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No wonder why people burn through tokens.
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)
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
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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…
(The best one I can think of is probably that recent Instagram account takeover hack, but that was so stupid it hardly even qualifies as a prompt injection!)
Having spent a bunch of time trying to build out examples of prompt injections, my current best guess is that the leading models are actually surprisingly good at spotting them.
I've had to drop back to smaller, weaker models for demos recently - it's definitely possible to prompt inject a frontier GPT or Claude but it's frustratingly difficult. I don't have the patience to figure it out myself!
So yeah, I do think it's likely that Mythos/Fable are "safer" than other models because they're better at spotting when they're being subverted.
That certainly doesn't mean that they're safe!
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…
It's trouble waiting to happen. Just the software's dangerous enough.
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
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
You should estimate how much time it would have taken a human
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
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.