What I find fascinating that there is so little substance in this article about the quality of produced code and the medium. Is the code documented and tested? Is it understandable and extendable? Is it secure? What language, framework, database was used? Author mentions judgement and taste - well, is the code tasteful? Will the model rearchitecture the entire thing if I ask it to add new functionality, spending another 9.5h in tokens? I assume that the research part is domain knowledge = how different types of travel translate to time making it presentable.

> This is a map that shows the distance you can travel in a given length of time, and the first one was created in 1881 showing travel times from London.

The first item on the article, the first thing it showed, was wrong.

It is 100% faster to go from London to New York in 1881 than Volgagrad. Or any of the Russian hinterland colored green or Turkey or Egypt.

Not a good look.

Anecdote: I fed Fable some models I’ve been hand verifying (basically, I sketch out a scenario for Opus to model, it builds it, I ask it to show me the math, I correct it, we iterate like this, then I double check its code to make sure the math matches the model logic). Fable found almost every error I found, and then had some interesting suggestions for additional variables.

It also burned through my usage quota like a late-90s Hummer.

I have been using it for less than an hour so take this with a grain of salt of being excited for the new tech.

In a project like mine (https://github.com/tsz-org/tsz) I am constantly frustrated that models were not doing enough research and were not taking into account other situations. Again and again models would produce code that would fix one thing and break 2 other tests that were "unrelated".

With Fable it seems like tasks are taking much longer (I have not seen a pull request from Fable sessions yet) but reading the transcription of those sessions I can see how it is doing the right thing by not leaving any stone unturned.

As the article says, it's hard to communicate this "feeling" about models because it is very project specific but I thought I share

What are people working on that they see such a substantial difference between Mythos and Opus? I'd say I'm working with advanced stuff and more than often Deepseek is even more than enough. Why is everybody a genius in here?

This is what he built:

https://isochronic-passage-chart.netlify.app/

Doesn’t work too well on mobile but looks interesting

> It worked for nine and a half hours.

> Again, it wasn’t perfect. As an expert, I was able to spot some errors and omissions (some as a result of the design I had asked for) that I had the AI correct

That's the bit that stuck out to me - that's longer than I would expect to work on a problem in a day or even expect to go back & fix the output of something that has a core reward loop of hours.

My customers are currently clamoring to push down my agent response times from 85 seconds down to below the 20s mark.

At the same time, it is very dissonant to see the industry heading towards hour+ long workflows with an agent.

> First, how good is Fable? In experiment after experiment I conducted, it outperformed basically every other public model I have used by a considerable margin.

What makes me excited is that GPT 5.6 (its actually GPT 6) is going to be crazy

What it feels like to work with Fable:

> Switched to Opus 4.8: Fable 5 has safety measures that flag messages on most cybersecurity or biology topics. They may flag safe, normal content as well. These measures let us bring you Mythos-level capability in other areas sooner, and we're working to refine them. Send feedback or learn more.

would it be possible for mythos to make the space bar scroll the pages on your website properly?

Was the condition of being granted early access to this castrated model writing a post praising it?

I think Qwen 3.7-Plus is better at reasoning than Mythos, and I've used both for quite a while.

Mollick runs the Generative AI Lab at Wharton, with all the corporate sponsors.

He is a professor but sadly also an AI shill. He should switch to advertising washing power.

I just can't stand this type of fawning language.

More Mythos Marketing.

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In Claude's defense (and I cannot believe I'm defending it), I know no single dev who could create what it did (Concord), from a 19-page design document, in 9.5 working hours.

We're gonna go back to the days where our bosses ask why we're just sitting around, but instead of saying "compiling," we'll just say, "waiting for Claude."

> At the same time, it is very dissonant to see the industry heading towards hour+ long workflows with an agent.

At this point, pay me significantly more, and I'll do it.

Work duration is also not that valuable of a measure, you're usually better off defining the process yourself in code and having that delegate chunks of work to the models. The only real issue there is that it's harder to take advantage of the providers' subscription discounts, but on the other hand it's easier to do your own model routing, and there's no way I've seen for the normal chatbots to maintain coherence on streams of work measured in days and weeks.

I think we hit the sigmoid back when the QWEN models were released. By properly structuring my project, I can point it at any extension I want and get it going for 30 minutes to extend whatever. It can't effectively do 'god mode' on all the code, but being a mindful observer and code "professional" I don't need more than what a 128GB VRAM needs.

I'm amazed we're so far into SOTA bloat that the chinese will kill once they start etching silicon with these models.

My Opus 4.8 regularly works for 10+minutes on a single non-trivial coding request.