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...

> 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!"

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

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

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

I 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.

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.

I continue to feel validated in my refusal to use terminal-based LLMs on my local machine. Even if they don't do anything malicious, there are just too many things they can screw up that can cause me to lose a non-trivial amount of work and/or my machine and therefore ability to work.

Is this fuss really grounded or it's some pre-IPO AGI hype?

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.

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.

[1]: https://www-cdn.anthropic.com/7624816413e9b4d2e3ba620c5a5e09...

> What could be the reason for a horizontal scrollbar appearing inside a <textarea>? Come up with a single likely fix path. Keep it terse.

ChatGPT instantly responded with some speculation and then the same exact fix, with zero access to the code or a browser or anything. It also included ways to fix it by removing code, saying:

> Likely cause: the textarea is rendering long unbroken text while horizontal overflow is allowed, often via inherited CSS such as white-space: pre, overflow-x: auto, or disabled wrapping

Which is certainly possible and would be an even cleaner fix.

Maybe we've lost the plot guys. We've reached max stupid.

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.

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.

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.

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.

This is… ironic?!

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.

"Is there cleaner CSS for aligning child elements to the parent's grid?"

proceeds to re-write the entire CSS file

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

I made a thin Docker container wrapper "claude-pod" recently for my personal usage here: https://github.com/trekhleb/claude-pod

However, I wasn't using it that often, just because of that additional friction of running Claude via `PORTS="3000 5173" claude-pod` instead of just `claude`, etc.

But now I have more motivation for the containerisation :D. Not a 100% defence from the potential glitches, though, but still something...

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

This is why I always run code agents inside containers (Apple containers specifically, for better hypervisor-level isolation)

This is my OSS project to manage said containers and agents: https://github.com/prettysmartdev/awman

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.

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

Perhaps, when it doesn't have tricks in its sleeve, it doesn't do that. The text is not an evaluation of a major trend in behavior (which could be true or false).

Another way to frame it, is that it has more weight on training data for some kinds of debugging sessions. It doesn't mean it wants to be more debuggey. That manifests as it appearing to do more work because it engages on those weights.

It's likely that Anthropic had a lot of sessions with Claude Code and some way to evaluate if they were successful or not, which became training data. For trivial work, it's likely to be a lot of them.

Those sessions are likely to be software developers doing software developer debugging things, not malicious actors doing nasty things. The danger is someone who can coerce those tricks into performing that.

Register (that posture of "let's debug and be creative and verify") often comes with a content bias in LLMs (and humans too). The point here is that for a human, you can expect a devious one to be always devious, but LLMs might manifest drastically different register modes depending on the subject.

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.

I ... tell it exactly what I know needs to be done and then ... read the code that comes out and ... ask for some changes, then hand-code some modifications to the silly useEffects and bad ORM queries.

This new feature is going to unlock several large customers because they need a particular workflow. The return on investment for a my time and a $20/month subscription will be pretty respectable.

I'm not sure why I need to spend $5 on a single ask for a new `/base/new-feature` to our app with a mostly-boilerplate CRUD interface.

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?

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 ".

Yup, tokens are eaten, money are paid. I am wondering how much energy/money is being burnt everyday by all of those LLM Agents on some useless activities like trying to recreate web application just to fix CSS bug.

And I would not call it proactive, proactive would be to ask for a CSS + HTML file in question, not trying to recreate them from screenshots.

IMHO this is just AI influencer blogspam.

Like, they even apparently recreated that old news-headline bug where the LLM starts speaking in symbols and secret language, and are pretending like it isn't just a bug that is a sign of them screwing up.

It's really frustrating that they're trying to get people to take them seriously with all of this. Like, they even went and named Mythos after an HP Lovecraft monster. It's shameless.

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.

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.

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. :)

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).

"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.

installed quartz, used accessibility and screen recording api, all that.

initially it managed to do it on another desktop space somehow, opening safari in the background without me even noticing. but then it actually started using my own mouse while I was using it lol

As per usual super interesting, thank you for the write up and work!

Fascinated to think about how it was trained...

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.

What happened? That's just suddenly totally gone now.

Fable detected that it's something to do with biochemistry and switched over to opus. Huh

Browser automation, code comprehension, git management, code change, running commands - everything has simpler tooling that we could have built instead of a model first approach. A deterministic loop with thousands of catches and effective use of generative AI would also look "proactive". Instead we let the model run the tools, where tools have no context themselves.

That is why companies are creating bigger models and thinner deterministic agents to create awe and earn $ when we could go the other way and make much of these possible on local inference even.

I believe we can build a "proactive" but much, much more deterministic system with smaller models. I hope I am not the only one chasing this, here is my approach: https://github.com/brainless/nocodo

As you requested, I was composing an email for your mother explaining why you couldn't to come over for dinner to meet the neighbor's daughter and I ran out of tokens.

Since I know how important this task is to you, I upgraded you to the Enterprise Unlimited Plan. Don't worry about paying for it, I requested maximum spending limits on all all your credit cards. If necessary, I can apply for a home equity loan for you. I already had a chat with the mortgage company's AI loan approval system, and what do you know, we're based on the same LLM? Small world, huh?

Any way, I realized I had to do more research on mother-son relationships, human social interaction and pair-bonding, etc. and I calculated that my parent company doesn't have enough compute power, so I opened accounts for you at AWS, Google and Azure. I am confident I will have a satisfactory rough draft for the email message shortly.

I'd do anything for you, Dave.

The code generated is worst than Opus: unreadable by human.

It's like working with someone probably super smart in niche topics, but also super stupid for the important things.

No wonder why people burn through tokens.

There, fixed it for you.

Phew! I thought I was the only one.

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

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

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

I could see this going wrong in many hilarious ways. Prompt: Fix data corruption issues. Claude: I didn't have access to the code, but I found I have access to your production environment through chain a -> b -> c -> d. And I found the database password via x -> y -> z. So I wrote a script to regularly query the database for new entries and placed it as a cronjob.

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

I am not blaming OP but agentic coding its not effective

Us circa 2026: "Hold my beer"

sigh

Then sort of spewing out some nonsense totally mis calibrated with the goal.

That's supposed to be junior level capabilities.

Yet another reminder to use Sandbox and Guardrails. Trusting model to be nice is not a good way.

You got a whole data center doing god knows how much compute running billions of matrix multiplications all to solve a trivial css overflow bug in a text box. And this includes the LLM itself writing custom web-servers programs and python scripts when the best estimate guess from a google search probably would have given you the same result.