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; how did the author verify this?
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.
I’m starting to realize that LLMs are really good at building low-stakes projects. Your questions mostly presume that the stakes are higher. The software will last a long time; the requirements will evolve; we can’t tolerate mistakes; etc.
The trick to getting good at using LLMs for software is to learn how to make _all_ projects low-stakes.
You don't need LLM for that.
You make _all_ projects low-stakes by working on green field project using (insert buzzword soup of the day) and leaving for a new green field opportunity (that requires experience with buzzword soup of the day) before the project ships.
Ah, but billions of dollars depend on those questions not being asked in a genuine manner. Don't you want a slice of that or are you an... AI skeptic thunder clashes.
I'm becoming more convinced these are questions of the Before Times. Yes, yes—heresy, I know.
Yet, I can't deny the reality that I observe working with LLMs every day. If this truly is a step-function (as some are sgguesting), then I have absolutely zero concern for the quality of the code.
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.
We know this model will be cheaper and faster with time.
And we have not even reached the timespan/timeframe were we have ASIC style models.
OpenAI has to do something which will beat Fable otherwise Anthropic won. China currently overtakes cars, pv, batteries and very soon silicon chip making, it has all the incentive to also take over AI.
The only thing they’ve overtaken is arguably batteries, and even that is questionable if the quality is as good as Korean manufacturers. I think it’s more likely that the Chinese chip industry overtaking competitors will remain like nuclear fusion, forever “just 5 years away”
That's the beauty of these AI advancements. You, a human, will have to compete against a model for the same job.
If you get $100,000 per year as a SWE, and Anthropic offers a coding model for $100,000 per year (but working 24/7), then you'll have to give up all of those addons that make the fully burdened cost of the employee. Say goodbye to vacation, sick time, benefits, etc.
Reading the first few paragraphs of what he calls "the most sophisticated academic social science paper I have yet seen from an AI" does not impress as much as I hoped.
"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.
I'm using Fable this afternoon and it's definitely a step up from Opus 4.8, finding and fixing things Opus 4.8 was blind to even perceiving. The next 13 days are going to be fun IMO. And Opus 4.8 was less annoying than Opus 4.7 FWIW.
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?
Just depends what you are working on. If you are trying to make a video game that's at a level of a decent indie game (think Hades/Baazar/etc), making UI elements/VFX/complex shaders/etc that are organic/interactive/animated that don't feel like a little dogshit vibeslop web-game, then none of the models are even close to good enough to get it done easily. Huge percentage of problems in top 3% games is really hard for any of the models to do with simple prompting.
Personally I don't really care, because I like coding and learning myself and DeepSeek Flash is all I really care about. But it's really easy to have a ton of benchmarks where the top models can't get anywhere close - and I like to test them on these problems to see how good they are getting.
We see the same thing when new laptops are announced and every employee all of a sudden needs to upgrade, despite the fact that 90% of people would be able to make do with a Macbook Neo.
I’ve been working on implementing some common web infra type projects in Rust lately. Basically trying to use a lot of the great primatives in Rust like rustls (modern openSSL) and Tokio (async) to build memory safe or close, nginx drop in replacements.
A small portion of this effort is having a high quality Lua in Rust repo. I’m using mythos to fix some of the performance issues with my Lua interpreter that gpt 5.5/ opus 4.8 had stone walled on.
Not sure if Mythos will be able to crack this but it has been running for a couple hours now with some promising results.
I had a few of the benchmarks left alone and was working on tech debt knowing that a new model is going to be released soon. For my project (tsz.dev) Opus 4.8 was running in circles without producing results for a while for those tasks
> 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 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."
This. I get told things like "you can't build all that on your own?" I've had Claude poop out full feature web apps in under 30 minutes, to a spec. Was it perfect? No, but sometimes even in a simple setup phase you can burn 15 minutes to some obscure setup step that's failing. I cannot just code nonstop at 900WPM or whatever ridiculous speed, and poop out an entire full feature web app, with maybe a few bugs here or there. If you can, come show me, I'll gladly have you race against my Claude prompting capabilities.
Will Claude's code be perfect in one shot? Probably not, will it get you 80 to 90% of the way there with your chosen design patterns in under a few hours? Absolutely.
Isn't it common to refer to all software like that? "Let my look at my JIRA", "I can't find anything using my Outlook's search function", "My Powerpoint is acting up today", "My browser just crashed" are all sentences I might say during a normal work day
In my mental model, "my Outlook" is the outlook instance running on my computer, on my data. My outlook crashed today. Yours might not have crashed. Similarly, my Jira contains tickets about my work, your Jira does not contain those same tickets. That might be technically the same instance on the same SaaS server, but the server I'm routed to accessing my data with my credentials turns it into "my Jira". My Jira is slow. Maybe you are lucky and get routed to a faster server, or your company is self-hosting. Then your Jira might be reasonably fast
Hmm, good point. "My outlook" might actually be correct.
Depending on if it is a webapp or the real one running on your device that is.
Similiar to "My game just crashed".
Jira otoh is not yours, because it's in the cloud. It might be "my internet connection", "my browser" or "my account" that is having trouble.
___
Hm. "My train got delayed" is interesting in this context.
I don't find that offensive. But that also might be because trains don't seek rent the way SaaS does? Not sure.
I guess trains do not hold me hostage. They might just be a container in which someone does that.
This is completely fine, as those are your own installs, but LLMs can't be owned by the users, your Opus is the same Opus as everyone else´s, your only difference is the suscription tier to their API.
If you had your own on-premises LLM, that would indeed be your LLM, and it would make sense to compare it to the on-premises LLMs of other people.
The copyright to the Outlook binary isn't owned by the users either, even if they're running it on local hardware. The Opus 4.8 weights are (we assume) the same between users, but the conversation/tooling state is not shared between them by default. I prefer to route around this construction myself, since I do think there's some ontological slippery-slope potential, but from a lexical perspective I think “my” is a perfectly defensible abbreviation in context.
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.
Man, that poem it made is terrible. Like just incredibly bad. Sure it's neat that software can make an incredibly bad poem but there is enough bad poetry in the world that we don't need it.
It looks interesting but, like a lot of AI, looks correct but is not. Most of northwestern Canada says you can get there by road. If you look at Google Maps, there's no roads there for quite awhile. I see one highway between Inuvik and Tuktoyaktuk but that's about it.
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
In general, sooner or later you need to restructure one thing or another when requirements are changing. Good code lets you reason about a refactoring, and experience tells you when it is necessary or appropriate. Coding agents aren’t very good at the latter.
the setup is solid. there are thousands of tests and CI won't let things to merge if tests are failing.
But overall, this is pretty normal for compilers to have this sort of "unexpected" tests failing due to some work in an area. It happened to me when I was coding everything manually back in the day too
> 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.
Nice, but I'm really curious about how many tokens have been used.
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.
> 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.
> because it involves researching thousands of potential trip distances and a lot of small judgement calls and decisions. I decided to try it on Fable ...
Because the author wasn't able to verify the output, it just meant that fable can gaslight better and in more detail? This is already familiar pattern for anyone using Opus today.
> 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
Ethan is a booster but I wouldn't call him a shill. He cites data and mostly in a fair way, though you could argue the sources he chooses to focus on are biased.
Instead of attacking the author, please respond to the content of the article. That is the HN way, and it leads to more substantive and interesting discussions.
Would love to see samples of the kinds of prompts you use with both. I sometimes wonder if the specific wording is the secret sauce, I have very few issues with Opus / Claude, but when I try premier GPT models, I get weird output from what I've grown to expect with Claude.
Reading it, I can't help but feel he's being paid to write this. Or maybe he hopes to be paid. The language he uses makes him sound like he's fawning over the lost days of his childhood. Pardon me for being skeptical, but a trillion dollar company running a net-loss is hoping to IPO, and needs to sway public opinion by any means necessary. I would imagine that no dirty marketing scheme is off of the table, even from the self-proclaimed "good guys".
It is not a sponsored article and he writes one of these every time a new model releases. Why would a professor at Wharton need to write sponsored Substack articles.
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.
The trick to getting good at using LLMs for software is to learn how to make _all_ projects low-stakes.
this doesn't really work in the real world. There are many things that actually matter, engineering is fundamentally about handling them.
Yet, I can't deny the reality that I observe working with LLMs every day. If this truly is a step-function (as some are sgguesting), then I have absolutely zero concern for the quality of the code.
It also burned through my usage quota like a late-90s Hummer.
We know this model will be cheaper and faster with time.
And we have not even reached the timespan/timeframe were we have ASIC style models.
OpenAI has to do something which will beat Fable otherwise Anthropic won. China currently overtakes cars, pv, batteries and very soon silicon chip making, it has all the incentive to also take over AI.
It's not just salary, but also safety/labor regulation, legal risk, vacations, sick time, personal conflicts, HR, benefits.
Even when automation is more expensive on paper, it's generally still cheaper
If you get $100,000 per year as a SWE, and Anthropic offers a coding model for $100,000 per year (but working 24/7), then you'll have to give up all of those addons that make the fully burdened cost of the employee. Say goodbye to vacation, sick time, benefits, etc.
"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.
Every sw dev knows this is a very dangerous, and unrealistic, assumption.
Personally I don't really care, because I like coding and learning myself and DeepSeek Flash is all I really care about. But it's really easy to have a ton of benchmarks where the top models can't get anywhere close - and I like to test them on these problems to see how good they are getting.
Fable 5 is def a little better than 4.8 btw.
A small portion of this effort is having a high quality Lua in Rust repo. I’m using mythos to fix some of the performance issues with my Lua interpreter that gpt 5.5/ opus 4.8 had stone walled on.
Not sure if Mythos will be able to crack this but it has been running for a couple hours now with some promising results.
Performance charts linked here if your curious https://github.com/ianm199/lua-rs
> 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.
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."
Will Claude's code be perfect in one shot? Probably not, will it get you 80 to 90% of the way there with your chosen design patterns in under a few hours? Absolutely.
https://xkcd.com/303/
At this point, pay me significantly more, and I'll do it.
Ha ha, that's how you negotiate yourself out of a job!
There are people that almost feel physical pain if something is unnecessarily incorrect.
+ That if the mental model of something is accurate, it is actually _more_ work to say something that is incorrect than just saying the correct thing.
Similiar to "My game just crashed".
Jira otoh is not yours, because it's in the cloud. It might be "my internet connection", "my browser" or "my account" that is having trouble.
___
Hm. "My train got delayed" is interesting in this context. I don't find that offensive. But that also might be because trains don't seek rent the way SaaS does? Not sure.
I guess trains do not hold me hostage. They might just be a container in which someone does that.
Jira, cloud LLM inference or similar otoh..
If you had your own on-premises LLM, that would indeed be your LLM, and it would make sense to compare it to the on-premises LLMs of other people.
There was a time where one actually bought software to own it.
This time is.. actually it is right now. Please leave at once.
I'm amazed we're so far into SOTA bloat that the chinese will kill once they start etching silicon with these models.
Is it a hard problem or is it just labor intensive?
https://isochronic-passage-chart.netlify.app/
Doesn’t work too well on mobile but looks interesting
I also see some logic flaws. It overlooks the option of going to a major hub to access faster aircraft, rather than hopping on local hubs.
Also, immigration and customs are cleared at the first airport you arrive at in the country, not at the last one.
In some countries, you need to clear immigration even while going to a third country, so 1 hour is not enough to do it.
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
But overall, this is pretty normal for compilers to have this sort of "unexpected" tests failing due to some work in an area. It happened to me when I was coding everything manually back in the day too
> 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.
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.
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.
Care to share the results you got from Opus working on the same prompt? It should be easy to compare quality.
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.
> because it involves researching thousands of potential trip distances and a lot of small judgement calls and decisions. I decided to try it on Fable ...
Because the author wasn't able to verify the output, it just meant that fable can gaslight better and in more detail? This is already familiar pattern for anyone using Opus today.
the map is for 2026, yeah?
What makes me excited is that GPT 5.6 (its actually GPT 6) is going to be crazy
Just an FYI this guy is an AI hype-beast. Some of his tweets are truly out there.
He is a professor but sadly also an AI shill. He should switch to advertising washing power.
What?