more slowly, and through metered subscriptions, with the eventual rolling out of "unlimited" packages like telephone, internet, or cell services once the market became commoditized.

We have seen a big inversion of that with the race to "win" AI marketshare. Now the true cost is being exposed, and the most competitive and capable models are hideously expensive to operate, so it makes sense that we are moving to metered billing for a utility service. If you want gas, you can buy regular or premium. If you have a premium car you definitely want the premium, but for most people regular is good.

Give it a couple of years, and the survivors will settle around fairly industry standard models of consumer grade services, pro-sumer accounts, and business/enterprise models.

Things are still shaking out, but I get the sadness. Luckily I work at a big tech company who is banging the drum on doing experimentation so I use my prosumer claude pro and other accounts at home for hobby stuff, and save my heavy lifting and potentially experimentation for work :P

Where?

I've been enjoying seeing how the quality of individual models differ based on the amount of reasoning effort you give them. If they were baking an a good pelican you wouldn't expect them to differ so much.

(Google Gemini are the only lab that have very clearly paid attention to the quality of SVG animals-riding-vehicles, see their announcement for Gemini 3.1: https://twitter.com/JeffDean/status/2024525132266688757 )