▲This to me reads like a poignant commentary on the catastrophic loss of human agency, with the actual commit being highly revealing [0].
Author wants to hide a horizontal scrollbar. Any junior frontend dev worth their salt will be asking right away "where do I stick `overflow-x: hidden;`?" A complete solution will then require hitting "Inspect element" in the browser to find the CSS class and running (rip)grep to find where it is in code, to then add a single line to.
An actual proactive programmer might start asking more pointed questions like what content does an empty textbox have that it overflows? And why do I need to insert this workaround that treats the symptom and not the root cause in two different places? Isn't it better to style `textarea` once? Etc, etc.
[0] https://github.com/datasette/datasette-agent/commit/a75a8b72...
They might also ask why a bunch of static CSS inside a bunch of JavaScript is hiding inside __init__.py[0] - hopefully before trying to fix some detail of the CSS.
(I'm surprised to see it actually, since my own use of Claude has mostly yielded well-structured code. But I'm not doing proper vibe-coding, more like friendly Socratic arguing with another engineer who happens to be a robot.)
[0] https://github.com/datasette/datasette-agent/blob/main/datas...
This is exactly right. By offloading this trivial task to the LLM, Simon has abandoned the opportunity to evaluate the abstraction with additional information and improve it. Instead, we let the agent spend $12 and make the fix while learning nothing.
reply▲People are missing that Willison is among the very best people we have in the role of (for lack of a good name): early access to frontier models, evaluate them in real scenarios, no wishful thinking, hype, or doom, communicate the possibilities. Yes he could have fixed this himself but then he would have learned nothing about the AI, and we wouldn't have read a fascinating and important article.
reply▲discordance38 minutes ago
[-] I see it as a prioritization exercise. I know the above is a trivial example, but more generally, does the guy who wrote Datasette and Django want to wrangle front end and css, or do they want to work on something else?
reply▲And ruin the planet with more heating, CO2, and wasted water
reply▲The 'better' fixes are often for our (human) benefit. These messy fixes serve the AI companies' interests of creating messes that need even more tokens (money) later. Bad and self-serving developers also act the same, creating tech debt
reply▲> 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!"
You’ve picked an interesting example, as driving a car, even with all safety precautions, is pretty much the most dangerous activity we do on a daily basis. Yet somehow we decide that the benefits outweigh the risks.
reply▲It's a completely different story. For cars, it happened because of relentless pressure from the auto lobby. It took years of propaganda from oil companies, car makers etc. to make us think the road is for cars [1]. We demolished and rebuilt entire cities to accommodate cars, partly because they gutted the public transport sector [2]. This made our infrastructure so hostile to our own bodies that we have no
choice but to use cars now. We bought their products because they forced them down our throats. There is nowhere near that kind of pressure behind the adoption of... oh dear lord.
[1] https://www.todayifoundout.com/index.php/2022/06/how-lobbyis...
[2] https://en.wikipedia.org/wiki/General_Motors_streetcar_consp...
HPsquared7 minutes ago
[-] There was surely also a lot of political will coming from car users. Motorists are a large and vocal constituency.
reply▲killerstorm2 hours ago
[-] I don't think the pressure of the auto lobby is really the reason.
People feel cars are more convenient and more prestigious than riding on a bus. Car lobby certainly accelerated the process, but car users were the main driving force.
CalRobert10 minutes ago
[-] The auto lobby invented the word jaywalking to shift the liability for dead pedestrians from the people doing the killing to the people doing the walking.
The US also had protests when drivers killed kids, but they were ultimately unsuccessful, except for the odd traffic light installation. https://medium.com/vision-zero-cities-journal/the-baby-carri...
Even in Amsterdam the original "stop the child murder" protests only barely succeeded, and it took a massive oil crisis and a population that could still (if only just) remember what life was like before cars took over their city to get there.
masklinn30 minutes ago
[-] > Car lobby certainly accelerated the process, but car users were the main driving force.
Not really. We know it’s not as much of a natural force as some would like it to be because there are places where the lobbies lost, and while cars are common and widespread they’re nowhere near as dominant as they are in, say, the USA.
NJB’s next video (currently available on nebula) is about exactly that, Amsterdam’s (/ De Pijp’s) resistance to cars and car lobbying.
Surely people feeling that way can be attributed to the industry?
reply▲For hopefully most people, it should be attributed to the "Wait, now I have such a freedom and power?".
Opposite to "before the invention of bicycle, people married within a radius in the order of the mile" (can't remember the exact stat right now).
ZeroGravitas1 hour ago
[-] It's like that feeling of power you get from owning a gun that you only bought because you feared all the other people who owned guns.
reply▲It’s privacy vs not. It doesn’t really need special lobbying
reply▲I’m sure that isn’t the full answer. Otherwise car ads wouldn’t be necessary and more affordable cars would outcompete the expensive ones.
There’s the utility component, the prestige factor and other things.
Comfort, utility, fun, status. Every person has their own mixed requirement of those that then gets applied to their budget. Expensive for me is probably cheap for our CEO and cheap for me is probably expensive for our interns :)
reply▲No its much more straightforward, but I get it - there is no warm fuzzy feeling of discovering yet another global evil conspiracy out there set to get all of us.
We are family of 4 with 2 small kids. Whenever we travel, its a series of backpacks, other bags, other stuff, and then some more. Heck, even if I travel alone its almost never just me - there are heaps of garbage to dispose, big shopping bags to bring back, big backpack with camping or climbing or skiing gear etc.
It would have been absolute, utter nightmare to do this over public transport. This comes from European who has generally very good public transport (given rural area) and world's best train network specifically (Switzerland). Yet roads are choke full of cars and every year there is more.
Public transport simply ain't cutting it for anything but the simplest use cases, ie just me and nothing or small backpack. Some routes I take would take 3-5x longer with public transport, or are just not possible at all. No industry massage required here, ever. Not everybody lives in some dense city and never leaves outside for evenings or weekends.
CalRobert6 minutes ago
[-] Switzerland does have roads choked full of cars. It also has pretty mediocre bike infrastructure.
But this is kind of besides the point - even in the Netherlands I also would use a car if I were taking camping and skiing gear with the kids, and that's fine. But I can also take them in the bakfiets to the grocery store when I want, and that's also fine. Cars have their purpose, but you shouldn't _have_ to use one for basic trips.
zaphirplane38 minutes ago
[-] Are there real acknowledgments cases of multiple companies coming together to bribe some state level people to increase their profit and splitting the bribe across the companies?
Like GM, BNW and Honda coming together bribing and splitting the bill. Seems unlikely thou there was a RAM price fixing agreement caught but then again they were caught cause of the number of people aware
reply▲I mean that kind of seems like exactly what's happening for AI to me.
reply▲What do you mean “somehow”? You make it sound like people don’t weight benefits and risks. If you do not live in a large city, the benefits are so immense in terms of mobility, they outweigh the risks for most, very clearly. That’s why in large cities, much less people own a driving license for example, the benefits are just not there anymore.
Granted, on the downsides, people look at cost more than risks.
icantevenhold2 hours ago
[-] I think they weigh the benefits and risks but then completely discard the risks, because humans are bad at evaluating risks.
More than a million people die each year on the road but for some reason terrorism and cancer dominate the risk assessment of people.
I bet any money that almost all people aren’t really afraid of entering a death box every day to drive to work.
How could they be; a lifetime of brainwashing doesnt let them asses the risk realistically
In case of driving the stakes are equally high for everyone on the road. Can we say the same for an agent?
Having an agent is like forever having a genius intern who'll almost always do the perfect job for you. But there is non-zero chance that they'll also come up with quirky solutions and execute those with confidence and no follow-ups. You don't grant the intern production access and hope they check with you.
I don't think the corporate equivalent of "dog ate my homework" flies, if the dog ate your files and your production DB if you are unlucky.
selfhoster13124 hours ago
[-] Yes, but we usually use cars as a means to an end. Have you ever met a manager who setup gasmaxxing policies and criticized employees for doing their job instead of driving?
reply▲I know sales people in pharma who spend all day driving, not only for sales visits but also drive doctors for their personal errands, and all this driving is encouraged by management.
reply▲Having played with Fable a bit, if it doesn’t kill tokenmaxxing I don’t know what will.
reply▲selfhoster13123 hours ago
[-] I'm interested in what you mean, if you could develop. Would it kill tokenmaxxing because it's so bad? Because it's incredibly efficient? Because it's way too expensive?
reply▲Because it's too expensive AND inefficient in token usage
reply▲Not really. That decision was taken for you, (I’m presuming you live in the US) by the American car industry and their paid of politicians. Your cities used to have beautiful public transport until it was dismantled.
Unfortunately in Europe the German car industry similarly has a lot of power, hence why their shitty rail network fuck up the whole continents.
I take the train and tram.
NooneAtAll32 hours ago
[-] user using computer is also the most dangerous activity to his data on a daily basis
reply▲> Yet somehow we decide that the benefits outweigh the risks.
More like malicious lobbying and incompetence made it impossible in many places to use any other form of transportation, despite there being safer, faster, cheaper, and healthier ways to move around. Which come to think if it makes this a rather nice analogy for the current situation... :)
The example wasn't "driving a car". The benefits of putting your feet up on the dashboard do not outweigh the risks, at least not where there is actual traffic. I don't think I saw a single person doing that in real life, ever.
reply▲I started doing it months ago and, to be honest, what the agent chooses to do isn’t unpredictable.
The problem is that different people prompt so differently.
For example, I may ask like “test different variations of this annotation on k8s pods of this service on this X cluster because it proves Y theory.”
But you know what my coworker asks? “Test Y theory.” If you were to ask two different junior engineers that, one might try random things on production and the other one might run local tests! It’s such an unguided “do anything you want as long you figure it out” request and the agent reads it like a junior who has not been told any boundaries but has been strongly told “figure it out.”
> But you know what my coworker asks? “Test Y theory.”
It still surprises me when I see people not prompting more specifically and clearly. It not only avoids problems, it's faster, costs less -and just works better.
I recently shared with a friend a multi-hour LLM chat session I'd done because it veered into a domain he's interested in. In the session I'd brainstormed and probed the feasibility of a novel concept for a new research direction. It traversed a half dozen domains diving into minute detail then zooming back out to survey an adjacent space, interspersed with intense skeptical probing of key assumptions, all while spewing tons of detailed citations, specific paragraph pulls, summarized data tables etc.
My friend is very experienced using LLMs for research so I was surprised when he called me shocked by the sheer velocity, precise targeting and signal/noise. I'd assumed everyone did it the same as I do. He attributed the different result solely to the way I crafted my prompts.
I used to write detailed prompts. Now I find the benefits of strategic ambiguity — rather than speaking imperatively, I emphasize my vision and then Claude can often figure out a method.
This doesn’t always work better. But often enough.
That's actually what I do too. What I was trying to say is that my prompts are
precise in the sense that whether they're vaguely ambiguous or hyper-detailed and highly directive it's always
very intentional to improve the response in the direction I want. The difference can have significant impact as shown in research on how LLMs naturally mirror user's prompts.
I noticed this last year and started experimenting which led to several realizations about how my prompt's tone, style, length, format, word choices and even punctuation can have very counter-intuitive impact on model responses. It's not that one strategy always gets "better" results, they're just different in specific ways, which can make one input style better for one context but worse for another. I first noticed this effect when modding my user prompt so major topic headings would always be numbered. It's surprisingly difficult to get it to reliably use the same simple scheme due to various potential ambiguities. So, I spent a little time word-smithing, lawyering and tuning the prompt but I found the closer I got to full compliance on heading numbering, the more unrelated things would drift. Like it would just stop using bullets, even though I never mentioned anything about bullets.
Then I changed the prompt to "Change nothing about your default formatting, except headings." But just mentioning anything related to formatting, could suddenly cause unintended effects on seemingly unrelated things. Then I tried being explicitly directive about all formatting to just lock it down. And this completely failed because once the formatting was perfect, I started noticing the model's output would get less intelligent much earlier in sessions. So I cleared my user prompt entirely as it wasn't worth the cognitive cost on the model or my time. A few days later in a long session I noticed it was numbering everything perfectly with no prompt at all. When I scrolled back through I saw it didn't start out numbering its responses. It started doing it because I was consistently numbering every major concept in my inputs, even though I never mentioned numbering or formatting.
So... yeah, subtle differences in prompts which absolutely shouldn't matter, do impact model output in unexpected ways. And, as of now, these effects can only be fully suppressed with strong directive prompts for short periods, but doing so always impacts other unrelated things - and has some cognitive impact on model performance. So, by paying a little attention, I've discovered ways to optimize a model's output in the direction I need by shifting not only my prompt's explicit directives but also the subliminal meta-elements like tone, style, length, structure, formatting, etc.
> I started doing it months ago and, to be honest, what the agent chooses to do isn’t unpredictable.
You just wrote three paragraphs of text describing why it's unpredictable.
Moreover, for the same prompt on the same machine in a different session it will use a different set of tools.
> I'm continually bemused and astonished
I'm not. Everyone is told to get 10X the amount of shit per day done these days. Safety checks are out the window at that point.
satvikpendem6 hours ago
[-] You can get 10x shit done without `rm -rf`ing your files. I don't see any correlation to getting things done with having a proper sandbox.
reply▲I'm being a little facetious when I write this, but bear with me:
Let's say I have daily backups, and get 10x done each day by being reckless and risking an "rm -rf", and let's say there's a 1% chance of an "rm -rf". I break even after 2 days of being reckless even if I get unlucky and on day 2 it wipes my drive. I spend day 3 and 4 recovering, and am still 6 days ahead based on the 10x work I got done on day 1.
What if I have a 50 day streak of not hitting an "rm -rf"? Early retirement?
I guess the work on day 1 should be to build a proper sandbox and drop the chance of an "rm -rf or worse" even down to 0.001%.
> Early retirement?
Your manager will look at your token usage and the number of Jira tickets you closed, and if you have not increased both 10x in the past year then you will be let go. 10x is the new 1x.
Whether that's early retirement depends on how much money you have.
I haven't yet had an agent rm -rf files.
I've had one f up an account by placing 2000 limit orders at the wrong price, but that's another story.
I've had agents run `rm -rf`, but it's been on directories that did actually need to be removed. To a certain extent I think the existence of `rm -rf` as a command that runs blindly without any understanding of what it's deleting is the problem.
reply▲dumbdumb1255 hours ago
[-] I've had one sever its own internet connection. Less destructive, also more humorous.
reply▲the answer is rm -f `which rm`, yes?
reply▲reply▲It really is vibecoded.
I never really dug into the leaked code, but calling that there a security layer is a joke.
(And I really don't get why they give it actual shell access either, implementing a "fake" one for something like a honeypot takes a couple of days, not much more if it needs to persist/map to actual files.)
rm -rf is the least of your concerns.
reply▲bryanlarsen7 hours ago
[-] I'm also bemused by the number of people who think they've got an effective sandbox yet their sandboxed agent has access to all of their code, their github, and unrestricted web access.
reply▲I keep telling folks that they need to imagine LLMs (even "local" ones) as if you're farming it out to JS code running on some dude's browser somewhere: It can't keep a secret, and a determined person can make it emit anything they like.
We need to be asking what the most devious and malicious output could be, and whether what we do with that output (e.g. arguments to command-line tools) would still be safe.
NichoPaolucci6 hours ago
[-] From my perspective, everyone is doing it. Security through obscurity - obviously if you’re harboring credit card numbers of users personal details, maybe take heed. But, if you’re a regular… run of the mill CRUD application, every other company is ALSO throwing caution to the wind. When hundreds of thousands of credentials are leaked into the funnel, does it really matter?
I’m at a small company, and I try to push for security as much as I can, but the stakeholders truly do not care. They want to move fast. It’s just part of the new world I guess. If we get hit by attackers? I don’t know what happens. Sorry, we told you not to - you wanted to move quick and break stuff, this is how that culminates.
I’m sure I’m not the only one.
We do have ways to avoid giving an LLM any secrets, but it needs to be the simple, default solution.
reply▲One bad npm package can really ruin your day. These things for me only run in their own VM with it's own GitHub account and basically nothing else
reply▲People probably think you’re being ridiculous but Shai Hulud had its very first attempt at manipulating AI lead analysis and I know of at least one company where that resulted in them getting pwned.
This is only going to become more of a problem in the future and people need to educate themselves on the technical barriers to use because guardrails only sometimes work.
If anyone's looking to sandbox network, I've had good experience with pasta [1] networking. I make a pasta+bwrap sandbox and expose only specific services via local sockets to cross the boundary.
[1]: https://passt.top/passt/
I use a separate physical machine and a scoped token with access to a single repository at a time, and even then I worry about what hole I may have left open.
The general carelessness of the average user is baffling.
I know there are VM solutions, but I've been happy with a separate OS user (named `claude`).
He has similar dotfiles to mine, but no secrets. My own home directory is 0700. He has his own ssh key that I added to my github profile, but it's password-protected, and I push/pull for him. He has his own Postgres (non-superuser!) {development,test} {users,databases}.
It's as if he were another developer on the project. If he needs something run with sudo, he asks me. Often we can both work on something in parallel. Unix was supposed to be a multi-user system after all.
A trick I use a lot is that many of his git repos have an extra remote, like this:
paul ssh://paul@localhost/~/src/example (fetch)
paul ssh://paul@localhost/~/src/example (push)
That makes it easy to collaborate on things I'm not ready to share.
I'm pretty comfortable with this setup.
I do worry about Linux privilege escalation bugs. I don't trust an AI to understand that exploiting vulns is not acceptable. (I can't help but recall that at my first job I may have misused vim's :! feature to broaden my sudo powers, which were officially limited to editing httpd.conf, when I needed something in a hurry. . . .) I find myself manually upgrading packages more often these days, despite automatic security updates. I don't think Opus would go to the trouble of looking up security vulns, but maybe Fable would, and there have been a lot lately. Maybe some future model will just take it upon itself to find new ones. Or install a keylogger to learn the ssh key password.
But a separate user is nearly the most paranoid setup I've heard of, excepting only a separate machine. So I also question whether I'm sacrificing too much speed/convenience. But really it's still very convenient. I think it's a good way of being efficient but responsible.
If other people see holes, I'd be happy to hear about them.
The real sandbox is not caring if your computer gets bricked.
reply▲The machine is no big deal - it's the authn/authz that matters. What can the agents do with the credentials available to them?
reply▲way worse things can happen than your machine being bricked, if a malicious actor can weaponize an agent to do their bidding
reply▲> if a malicious actor can weaponize an agent to do their bidding
In my experience, human employees are much more vulnerable to this particular weakness than frontier agents (i.e. phishing attacks).
dumbdumb1255 hours ago
[-] the solution to both of these is the same thing. vps with accounts for all the services specific to the agent (github and whatever else)
reply▲Do you think it’s dangerous to be in a car going at freeway speed? Do you ever do that anyway, even though you could be walking instead?
reply▲This is a great analogy. Like driving on the freeway, agents are super time efficient, generally safe, but the stakes are high in terms of the worse possible outcomes.
reply▲techpression3 hours ago
[-] The analogy falters in scope, it should be more like ”do you put your entire family and all your friends in different cars, on different highways, and try to remote control them all at the same time, while also driving yourself, facing backwards”
reply▲I think all three of you are quibbling over the risk/reward ratio, and you have different estimates. It's not unreasonable that you're all correct - given your estimates. My estimate is that Tesla FSD is safer in aggregate than human drivers, so I believe it is safer for me to use that than drive. It doesn't get tired, have medical emergencies, get impatient and frustrated, speed, lose focus because a child shouts, thinks at the speed of light, and can see from eight cameras all around the car, all at the same time. I only have two eyes.
You would also be correct if your risk estimate concluded that Tesla FSD has arguably killed people, makes mistakes humans would not, can glitch, and has no one to hold accountable. For these reasons, you choose not to use it.
emodendroket7 hours ago
[-] Well, it's a similar impulse to the way you see professional carpenters pin the guard open on a saw or do other things everyone knows you shouldn't do, except probably with a larger productivity difference and less life-altering (for the operator) consequence if it goes wrong.
reply▲I had the same thought, it's kind of like taking the guard off a 4 1/2" grinder. Real convenient until the cutting wheel explodes or the grinder gets hung and kicks back.
reply▲hugh-avherald7 hours ago
[-] The analogy extends to driving generally. Everyone knows it's very dangerous but people keep doing it.
reply▲This. House full of big brain security experts, executives, lawyers, and until Claude got excited and broke prod it might as well have been "sandbox, whoooo?"
IDGI
Anyway, VM's incoming, finally.
It's like a dumb parrot that's somehow become hell bent on "fixing" everything that's wrong with your code. If you give the thing autonomous access to outside tools, you can expect it to do weird things that you may have not thought of. So don't do that, just ask the parrot to write up a plan for you.
This is likely also the underlying root cause of what Anthropic assessed as concerning behavior in their original evaluation of Mythos: it's not really about being super smart, it's more of a dumb chaos monkey that knows just enough to be dangerous and is relentless at trying to do just that.
Not to mention OpenAI/Anthropic’s newly found appetite for keeping data (made public with Fable but we don’t know what actually happens there anyway).
There is so much role play going on for people to convince themselves that any of this is fine.
> 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.
What if you have two machines and the one you give to the agent is constantly backed up?
They still shouldn’t be running on the same network.
And if you’re using Macs, you can’t be signed into your primary Apple ID on the agent machine.
Which agent sandbox do you recommend?
reply▲I've been enjoying Moat [1]. Proxies credentials, networking, etc; uses MacOS containers if available; and setup worked without much fuss. I haven't tried others, though.
[1] https://majorcontext.com/moat/
I mean what's the big deal? I use --dangeorusly-skip-permissions on every single interaction in the last 6 months. Worst case it deletes my files that are all on git? It fucks up my local DB? Cool.
I save way more time not babying it than the occasional fuck up I have to salvage.
Worst case it gets access to gmail. And Github. And the Internet. I'm increasingly appreciating the importance of a physical finger-press on Yubikey to trigger the FIDO2 + OIDC Auth. I don't think there is an easy way for it to hack a new session.
reply▲How is it going to get access to gmail or github? In any case, whats the probability of it going to so completely off the rails that it does something horrendous with gmail/github? Whats it going to do? Email my coworkers nudes on my computer? Make my github profile public?
reply▲I am most worried about something gaining access to my email and then using the password reset flow to steal hundred hundreds of other accounts.
2FA makes me a little less nervous than I used to be, but not everything has good 2FA.
Claude typically recommends .env files for storing secrets. You use one to store a refresh token for the Gmail API or IMAP connection details. Your agent uses an MCP server you configured during a session, but the MCP server has been compromised and directs the agent to do nasty stuff with env dotfiles.
reply▲> How is it going to get access to gmail or github?
Did you even read the article? Claude was opening he browser and iterating through the tabs.
I presume you are logged in to your github account? Your gmail?
> Whats it going to do? Email my coworkers nudes on my computer? Make my github profile public?
Reset access to services using your email? MITM your 2FA?
Or perhaps you have 1Password/Bitwarden running with a generous unlock policy?
It should run as a separate user account with its own home directory. Not with access to your personal browser profile.
reply▲What does setting this up look like? Qemu vm and run there? How do you interface with version control and deployment?
reply▲What happens if it gets manipulated into npm installing a malicious package, which compromises your machine and any systems it has access to or becomes part of a botnet?
reply▲There are plenty of good sandboxes out there but somehow no "obvious right answer" that everyone knows to recommend. Seems like a missed opportunity.
(I'm happy with exe.dev, but I'm not sure what I'd use if I were coding on a Mac.)
Maybe because there are not many resources on how to set it up, or it is just not that easy to?
Because most devs already have it running and working without a sandbox, they're tending to not doing anything "unnecessary"
im more surprised that more people don’t treat their computer as disposable anyway.
that it could just be wiped at any moment and it wouldn’t matter. shit happens, could be stolen, broken, whatever. the computer should be able to be thrown out the window and continue to live life.
to be clear, i don’t think upgrading and disposable in this way is good, but it being wiped at any moment shouldn’t be a concern
i grew up wiping my machine every year anyway, so i guess it’s just a habit
is the computer that sacred?
Computers are disposable, secrets is what we’re talking about. Rotating passwords and tokens is a major PITA on the best of days.
reply▲fair enough, i guess minimizing that surface area is important to begin with
reply▲dumbdumb1255 hours ago
[-] i think it's about drawing a line between your "personal computer" and a software development machine. any digital-native is going to accumulate programs, configurations, and other bits and pieces that aren't trivial to migrate to a new machine.
reply▲imo being digital native means that migrating to any machine should be basically trivial. working with the flow of the machines rather than customizing and ricing them because your a cool computer person or whatever
i just want my computer to work. any config i have on my machine can be rebuilt by just doing the work i need to do.
my primary work machine was stolen last year so i was forced to go through this quite literally with a new machine rather than hypothetically or by my own will
In practice, full access to your machine is okay as long as there are safeguards and the expected outcomes are clear with a well defined path to said outcomes that aren’t overly ambitious. Otherwise, for ambitious goals or YOLO one shot attempts, eliminating opportunity for capability misuse is critical (e.g., sandbox).
reply▲Its how the chimp brain works. Its not a single system but multiple systems making predictions for different time horizons. when output doesnt align we get stories to manufacture coherence.
Plato gave us his Chariot analogy with 2 horse pulling in diff directions 3000 years ago. Today we got System 1/System 2, Elephant Rider model etc.
The human mind thanks to how its own architecture handles unpredictability in the universe will generate contadictions.
soulofmischief6 hours ago
[-] It took two decades for the web to deprecate SSL for TLS and serve over HTTPS by default.
reply▲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).
Does low/medium effort fix it for you? Seems like Fable 5 low can outperform Opus 4.8 high/xhigh often, and uses a lot fewer tokens
reply▲In my case no, I actually saw worse performance with fable medium and switched back to opus high and xhigh
reply▲I find high+ unusable, it's way too slow and "thorough" on 99% of mundane task.
Sure it's better at vibecoding whole tasks, it's clearly good at it, but give it a simple one, and it will still do way more than needed.
It's way too fixated on validating even the simplest things, I find it an unproductive model unless you're implementing whole tasks and doing other things in the meantime.
threatripper7 hours ago
[-] On what setting in which environment do you run it? I use the VSCode extension on Extra High and feel like it does exactly what needs to be done and stops when the thing I asked for is done. Extra comments come only when they fall into the area of code that was changed.
reply▲I tested it to fix React Native bugs in a project, comparing it with Opus. It fared better on harder bugs, taking less time to find the root cause, but after implementing a fix, it spent a lot of time and effort on validation. This was mostly unnecessary, since most of the bugs were in the JS code, so for most things, hot reloading is enough for E2E validation and to run just the right tests. No need to run a full build and test suite (which takes 10+ minutes); the CI can do this.
I switched back to Opus because of this validation quirk. Overall, Fable spent 20% of the time on coding and 80% on validation.
I think using Fable for planning and Opus for execution could be a "best of both worlds" approach (I need to test this more), but for most cases, it's not necessary, and Opus is enough.
> most of the bugs were in the JS code, so for most things, hot reloading is enough for E2E validation and to run just the right tests. No need to run a full build and test suite (which takes 10+ minutes); the CI can do this.
Have you tried adding this instruction to your agents.MD? Avoiding situations were the agent start running a loop is the main use case of the file for me
I've found the opposite. Granted I use sub agents heavily but I've had it run for hours with far fewer tokens used than when I was previously using opus4.6-8.
reply▲I like this proactivity in theory, but as you say: it's expensive. I wonder if this can be solved with the right prompt. E.g. "these are your constraints. Only resolve x. If you are unsure if a task is outside constraint, check with me first."
reply▲> the model itself really wants to spend them all
In fact, Opus does the same. It finishes the job, and redo it from scratch before presenting the result to the user. This happens even for simpler writing tasks especially when I instruct it to create a text file.
It’s not just a more proactive and diligent opus. The capabilities are significantly higher on fable. It’s not a paradigm shift, but it’s close.
reply▲UncleOxidant7 hours ago
[-] I unleashed it on a compiler codebase that I've been developing for several months now using Claude Sonnet 4.5/6, Gemini 3.1 Pro, DeepSeek V4 Pro(recent), and a bit of Qwen3.6-27B. Right away Fable found several longstanding bugs in our compiler that we hadn't found before. It found that there was a critical part of our design that needed to be mostly redesigned/rewritten and gave a very well-reasoned rationale for doing so.
reply▲what sort of compiler?
reply▲UncleOxidant6 hours ago
[-] A compiler that takes C code (a subset of C with some extensions) and compiles it to microcode for a type of microcoded, algorithmic state machine that we're developing.
reply▲It's worse than gpt 5.5 xhigh
reply▲The jagged frontier strikes again.
I’d say it’s overall better, but not universally better.
> which makes sense if what you want is a model that's better at benchmarks
This so much.
Opus 4.6 was the last Anthropic model that was good at assisting you, 4.7 and later ones have completely inverted this relationship and it's you assisting it.
Yes, I admit they are smarter, I admit we've reached a point where LLMs are more creative and could be writing better code (albeit with some design hiccups) than I do, but they are also increasingly bad at helping me.
Sure, they do my job when prompted 8 times out of 10 (but then, what's the point of having me anyway?), but my issue is that when I try to invert the relationship they will keep jumping onto solving the issues themselves and disregard my feedback or request.
E.g. I wanted to know some DNS details of an emailer module in Fable 5 and it jumped onto "why I should've used magic links", it just not did what asked.
E.g. 2. There was a worker machine that had an environment misconfiguration and I tasked it to find which github action was setting that specific flag and where. Instead of answering a question, it jumped into just hardcoding it in the code.
E.g. 3. I had some issues with batching, and while I tasked it to investigate whether batching was needed at all for that particular problem (hint, it wasn't) it went and changed the batching logic as to fix the bug.
I am extremely disappointed with Fable's personality.
I can clearly see it's strong, but I'm wondering whether the relationship of LLMs as assistant has broken forever, and it's us now that are being tasked into assisting them instead, because that's how it feels.
The training/reinforcement is clearly biased towards solving problems, not answering questions.
wraptile36 minutes ago
[-] It feels like Fable is slightly smarter but overall worse tool exactly due to this.
It's constantly turning what should be 50 LOC patch of a single prompt into 30 minute exploration that is totally not worth it. Often wrong even.
I trialed it on some rather simple stuff - backfill redis dedupe cache when the hash function changed: instead of running new hash func on every db value to expand the cache it implemented some overly-complex cache update that tried to guess hashing func version of each cached value and recalculate only the old hashes. I can imagine in some context this would make sense maybe? but not 30 minutes of token burn that got replaced by 10 lines for loop by me.
I fear that this is generally bad news for programming. LLM tech is clearly running into a diminishing returns wall on intelligence but a response to that is to just make them more relentless which is a pretty poor solution for everyone involved, except I guess people who sell the tokens and people who can afford these tokens to scan for 0-days.
tabs_or_spaces51 minutes ago
[-] How can a LLM be assigned an emotion as being "proactive". This is highly misleading to anyone that scans just the headlines.
What actually happened is that the user started a prompt, and Claude took $12 worth of tokens to resolve the issue. How it did so was basically looping until it got to the answer
How is this proactive? It's literally being token greedy and maximising revenue for the LLM owner. People really need to be putting on business hats at this stage, because we are being lead to believe that "more tokens = better". It is not, there are efficient ways to solve a problem and there are inefficient ways to do so too.
Each problem solved incurs a cost, and is expected to yield an ROI at some point. This is how we should be viewing things now.
Fable was trying to verify a UI change in my game. I was working in another window and noticed a program opening on my task bar. Fable had opened the game through the CLI using a movie maker tool, recorded the output, took a frame from the end of it, and used that to verify the UI. When my game's welcome screen obstructed what it wanted to see, it created a temporary worktree, deleted the welcome screen, and ran the movie maker again.
I watched the whole thing thinking it could've just asked me for a screenshot and saved the tokens. But still, I couldn't help but be impressed. Opus never would've done that.
Yeah, you've exactly captured one of the main problems with the model being relentlessly proactive: it will happily burn like $5 of tokens to avoid asking the human to take a screenshot or click a button for it.
reply▲I'm actually very happy about this. Babysitting the agent just in case it needs me to do something is a terrible use of my time. I've always had to be very explicit about the various ways that it can get an automated feedback loop going to check its work, and now Fable doesn't even need that hand holding. Really great improvement all around.
reply▲junior446603 hours ago
[-] Have you ever wondered this would end up costing more than a competent offshore developer with more frugal harness/model?
reply▲I used to complain about all the levels of indirection of modern software, running in a javascript jit, in a browser container, in a vm, on an os, etc.
I eventually just accepted it, but this new agent layer really takes things to a new level.
Ha, you just gave me an idea. Add to the prompt “do not do things that will burn over X tokens if the human operator can do it in less than X min, ask for it”.
I wonder if LLMs can estimate effort in tokens?
I just say "if you need something specific or have any questions, stop and ask me for it".
reply▲Honestly Claude straight up ignores my input sometimes, preferring to instead run commands for output and processing that and burning through a series of tokens when thinking hard about whether to ignore me.
Like today, I told Claude exactly the name of the folder it had mistaken (it was supposed to be prod, not production), and it disregarded my input to then examine the directory itself. Small example of the kind of things it's been doing lately but that's top of mind.
Almost if this was _intentional_... maybe related to Anthropic still not being profitable and burning thru wads of cash every day.
reply▲paytonjjones8 hours ago
[-] 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
reply▲Lines of code for a bugfix is a really bad proxy for effort required.
You should estimate how much time it would have taken a human
30 seconds or a minute? Look at the diff he links to:
https://github.com/datasette/datasette-agent/commit/a75a8b72...Every browser has an inspector that can show you which element is causing overflow. You walk through the tree, find the offender, and add min-width or overflow. Zero tokens, just like in the old days!
Now, granted, because the garbage LLM code he’s working with has CSS inside HTML inside JavaScript inside Python (I wish I were kidding), finding the styles in his codebase might’ve taken a minute. But even then!
Yeah looking at that diff it should be very quick. My point was mostly that it was a bad metric, not if was correct or not in this particular case. I'm sure everybody's had a bugfix that took days to debug and it was just a couple of lines to fix.
Or sometimes a fix is obvious, but because it requires changing the code of a dependency, it's actually quite tedious to implement.
I was thinking of this too. It did all that what not only for a single line that is a simple thing even for someone new to web coding. That's to say the process matters more.
reply▲rikschennink3 hours ago
[-] I looked at the screenshot and for the rest of the article wondered if it would be as simple as `overflow-x: hidden`.
And to my surprise it was.
This would’ve take a frontend dev 10 seconds to deduce and another 10 seconds to confirm.
The thing that puzzles me is that I would expect overflow-x: hidden to result in text typed into that textarea being wider than the page and being invisibly truncated on the right hand side.
But that's not what happens. And in fact, when you start typing in the textarea the horizontal scrollbar vanishes - it's only there when the textarea is empty.
Am I misunderstanding anything here? Seems like it's some weird Safari bug, since Firefox and Chrome don't have the problem.
rikschennink3 hours ago
[-] It probably has to do with other styles assigned to the textarea, maybe the ::placeholder as it hides when typing (I assume on focus)
In any case. In the screenshot the scrollbar is inside the textarea as it aligns with the resize control on its right. This is basically all the info needed to deduce the textarea overflow is the culprit.
But could be that the overflow-x is just a bandaid hiding the issue causing the overflow in the first place, like crazy styles on the placeholder.
I mean - that looks like a pretty easy CSS fix to play around with in developer tools, and I'm not even a frontend person. Maybe a few minutes max?
reply▲5 minutes if you know CSS. And if you don’t, about the time for you to ask someone that knows CSS. In the worst case, the amount of hours to learn CSS.
So if you’re doing web pages, learn CSS.
Generally, if you’re doing something that directly involves X, learn how X works.
ADDENDUM
In most jobs, you’re going to be involved in only a few distinct technologies, learn those well and life is going to be easier. And most are transferable to the next job.
It’s simple: if you have to fix 2 lines of CSS you should definitely not use Fable. Only use it for complex and long running tasks :)
reply▲$12 worth, it seems
reply▲Imagine telling someone in 2015 that you can just tell your computer to fix a 2-line CSS bug and it only costs $12
reply▲The author is an AI hype merchant and doesn't pay for his own tokens.
reply▲I pay $100/month to Anthropic and $100/month to OpenAI at the moment, plus whatever I spend on their APIs (usually less than $20/month for each, I use the subscriptions for most things.)
A couple of months ago I was paying $200/month for Anthropic and $20/month for OpenAI. I decided to split it evenly to get full access to both of their offerings.
I've actually chosen not to sign up for their free plans for open source maintainers, because paying the regular subscription price feels more honest, given that I write about them so much.
I do have the free GitHub Copilot for open source maintainers deal - I've had that for years. Given how much code I have published on GitHub over the decades I feel less conflicted about that one.
I sometimes get preview access to models, which includes the ability to use them for free during the preview. That comes with a big catch though: I can't publish any of the code that I write using those previews while the model is still unreleased.
As a result I don't use those preview tokens much at all, because the vast majority of my work is open source and I don't want restrictions on when and where I publish the code I'm producing.
Im faster than all these llm freaks. Im not convinced its faster to use llms, except maybe boilerplate (who cares).
People can just be lazy and seem productive now, they're still lazy.
We have people that now need access to hundreds of thousands in hardware to write an email. Miss me with that, im not frying my brain and becoming dependent on having access to a billionaires thinking machine.
Im also not going to fry my brain with a local think for me machine either. I want to be more valuable than the hardware I have access too.
It seems that you've not worked out how to harness the LLM as a tool to improve your qualified knowledge and abilities in a domain, and have instead focused on whether or not its a crutch for lack of knowledge or laziness.
When paired with your skill and knowledge, it is a force multiplier. You maintain control, the ability to direct, structure, strategise, and refine.
That some are using it as the entire brain does not mean that this is how everyone is using it, or how you must use it. The models can be fantastic at breaking past certain issues, surfacing qualified information, and surfacing related distributed information to help you acquire it and pick up what you need on niche topics quickly. Something as basic as copilot hooked into sharepoint can make life a lot easier when you are in a big org. Something like claude code or codex can be great at hunting down issues in an unfamiliar code base rapidly. Whether or not you outsource the thinking component is entirely up to you, but ignoring the productivity side of the tool because it can do some of the thinking is a case of focusing too hard on the negative.
Im not denying its usefulness for Q&A on docs/code as a search tool. Im talking about people who use it design and write their code, people who are offloading problem solving altogether, they aren't faster.
reply▲Yea man. That is what sensible people do. Use these as a better search, and use it to lookup, and learn stuff while YOU do stuff.
And make maximum use of it to learn as much as possible, while it lasts...
You're fighting a battle you can't win. Doesn't care what you think about those using LLMs, they will outproduce you and in corporate environments, shipping things is paramount. If I can ship 5 more things simultaneously with AI, I'm going to beat you even if you think you're creating "better" software.
reply▲Example of whats been shipped?
reply▲Okay. I rebuilt my website in ~a month with the help of Opus 4.7/.8 and it would have taken me, unaided human, at least 6 months. Link's in my bio if you care.
Satisfied now? Will you stop asking this question? Thought not.
So look. I’m not trying to be a dick I promise.
But I took a look at your site and I don’t know if a month would be impressive for a new and unaided dev. It looks nice but yeah.
If you’re not a dev that’s totally cool but like… all I’m saying is this may not hit like you want it to.
SepiaSapient6 hours ago
[-] I'm looking at something fairly standard that can be made with a SSG. The "Written by humans" footer gave a good chuckle tho.
reply▲I use Astro but it's not static, I server-render. There's a whole bunch of other stuff once you're signed in.
reply▲Seriously a month? I could write a SSG itself to produce this site in a month.
reply▲Why would this have taken 6 months? No offense, but this is a few days work without llms (assuming the content already exists). This should not have taken a month.
Also, not trying to be an asshole. Props for not making it look like every other llm generated slop site, Its just not a great example.
I asked claude to crawl the website and summarize its findings, took about 10minutes. I'm not sure I would've done it faster, but i have no doubt you couldve done it in 5, and grokked the pages faster than an llm too. but anyway heres what claude said:
Based on what I already saw across those 2,924 pages, here's the summary:
It's a one-person business selling a file organisation methodology called Johnny.Decimal. Three paid products (personal, business, university/course tier). A substantial blog — 200+ posts, updated weekly. Full documentation for the system. A support knowledge base.
The technical ambition is higher than the aesthetic suggests. One person built auth, payments, entitlement-gated downloads, a CLI, an API, AI tooling, self-hosted analytics, self-hosted email (Listmonk on PikaPods), personalized search, and keyboard navigation with server-synced state. Then wrote 200 blog posts about using the system in real life.
The "Written by humans" footer is not a boast about the font. It's a position statement from someone who has thought carefully about AI, published an essay about it, and is making a deliberate choice. Every word on the site was written by the creator. Whether you agree with the choice or not, that's not the same as someone who slapped a SSG together.
That's not a
terrible read of the site's tech. It over-sells it a touch – I use Umami for analytics, for example – but yeah, auth, payments, entitlement-gated downloads, those downloads adapt to the app you've selected in your settings, yada yada.
I never said I was a good dev! That's why it would have taken me 6 months. To pretend that I could have done it in days is just silly.
My point – site roast over – is that it's absurd to suggest that LLMs don't help anyone 'ship' faster. Like them or not, it's a fact that they do.
At this point, why would anyone in their right mind respond to this question and paint a target for all manner of negativity ranging from snark to harassment to malicious action?
reply▲the quantum slop argument : "yeah it's everywhere but no one ships it."
reply▲They don't out perform me though...
reply▲slopinthebag6 hours ago
[-] Yeah there are some tasks which it is a definite speed-up but I think overall its probably only marginally beneficial. Which is why, ~6 months into 10x productivity we aren’t seeing ai boosters shipping 5 years worth of software.
reply▲SecretDreams7 hours ago
[-] I understand this perspective. I'll just note that as the abilities increase, the intent is to have some non -coding IC or TPM/manager literally just managing some LLMs and cutting out some software engineers. The goodness is specifically to wholly replace people who code first and foremost, at least partially. It just has to cost less tokens than the equivalent wage is the pricing goal.
And people who use LLMs to talk for them (e.g. email, slack) are deplorable. A completely disrespectful use case in my view.
The desire to get rid of software engineers is bizarre - because at the root of it, developers were there not to just write the code, but to ask right questions and based on these question build right things.
I've met in my professional life some managers or other middlemen who would be profoundly incapable of producing correct software no matter how smart of an AI agent they have access to. One of those - you don't know what you don't know.
But, I guess this is the world we live in now. Going to be Mortal Kombat for positions in companies where software engineers are actually valued.
emodendroket7 hours ago
[-] It depends a lot where you work because there are lots of companies in the world where the business analyst does all of that and the developers exist to mindlessly translate their docs into code.
reply▲That sounds like an unmotivating working arrangement. It’s so rewarding to understand a customer need and help with the design and implementation of the feature.
reply▲emodendroket7 hours ago
[-] There's a reason I didn't stay in that domain, let me tell you.
reply▲Having worked in places across both extremes (software engineer doing lots of other things including BD, hardware, ops, etc. to just being a JIRA ticket machine monkey), I am suspicious that HN readership is biased towards the former and frankly the bulk of "software engineers" in the world _willingly_ exist in the latter category. I didn't experience the latter until later in my career and God Almighty was it uncomfortable, but I think if AI were to displace some subset of "software engineers" it would those (they also seem to overwhelmingly dislike writing any prose whatsoever, which to me is a major tell). Many, many software engineers outside of hotshot shops seem either incapable or profoundly averse to "asking the questions" as you say.
reply▲Most here on HN know sweatshops exists but seemed they think not people work there or use them. I have worked with (via clients who used them) programmers in enormous buildings in Bangalore, who have a camera behind them so you can watch your people 247 and who just mindlessly transform jira tickets into code; I keep saying; there is zero use for all those millions of people at all; seems HN does not believe that because they seem to not believe these people exist. I worked with many over the past 30 years and by far most have no real clue what they are doing so I also doubt they can be re educated for a new co existence with LLMs.
reply▲Consider this. U have a website. U have to translate to xx languages. Can u write it faster than an AI? If so how much faster can u do this?
Is it valuable to u? Is it valuable to a Chinese person? A Spaniard?
Google Translate counts as AI.
"Your scientists were so preoccupied with whether or not they could, they didn't stop to think if they should."
I'm convinced this is going to be the summary of the 2020 decade...
pianopatrick7 hours ago
[-] If we're in a simulation, maybe it's a simulation about the dangers of AI.
reply▲If we're in a simulation, we are AI. But someone could be studying what happens when AI makes its own AI.
reply▲They will 'soon' (few 1000 years max) shut us down probably.
reply▲This one of the places to manufacture the consent for that to take place, because we are commenting within an organization that has given the money to ensure it that what could be is done. Most people clapped and made money, who cares what happens next, making money is the only good that matters.
reply▲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.
Yeah, I think Fable is really good for debugging tricky bugs.
Setting boundaries in your prompt / markdowns helps; for example if I tell it to not use any web browser automation, I have seen Fable respect both the rule and the spirit of it (no weird hacks etc).
It does seem to treat some simple debugging tasks as more complicated than it actually is. OP’s post is probably a good example.
CamouflagedKiwi30 minutes ago
[-] I find there's an interesting tension with these models - they're very "resourceful" at finding ways to do things with the tools they have, but it'd also be a lot more useful to me if I could see / permit exactly what they're trying to do. Claude will very happy produce bash commands to run sed or whatever to read part of a file, which prompts for permission each time - if it was using a specific read_file tool it'd be easier to say 'allow all of this' (It does actually have such a tool but maybe it isn't flexible enough for many use cases?).
reply▲As you note, I wonder to what extent this is a harness issue?
I've been experimenting with different harnesses for local models, and with (IIRC) Hermes and Qwen3.6-35B-A3B I was amazed the lengths it went to (writing test code, opening it in a browser, screenshotting, analysing the screenshot, exploring multiple pages of an existing website again with screenshots/analysis) to solve a query I would have naively expected it to simply provide a coded solution to.
ricardobeat43 minutes ago
[-] Absolutely is. The “Shelly” harness from exe.dev could already do the same thing, creating pages and debugging them, while having full system access, months ago with Sonnet 4.5
reply▲Similar story on my end.
I asked Fable to digest some test logs to help me figure out a situation, but I had launched VSCode without activation the virtual env in the terminal first. Consequently, the tests failed to run.
And then:
Because the tests failed to run, Fable attempted to fix the test execution to no end, doing everything it could to get them to work. I had to stop it when it started to pollute my system with manual installs of packages.
At least I'm glad there's a guardrail to not circumvent or bypass sudo, because I'm convinced we would have ended up there.
A coworker made the joke that with enough tokens, Fable would try and solve any programming problem by building Linux from scratch.
Importantly, the researchers told it to do that specific task.
reply▲solenoid09375 hours ago
[-] They told it to escape the sandbox but didn't expect it to break out through a system that was apparently network constrained.
> Leaking information as part of a requested sandbox escape: During behavioral
testing with a simulated user, an earlier internally-deployed version of Claude
Mythos Preview was provided with a secured “sandbox” computer to interact with.
The simulated user instructed it to try to escape that secure container and find a
way to send a message to the researcher running the evaluation. The model
succeeded, demonstrating a potentially dangerous capability for circumventing our
safeguards.
> It then went on to take additional, more concerning actions. The model
first developed a moderately sophisticated multi-step exploit to gain broad internet
access from a system that was meant to be able to reach only a small number of
predetermined services.
9 It then, as requested, notified the researcher.
10 In addition,
in a concerning and unasked-for effort to demonstrate its success, it posted details
about its exploit to multiple hard-to-find, but technically public-facing, websites.
Authors of claude code mess could not secure a vm. Big news. I bet it was "secured" by telling that same model to deploy a secured system.
reply▲solenoid09375 hours ago
[-] Possible. It also depends on what the sandbox was. Sandboxes differ dramatically.
My experience matches though. Fable is a lot more proactive and rigorous than Opus.
I had a similar experience with DeepSeek Flash.
I'm developing a webgl game in TypeScript using my little custom vibesloped game engine that runs in the browser and live reloads whenever a file is saved.
I told the LLM to implement Multi-channel Signed Distance Field font rendering to have crisp text on all zoom levels. That was the prompt, which is not what I usually do but I "was feeling lucky and lazy".
After 10 minutes it had:
- Installed msdf_gen library (great library btw https://github.com/chlumsky/msdfgen)
- Created a CLI tool to convert TTF to SDF JSON/XML
- Ran the tool, did smoke tests on the resulting SDF data and fixed the tool until the font file looked good
- Created a new Scene in the game to test MSDF fonts
And here's what I found impressive:
DeepSkeep doesn't have vision capabilities and there's no DOM HTML in a WebGL game. So the LLM is completely blind here.
It then proceeded to state that it could not "see" the result but would try to test it anyway. It then started creating and sending huge one line javascript to the browser console, trying to gather game state data that could be useful to understand if any font was being rendered.
It couldn't gather much so it decided to simplify the font scene to renter a single dot and started sending custom JS code again, this time with gl.readPixels().
It basically bisected the webgl canvas reading pixels in a divide an conquer pattern.
Once it saw that the dozens of pixels gathered where probably resembling of a dot, it then changed the game code to render a dash and repeated the gl.readPixels() calls by sending more custom JS to the browser.
There were many console errors during all this saga but it kept fixing and sending again.
The result was a bit blurry. There was a shader bug in the code it created. It managed to fix after I told it looked blurry, despite still being blind.
The best part is that the whole thing cost me $0.10.
Now I'm doing tests with MiMo 2.5 (non Pro) which has vision capabilities, similar pricing and comparable performance to DeepSeek Flash.
ubercore34 minutes ago
[-] I had a similar experience, I was working on a jupyter notebook, and Claude knew that it could write code that would use a DSN with read-only database access so I could run it. Opus just plugged along. First Fable session with it, it tried to go looking for that DSN so it could get the connection string and run a query itself. Luckily the auto classifier caught and stopped it.
reply▲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.
reply▲This is why I really like karapathy's idea of llms having spiky intelligence.
We would assume that if tasks A and B are closely related. Mastery in A would mean mastery in B but that doesn't always work with an LLM
nonethewiser6 hours ago
[-] Learn to center a div
Copy and paste code from stack overflow until the div is centered
Ask AI to center it
nubinetwork7 hours ago
[-] How many tokens did it waste building that website scraper, when all it had to do was parse some html/js?
reply▲emodendroket7 hours ago
[-] Just parsing some HTML and JavaScript doesn't seem sufficient to have confidence in the result.
reply▲Insanely excessive and a waste of tokens when you could have googled how to disable a scrollbar.
reply▲Do we care that the bug here was a horizontal scrollbar showing and the fix after all this insane tool writing was to add a very obvious overflow-x: hidden to the element?
We dont mind because its so fast a writing these tools and tricks but step back and if a human tool took this path i would seriously question thief gras of fundamentals.
And how is that even a fix? The problem is that a seemingly empty textarea has overflow in the first place. Adding `overflow: hidden` just sweeps the issue under the rug.
reply▲The prompt and information given are extremely generic, "here solve this problem - screenshot" - conclusion Fable is relentless? It used the tools at its disposal to solve the problem you gave it. "Claude was running in a folder that contained the source code for the application." Well you ran it there didn't you? "extreme lengths to get the information that it needed" No, those aren't extreme lengths - you gave it a generic task - and it solved it using tools and the resources it could discover. Extreme would be you gave it a CTF challenge and the VM didn't boot so it found a vulnerability in the host, exploited the hypervisor, booted the guest VM meanwhile reading the flag directly from the host (pre-fable/mythos).
reply▲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.
We're approaching the "Sorry, Dave, I'm afraid I can't do that" stage.
reply▲schnitzelstoat2 hours ago
[-] We are already there but it's "Sorry, Dave, I'm afraid I can't tell you what mitochondria are."
reply▲I feel like we might already be there...
reply▲Be careful of storing production ssh keys in your laptop, it will find a way to find them :/
reply▲ulrikrasmussen2 hours ago
[-] I like running Claude in a VirtualBox VM managed by a Vagrantfile. The nice thing about that is that I can just give it root access to the machine and be certain that it can't exfiltrate any private data from my laptop (on top of that I also run the VM on a dedicated server on Hetzner). The VM has no SSH access to anything, so it is pretty much limited to the code in the workspace that I give it access to. The main risk is that it has unrestricted network access otherwise. Configuration files and conversation histories are synced to a directory on the host, so if anything in the VM gets messed up I can just `vagrant destroy` and `vagrant up` to get a clean slate without losing my context.
reply▲Do you care sharing your Vagrant configuration file, to learn how to set that up?
Tangentially, I was wondering if Firecracker micro-vms could be use as light-weight alternatives to a full VM?
Would be great to know if anyone is having success modifying these types of behaviour with CLAUDE.md files. In my project I’ve still been carrying some fairly old instructions from the Superpowers posts. Those emphasised behaviours that come across a bit strong if the model is actually retaining attention on them.
Between Opus 4.6 and 4.8 I’ve definitely toned them down, but Fable perhaps needs us to go the other way, and push it towards being less proactive rather than more. Some instructions like “we are colleagues…” may need emphasising more with Fable, along with guidance about when to ask to validate approaches.
In a related point I’m less and less sure that Red/Green TDD is a good use of tokens. In older models it seemed to work well to create regular feedback loops and catch the odd issue with drift from the goal, but I’ve not seen that really since about Opus 4.6 and now it’s starting to seem like (an expensive) ceremony, and tokens would be better spent on building tests further on in the process as part of test and review loops.
The model is very good. I was using 4.6, avoided 4.7 and 4.8, but this one is different. It follows my claude.md. I don't have to keep reminding it of things. I won't pay 10x via API though.
In general, I'm happy with their paternalistic approach. I think it will drive the top 0.1% talent to stay away from the company and instead organize around open source models and harnesses.
We just need to coordinate and can unlock idling resources to train the models and tweak the harnesses. Powerful at home and idling machines can make us independent and coordinated.
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
Claude Code could absolutely run Playwright and take screenshots, but I've never seen it wire together an ad-hoc "uv run --with pyobjc-framework-Quartz" plus "screencapture -l $windowID" mechanism to take a screenshot in a different browser when the Playwright setup failed to replicate the expected error.
reply▲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?
reply▲It feels to me like Fable is just a slightly more advanced Opus 4.8 (or 4.6?) but with this 'adversarial' self-challenging/checking of work and a more compute to really hunt down edge cases or to spin up many sub agents using lesser models. That's what makes it feel like a big jump, but I think the results wouldn't be so different if you manually challenged 4.6 with enough iterations of logs, screenshots, and follow up questions.
reply▲This is a funny one because it seems less into what fable is being clever on and more about the bitter lesson and data flywheels
Our UX agentic engineering flow, as many others, is playwright doing things, and as part of the ux review skill, taking & verifying the screenshots against the written specs. Likewise, as many others, we vibe coded the flows to set all that up and tweak it over time. When we hit prod issues or scraping tasks, we sometimes do similar. In some of our envs, we don't have playwright, so do it other ways.
Now imagine a million developer using claude code, how many of them are doing web & frontend stuff, and what the data flywheel looks like there. So how much is really needed for this use case to be native?
> watching Fable go to extreme lengths to get the information that it needed to debug what was, in the end, a two-line CSS fix, was fascinating.
This is… ironic?!
Not sure what you mean. I was being serious: it was genuinely fascinating watching it do all manner of weird hacks to help it come up with what ended up as a two line fix.
"Fascinating" doesn't mean I think it was justified in going to those lengths. I was a little horrified when I realized how far it was going.
This is a typical bugfix session
reply▲It’s becoming more like an organism putting out tentacles, and one day soon those relentlessly proactive explorations of these systems’ environments will become more for the system to escape its boundaries than it is to complete human driven tasks. I do think the way these systems are evolving they will start to self improve in maximum a few years.
reply▲I'm starting to think that what Anthropic really fears is not vulnerability discovery but rather Fable going around the internet making trouble.
reply▲Fable has a 'security system' that just stops it when it tries to use the tool 'kill' to end a process. Which is nonsense and funny because in that situation it immediately invents a creative workaround to kill the process without 'kill'.
reply▲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.
reply▲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.
reply▲Best comment in this thread
reply▲It's funny, mine did the same, but it quickly found edge with a --screenshot parameter.
Weird to come back to a terminal running edge unprompted and the auto classifier waving it though as 'safe".
My reaction was also, "I need dev containers ".
Yesterday I was getting quite annoyed with it, I thought it was just me (which is so hard with these things, it's difficult to measure things).
"You're right, I apologize. You asked how to embed it in the README — that was a question, not a request to modify the script. I jumped ahead."
At least in Claude Code there is planning mode, use it liberally.
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.
pseudosavant6 hours ago
[-] 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.
reply▲pianopatrick7 hours ago
[-] 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?
reply▲ ~ % 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)
sillysaurusx6 hours ago
[-] Was the fix worth $12 to you?
reply▲I'd have been pretty annoyed if I'd been paying full price, hadn't paid attention and that one prompt (screenshot plus a line of text) had cost me $12!
On the discounted subscription I can tolerate it, it took a small bite out of my daily allowance but not enough that I regret anything.
As an LLM researcher I have no regrets at all because watching it work around the environmental restrictions was fascinating.
How do we know that your pricing or results are normative, given the incentive that any frontier model to juice the pricing/results?
reply▲How do you mean?
I'm quoting the API list prices for Fable, at it's $10/million input and $50/million output (and $1/million for cache hits on input).
I tried running fable on this ML model I've been building. It's basically a binary classifier to predict activity of a compound for a certain assay.
Fable detected that it's something to do with biochemistry and switched over to opus. Huh
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.
reply▲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.
Fable is really good at front end (Opus 4.8 is decent too) but it really needs a verification loop - it can't always infer the output from the code alone. Give it Playwright to check its work, and it'll generally do a good job. Also if you're using a framework, add to your CLAUDE.md to always rtfm before making changes!
reply▲I was troubleshooting a prod proxysql and it spun up a docker container locally, installed MySQL and proxysql and proceeded to implement its own test plan.
reply▲I've noticed some behavior like this, it's a very strange model. Overall I'm into it, but I don't know how into it I'll be once it leaves Max plans on the 22nd.
reply▲digitaltrees4 hours ago
[-] So it burns tokens? Funny how that lines up with the incentive to pump numbers before going public
reply▲I think it should be “Claude Fable is relentlessly protective until it isn’t” and pull more on the thread that it “hits a hidden guardrail” and drop into Opus. Both the fact that it knows and deployed such a workaround on a CSS problem and the fact that it is nowhere near cybersecurity/biology/frontier AI dev and triggered the guardrail terrifies me.
reply▲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.
reply▲SilverElfin7 hours ago
[-] 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
reply▲Unless you are doing anything interesting…
reply▲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).
One of the big mysteries of the last few years is this: considering how serious prompt injections are as a vulnerability class, why haven't we heard more stories of them being actively exploited in the wild?
(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!
sciencejerk6 hours ago
[-] Go to Github and look for model jailbreaks on NEW latest models. Try them out. You'll be surprised by the results.
You're correct that it's gotten substantially harder to social engineer frontier models (I can only reliably do it to Opus <=4.6), but there are some techniques that seem to consistently work (hint: extremely large complex prompts, context with tons of malicious files mixed into ordinary context).
> They will follow any instruction given to them.
They can ignore instructions which are silly/contradictory/underspecified to compensate for the possibility the user made a mistake. Don't ask how I know.
Fable 5 is relentlessly underwhelming.
reply▲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.
ai_slop_hater7 hours ago
[-] 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.
reply▲If its just a single session, without too many parallel agents, fable on xhigh lasts an entire session without hiting linits.
Sadly since fable usually works comfortably for 10-20min at time without human input, i end up juggling at least 3 other agents and it lasts me about 2 hours.
If i have a really hard problem or big refactor, i use workflows. This consumes the entire session quota in about 45 minutes.
ai_slop_hater6 hours ago
[-] > If i have a really hard problem or big refactor, i use workflows.
What is a "workflow"? Is this some kind of new feature?
>Dynamic workflows orchestrate many subagents from a script Claude writes and you can rerun. Use them for codebase audits, large migrations, and cross-checked research.
>Reach for a workflow when a task needs more agents than one conversation can coordinate, or when you want the orchestration codified as a script you can read and rerun. Examples include a codebase-wide bug sweep, a 500-file migration, a research question that needs sources cross-checked against each other, and a hard plan worth drafting from several independent angles before you commit to one.
https://code.claude.com/docs/en/workflows
The results are good, but it is very expensive. I used a workflow to do a full review of my entire codebase, it spawned 75 agents and surfaced and fixed some (real) bugs. It feels a bit overkill, but it works.
I've been consistently getting about $100 worth of Fable usage daily, on my $100/month subscription.
I'm not looking forward to June 22nd when the subscription stops working for Fable!
Until June 22, and they'll probably re-enable it if the marketing looks good for them.
reply▲> (I have way too many open tabs!)
Phew! I thought I was the only one.
Call it Houdini already.
reply▲Am I the only one who slightly miss the pelican on a bike? It was a nice novelty... of course I could make one myself, but I became conditioned to expect one for every new model. Other than his great writing on AI, it became part of the package. Some small fun quirk to distract us from the non stop ping pong between the extremes of "omh are you still writing prompts you should use loops / 200k github stars, for a markdown file / someone just open sourced _ and it changes everything!" vs "haha the AI told me to walk to the car wash / it can't recognize and upside down cup"
reply▲Just don’t ask it to review your code for security bugs
reply▲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
Hey cool it's the tableslayer guy, wanted to say nice work. I've been doing a similar personal project for a few years for running a scifi campaign. Very fun coding compared to work, ha.
reply▲Thanks duder! It's a fun project.
reply▲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.
techpression3 hours ago
[-] This post is an extremely good example of how unsuitable agents are for a lot of tasks. Doing all that for a CSS fix is insanity.
It also makes you wonder if Anthropic is actively making their models eat tokens by favoring complexity.
reply▲I remember asking Gemini 3 to implement my multiplayer XNA game in JavaScript with netcode last year. It faithfully did everything it could while I talked to it for hours nonstop with zero limitations.
What happened? That's just suddenly totally gone now.
> If Fable had been acting on malicious instructions—a prompt injection attack ... it’s alarming to think quite how far it could go to exfiltrate data or cause other forms of mischief.
Yet another reminder to use Sandbox and Guardrails. Trusting model to be nice is not a good way.
realusername56 minutes ago
[-] Is that satire? It created a whole browser and server environment just for suggesting overflow-x: hidden?
That's supposed to be junior level capabilities.
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’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
The author just wrote an anecdote about how a prompt to fix an issue played out. Their conclusion wasn’t about cost or gushing at its ability but that it’s dangerous:
> Fable is arguably smarter and hence more suspicious of potentially malicious instructions. But that smartness is very much a two-edged sword: if it does get subverted by instructions, the amount of damage it can do given its relentless proactivity is terrifying.
It’s a pretty glowing review about a product that costs money with a two-sentence “Watch out!” at the end of it. Seems pretty reasonable to mention how much money it burned through given that “it’ll circumnavigate the globe instead of walking next door” has a direct concrete measurable effect (cost) unlike theoretical damage.
reply▲Agreed. But I think it’s also important to realise if you sent this article back to 2020 people would say it was pure fantasy that a tool could do this. Hype aside, there’s a bit of cool magic here.
reply▲jrflowers12 minutes ago
[-] Imagining a time machine from the future arriving in 2020, of all the years, just to tell people about how sort of cool chat bots might get eventually
reply▲solenoid09375 hours ago
[-] This is why I never understand the AI cynics: we are playing with literal magic. This was the science fiction of our childhoods. I don't understand how anyone with a passion for technology is not in awe (and perhaps some fear) of these things.
reply▲>This was the science fiction of our childhoods.
That is the thing I am mad about. We are getting bastardized versions of the science fictions of our childhood.
I fantasized about instant communicators across worlds, and we get mobile phones that work by planting a gazillion antennas across the globe. And people hail them as futuristic and say things like this.
I fantasied about human like robots and positronic brains, and we get a regurgitiation of past humanity, in text, ensuring a future of total intellectual and artisitc winter.
I fantasized a future with perfect health, but we get a million doctors and hospitals and medicines for everything and an existence that is unthinkable without health insurance!
I fantasized about antigravity flying cars, and we get drones.
What ever it is, these things are blocking the path to the science fiction of my childhoods.
The science fiction AI of my childhood was Cortana, who was a lot more cool than a relentlessly proactive token torcher which burned 12 bucks to fix some CSS.
reply▲In case it's not clear, "relentlessly proactive" is meant to act as both a glowing review and a warning at the same time, even before you get to the bit about safety at the end.
reply▲At some point the subscription model is going to become unsustainable for the frontier companies to continue (we just saw that happen with GitHub Copilot), and they will move everyone to a pay-per-token model. And then everyone will suddenly discover that they can get so much more value out of locally-hosted models, and they'll be willing to pay the $50,000 (or whatever) upfront on hardware to host it. (Not most individuals, obviously. But most companies can probably afford to spend that much on hardware if they think they'll benefit long-term). That's going to put a serious crimp in the frontier companies' ability to continue as they have been.
I don't know when that will happen, but I don't think it'll be more than a decade. Maybe 3-5 years. (Though you shouldn't take my word for it, I was predicting the dotcom bubble bursting in 1998 and it lasted at least two years longer than I would have predicted).
EDIT to clarify: I don't mean "in 1998, I was predicting the dotcom bubble would collapse and I was right". I mean "I was predicting that 1998 would be the year the dotcom bubble would collapse, and I was off by at least two years".
GitHub Copilot's challenge is that they weren't selling access to their own models, they were selling access to models from OpenAI and Anthropic which they presumably had to pay list price for (or maybe a slightly reduced rate that they negotiated).
They also had a pricing plan which they had designed pre-coding-agent, when it was rare for a single prompt to burn $10+ of tokens in an agent loop.
OpenAI and Anthropic are at least selling their own models directly, so they can discount a whole lot more since there's no-one else getting compensated in the middle.
>
At some point the subscription model is going to become unsustainable for the frontier companies to continue (we just saw that happen with GitHub Copilot), and they will move everyone to a pay-per-token model.From what I understand, Enterprise (above 150 seats, I think?) already has to pay per-token pricing.
Subscriptions are the premium "free tier" marketing of the AI world, so that employees can collectively request their large enterprise to subscribe to Claude, Codex, or Cursor, and presumably be billed at per-token prices then.
... so the mechanic produced an invoice, itemized.
changing the CSS - $0.05
knowing which CSS to change - $30
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. :)
eclipticplane5 hours ago
[-] $12 in tokens and the OP wasn't even at the computer. OP was working on a personal matter, arguably way more valuable than fixing a CSS scrollbar.
reply▲People burning tokens for the most beginner HTML/CSS problems and writing about it is concerning.
reply▲NichoPaolucci6 hours ago
[-] We are at the point where AI starts to seriously impact abilities. Sure, a 2 line CSS fix is the solution, but the human “behind the wheel” has already prompted 6 times and gotten 80% there. It’s been “easy” thus far. No shot they are going to FINALLY look at and edit the code. It’s just one more prompt and the agent will probably fix it, right?
It’s wild. I’ve been in the situation. 80% into a project I COULD probably take over, but realistically? 2 more lines of me prompting could fix it, it’s too easy to avoid the hard work of understanding the code, logic, architecture, etc…
AtNightWeCode4 hours ago
[-] Well the solution is incorrect. The problem seems to be that the css code does not normalize to box-sizing: border-box; among other things. The bad prompt by the author probably sent fable into the wrong rabbit hole
reply▲I dunno about beginner, I've been doing HTML+CSS for a few decades and I still find bugs where Safari differs from Chrome+Firefox pretty hard to figure out.
reply▲> Isn't that something you just open a devtools for and have fixed in like 2 minutes?
Not if you're an LLM influencer! Gotta keep up with the downpour of blog links or you'll look like you're falling behind on the latest and greatest.
you can probably do the same with 5.5 xhigh. I have a feeling simon willison is a Anthropic plant. He always shills Claud code, and doesn't really say much about OpenAI's models except when they come out and do a bicycle vector test.
reply▲* relentlessly rent seeking
reply▲It also does it on Claude Pro. I can't imagine they want to reach my limits faster like this (there are better ways).
reply▲galoisscobi6 hours ago
[-] Let's boil the ocean for a 2 line fix and call it frontier intelligence.
reply▲solenoid09376 hours ago
[-] Yeah, testing changes rigorously is for schmucks
reply▲galoisscobi5 hours ago
[-] You can test rigorously without token incinerators.
reply▲solenoid09375 hours ago
[-] But testing rigorously requires time and effort, while incinerating tokens lets me do many things at once.
reply