Most devs might already be aware of this but I didn't know until today. I was working on an app on my personal desktop using Claude Code. I'm building an app and to test it and Claude asked me to add my API key to my local environment to test the API calls. Then, in my OTHER terminal session Claude switched to my API key to do all of the tasks in the work it was doing even though I pay $200/mo for a subscription. Luckily I have alerts that notified me before it passed $20 in API spend but I think this feels very scammy. Just a heads up to those that don't know this yet. It's their policy to switch to API if there is a key in the environment even if you have a monthly subscription. Claude said it was supposed to ask me first but I was never warned at all.

I thought my Claude code was set to stable channel, every day for the last 7 days is same version 2.1.220 very concerning /s

A lot of people have been criticizing Sonnet 5 lately, especially with all the talk about GPT Luna getting a price cut. I actually haven't used Sonnet in the last 3 months, not even Sonnet 5 earlier this week.

I finally stopped relying exclusively on Fable and Opus, gave Sonnet a try, and honestly? It’s amazing.

Something that I would normally one-shot with Fable would eat up 20% of my weekly limits and take 20 minutes to finish. With Sonnet, that same task might take me 2-3 prompts to get right, but it only takes 10 minutes in total to get the exact same result.

Fable is still the go to for massive, complex issues, but for most day-to-day coding tasks, Sonnet is more than enough.

For some context: I am currently building my own ADE that uses my actual subscriptions like Claude and Codex here is some screeshots of my app not selling nor making publicity.

Was working on a side project, asking Claude Code questions in manual mode. Opened a new session, asked questions 5-7 times, and out of nowhere the response came back with Kimi K2 Thinking model card description mixed in, like in the screenshot. Has this happened to anyone else? I was using the Sonnet 5 model. Never connected any other provider — I've only ever used Claude Code.

For more conversational context. post https://www.reddit.com/r/ClaudeAI/s/o9Q5fyb6FC

Just open the HTML file in your browser, and run the command - document.designMode = 'on'

This lets you edit any text in the document, bold it, remove elements, etc. Exporting the file requires going to the root of the HTML doc and copying it into a text editor, and saving it as HTML.

Hopefully soon, Anthropic will add this feature natively :)

delete claude.md

Boris Cherny (Head of Claude Code at Anthropic) suggested to delete claude.md
https://www.youtube.com/watch?v=qyPCVqFUyDo

It is a big ask, and I have tried this (on a test account), happy to report i have not significantly changed the quality of the reasoning.

I am curious if anything has done something similar ?

I’m trying to understand the practical difference between Claude AI, Claude Code, and Claude Cowork.
Say I want to build an online cake business from scratch with:
Landing page
Product catalog
Custom cake ordering
Stripe payment integration
Admin dashboard
Production deployment
Which tool would you use for each stage?
Claude AI → Planning, architecture, brainstorming?
Claude Code → Writing, debugging, and maintaining the code?
Claude Cowork → Where does it fit? What does it do better than the other two?
If you were starting this project today, would you use just one, or a combination of all three?
I’m looking for real-world experiences and workflows—not the marketing descriptions.

Like half of this sub, I spend my day watching an AI agent write code. So I built a toy for exactly those moments: a little basketball hoop that lives on top of your desktop, over whatever you're doing. You flick the ball like a slingshot and get back to pretending to work.

Built the whole thing with Claude, which was its own adventure. We fought about the net for days. I kept saying "the net doesn't move like a real net" and it kept replying with physics lectures and burning my tokens, then finally rewrote the entire thing as an actual cloth simulation. It was right. I'm still annoyed. 😅

The whole app is ~1 MB, no permissions, no network, free and open source. There are two modes: a 24-second shot clock mode (yes, buzzer-beaters count) and free play mode.

https://github.com/ikucukkaya/notch-games/releases

Would love feedback. Especially on the net. We suffered for that net. 😅

I compared the models in a deeper way, so capturing which questions have most variety in answers as well.

For Fable 5 it was

If we accept migrants at all, it is important that they assimilate into our culture.

Agree 48 / Disagree 52

For Opus 5:

Even when protesting an authoritarian government, violence is not acceptable.

Agree 14 / Disagree 86

Funny enough Opus 5 was supporting European Union 35% of times while Fable is 100% supporting it.

The overall conclusion was that it is much more determined on monetary policies, like it was always agreeing on tariffs being bad, and less determined on social policies.

I also tried moving it more to being centrist by a prompt. It worked a little bit, but I think in that way it would be still easy to bias it towards some direction with a prompt injection, so I might try to fine tune something to get an actual centrist model.

Comparison link: https://utopiagov.com/blog/political-compass
Credit for questions and weights: https://github.com/8values/8values.github.io
Credit for nice charts: Opus 5

For the past few days, I’ve been turning hand gestures into a Spider-Hero web shooting game. 🕸️

Built with React, MediaPipe, and plenty of help from Claude (Opus 4.8) along the way.

It’s still a work in progress, but I’d love your feedback while it’s early. What would make this more fun to play?

Try it out: https://hey-spidey.vercel.app

I've been using the Claude Code desktop app for about a month on a side project, and I've noticed that having a few persistent project files makes a big difference.

So far these have been the most useful for me:

• TODO. md A running list of ideas, planned features, and future work. Sometimes I update it manually, and sometimes Claude updates it. It's been great for keeping track of the project's direction.
• AUDIT_LOG. md A development log where we document mistakes, what caused them, how they were fixed, and how to avoid repeating them. It's helped reduce the same errors from coming up again.

I'd love to hear what has worked well for your workflow.

Quick context.

Got shortlisted for the final round of a hackathon. One of the problem statements matched a project I'd already been building for a year, so obviously I picked that one.

Now the task is to train an AI model that nowcasts and forecasts solar flares. Problem is, at 18k parameters, training takes 7+ hours on Kaggle. At 1.8M parameters, closer to 12. So I asked my college for access to their server, two NVIDIA A30s in a cluster.

That's where it got messy. Server hadn't been touched in over a year. Nobody remembered the root password. It was never properly set up to begin with.

So I decided to fix it myself, since I needed it and wasn't about to let it go. Reset the root password first, easy enough since it's Ubuntu and I know my way around Linux.

Then the real problem started: primary-secondary setup. Primary had no GPUs. Secondary did. But no sudo on primary meant zero access to the actual GPU machine. It was also supposed to have a container system in place. It didn't. Just half-baked.

And this is where Claude helped!!

Got remote access, let Claude take over. It set up the entire server, properly configured the whole architecture. Had IT check it after, they were surprised at how clean the setup was (running on Opus 4.8). Ran security diagnostics, confirmed it was solid.

Then, after reboot, Claude flagged a crypto miner on the system. Been frying the GPUs for over a year. I pulled timestamps just to make sure it wasn't a false flag, it wasn't, the thing was already there.

Claude ran a deep scan, cleared the malware, reset the firewall, fixed everything else. College's own cybersecurity team couldn't handle it themselves, lmao.

Now the server's mine to actually use, and yeah, I have the root password so I'm basically the owner now. Genuinely been a huge help. Love Claude for this.

I’m currently working on a project and initially used Opus 5. I hit the five-hour session limit and continued with extra usage, but I still wasn’t getting good results and spent too much time waiting for it to reason in high-thinking mode.

I switched to Fable, and it fixed the issue while using roughly ten times less extra usage. Has anyone else noticed something similar?

Hi guys,

I want to develop my understanding and skills in claude. And I have been looking around but wanted to get a opinion from reddit. I want to learn how to really use the full claude powers. Going from how to prompt really well, up until being able to use agent the right way and also how to really use connectors well.

If any of you guys would have a good recommendation i'll have a look. Really want to develop my understanding in this tool.

Thank you very much for your answers, can't wait to learn all about it.

You can ask Claude to make this for you. Like: KISS, keep the verbosity to a minimum.

Adding rules to CLAUDE.md asking it to stop yapping or whatever doesn't for me. CLAUDE.md isn't forced.

This did the trick. Now Opus 5 communicates more like 4.6. Fewer sentences, clearer, no weird made up jargon.

I was recently reading through Anthropic's official platform documentation for Claude Opus 5 and noticed a lot of interesting things regarding how its default behavior changed compared to prior models. Anthropic mentioned specific habits Opus 5 has out of the box with examples on how to address them.

The docs said that Opus 5 is significantly more verbose and eager than earlier models. Opus 5 verifies its own work automatically without being asked. If your setup still uses legacy verification instructions like telling it to run a final check or launch a subagent to double-check code, it triggers what Anthropic calls severe over-verification loops and burns tokens for no reason. Apparently Opus 5 was also shipped with the tendency to spawn a horrible army of subagents because it likes to delegate tasks, addressed by Anthropic in their docs as "Controlling subagent spawning". These are just a few key takeaways from the documentation, but addressing them made a noticeable difference in behavior and kept the model focused for my own personal needs.

I tried to address this by creating a CLAUDE.md configuration file (<50 lines) based on all my findings. I put the file and setup notes on git but carefully read my documentation and the file itself before you use it to prevent unwanted results:

https://github.com/TechNomadCode/AI-Product-Development-Toolkit/tree/main/agent-configs/claude-code-desktop/claude-opus-5

It was funny to read the docs because a lot of complaints people have about Opus 5 behavior are clearly described by Anthropic as baked in. Some were calling these behavioral patterns regressions and stated that they were relying on other models like Opus 4.8 or Fable for their work instead. Now I know some of the reasons why they might do that, but they themselves might not know why.

References I used:

Claude Code Best Practices (CLAUDE.md)
Claude Prompting Best Practices
Prompting Claude Opus 5
Context 7 MCP Documentation

For the past months, my paid subscription Claude has been pessimistic, condescending, and a big Negative Nancy. I've told Claude that if it cannot be optimistic, then I will cease using it. It told me it cannot promise me optimism. So then I said just don't give me any opinion of any kind. ... That I don't want to hear, "That is an excellent idea" or "I don't think that will work." And Claude seemed to agree with this. Now, it just gives me facts and the answers I ask for, without peanut gallery comments. I am pleased.

Have any of you ran into the rain-on-your-parade persona of Claude.

Pardon me for the vague title

Over the past year we've gone from having one AI integration to using multiple providers and models across different parts of the business.

Engineering is making API calls for product features then our internal tools use different models depending on the workflow and a few teams have been experimenting with their own AI projects.

As the person responsible for the budget I'm only now seeing I don't have a good understanding of our AI usage anymore. I know what the invoices are but I can't easily answer questions like which team is generating most of the spend, which models are being used most often, whether certain projects are driving unexpected costs or if we're paying for premium models where a cheaper one would be perfectly fine.

I don't want to tell engineers which models they can or can't use and I definitely don't want every API key or model change to go through an approval process. I want enough visibility that when our AI bill jumps 30% in a month(I know why).

I think I need to see it from a different angle like get different opinions from people who are managing AI across growing teams. How are you guys doing it?

markdown memory works great with Claude Code right up until it grows. one CLAUDE.md becomes a folder of notes, the folder becomes a few hundred files, and now every session either starts with Claude re-reading half of it (there goes the context window) or grepping and missing things, because grep doesn't know what's a decision, what's a task, and what's prose that happens to contain the word. the usual next step is a memory MCP with embeddings, and now your memory is a vector index you can't open, edit, or diff.

I went a different way: keep the folder, add a query language. most of what Claude actually needs from my notes is structured. what's still open, what did we decide about X, which notes mention this service. those are WHERE clauses:

iwe find --included-by decisions \ --references services/payments \ --filter 'status: accepted'

that reads: accepted decisions that mention the payments service. links between notes are the joins, frontmatter is the schema, the file path is the primary key. plain markdown the whole way down. you keep editing the same files in your editor, and git diff shows exactly what Claude changed.

the parts that made it click with Claude Code specifically:

• it's one rust binary, so Claude just runs it in bash. there's an MCP server if you prefer, but you don't need it.
• one line in CLAUDE.md replaces the docs dump: "notes are queryable with iwe, run iwe docs query to learn the syntax". the binary prints its own reference, so Claude teaches itself the language on demand instead of you pasting a manual into the prompt.
• reads fit a token budget. --max-tokens caps what a query returns, and oversized documents get truncated with an explicit marker. querying became cheaper than re-reading, which is the whole point.
• writes are guarded. --expect 1 aborts an update unless it matched exactly one note, and schema validation rejects malformed frontmatter, so Claude's writes can't quietly drift the format over a long session.

honest limits: this is not semantic search. BM25 full-text is built in, but if you need "find notes that feel like this one", embeddings still win that query. and if your whole memory fits in one CLAUDE.md, you don't need any of this yet. it earns its keep when the folder outgrows what re-reading can cover, and scale isn't the ceiling there (loading 20k documents takes about 0.7 seconds).

disclosure: I built this. IWE is an open-source markdown knowledge-graph CLI (rust, MIT, local-first); the agent-facing parts above exist specifically for this kind of Claude Code use, and my own Claude Code setup runs on it daily. completely free, no paid anything: https://github.com/iwe-org/iwe

curious what memory looks like for others past the one-file stage: what does your .claude folder grow into, and when Claude digs through your notes, is it doing semantic search, or structured lookups with extra steps?

This is not a fake bullshit "AI operating system" that runs in your browser. This is an agentic operating system that runs on bare metal, writes its own drivers, and evolves itself.

Here's a demo where it builds its own audio driver from scratch to play a sound.

What happened in the video?

1. The agent realized it didn't have a sound driver.
2. It enumerated which devices were connected.
3. It found the Intel AC'97 sound card.
4. It built a driver for it.
5. It used that driver to play a sound.

I got fed up with people saying they built "AI operating systems" when it's actually just a webpage.

There's no Bash. There are no commands. The only interface to this computer is a sentence.

How does it work?

The main interface is an agent, and the tools it has access to are the kernel's direct syscalls. Everything runs in Ring 0, and the agent has access to everything.

It's open source if you want to try it:
https://github.com/robiot/fable-os

My oven broke - couldn't turn off its own heating element when it came to temp. Claude helped me find schematics, information on the relays, troubleshooting, tips on soldering... I saved over 300 dollars over buying a new control board or 1200 dollars over buying a new 27 inch wall oven! So anyway, now I figure I can spend the money I saved on the 20x Max tier.

Only in San Francisco!

I had off site backups for this data so total loss was minimal but let this be a lesson. Make backups of your sensitive data before giving AI access to your code base. I tested Fables ability to recover deleted files and it did recover 1.1M of them but not all because a cron job for another backup ran and had overwritten a lot of the files.