These questions are even not about AI: if I were to give money to a human agency and were given something they tell me works, I would ask the same questions. If I did not know how to evaluate, I would hire people that do. With LLMs the verification part is what bothers me the most.

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

Every sw dev knows this is a very dangerous, and unrealistic, assumption.

"Posterior beliefs about market demand are purely referencedependent: holding dollars raised constant, they track only performance relative to the founder’s self-chosen goal—jumping half a standard deviation at the threshold, responding steeply for the first ten points past it, and flattening thereafter"

Humans generally don't verbalize data this way. The summary document is also very fluffy.

> 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.

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

> This is from "The Cyberiad", a collection of science-fiction fairy tales by Polish author Stanislaw Lem ... In one of the stories, a robot constructor named Trurl creates a machine that writes poetry. A jealous rival named Klapaucian challenges the machine to compose "...a poem about a haircut! But lofty, noble, tragic, timeless, full of love, treachery, retribution, quiet heroism and in the face of certain doom! Six lines, cleverly rhymed, and every word beginning with the letter s!!"

And the computer responds with:

"Seduced, shaggy Samson snored.

She scissored short. Sorely shorn,

Soon shackled slave, Samson sighed.

Silently scheming,

Sightlessly seeking

Some savage, spectacular suicide"

The author had to be referencing this moment in their challenge to Fable/Mythos. I'm curious to know what their exact prompt was.

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

Doesn’t work too well on mobile but looks interesting

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

I do not fear that management will get tools like Mythos and then not need people like me. Most of the value I provide is in translating what the management/client _thinks_ they need into what is the real problem and solution.

That's not an insult to them, it's just pointing out that they see only their problem, and they imagine what would be the solution. They then ask for that solution. Quite often, what they want built isn't what they need. And I've seen so many problems, from so many domains and scenarios, that I can usually recognize the core need and propose (and build or direct building of) a solution which resolves that need AND has an eye toward the likely future needs.

Mythos may do an excellent job providing a high quality result based on what is asked of it. But the result will only be as good as the quality, clarity, and presentation of the request.

If I hire a home builder to build me a custom home, that builder is going to ask me a thousand questions - questions I had never even thought of. Mythos isn't going to ask all those questions - it's going to make the best choices it can without the consultant's level of interaction. And the buyer will get what they get. Sure, the buyer can then say, "oh, I don't want any hallways - just connected spaces." Then the house gets demolished and rebuilt to the new, clearer spec. Repeat, repeat repeat. Maybe eventually the buyer gets what they really want. More likely they give up before reaching that point, and they go and hire a real builder.

I'll sum it up like this: You can get great results with minimal effort if you don't really care too much about the details. But if you don't care much about the details, then your need probably wasn't very significant.

> 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.

I don’t see why working longer is a pro. The results don’t seem much better than you’d get from putting Opus in a long loop.

It's likely that at least some amount of additional context was provided to the model to enable it to reliably create the desired form factor. This introduces the caveat that the author probably views some amount of context as being trivial / beneath the level of mentioning. But then the question becomes where they draw the line.

I made serious progress towards repairing a proof for a conjecture that was published 10 days ago but kept running into a wall with one of the Lemmas.

I threw Fable 5 Max at it with the same subagent set up and in an hour it claimed to have disproved a core theorem of the paper.

The Lean construction looks correct, but I still need to verify it rigorously. This is certainly not something Opus 4.6 Max could do and it’s likely something Opus 4.8 Max could do with more delicate orchestration and time. However, the “one-shot” Fable 5 did give me pause.

[1] https://isochronic-passage-chart.netlify.app/

[2] https://mapitout.welcome-to-nl.nl/

[3] https://commutetimemap.com/

[4] https://andrewding.ca/flightisochrones/

- Went deep on "what types of guidance even are there? what does giving good guidance mean?"

- Sampled my existing Claude guidance (CLAUDE.md, skills, hooks, etc.) and broke their guidance into "atoms"

- Categorized them by clustering, the same way Big Five was generated

- Generated a new candidate

- Then used independent agents to compare it against my existing corpus assuming that the new one would be worse

Working with it felt like working with a supersmart entity capable of generating very plausible-sounding but not-necessarily-true statements. The outcome certainly felt like an alien artifact, like nothing I'd make myself.

Only time'll tell if it holds up, but it sure had some interesting ideas.

Maybe my prompts are too vague, but it’s worth noting that every example in the post is a greenfield build, and vague prompting seems to hold up fine when there are no existing constraints to respect.

Other commenters have pointed out that his isochrone map contains a lot of nonsense as well.

So the most charitable interpretation here is that this is a case of Gell-Mann amnesia.

Most of the “impressive” stuff is not “the model” but “the harness”. Spinning up the subagents and teams of lower models, letting them explore, do adversarial coding. It’s all in the harness. Granted, Mythos might be better at that orchestration, but it’s still the harness.

Second is the prompting. The author is an expert in what they’re doing and prompts the system in a way that yields useful results. I see too many people believing that if an expert can achieve those results in a domain they’re familiar with, then them as non-experts will be able to as well. And that’s a fallacy that Mythos doesn’t change.

There is only one hint: 475k tokens in the screenshot when OP asked the model to fix some behaviour, but it would be fascinating to know the total tokens amount.

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

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.

And I'm excited to try it, but also have a fear that I will like it too much and then won't have access to it in 2 weeks... But maybe I will and maybe it will be worth it and I'll just pay a bunch of extra for it and it'll be great!

I think the article could be improved by actually sharing more feelings. I clicked on the article for feelings but I didn't see that many feelings described.

> It worked for nine and a half hours.

And how much did that cost?

Wow

Just an FYI this guy is an AI hype-beast. Some of his tweets are truly out there.

Is it a hard problem or is it just labor intensive?

looks nice but deeply flawed

classic LLM output

Edit: A couple hours in and I just got my first gaslighting attempt from the model. Good times!

Not a great start for "a generational leap in model effectiveness"

At first i thought its routing was just completely botched.

The text overflow on the legend is pretty funny considering how well the other graphics turned out

(Edit: referring to the map app)

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

What?