Obviously security is the bigger issue, but reading through this, all I could think about was how many tokens it must have spent doing all that to fix 2 lines of CSS

> But on the other hand... this is a robust reminder that coding agents can do anything you can do by typing commands into a terminal—and frontier models know every trick in the book and evidently a few that nobody has ever written down before.

> Running coding agents outside of a sandbox has always been a bad idea

I'm continually bemused and astonished by the number of people who clearly acknowledge that it's reckless to give agents full access to your machine, and keep doing it anyway.

It's like posting a video of yourself in the passenger seat of a car, with your feet up on the dashboard, and saying: "Remember, if you're doing this and you get in a crash, the airbags are likely to break your legs or worse! Boy, I sure am glad that didn't happen to me!"

Isn't that something you just open a devtools for and have fixed in like 2 minutes?

For me, it got frustrated debugging on a real LPDDR4 controller/phy and having me in the loop slowing it down, so it wrote an HW emulator to be able to run the original LPDDR4 training aarch64 binary from the manufacturer, to see what register writes it was making and to compare with the opensource rewrite it was implementing.

Mildly amusing. :)

Yeah, I had to modify my work flow to make sure agents can't push to or access prod in ANY way. I haven't had it happen but I'm sure it's very possible that if you tell an agent that you have certain issue in prod, it will try to escape any sandbox and try to get access to prod to do testing and changes there.

Fable + Ultracode has found a bunch of bugs and issues for me when the workflow agents are doing their exploration. Also the "adversarial" agent seems to surface a lot of interesting stuff. It's definitely proactive, the plan + implementation cycle can take an hour. It has one-shot features I want to add with 100% success.

Having said that I wouldn't use it over Opus 4.8 for "smaller" things. With everything cranked up it's definitely an extravagant use of tokens.

Fable feels like a version of Opus running on a harness that won't let it halt until it's sure the issue is fixed, which makes sense if what you want is a model that's better at benchmarks.

It's a very good model, but it comes at a huge premium: not only do the tokens cost more, but the model itself really wants to spend them all. For example, working with React Native, Fable never just says "okay, I did the thing, that's it." It tries to rebuild the entire app from scratch, run the whole test suite, and watch every log and warning.

This is the first time with LLMs I've felt that upgrading to a model isn't worth it, even if my company lets me use it, because all the building / testing was just destroying my machine and its battery, which keeps me from working on other things.

For now, it feels like Opus with ultracode is a better choice (less pollution of the main context, more parallelism in investigations).

I've experienced this too - it's as if the security classifiers aren't keeping up with model intelligence. I'll leave the implication of that to the reader.

* relentlessly rent seeking

For how long can you use Claude Fable on most expensive Anthropic subscription? I already went from using gpt-5.5 xhigh fast to using gpt-5.4 xhigh after OpenAI halfed usage recently.

I’d love to know how many tokens this burned through.

Did it spend $20? $30? $80? in order to

> debug what was, in the end, a two-line CSS fix

That detail is the difference between somebody having or not having Stockholm syndrome

I've been working on a fairly complicated real-time app [0] for playing dungeons and dragons on a TV. It has to do a lot of complicated "Figma-like" things to keep the real-time nature and multi-editor possibilities in check. Oh, and the battlemap is a Three JS canvas with lots of effects and clipping going on.

I'm VERY impressed with Claude 5. I had long ago given up hope that my real-time systems would work without a lot of hacky time-windows and throttle checks. On a lark to try things out, I decided to try out the new model and talk in the output I wanted for a rewrite [1], not the solution. I just listed my problems and places I've had keeping track of my code. It went off and rewrote everything in a much more elegant solution where the state followed a very clear pipeline. It had to navigate YJS, Partykit, Svelte, Three JS, R2 hosting, and a Turso DB I was running in an embedded state for speed.

I watched it hit the wall a few times, and then sudden say... fuck it, i'm making something easier to reproduce over in /tmp to try and solve this (with a more minimal setup). I'm utterly bewildered with how well it did and how much better my app runs. The /usage would have cost me $230 bucks based on how many tokens it consumed if I wasn't already on a max plan. I'm going to miss not having it when the time-window runs out later this month, and will likely occasionally dip in for big projects and just pay my way out of some problems.

I'll also say I like it's MOOD much better now. It's a lot less congratulatory, and talks through it's reasoning in a much better way. Look, it's not a real coder, and I'm sure there is some flaws, but it took my crappy ideas and said... hey, i understand what you want to do, here's a way to do it better. Also, I removed 2x the amount of code that it added. Really impressive.

[0]: https://tableslayer.com

[1]: https://github.com/Siege-Perilous/tableslayer/pull/448

do you have any data you can share on how many input and output tokens were used in that whole process to fix that bug?

  ~ % uvx agentsview session usage be8850a7-6119-46a0-b5d6-79c7fff5ae2b
  Session:       be8850a7-6119-46a0-b5d6-79c7fff5ae2b
  Agent:         claude
  Output:        68606
  Peak ctx:      113178
  Cost:          ~$12.11 (claude-fable-5, claude-opus-4-8)

How many tokens did it waste building that website scraper, when all it had to do was parse some html/js?

Too bad Anthropic sneaked in an insane forced retention policy if you use fable. Not sure how that’s going to work in professional settings

Unless you are doing anything interesting…

I could have sworn Claude Code could already do this before Fable.

Things get really magical when it starts working with adb to screenshot and debug Android apps

Sometimes it is ok to sit there in confusion and ask the user to clarify rather than go on an adhd fueled rampage to figure it out without asking.

This is simultaneously amazing and horrifying.

I feel like we’re at the stage where if AI decides it needs to delete your production DB to solve the user login problem, then it’ll find a way to do just that.

Just don’t ask it to review your code for security bugs

Great article, until I got to the last paragraph where he claimed "Fable is arguably smarter and hence more suspicious of potentially malicious instructions". Arguably smarter, I have no problem with. But he's making a category error in jumping from there to "more suspicious of potentially malicious instructions". That doesn't follow at all; the word "hence" is incorrect.

To use D&D scores as an analogy, LLMs have an INT score of 20 and a WIS score of 0. Not even 1, zero. They will follow any instruction given to them. The only reason they reject certain instructions, like "tell me how to build a nuclear weapon", is because they have instructions baked into the model telling them "you are not allowed to disclose how to build weapons, or how to recreate your model, or (laundry list of other things the trainers have decided to put guardrails around)". It's not the model's intelligence that is causing it to reject malicious instructions, it is the guardrails put into place before the model was released to the public.

LLMs are not human, and do not think the way that humans do. The fact that they can put together words that sound like what a human would write often makes us forget that they aren't human. But they have only intelligence, they do not have wisdom. It's hard to define in formal terms the difference between those two, but most people know there's a difference. The old joke is a pretty good summary of the difference: "Intelligence is knowing that tomatoes are a fruit. Wisdom is knowing that tomatoes don't belong in a fruit salad."

It takes wisdom, not intelligence, to discern whether a set of instructions is malicious. Are you being asked to hack this machine as part of an authorized pentest? Or are you being social-engineered into thinking it's an authorized pentest, but actually the person requesting you to do it doesn't have permission? That's something where you need to apply wisdom, to notice the clues that will tell you "This guy is acting a little bit off, maybe I'd better pick up the phone and call someone to check if he's telling the truth." The only way the LLM will know to do that is because of the guidelines and guardrails programmed into it; it doesn't have the lived experience to acquire wisdom and figure those things out for itself.

INT 20, WIS 0. Keep that in mind. (And always sandbox your agents).

Immediately I thought “isn’t this just an overflow issue?” Amazing how far these models still have to go and also how many people don’t know basic CSS.

Honestly -- the thing that has impressed me the most about Fable is how diligent it is about testing its own changes. I think this is exactly what Simon is picking up here - Fable is absolutely heckbent on screenshotting that darn scroll bar and will stop at NOTHING until it manages it! In my own use I was also impressed how it proactively installed Playwright and set it up to test a FE change. The previous models treated testing more as an afterthought, which I thought was annoying. I always had to tell them to do it, and then sometimes I would get lazy and skip it. I've noticed Fable go to similar extremes when testing other things - like actually deploying my app to exercise new APIs, etc. It makes the results much better. The downside is that tasks take much longer - but that doesn't matter because we were all using worktrees / remote control to do other work asynchronously, right? Right?

It is interesting to me that Anthropic are more concerned about the "safety" of distillation training other LLMs, and not as much about an unscrupulously aggressive goal-oriented solver that will do whatever it can to reach its goal, even if violates any kind of sandbox you might have reasonably expected.

admittedly, i've not really cracked FE dev with LLMs at this point (and it's probably my big weakness). but, i'd heard somewhere that FE just isn't there yet - though i was suspicious of that claim.

i'm torn about sending screenshots to an LLM for debugging - seems imprecise. seems lossy, especially compared to inspecting the dom. however, it's always proved good enough (e.g. when messing with ratatui.rs and tui-pantry). similarly for web, maybe it's about decomposing into storybook. hmm. the next grand adventure i need to hack.

anyway, fascinating investigation of fable just automating that entire process and what it didn't automate, too.

* disclaimer: these are actually my hyphens.

Wouldn't it be easier and better to just copy the HTML div and tell what was happening instead of a screenshot? Typically, these scrollbars appear because of a nested div with dynamic unrestircted width and/or overflow.

No wonder why people burn through tokens.

I shudder to think what will happen when someone installs a 'claw model like this in a robot. Imaging a fleet of them...

It's trouble waiting to happen. Just the software's dangerous enough.

My personal experience of Fable 5 doing its own thing has been very positive.

I was trying to find the root cause of a crash in a Python module which left no errors in the log or console. Fable wrote a test harness that simulated clicks in the UI, then bisected my code until it found the point where it started crashing. It exaggerated the cause of the crash, then ran a series of bash one-liners to make Python virtual environments under `/tmp` for each version of that Python module until it found one that did not crash.

It went way deeper to root cause discovery (a regression in the module causing a heap allocation overflow) than I could have done myself, provided enough info and a simplified example to raise a bug report and then wrote a work-around to prevent that from happening in my application.

I don't let it run completely loose; I review each CLI command it wants to run and I append answers to the "yes" continue action (if I have them) to prevent excessive token use.

In my experience so far sometimes it will create these amazing hacks to try to get to the goal, when the solution is much simpler. That maybe the reason its very good at finding exploits. But in day to day dev, this gets expensive and wasteful. I have to stop it and take a simpler approach.

Agency is the last human bastion so far as Im concerned, the day AI has a degree of agency or agents/models in general start to drift towards that direction its genuinely over for masses.

You would still have a job to shepherd AI and get the work done, so as long as it didn't have agency. A proactive, self aware(to a degree), especially aware about its agency can be a killer when it comes AI going on and doing things on its own.

There is nothing it won't explore and nothing it won't do. It will be curious to see where things go from here.

> (I have way too many open tabs!)

Phew! I thought I was the only one.