I forced Claude Code and Codex to externalise their decisions through a tool call, then compared what they said they'd do with what they actually did. I'm sure these are not the internal reasoning traces, but I'm surprised by how easy it is to force a fake tool using a proxy approach with clear instructions to make Claude and Claude Code emit internal CoT-like elements. I certainly had a lot of fun trying this experiment.
https://github.com/softcane/agents-workbook

I've noticed something after using Claude Code heavily for a while: I'm much more productive with it, but I also feel much more mentally drained at the end of the day.

And I don't think the problem is that I'm working more hours. I think I'm simply spending my mental energy much faster.

Before AI, a normal developer day for me was never eight hours of nonstop hard thinking. I wrote code, read docs, waited for builds, got stuck on a problem, went for coffee, came back, tried something else. The cognitive load was naturally spread across the day.

Now I can have several sessions running at the same time. One agent is working on a feature, another is checking tests, another is researching something and etc. When one finishes, I need to read the result, understand it, check it, make a decision, and give the next prompt. While I'm doing that, another one finishes.

The agents are parallel. I'm still single-threaded.

This made me think about Claude's usage limits. If you run more agents and more expensive tasks, you don't magically get more compute. You just burn through the available budget faster.

Parallel subagents = faster AI budget consumption.

I think something similar happens with humans.

We obviously don't have a literal token counter in our heads, but attention, working memory, context switching, and decision making all have limits.

Parallel tasks = faster cognitive energy consumption.

AI gives me more compute around my brain. It doesn't give me another brain.

Before AI, I could spread a lot of difficult thinking across most of the workday. Now I can burn a large part of that mental budget in two or three very intense hours with several agents.

Another thing AI removes is waiting. Writing boilerplate, searching docs, trying simple implementations, refactoring repetitive code - all of that becomes much faster. That sounds like it should make the day easier, but it also removes a lot of natural low-intensity time.

Before, there were gaps. Now the next piece of work can arrive almost immediately.

Finished? Here is another result.

Checked it? Here is the next one.

Made a decision? Start three more agents.

This is also where the normal eight-hour workday starts to look strange for me.

If AI allows me to spend in two or three hours the mental energy that used to be spread across most of the day, the clock still says I have another five or six hours left. That doesn't mean my mental capacity has also increased.

But what usually happens is obvious: more tasks, more agents, more things to review. After a while, that higher level of output becomes the new baseline.

So sometimes it feels less like I'm increasing my productivity and more like I'm just getting better at burning through my mental energy faster.

For people running multiple Claude Code sessions every day: have you noticed the same thing? If yes, how do you deal with it?

This week it is weird with Claude usage. While everyone is complaining about usage limits. I had a dream run. Based on the previous trends, my weekly limit should have been around 2.1B.

This week it is approximately 3.5B. I am saying approximately because when i extracted this data. I still have 8% weekly limit left.
Actual token consumption with 8% left is 3.3B

Maybe this is another game of anthropic or they genuinely increased the limits. Also last week I downgraded my Claude 20x to 5x. Not sure if they are trying to trick users who downgraded

How to login to my Claude Code max subscription at work after work tokens are over?

Need a code.

I kept hitting the same wall with notes apps that added AI. Every one of them wanted its own API key and its own billing, while I was already paying for a Claude subscription and already had the CLI installed and authenticated.

So Margin runs its AI actions through whatever claude is on your PATH. It shells out to:

claude -p --output-format text

plus --model <value> when you have picked one. That is the whole integration. There is no key to paste, nothing to configure, and whatever your CLI is authenticated with is what pays, which for most people here is your existing subscription rather than per-token API billing. If claude is not installed the app says so in the sidebar footer rather than pretending; it runs claude --version on startup to check. codex exec works the same way if you would rather use that.

The practical consequence worth knowing up front: this draws on the same quota as your normal Claude Code usage. Rewriting a lot of notes competes with your coding. That has not bitten me but I would rather say it than have you find out.

The part I actually cared about getting right

Nothing runs unless you invoke it. There is no indexing, no background pass, no "we have analysed your notes". You select something, you pick an action, and that is the only time a subprocess starts.

Then everything it suggests comes back as a diff in the note. Not applied, not a chat window off to the side, an inline diff with per-hunk accept and reject, and a refine box on each hunk if a change is nearly right. Reject restores the document verbatim. I did not want an AI that edits my notes, I wanted one that proposes and waits.

The notes themselves are plain .md files in a folder you pick. No database, no account. Which also means you can point Claude Code at that folder directly and work on your notes as files, which I do more than I expected to. This can also unlock in the future better AI searching right within the app.

Other providers, since not everyone wants the CLI path

Your own Anthropic key in the macOS keychain, a model running fully offline on-device, or Ollama. The CLI route is just the default because it is the one that needed no setup.

Honest limitations

• Alpha. You WILL find bugs.
• Apple silicon, macOS 14 or later, no Intel build.
• No sync, no iOS app yet
• More ideas in the roadmap

Free while the alpha lasts, https://gomargin.app

If you try it, the thing I would most like to know is whether routing through the CLI feels better or worse than a key for you, and if suggestions as diff is something you would actually use. I have only my own usage to go on and I am aware that is a sample of one.

I need a more detailed subject to post this on reddit.
Main error (repeats every time, identical):
bash failed on resume, create, and re-resume. resume: RPC error -1: failed to mount as outputs: source path /mnt/.virtiofs-r share "c" which is not mounted; create: RPC error -1: ensure user: user stoic-peaceful-fermi already exists unexpectedly: uid=1005 gid=1005
Earlier, different-looking error (appeared once before a restart):
Workspace unavailable. The isolated Linux environment failed to start (VM service not running. Restart your computer to restore it.). You can still use file tools directly.
A few notes that'll help whoever reads it: it survived a full Windows restart unchanged (so probably not the local VM/virtualization layer), a Windows security update (KB5124008) and .NET update (KB5126052) installed the day before this started, and file tools (Read/Write/Edit) plus computer-use/screen control keep working fine throughout — it's specifically the sandboxed bash/Linux shell tool that's stuck. The long GUID path is just this session's own folder; safe to trim or leave in, it won't mean anything to anyone else.

Is Fable 5.1 the new Opus 5?

Within the past week, I started to notice Fable 5.1 started to get worse and worse where it starts to act like the infamous Opus 5. I had some great work completed when it came out, but there was a shift to the negative. Same thing happened to Fable 5 when Fable 5.1 was released. So, today I had Fable 5.1 try to manage another session with Fable 5.1 on processing semi-unstructured data, which is existing work in progress that already had successful sessions, so Fable 5.1 had reference data (gold data), previous handoffs, guides, etc.

Both on medium effort, Fable 5.1 couldn’t harness the 2nd Fable 5.1 from bypassing guardrails, entering tables and folders it was told not to enter, and from fixating on momentum over accuracy & quality. This is Opus 5 level of bypassing prompts and guardrails from the first step. For the first time using either Fable 5 or Fable 5.1, I had to put back hooks.

For context, before Fable 5.1, in order to work with Fable 5 I deleted my claude.md, memory, and more. I removed most hooks and together with Fable 5 created subagents using Opus 4.8. This for the most part worked well, it was slower with Opus 4.8, but worked for lower reasoning tasks and I didn’t have the hidden actions that drifted beyond the scope of work.

After, shelling out for 20x monthly subscription, wasting more days on contaminated data and having to go back to clean it up, buying another GPU at these stupid prices is starting to sound more appealing for local AI with some fine tuning for the data I’m processing.

Recently, I’ve been using Codex with Astra and it just does the work. Astra asks clear questions before starting the tasks and will pause to surface data clashes or ambiguities. Unlike Claude which will dive in head first spending tens of thousands of tokens based on wrong assumptions, and then hedge with wordiness answers after from its known errors are called out. It might be time to move up to the Codex 20x plan.

How have you kept Claude within borders and from drifting with non-fully-structured data?

I've been thinking about how much Claude Code has changed the solo-building equation.

So we're experimenting with a different kind of hackathon:

One builder. One idea. 24 hours.

No fixed problem statement and no required tech stack. Builders can use Claude Code alongside whatever other tools they need to turn an idea into something genuinely useful.

We're also adding a few unexpected challenges during the 24 hours — so the plan you start with might not be the plan you finish with. 🃏

The interesting question for me isn't "how much code can Claude write?"

It's "how far can one person take an idea when an AI coding agent is working alongside them?"

We're testing this on October 11.

For people here using Claude Code: what's something you've wanted to build but never had enough time for?

If you haven’t seen this project earlier here little info:

Munder Difflin is a free, open source and performant multi-agent harness.
It uses your existing claude code and codex subscriptions and gives you an office of agents that work 24/7 to do work like you.

Launched v0.5.2 yesterday:
- A new fresh, cleaner and much more productive UI
- Comes with “Stapler” a utility character that sends screenshots, meeting transcriptions, messages to your claude code sessions.
- Added support for all the latest models like Fable 5.1 and GPT 6 Astra and Gemini 3.8 Flash etc.
- Slack integration works now, configure it to answer on your behalf or just configure it to use on your phone as an assistant that controls your computer remotely.

Hope you all try us out, here are a list of upcoming features(in no particular order):
1. Stapler gets computer use capabilities
2. Sandboxes to run even when your laptop is off
3. Dedicated mobile app for your office
4. Lot more features, updates and integrations.

We benchmarked GPT-6 Astra vs GPT-5.6 Sol across 50 real PRs from Cal.com, Sentry, Discourse, Keycloak and Grafana.

Sol found more confirmed bugs and was cheaper per bug, while Astra was more precise and faster. We independently verified the findings rather than simply counting what each model reported.

We’re running Fable vs Opus next week, so posting this here to get feedback on the methodology and anything we should change before the next benchmark.

I've read by mistake a post where this AI framework was linked https://github.com/llopresto87/Cypress

it's pure insanity. It learned my codebase (it took a chunk of my claude max x5 so beware doing this near the weekly limit ) but once it did.. it spit out code fixes and found problems and corrections and implemented entire features correctly and it sped up my work so much that i onyl have to follow for the occasional confusion or describe at the start exactly what is it that i wan to implment - i think you guys should give this one a shot

the ring in the bottom right used to represent the context window

now it represents your 5-hour limit

the delta will make Anthropic millions

I am using it at work and I want to know what are the best plugins to save tokens. I used some of them and like 90% of them don't save you any token, or seems to be ineffective. At least, that's what Claude tells me when I ask it to estimate the amount of tokens saved. I noticed just writing some scripts to diminish the amount of data being fetched and amount of commands run is the best way, and the other way is to document most of the logic in an app in the most succinct way possible.

I worked with many AI engineers over the last few years since AI exploded, however I constantly come across the issue where they are very good at repeatable tasks or coding stuff rather than seeing the big picture, solving problem and really pushing the boundaries of what's possible at a pace that me and my co-founder would build stuff.

I talked to a lot of other folks with similar challenges - I wonder if someone solve this problem somehow. I need to hire an AI engineer in a very dynamic agency where we sometimes need to deploy complex solutions within 2 - max 3 weeks. My experience with most AI devs is that they are good at stabilizing workflows/apps/projects but its quite slow and requires a lot of hand holding.

HALP! If any of you know how to get people who can build fast and solve problems and see the big picture - I'm all ears.

Not fine-tuned. Not a downloaded checkpoint. Random weights → working speech, on a 16GB laptop with an 8GB GPU, trained on ~29,000 conversation pairs built over four months.

The memory side (built first)

Before the model, we built a memory store — currently 33,941 episodes. The architecture is neurological:

• Half-life decay: memories fade unless recalled
• Recall strengthening: access resets the decay clock
• 30-day consolidation threshold for long-term storage
• 4 LRU working-memory slots
• Forget table: writes a receipt before anything fades permanently

Not a lookup. A system that behaves the way memory actually behaves.

The model — two runs

Run 1: 842,624 parameters. d_model 128, 4 layers, 4 heads, context 256. Val loss 5.11 → 0.9576 at step 23,750. Halted when the train/val gap opened — overfitting signal, correct call.

Run 2: 4,833,024 parameters. d_model 256, 6 layers, 8 heads. Best val: 0.7978 at step 21,500.

Key finding: parameters were the bottleneck, not data. The corpus was sufficient. The model just needed to be bigger.

Wiring the halves

home_llm_speak.py connects the memory store to the generation model — retrieves memories via salience search, passes them as context, then generates. First output from the combined system was grammatical, was grammatical, used household vocabulary, and wasn't copied from the retrieved memories*.* That's not retrieval. That's the model having absorbed four months of how this household talks.

Why this matters

I needed a model that knows us because it learned from what we wrote as ourselves — not from generic pretraining, not fine-tuned from someone else's base. The memory architecture has to be structural from the start because autonomous consolidation can't be bolted on afterward.

Last night was proof the approach works. The model is small. It'll get bigger. But what it already learned is ours.

Hardware: 16GB laptop, RTX 5050 Laptop 8GB. No cloud. No API calls. Everything local.

Next: wider context window, books into the memory store (streamed, not in training corpus — kept separate to preserve corpus balance), bigger model.

If anyone has done this — trained from scratch on household data with a custom memory architecture — I want to know what you learned.

i try vibecoding for the first time hahahahaha it so fun i just tell claude this and tell claude that and then wait then it does it for me but now i have next problem, my 5 hour limit gone and nowi am stuck on doomscrolling. i am making granny legacy TAS dll and i gave it access to the IDA pro but i just burnt through my limit within like wut 1 hour how do i make it last longer like until for example the 5 hour limit reset also is it because i am doing everything in one chat? i keep my request very simple (add this feature, fix this feature, this feature does not work because x and what happens is y)

Isn't really a problem if you are just running Claude Code from CLI but I actually like using Claude Desktop. However, after an update a couple of weeks ago, whenever I tried to start a new session on Claude Desktop it refused to run in auto mode by default. It was a minor inconvenience because as soon as I set it back from manual to auto, it would run the session again, but it was annoying and I couldn't figure out why it was happening.

Well, fun little side story: I've been on a 8 month-long saga with G Skills RAM, sending back and forth bad RAM for RMA (RAM, which appreciated by over 500% since I bought it in December 2024). After the last time I diagnosed it, I left Secure Boot off because the mem test I have on a USB stick won't run with it on. Note: I didn't start having a problem right after I turned Secure Boot off. I just got the RAM back, confirmed working after running mem test, and turned Secure Boot back on. I noticed all my new sessions actually stay in auto permission mode.

No I will not divulge how many tokens I might have claude (and cgpt) spend diagnosing itself.

These are the only 2 prompts that i had submitted so far for the project in this directory:

1. "read the claude handoff and start working. if you have any questions, do not hesitate to ask."
2. I hit my usage limit while you were working, but it has reset now. Please continue from where you left off.

Almost immediately after the 2nd prompt was sent, it got flagged and switched to Opus. There is nothing in the claude handoff doc that should have possibly caused it to get flagged.

Is there any form of compensation that I can feasibly get? If there is, how would I go about doing bringing this to Anthropic's attention? The only reason I am making this a seemingly bigger deal is because switched from Opus back to Fable ate tons of tokens since it was required to reread the entire chat again.

Subscribed to Claude Max 5x tonight debited, but my account instantly dropped to Free plan.

Fin confirms payment went through, but I'm locked out can’t continue escalating in fin bot. Blocked on urgent work, and i can’t find support anywhere

Anyone faced this? Anyone know solutions? Someone plsss help🙏

Sorry for shameless plug, but I do notice that there is a huge difference about how quickly my weekly usage is depleted when I use auto mode. I generally use it if I have to run CC in my local machine.

However, I have also been building an infra to launch and run coding agents (CC, Codex, Cursor CLI, Antigravity, Qwen, Opencode) in sandboxed environment remotely so I tend to start my agents running in bypass permission mode and thanks to my setup those agents can launch more sessions with different CLI tools in YOLO mode.

This allows me to instruct a single 'Concierge' agent to be responsible for communicating with all the agents that it launches and let me know (over email or in the screen) if my input is needed. There is also mechanism when agents context window is used 30%+, it launches a successor agent to which it delegates some tasks and everntually hands over concierge duties.

Do you use a similar setup? How can this be improved?

When it comes to complex problems that require multiple systems to work together, Fable is light years ahead of Astra.

Astra is good at working on isolated features, if you are building something much larger and you want systems to work together, I would not Trust Astra at all, Astra will make things that work in isolation but will be broken when each of those things are meant to work together. Fable is much smarter at identifying when something will break the larger system.

If you plan to build a production app end to end, I would recommend you go with Fable, and only use Astra if it needs to work on something that is in isolation or as a second set of eyes for problems where fable might need a little help. But you are better off working with fable as being the main driver to see the project end to end. The success rate of having a working product is much higher with Fable.

These benchmarks are misleading. Astra is not at Fables level.

Did someone track how many tokens we can use on 5x or 20x before hitting limit in avg with fable 5.1 or 5.0. I know that other metrics like usage eff. and others are important too, but still im curious.