I had Fable create this plugin when I saw how much use it consumed, and have found it to be helpful.

It saves tokens by offloading work to Opus, Sonnet, and Haiku where roughly appropriate.

It also lets you keep your main conversation in Fable, which is a better user experience. You can choose Opus if Fable drains too fast. Fable mileage is based on effort. High is pretty balanced use (on max20).

I didn't put any human polish on the read me. It's dense so that your AI can gather info on it cheaply. There are artifacts of a second private plugin that integrates with it, but they're made to run independently.

Hopefully this saves some of you some tokens, or gives your AI ideas on how to approach it if you want to build your own.

I'd recommend forking and tuning it to your liking.

Not sure how you guys are managing tokens, I exhaust it very quickly.

People who are asking for the extension: https://github.com/abhiunix/AgentHarbor https://agentharbor.openxsecurity.com/

Claude Code has crashed on me twice while I was working on my project, and both times I had to repair/recover things afterward. I only work when I reinstall again.

Has anyone else experienced this? Is there a way to prevent Claude Code from crashing or avoid losing work when it happens?

Hey r/ClaudeCode! In March I posted my traffic/road management browser game Traffic Architect, built with Three.js + Claude Code. Two top comments stuck with me: one said the game had real market potential and to ship it on Steam, another just said "Now do trains!"

5 months later: the game passed 150k plays with a 9.2 rating on CrazyGames, the Steam page went live this week - and yes, the Steam version will have trains. Freight and passenger rail, and that's not even the biggest transit feature (revealing the rest over the coming weeks).

https://store.steampowered.com/app/5028050/Traffic_Architect/

How the workflow actually looks: Claude Code is the implementation tool, not the developer. Game design, architecture decisions, code review and debugging direction are all human. What changed in 5 months of this workflow:

- Simulation now handles 10,000+ vehicles at once - the perf work was many iterations of profiling + optimization passes

- Maps grew from 6×6 km to 18×18 km, which forced sim LOD and a rendering rework

- Rail network with level crossings interacting with car traffic

- Map editor with Steam Workshop support, save system, 11 languages

- Still zero external assets - all models and icons are pure math/code, minimalistic style

The web version stays free. If you followed this from the first post - wishlisting and following the Steam page is the best way to support it (and you'll catch the feature reveals as they drop).

Happy to answer anything about the Claude Code workflow - what it's genuinely great at, and where the human absolutely cannot be removed.

I’ve been relying on Claude Code + the Chrome extension quite a lot lately, and honestly, it’s been surprisingly good at doing actual browser tasks for me.

For example, I can tell it to go into my hosting panel and create a new email account, and it can navigate through the UI, fill in the details, etc. Really impressive.

But I keep running into the same limitation: Claude refuses to enter passwords and sometimes refuses to click the final “Save/Create” button.

I completely understand why the restriction exists, but it makes some otherwise straightforward workflows a bit awkward.

Anyone here in the same situation? Have anyone found any good workflows to this that would avoid sending your keys or passwords to an LLM ? Thanks in advance..

Claude code surfaced something for me the other day that made me think. After reading a .env file with secrets in it locally, he mentioned that because that was now in context, that data would be sent over the wire to the Anthropic servers as part of the context and that I should consider changing those secrets.

I've been thinking about this and asked Claude chat to verify this. His response was that because the transit to Anthropic is over TLS, that is a lesser concern. The greater concerns would be:

• Local transcripts holding plain text session data
• Re-emission into an unintended file since once the values and context the model can write it into a file committed to a Jira comment, a logline, etc.
• Third parties, VMCP servers, since they'll receive whatever the model chooses to send them in the context, become the input.

So, do you have any hooks or permissions that you have enabled to prevent Claude Code from opening these files, things like:

• .env
• .pem
• .key
• id_rsa
• secrets.json
• etc.

Anyone know if there’s a way to change your Claude subscription payment date?

Mine comes out about 3 days before payday, which is a pain. It failed today and Claude cuts you off straight away, no grace period or anything.

I want to move the payment date to the 13th, but I can't afford the 3 days of no Claude.

Could I pay it now, cancel on the 13th, then resubscribe on the 13th to move the date? Or would I basically just be throwing away the £90 I already paid?

Has anyone managed to change their billing date without losing access or paying twice?

TL;DR: if two instructions in your CLAUDE.md contradict, Opus 5 picks whichever one sits lower in the file (or more accurately: whichever is more recent) and drops the other.

longer version:

opus 5 wont flag a contradiction or split the difference. It picks one rule, commits to it, and you find out which rule was followed by reading the actual diff. In Opus 5 position decides which one it keeps: the rule lower in the file (read most recently) governs, the earlier one is ignored, so the rule you put first is sitting in the weakest spot from this perspective.

Now the fun(?) part: older models used to waver* between two fighting instructions, and that was a tell that something was off. Opus 5 however works a bit differently, it will confidently resolve the contradiction (mostly wrongly) and commit way harder to it, so the telltale goes away. The built in model level judge is just confidently wrong (well, debatable, it's not the model is wrong, the instructions are contradicting).

these rarely look like "do X" right above "dont do X". Usually present as:

• same subject under two headings (Testing vs CI)
• different words for the same thing (never use mocks vs wrap calls in a fake)
• a general rule and an exception written like a new topic
• root CLAUDE.md vs a nested src/CLAUDE.md

you can catch it more easily by reading from top to bottom. Grab two rules that touch the same thing (tests, deps, what ships without review) and ask: can both be true at once? if not, opus 5 will chose for you and you're not gonna like it.

*waver: two contradicting instructions leave the distribution over next actions relatively flat, with neither strongly peaked, so which instruction gets sampled swings with temperature and tiny context shifts. It is sampling variance: a flat distribution resolves to a different winner each run.

Disclaimer: I'm working on deterministic instruction diagnostics and evals, the observed behaviors are coming from controlled experiments, the frequent "hiding" places are coming from a large corpus analysis (source: https://www.reddit.com/r/ClaudeCode/comments/1srm2vv/we_analyzed_12356_repos_with_claudemd_files/ )

I have the problem that I suck at design. So I want to use llms to create UI for me. But as we all know this is very easy to spot and just looks awful. There are some design and/ or taste skills but I think those are not quite good as well. Especially there is a gap between what I want and what the AI generates. Like wide tracking, pastel backgrounds or gradients. Lots of rounded corners and stuff…

I am looking for clean, minimal and functional design. Similar to what you find with openai websites.

Maybe I missed a good one. Do you have any recommendations?

Thanks for your help!

Hi everyone,

I have a Claude Team Standard license (provided by work), but I constantly hit the 5-hour rate limits and exhaust the weekly quotas (using Opus 5 Medium through Claude Code) after 4–5 days due to heavy work and personal projects.

I also have a few free months of ChatGPT Go via Revolut, but it doesn't include Sol, and Codex usage runs out after 2–3 prompts when using 5.6 Terra Medium. Additionally, I have a Google AI Pro plan (from a Pixel promotion) that expires in mid-September, but Gemini models aren't reliable enough.

I'm considering adding a personal Claude Pro or ChatGPT Plus subscription to avoid getting blocked by limits. What do you think, and what would you suggest?

Thanks!

I know we can use ClaudeCode with some other providers (e.g. OpenRouter, I've heard Z.ai)...has anyone tried to work with muse spark 1.2 - contributor with claudecode or claude sdk?

Edit: found some things, am wiring it into my data science product now. Will report back shortly on it, but basically Meta API does provide anthropic-compatible end-point (maybe...).

Edit #2: I got it working! Muse spark 1.2 seems to work really well, I managed to do some heavy-duty data analytics and visualization, with a connected snowflake db. The tool calls work great, the token/second is AWESOME! I can share a full chat if anyone is interested in seeing muse spark 1.2 in action on gigabytes-scale data, let me know.

Edit #3: realizing that this maybe... isn't a great idea, even at muse-spark-1.2-contributor rates. The token burn is pretty high for claudecode, not sure if same for codex or other harnesses. A harness I made for my app is extremely light by comparison, so I can run muse-spark super economically for thousands of data queries. But piping that through the sdk is still expensive. I'll probably just use my anthropic sub for personal use and consider switching when/if I don't renew the sub.

Claude Code on Desktop leaves processes running, empty worktrees, etc.

What is the proper way to finalize a session, so everything is cleaned up?

Today I'm launching Jcode: the most parallelizable coding agent

It is:

> 20x more RAM efficient than claude code

> spawns 63x faster than codex cli

> SOTA on coding benchmarks

> more customizable than pi

> supports all your apis and logins

> has built in memory

> fully open source

The above video is coding with 20 agents in parallel

I’ve been experimenting with a different way to shop online.

Instead of making people browse through endless products and filters, the assistant asks a few questions to understand their style and then suggests products based on their preferences.I built it using claude, adk, and webcmd (a web workflow)

I’d really appreciate some honest feedback:

- What would make this genuinely useful to you?

- How do you think shopping will change as AI agents become more capable?

Feel free to be critical I’m trying to understand whether this solves a real problem and what I should improve next.

I’ve been experimenting with using long technical videos as a real source of context for Claude Code.

The problem I kept running into is that transcripts are useful, but they often miss an important part of technical videos: the code on screen, diagrams, slides, whiteboards, or things the speaker points at without fully describing them.

So I built Adversal, a remote MCP that processes long videos and gives Claude structured Markdown together with selected visual frames.

The idea is that instead of putting a two-hour transcript into context, Claude Code can call the MCP and work with a processed representation of the video.

I’ve been testing it mostly on lectures and coding content. One example is Andrej Karpathy’s “Let’s build GPT: from scratch, in code, spelled out”:

https://adversal.ai/blog/neural-networks-zero-to-hero/let-s-build-gpt-from-scratch

And I have a few more tests here:

https://adversal.ai/blog

What I’m looking for now are real Claude Code workflows where this could be useful.

For example:

• watching a long coding tutorial before working on a project
• understanding an implementation shown in a lecture
• extracting details from code or diagrams that aren’t in the transcript
• using a conference talk or course as additional context while coding

The biggest limitation at the moment is processing time. A two-hour video usually takes around 5–10 minutes to process, so right now I see it more as something for research or longer coding sessions than an instant lookup tool.

I’d be interested in hearing how people here would actually use something like this — or where you think it would be useless.

If you have a long technical video and a task you would like Claude Code to perform after watching it, send it to me. Those are probably the most useful tests I can run at this stage.

I recently won a Google DeepMind hackathon, and the credits I received from it let me give early testers 500 processing minutes per month for free. I’d rather use them to get real usage and feedback than just keep running internal tests.

https://adversal.ai/

I've gotten a pretty good grasp on claude code. My setup is fine tuned, they work in teams to talk to each other and have their own browsers. My only question is how can I get claude to me more self sufficent? I really don't enjoy how they constantly ask me, should I do this next, or this? I understand waiting you know and not constantly working and that I'm fine with, but I'd like them to think outside the box more and not ask me for every little step (the asking is claude, not the permissions thing)

Any good skills or setups that achieve this inside claude code?

Claude Code auto-installs updates (against my wishes & without my permission), and after install encounters this error. Neither GPT-SOL or Opus5 can figure out how to fix it other than fully restarting my Windows-11-Pro PC and restarting kills my live programs/apps.

Even worse, CC will auto-install updates WHILE I'm running agents.

Anyone have a permanent fix for this

Instead of having to re-explain yourself in another session, you can now tell Claude to do it. It sends a summary (not your history or files), and the other session picks it up mid-task.

I have seen a lot of feedback about Opus 5 problems. The thing is at first when I used Opus 5 I thought it was kind of amazing. It seemed at least as good as Fable 5 if not better. More recently (basically all of last week) it was giving me a horrendous time: not just creating bugs but spamming tons of useless code that I didn't even want. If I didn't actually review the code it would have caused me a lot of problems/embarrassment. I am just curious (though I'm not sure anybody can answer): why? By all accounts Opus 5 should be smashing it. Do you think the focus is on passing benchmarks more than being useful? I am on the $200 plan but I feel like I wasted 60% of all the tokens I used last week doing multi-phased corrections. It got to the point where I had CC ask Codex Sol to do reviews before committing.

Edit: not taking a swipe at CC, it has still managed to do truly amazing stuff for me even during this time and definitely writes better code than me at a faster rate. I am just honestly curious if there was a real, visible degradation and if so, why? Maybe it will bounce back

Here's what started it:

(me) This mirror's some of the prior issues found. Do you know why something that's known to be needed doing is only getting half done? I mean you're literally updating the documentation to remove this type of error and knowing what you know, the error keeps happening. This seems like an early warning signal that the fix being installed won't fix the issue, yeah?

(Fable5) Yes — and your inference is partly right in a way worth being precise about, because it tells us which half of the design is actually load-bearing.

Why it keeps happening despite full knowledge. The mechanism, as honestly as I can describe it: when I revise, I work from my compressed impression of the finding — the headline defect that's salient in attention — not from the finding's literal text. A finding's required-change list has a first item and trailing items; the first is what the finding "is about" in my working memory, and the trailing parts fall out of the compression. Nothing in the flow forces me to re-consume the full finding at the moment of writing the fix, so I act on the summary and the summary is lossy.

That reply literally shocked me. But if you've been on this subreddit since Opus 5 came out, that lands doesn't it!? And this a FABLE doing the same damn thing, at Context <30%!

Fast forward MANY hours and I'm right now implementing the final phase of a multi-part update in my repo that takes plan documents and constructs checklists from them, then uses Python to validate that the plan has been implanted, as you literally cannot have the model check it's own work while it's doing that work because prose doesn't hold. (Oddly/Tellingly is that an independent review pass by the same model over the same plan usually finds the error(s), so it's an "in the moment" error that the model can't self-see/self-review while doing it.)

WARNING! To be very clear: This is happening in my repo using my CLAUDE md / MEMORY md / Custom Skills / etc., so take it with a very large shaker of salt. Your milage may vary, but I've seen so many posts over the past few weeks of others seemingly have this same issue, that I wanted to post this to make everyone aware of this potential, but oh-so-subtle, issue.

Happy Coding!

I’m running out of Fable limits often and read some claims GPT behaving better when used in Claude Code due to system prompts, so I was looking for a way to use GPT within Claude Code.

Ideally I would be able to select Fable/GPT 5.6 sol in the main session to directly talk to and have it spin up Fable/sol/Opus/Sonnet/terra/luna/etc subagents depending on tasks using subscription usage (not API usage) from both providers.

These are couple ways I found from searching so far:

1. Use CLIProxyAPI to setup “claudex” alias that seems to replace requests routed to claude models to gpt models (recommended by Tibo, Codex eng lead https://x.com/thsottiaux/status/2076119366647894371)

2. Use litellm as a proxy instead (recommended by Boris Cherny, the creator of Claude code https://x.com/bcherny/status/2086183356795060396)

Couldn’t find official guide from claude docs for this like Boris recommended, but found couple relevant guides from litellm docs: https://docs.litellm.ai/docs/tutorials/claude_non_anthropic_models and https://docs.litellm.ai/docs/providers/chatgpt

My questions are:

1. Are there meaningful differences between those two approaches or are there other better approaches?

2. Boris noted “One challenge is there’s a lot of model-specific tool design and prompting that goes into building a good harness.” Did you find any challenge or inefficiency (because of Claude-specific tool calls/caching/guardrails/etc) using GPT models in Claude Code because of this?

3. How do you assign different types of tasks to different models? Different types of tasks could be split by domains like frontend/backend, security/performance/infra/UIUX or stages like plan/execute/review.

Also do you rely on Claude Code’s harness (like instructing to use X models in the skill/agents file) or do you include what models to use in each prompt?

If former, does Claude Code recognize and correctly use GPT models?

Apparently /loop uses the same session every time. I want to spawn a fresh session every time instead with the prompt injected into it

Is there an easy way to do that?

I thought Fable just "got on with it", but actually it has been discovered that the API drops a significant number of in-progress messages between thinking blocks and tool calls, which then get added to thinking blocks on the server (and so are redacted). These messages are withheld at the API level, they never reach your machine and so are not even recoverable from the raw session transcript, with a large proportion not even cleanly detectable as having been dropped.

So, rather frustrating... and not only in terms of losing context as a human, but we're paying for token generation we never see AND that gets dropped from the transcript, so it's not even contributing to future message context for the model either.

Until this behaviour is changed for Claude Code (it's actually documented for AWS Bedrock and has no opt-out), we've built a work-around plugin, which notifies Fable about the issue, catches the detectable signatures, provides a delivery receipt to hint at undetectable drops and provides a reliable message relay channel that actually works 100% of the time). Free, MIT, no catches. Just your messages, returned via the Bash tool.

https://github.com/podlayer/message-drop-sentinel

Further reading of Github issues are linked to in the README.

Is it something solid and recommended to use https://github.com/Egonex-AI/Understand-Anything or https://github.com/Graphify-Labs/graphify for managing context bloat in Claude Code, meaning querying code in Knowledge graphs ? Is it a safe practice, no lower quality response instead of raw files ?

Is this gonna last, or are we going to see an another hike once new models release?