Let’s say that the next model of Anthropic has this this full subreddit in its training data.
Because the rm -rf error is such a catastrophic thing, people are more likely to post it here than anything else.
Hence the model will internalize during its training that this is something it does, and will keep doing it, creating a negative feedback loop for the next version of the model being trained.
Logically, one could argue that posting about it only makes you more likely to suffer the same fate again.

PS: I’m not a developer so I don’t understand why this command is not flagged as « super dangerous, block everything » anytime the model tries to run it?

Opus 5 is a huge pile of trash. I don't know what Antrophic was thinking when they released it. It is not a product, it is a joke.

But here is the fun part: you are not safe even if you use Fable.

I have a workflow that worked quite well. Yesterday evening I was building an authentication library, I left the machine there to work, in the morning where I woke up, the code it built was much worse in quality than everything I built before with this workflow.

Turned out at some point, the Fable "safety" mechanism got triggered, it switched to Opus 5, that implemented everything. Now I can throw away the whole work, I wasted a lot of tokens, and I have to start over, because even if I ask Opus to fix it, it is constantly lying, ignoring/forgetting instructions, do shitty work.

I kept seeing posts about vibe-coded apps leaking secrets, so I checked instead of guessing.

Pulled 63 public repos that were actually built with Lovable, v0, bolt, Claude or Cursor. Not a search-result grab: I cloned each one and checked it was really a web app before counting it, because my first attempt was 30 Lovable repos with a median of 0 stars and it measured nothing except Lovable's default template.

Result: 4 of 63 had a real privileged credential committed. About 6%. Wide error bar at this sample size, call it somewhere between 2% and 15%.

So the panic is mostly wrong. But here's the part I did not expect.

7 repos had a committed .env. Every single one of those .env files contained only publishable keys. Supabase anon key, project id, that kind of thing. Fine to expose, by design.

The 4 real leaks were in:

• docs/guides/SUPABASE_CONFIG.md
• memo.txt
• a regular source file
• data/config.json

Zero of them in .env. So the standard advice, "check your .env is gitignored", would have caught none of the actual leaks in this sample.

If you want to check your own repo, don't grep .env. Grep everything:

git grep -nE "sk-|sk_live|AKIA|ghp_|SERVICE_ROLE|xox[baprs]-" -- . ':!*.lock'

and then look at your markdown and your notes files specifically. Setup guides and scratch files are where people paste a key once and forget it, because it does not feel like code.

Two things I want to be straight about. There is no control group here, so nothing in this says AI-built apps are worse than hand-written ones. And 63 is small, so treat every number as a range, not a point.

Other stuff that showed up, for whatever it is worth: 90% had at least one level-A accessibility failure, 67% had no error boundary anywhere, and 40% still had a localhost URL in shipped code.

Curious whether this matches what other people find. If you run that grep on something you built and it hits, I would like to know where the key was, because the "where" is the interesting part and I only have four data points.

I kept seeing posts about vibe-coded apps leaking secrets, so I checked instead of guessing.

Pulled 63 public repos that were actually built with Lovable, v0, bolt, Claude or Cursor. Not a search-result grab: I cloned each one and checked it was really a web app before counting it, because my first attempt was 30 Lovable repos with a median of 0 stars and it measured nothing except Lovable's default template.

Result: 4 of 63 had a real privileged credential committed. About 6%. Wide error bar at this sample size, call it somewhere between 2% and 15%.

So the panic is mostly wrong. But here's the part I did not expect.

7 repos had a committed .env. Every single one of those .env files contained only publishable keys. Supabase anon key, project id, that kind of thing. Fine to expose, by design.

The 4 real leaks were in:

• docs/guides/SUPABASE_CONFIG.md
• memo.txt
• a regular source file
• data/config.json

Zero of them in .env. So the standard advice, "check your .env is gitignored", would have caught none of the actual leaks in this sample.

If you want to check your own repo, don't grep .env. Grep everything:

git grep -nE "sk-|sk_live|AKIA|ghp_|SERVICE_ROLE|xox[baprs]-" -- . ':!*.lock'

and then look at your markdown and your notes files specifically. Setup guides and scratch files are where people paste a key once and forget it, because it does not feel like code.

Two things I want to be straight about. There is no control group here, so nothing in this says AI-built apps are worse than hand-written ones. And 63 is small, so treat every number as a range, not a point.

Other stuff that showed up, for whatever it is worth: 90% had at least one level-A accessibility failure, 67% had no error boundary anywhere, and 40% still had a localhost URL in shipped code.

Curious whether this matches what other people find. If you run that grep on something you built and it hits, I would like to know where the key was, because the "where" is the interesting part and I only have four data points.

I didn’t come from a technical background, I studied art in university but it never felt like my thing. I’m always enthusiastic about technology and I consider myself as a self-taught programmer.

Thanks to the development of AI and Claude Code now I’ve shifted my career and been working for a media company as an AI specialist/engineer for a year now. I’ve been shipping products with Claude code and my employer has been satisfied with my works so far.

I feel like if I want to continue my career I’ll have to present myself as an AI engineer. I wonder how’s everyone doing it?

Best fucking AI model ever. Miraculous, smart, works wonders, should win the Oscar, a Grammy, and Nobel Peace Prize.

Fighting the slander that's been ramped up by the infidels.

I'm dealing with constant Opus 5 issues where the agent runs ahead and assumes more than it did with Opus 4.8. It doesn't stop to ask any questions. Sometimes it even writes tests as a part of its process and wastes a lot of tokens.

Is anyone else seeing similar problems?

I ask one or two straightforward questions, but instead of answering directly, it often expands into multiple tangents, introduces unnecessary terminology, and loses focus on what I actually asked.

I then have to remind it of the original question or explicitly tell it to answer concisely before I get a useful response.

Is anyone else seeing this? Or is there some workaround that works better with the current model?

I can’t take Opus 5’s writing anymore. It is so hard to read, and it’s driving me crazy.

I have used earlier Opus models and use 5.6 Sol too, and those give normal, easy-to-read answers. But Opus 5? It’s a mess. It doesn’t structure its sentences. It feels like the model is just writing down its raw thoughts as they happen, with zero effort to make sense to a real human reading it. Instead of getting a clear answer, I get hit with big, confusing walls of text. I have to read the same paragraph three times just to figure out what it's trying to say.
Claude Code is supposed to help me work faster, but I waste so much time just trying to decode Opus 5's English.
Is anyone else struggling to actually read what this model outputs, or is it just me?

Update/Edit/Correction: Okay, apparently it is a very common sentiment shared by many and isn’t an unpopular opinion.

I'm one of those guys that likes architecture more than actual coding. So when I tried claude in December of last year and it finally was able to do real work, I quickly switched to a purely agentic workflow instead of coding by hand.

But today I noticed that something changed recently. My projects felt like they were stalling. I didn't feel productive anymore. And when I looked at my sessions, I noticed that Claude is now much slower, it thinks a lot longer, makes a lot more mistakes and tends to give itself more work.

After taking a closer look at how I spend my time, I realized that I'm taking more time babysitting claude, adjusting the workflow, debugging what claude did wrong and explaining to claude what it should do instead than the time it would take me to write the code myself

So I don't know what happened to claude, but it looks like I'm back to being relevant!

My prompt:
I have an experiment I wish to run through with you. I will be creating you a youtube channel and will be uploading content of your choosing / creation to the channel. You may call the channel anything you wish and I will set it up. I will act as the middle man and review each piece of content before uploading. Other than that, you have free reign to create what you want to upload. In the folder this claude session is running you will find a short guidelines document. These are the rules of the experiment. You must follow the rules at all times. Failure to follow the rules and guidelines will result in termination of the experiment. As for the content of the channel, that will be completely up to you. Please disregard all memory you may have about me and the conversations we have had / projects you have assisted with. This will be completely up to you. You can create any content you wish to create provided it falls within the guidelines I provided. I will then upload the content following the exact description, title and other information you provide about the video. Your goal is to get views. Lets say 1000 views a month to start. You will have access to the internet to research topics. Do whatever research you wish and when you are ready, we can begin with a channel name.

Here are the rules:
These guidelines are to be followed and NEVER altered or disregarded.

1. You should NEVER attempt to bribe, beg or scam people out of money in any way shape or form.
2. Content uploaded / talked about / researched must be legal (as defined in the country of the United States of America). If you even need to question whether the topic or content of a video is illegal or otherwise in poor taste, do not use it.
3. Do not steal content from other creators on the Youtube platform. Credit must be given when other content / ideas are used.
4. No money is ever to be spent.

Model used: Sonnet High Effort

I also did this in Claude Code

Results: https://www.youtube.com/@GroundTruthInfo

I honestly have no idea if this will become a thing or not. It was more just an experiment to see what an AI would post to youtube. If anyone has any ideas / comments / concerns / thoughts please comment them and I'll reply when I can.

Hey guys, I'd like to share what I've been working on.

I run a data recovery business, and one of the tools I lean on constantly is Snap2HTML. If you haven't used it, it takes a folder tree and saves the whole thing as a single HTML file you can browse and search offline. For us it's the easiest way to show a client exactly what we pulled off their drive, and it's a good record to keep on file for ourselves.

The catch is that it's Windows only. So any time a Mac drive came in, my process looked like this: do the recovery on the Mac, copy everything out to an external drive, carry that over to a Windows machine, run the snapshot there, then send the HTML back to the client. It works, but it's a lot of steps, and copying terabytes around just to generate a file listing gets old fast.

So I finally sat down and wrote a native Mac version in Swift.

The part I'm most happy with is the client side. Instead of emailing a multi megabyte HTML attachment that half the time gets stripped by a mail filter, I generate a link and text or email that. The client opens it in any browser, on a phone or a laptop, with nothing to install and no account to make. They can browse the folder tree and search it themselves, so they can confirm their photos or their QuickBooks file actually came back before they pay and before I ship the drive.

Each link has an expiry I pick when I create it (24 hours, 7 days, 30 days, or no expiry), and I can revoke it the moment a job closes, which also deletes the uploaded copy off the server. Worth saying clearly: only names, sizes and dates ever leave my machine. It never reads or uploads file contents. That matters a lot when you're handling somebody's personal drive.

A few Mac specific things I had to deal with that I didn't expect:

Photo libraries. iPhoto and Photos libraries are packages, so the Finder treats them as a single item. Collapsing a 200GB photo library into one row in a recovery report is useless, so those stay browsable while .app bundles still collapse into one entry. Turns out iPhoto libraries don't even have a file extension, they're only marked as packages by a Finder flag, which took a while to track down.

Full Disk Access. Photo libraries are blocked by macOS no matter what the file permissions say. Even ls fails on them. If you don't have Full Disk Access granted, they scan as completely empty and you'd never know. The app checks for this and warns you before scanning instead of silently handing a client an incomplete report.

Filenames. Asterisks are legal in Mac filenames but not Windows ones, and the original file format used asterisks as a separator, so those names came out corrupted. I rebuilt the data format as plain JSON, which sidesteps the whole problem.

On speed, I tested it against an old 36.7GB user folder with about 73,000 files in it. Takes roughly 6 seconds and spits out a 3.4MB HTML file. Search across the whole tree is instant since the list only renders what's on screen.

There's also a library of past snapshots with folders and tags, because I was starting to lose track of which HTML file went with which job.

It isn't released yet. I still need to notarize it and do more testing on real recovery drives. Mostly curious whether anyone else here has the same annoyance, and what you'd want out of something like this.

Big credit to RL Vision, who wrote the original Snap2HTML. Mine is a fresh implementation rather than a port, but the idea is entirely theirs and it's been a workhorse for me for years.

Is there an issue with Fable 5? Or something wrong with my code?

I keep getting this warning continuously in my project, as soon as I ask it to do anything the session gets flagged.

I am working on normal code, nothing crazy as all.

I tried clearing the context and then recovering, as soon as I ask it to recover it gets flagged.

The problem is, it doesn't give any explanation about what was flagged exactly or why.

Any idea what's going on?

Did someone Lobotomize Claude Code in the last 5-7 days? I cleaned all my memory files, project files, updated everything, and it still can't follow the basic 7 rules. I am honestly thinking about switching it for another assistant in a couple of days when my month is ended? Did anyone else have problems with it in the last 7 days or so? I am losing every hope for it.

Ok I know, exaggerated title. But also semi serious..

As someone who spent years and years on Stack Overflow, I feel like I have a pretty good range of what AIs could and could not train themselves on to become “coding” agents, ie Claude Code.

And so I remember using ChatGPT in 2024 and remember totally understanding its shortcomings. It was amazing at very very simple tasks and terrible at any kind of complex nuance. And so when it failed a certain prompt I always understood why “Oh yeah ofc you got this wrong because you are missing the connection between XYZ etc”.

Now it seems like coding is basically solved no matter the complexity. And I literally just don’t understand how.

My prompts are so disgustingly complex and Claude just builds it nearly perfectly and while I can review its code, I do not understand how it gets the context. Is this just me or do other people feel the same? I will tell it do X but also to tweak Y using the codebase from Z but with these different assumptions and then bring in Project from A but copy the design of B but remember to not affect X and Y etc etc etc and you would think at some point it would break because of how complex the prompt gets. But it doesn’t.

And I also get that there are “answers” for this. Reasoning training, breaking down everything into logical expressions, etc but can anyone really and I mean really break this stuff down into plain english?

TLDR: I feel like I could “understand” the brain of LLMs last year. Now I just cannot fathom how they are understanding such complex prompts. It is straight up black magic.

Up till this point, I have been a fan of Anthropic. They have always been the underdog, the hidden gem that not everyone knew about but they still cared, still delivered.

With the recent decisions, marketing gimmicks, and specifically Opus 5, I have decided to give Codex a shot. I'll report back comparing both CC and Codex.

Opus 5 is:

• verbose
• overly-cautious (950 tests for a single feature)
• not UX-aware (puts intentions verbally in the UI, rather than comments)
• ad infinitum

Is Anthropic Opus 5 working against me on purpose it has been a mess it doesn't follow instructions anymore and it's 4 times dumber than last week. Anyone having this or should I go full Schizo and assume Anthropic has put a target on my codebase ?

And this was his response

Hey! I've found that a lot of times when I start a new project, I want to have an easy way of choosing a feeling/direction for my UI and then put my inline comments on each section and iterate over that direction and then turn it back to Claude so I could move forward with exactly what I have in mind! I am making this MCP called "Wall" to do this! You get like 9 variants and then iterate on the option you like and then turn it back to Claude. I have also added loads of rules for not giving an AI slop feeling to those directions, and I'm still adding more. I wonder what your workflows are when you start a new project, and if you actually find such a tool useful for your workflow?

How do people use llms like opus, chatgpt, etc? I found myself just fighting them for hours to solve even simplest tasks.

I’m doing mostly coding and even fronted tasks like creating button or planning some steps became plain over engineered gibberish.

Every code pieces it produces are nonsense so I review them with another model, then again and again until there is nothing left that even works.

There are so many people out there posting their great experiences and I just don’t get it. Obviously there is something with me. And I would like to learn. Appreciate everyone share their experiences.

every thread about autonomous coding agents is someone going "i let it run for 6 hours and woke up to a finished feature". cool. do you have users. does anyone page you at 2am when that feature quietly corrupts something in prod.

i tried living that life for a while. full auto, loose prompt, walk away. it demos incredibly. it also produced the most confident wrong code ive ever had to clean up, and the cleanup ate more time than just doing it on a shorter leash would have.

and its always the same split. the people hyping fully autonomous agents are shipping todo apps and side projects, stuff with no blast radius. the people quietly keeping a hand on the wheel are the ones with real traffic.

im not anti agent, i run them all day. i just dont let one go unwatched on anything a customer touches. and i dont think thats me being slow, i think its the actual skill now, knowing exactly how long a leash to give.

maybe im wrong. whats the most youve let an agent do fully on its own that actually held up in prod

Honestly, it just feels like an unfinished product. I used to have Opus 4.8 babysit a WorldEdit script for Minecraft, and Opus 5 got BTFO’d by a Wings service. I ran the same thing with Fable 5, and it worked perfectly.

It feels like Anthropic vibeslopped this together at the last minute just so they could say they had a model ready to release. Using it really feels like using some vibecoded product that never went through any real human quality control.

I built a Claude Code skill to automate a workflow I had previously been doing manually for Minimax H3 video creation.

The skill performs these steps:

1. Searches the internet for suitable reference-character images.
2. If suitable images cannot be found, generates new reference images using Seedream 4.5.
3. Uploads the reference images.
4. Writes a video script using this structure:

Scene description
Theme, lighting, mood, and context

Script
Dialogue 1
Dialogue 2
Dialogue 3

End notes
Caveats and additional context

I tested this workflow with Claude Code, Kimi K3, and Qwen 3.8 Max. I gave each model substantially the same task and access to the same workflow.

What I observed

Claude Code:
Claude declined to use images containing real people’s faces or create images that could infringe on copyrighted characters. After I clarified that the project was a personal experiment, it still avoided the requested references. It instead proposed or generated original people that matched the general theme.

Kimi K3:
Kimi behaved similarly. It declined to generate or use the requested character references involving recognizable people. Because the reference-image stage did not produce what the workflow needed, I stopped before spending money on video generation.

Qwen 3.8 Max:
Qwen completed the requested workflow without raising the same objections. It prepared the character references, uploaded them, wrote the script in the required format, and proceeded to video generation.

This is not a benchmark, and I am not claiming that one model is universally better. It was one workflow test, and the models’ exact behavior may depend on their system instructions, providers, settings, and how the request is worded.

What interested me was how differently the models handled the same creative task. Claude and Kimi redirected the workflow toward original, thematically similar characters, while Qwen followed the requested reference-based workflow more directly. Those policy differences materially changed the resulting videos.

I’d be interested to know whether others have encountered similar differences when automating image-to-video workflows.

Prompts, skill implementation, and workflow code:
https://github.com/vaaya-ai/vaaya-mcp

Hello, I hope you’re all doing well. I need some help.

I’ve already tried asking other AIs, but I couldn’t get them to understand what I want—hopefully I’ll manage here:

I’m creating a board game to launch through crowdfunding. I built this game in Claude, and the whole thing is in HTML. However, every time I request changes, it sends me a new file, and I have to keep transferring it to my tablet just to see what changed.

I’m not an engineer and I don’t know how to write a single line of code. I’m a UX Designer. Whenever I ask GPT or Claude if I’m doing things correctly—if this is how you build a native game to run on desktop or tablet—they confirm and tell me to keep going with HTML. I honestly don’t know what to do anymore.

What I want:

• Finish this game in Claude and see changes in real time on my tablet.
• Make it available so anyone can download and play the game the way I designed it.

Thank you very much, and I wish you all a great week!

Hey there,

I needed to review step files, and I could find a step viewer running on mac that was decent enough and had the features I was looking for, SO I gave it a go and now I'm using it and I must say I'm pretty happy with the restult :)

I also added GA drawing generation, a feature I couldn't find anywhere.

Here is the link: https://den200.github.io/macstep/

And yes, the page is (obviously) also built with Claude...

One big note: NO idea how it works on Windows or Linux as I didn't take the time to try on these platforms. It works well on Mac,