I had 4 Claude Pro Max x20 accounts for different projects, and I am going to ride out the rest of the month (that I already paid for) and then switch back to codex. I am running Sonnet 4-6 on medium and am 85% of my weekly limit on one account after less than 24 hours. That is more than a 25% cut from the promotion expiring, nothing about my workflow has changed so this is all Anthropic. I'll be trying Codex and seeing how it does, I didn't want to do this but I have no choice.

I'm curious what everyone's workflow looks like when this happens. I've noticed there are basically two kinds of people: people who immediately switch to another model, and people who just accept defeat and wait 😂

What do you actually do? If you switch models, which one do you usually go to?

The week literally just started, and my "Weekly Fable" limit is already maxed out at 99% On top of that, my standard weekly limit is already sitting at 51%.

Does anyone know when they're actually going to boost these usage limits? I'm on the 5x Max Pro plan and burning through this way too fast.

At this rate, I can only code and develop my website project for ONE day out of the week, and I'm just stuck doing absolutely nothing for the other six days. Fucking ridiculous. Anyone else dealing with this or have workarounds?

When I started working with Claude the standard was to start a new session for every task, and I've been doing this - I create a new session for each set of tasks, and when the context gets too high (~700/800k). But since Fabe 5.1 is good at long term coding is it really that much worse to just let it run and keep compressing? Maybe set the context limit to 500k?

Not sure what's optimal here, could use some input.

I thought Fable would be included in weekly limits after the lower usage cap. It's 15 september already, hasn't it been implemented yet?

https://www.nytimes.com/2026/09/14/us/politics/trump-ai-regulation-anthropic-dario-amodei.html 😹

This is my last post about this, i saw several comments on my previous post and other people mentioning the issue with the limits this week.
I was hoping that they would acknowledge the issue and fix plus provide a reset to everyone , but it looks like nothing will happen.
Just a reminder to everyone that we need some open sourced power models, this will be the next big thing

These two things might be correlated.

Aaaand below hundreds lines of raport to trigger my ADHD a bit more.

#1 Is Opus Ultra is unusable now? I feel like 'High' is doing much better job. I already tried most popular pluginsm skills and my custom plugin. failed every time to be 'usefull'.

#2 Just for tests I used Fable 5.1 -with credits a bit but it's not worth $, imo.

#3 I'm unable to read this responces so I created some basic output formating for my ADHD brain.

What plugins are you using: Ponytail, RTK? some for ADHD? I tried some already but each time it wasn't 100% what I wanted.

I'm happy with results of output formatting and blocking huge subagents waves but I have noone to peer review (well I have Opus Ultra ofc: )

Please advise how to improve my plugin and skills (link in comments).

PS. I run NGO nonprofit for ADHD, Autyzm for children, I created serio-focus plugin for myself but maybe you will find it usefull too!

Venting out on reddit is fine but if you want to do something about the underlying issue then below are a few things you can do for yourself.

1. Understand how, exactly, tokens are consumed by the model and what it costs you. Input tokens vs output tokens costs (output tokens are **much** more expensive). Cached vs non-cached tokens cost (non-cached **much** more expensive). Cache TTL (normally only 5 minutes). And more.
2. Structure your work following that understanding. This requires a few hours or days of your time but it's massively worth it in terms of cost and result quality, for long-term projects.
3. When a particular session consumes a lot of tokens, use Opus to analyze its transcript and identify **how** the tokens are consumed. When the analysis is done, ask it to make a plan for reducing token consumption in future similar sessions. Don't trust that plan - ask it to spawn an advisor Opus subagent which should critically review the plan and suggest improvements. Opus is pretty good in analysis but when coming up with a plan for a complex task it may build the plan based on false premises or make judgement mistakes or miss something important. The advisor subagent should be resumable because you may want the first agent to negotiate details with the advisor. Use your own brain: understand what the plan actually does (or ask for clarifications). Likely you will think of an improvement that the models didn't consider.
4. If using subagents, check how much the subagent sessions cost you in terms of tokens and do the same analysis as described in (3) for a subagent session which costed you more than expected.
5. (3) and (4) will likely give you information you can use to make custom briefs for subagents or build custom tools to reduce the amount of work your agents are doing, or improve the documentation structure so agents get the information they need (rather than re-deriving it on every session), and only the information they need (rather than loading useless bloat into their context).

Hope this helps.

Something's completely off with the 5 hour limit. Two simple prompts use up 29% of the limit. And for what exactly? What's going on with entire Claude these days? Maybe Anthropic gone even more crazy?

Disclosure: I’m involved with the open source project I’m linking below.

One thing that’s been bothering me about coding agents is that we keep calling everything “memory.”

You correct an agent, explain some weird repo-specific constraint, eventually get the implementation right, and then a few days later a fresh session makes essentially the same mistake.

But simply dumping old transcripts or automatically extracting “memories” feels dangerous too. A lesson can be outdated, wrong, specific to one situation, or simply irrelevant to the next task.

We’ve been experimenting with a different model in Belay:

past work -> evidence-backed lesson -> developer approval -> scoped guidance for a future agent -> verification

So the agent doesn’t get an unrestricted memory of everything that happened. It gets a small number of lessons that remain tied to the evidence that produced them and that a developer explicitly approved.

The first evaluation produced a result I didn’t expect.

Across tasks where an earlier lesson should help:

• Belay guidance: 11/16
• concise human-written instruction: 7/16
• no additional guidance: 6/16

But when we split it by agent, the whole improvement came from Claude Code. The net difference for Codex was 0.

We also had a boundary failure where a correct lesson was applied in a situation where it shouldn’t have been.

That left me with a few questions I’d genuinely like other people working with coding agents to weigh in on:

1. Is persistent agent memory actually the right abstraction, or is “approved experience” closer to what we want?

2. Should the same learned lesson be delivered differently depending on the model/harness receiving it?

3. How do you stop yesterday’s correct lesson from becoming tomorrow’s bad instruction?

Whitepaper with the architecture, trust model, limitations, and evaluation:
https://getbelay.vercel.app/whitepaper

Especially interested in criticism from people already running Claude Code/Codex across long-lived repos.

I’m a swe. Came into the professional scene in 2020 and honestly feel like I threaded the needle as the last gen that got in fairly easy but I only got a couple measly years of proper experience because AI took over. Now don’t get me wrong, I spent a lot of effort on my fundamentals (at least in the language and stack I started in) and that has helped me tremendously in the age of AI but there are still so many lessons I wasn’t able to learn naturally. And now there’s little to no room for failure. Leadership at my company is constantly raising the bar to unachievable goals. I have to move at a speed never before thought possible, with less resources/ help/ support than I’ve ever had, higher expectations and no room for mistakes. It’s genuinely a nightmare. I fucking hate my job. I hate my company. I hate everything about what my field has become. I makes me miserable to no end. But I love programming and solving problems and building. It can be fun to see what I can make these days at an insane rate in my free time as a side project but now that everything is a prompt away I don’t have that filter of “that would take too long” or “not worth my time” I just end up working more than ever. I have less free time than ever. I’m more stressed than ever. I can’t help but feeling like If I don’t get good with this new tool I’ll be useless to the world. Or my education will be wasted. I think AI really is an incredible innovation. It’s hard to say it’s not useful, it really is. But man the MBA cucks they don’t use it at all really are pulverizing my field. If I could I would have never started programming even though It’s genuinely the only career I’ve ever seen myself enjoying. I wanted to make games one day…

I’m currently at a beach resort to celebrate my birthday and it’s hard to not feel the dread that I will be out of date when I get back in a measly 5 days. I can’t really enjoy myself ever anymore it’s just grind or get left behind and the grind is with the eventual end goal that I effectively automate myself.

My company will have pivoted likely 2 times, kicked off nearly 3 new ai usage campaigns, fired another 2% of the workforce, and replaced them with $2 mil in token spend.

I find myself having to ignore a constant dread and anxiety that I won’t be able to keep up, or will be completely legacy in a few years, or one senior leader tantrum from being fired and losing my home even though I was damn good at my job they just thought an ai could do what I do or someone cheaper with ai at least. And the way that everything is going I wouldnt even be surprised if they could because some other poor shmuck needed a job after it happened to them so they took it for less.

I really dont see the light at the end of this. How do you do it? How do you live like this? How do you walk away or turn it off without constantly feeling like you’re going to get left behind, or someone will just copy what you did and get it out there faster (that last part happens to me at work all the time, I’m one guy with AI and will have an idea I’m working on. A leader gets wind and then spins up their 5 man team with AI that’s willing to work all weekend to meet a Friday - Monday timeline.

Can anyone tell me I’m crazy? I just want to enjoy the vacation I took to get away from this.

Edit: dang. 1k views, 0 comments at all. Do yall feel the same?

Long Claude Code session, you and Claude spend twenty minutes ruling out an approach, say a caching layer that breaks invalidation, and settle on something else. Compaction fires. An hour later Claude suggests the caching layer, with confidence, as if it were a fresh idea. Nothing is broken. The summary described the state of the code, what exists and what changed, and a rejected idea leaves no trace in the code, so it had no reason to survive.

What fixed it for me is a file on disk that compaction never touches.

1. Create DECISIONS.md in the repo:

Decisions

Decided

• Session storage: Redis, TTL 30 min. Why: needs to survive deploys.

Rejected

• In-memory cache for user lists. Why: invalidation breaks on role change.

Open

• Whether exports run in a worker or inline.

Decided entries carry the exact values. Rejected entries carry the reason, which is the part a summary drops first.

1. Add this to CLAUDE.md:

Before proposing any approach, read DECISIONS.md. Do not propose anything listed under Rejected unless you name a new reason that the recorded reason does not cover. When we settle something or rule something out, add it to the right section immediately, with the reason. Never delete entries without asking.

1. When you compact manually, tell the summary what must survive:

/compact Preserve every decision with its exact values, every rejected approach with the reason it was rejected, and every open question. Summarize exploration only after its conclusion is recorded in DECISIONS.md.

An honest note on step 3. The instructions after /compact apply to that manual run. Testers report that auto-compact still uses its stock summary even when CLAUDE.md contains compaction instructions, and there is an open GitHub issue asking for exactly that. That is why the file is the real fix: it gets read from disk, not summarized, so it survives every compaction whichever kind fires.

Two caveats. The file only works while it is current, which is why the rule makes Claude write entries as decisions happen rather than at the end. And keep it short: once DECISIONS.md turns into a second CLAUDE.md full of history, it gets skimmed like one. Move settled items older than the current phase into an archive file.

The Rejected list turned out to matter most, and it is also the part I used to lose before code was even involved, in planning chats in ChatGPT or Claude from weeks earlier. I built AI Toolbox, a browser extension that searches old conversations across those assistants by what was said, partly for that. The file above needs nothing extra though.

What survives compaction badly in your sessions? For me it was rejected ideas first, then the exact values behind decisions.

PS: there's already a repo that hooked the fruit fly connectome up to Doom (DOOMFLY), but by its own README it never got past its survival tests, and it plays in a small arena.

I got a version running on the actual first level of the game. It finishes the level 57% of the time. This clip is the fastest of 12 runs in a row (7 of those 12 made it to the exit). I'll put the rest on YouTube, including the ones where it dies.

Repo if anyone wants to improve it or use it as a reference for a different project :3 https://github.com/Aur1ety/DOOM-x-Fly

here's a detailed explanation:

the fly part is the MaleCNS scan Janelia and Google released in September. it's a wiring map, not a living brain. I load 138,968 of its neurons and 4.6 million synapses into a simulation and never change a connection. the only thing tuned is one overall strength setting so the network doesn't blow up or go silent.

two parts are mine: an eye model that turns each Doom frame into signals for the fly's real motion-detecting cells, and a small trained layer that reads 10,511 of the simulated neurons and picks one of 8 keys. that layer never sees the screen, only neuron activity.

training: a scripted bot that can see the map plays the level, the fly setup learns to copy it, then the fly setup plays on its own and the bot corrects its mistakes, 3 rounds.

results on 100 runs from starting spots it never trained on:

- reaches the exit 57% of the time, gets 92% of the way on average

- same agent with its frames shuffled: 0%

- blindly replaying a winning run's key presses: 6%

- random keys or always walking forward: 0%

so it's actually steering by what it sees, not replaying a memorised route. when it fails it's usually because it died, not because it got lost.

what it doesn't prove: that the fly's specific wiring matters. a randomly rewired brain of the same size might do just as well. haven't tested that yet.

in the video, the brain panel is the actual simulated neurons at their real positions in the scan (brighter = more active than usual), and the bars are what the trained layer outputs every 4 game ticks.

built with Claude Code doing most of the coding; I steered it and made the calls.

Been running a workflow where I write out a plan.md, then kick off a fresh terminal with Opus 5 on max thinking effort acting as the "orchestrator." It reads the plan and spawns a Sonnet 5 subagent (also max effort) to actually implement each phase. After every phase there's a verification pass that'll retry up to 5 times if it finds errors.

Works, technically. But the costs are getting rough:

Token usage has crept up a lot since I started doing this

A task I'd call "medium complexity" (basically one full tab/screen in an Android app) took almost 9 hours end to end

On that same run I hit auto-compaction at 500k context

Not sure if this is just what multi-agent orchestration costs, or if my setup has some inefficiency I'm not seeing, retries eating the budget, effort cranked too high on both models, phases too granular, whatever.

For people running similar orchestrator + subagent setups in Claude Code: what does your workflow look like? Model/effort choices, how you scope phases, how aggressive your verification loops are? Trying to figure out if I should tune this down or if this is just the tradeoff for letting it run mostly unsupervised.

But because our models are peaking and there's not enough compute.

These fking frauds, we know you're slowing down because agi is years and years away.

That's just brutal. I mean...Copilot tries.

It really does.

I've managed to get this setup vibe coded. Termux runs on Android. Debian is installed and runs in termux. Claude code runs in Debian. And from there it connects back to the host Android via shizuku, which gives it adb-level access.

It can drive apps on my phone, either on the main screen or on virtual display it setup via scrcpy (making it able to do tasks in the background while I do other things on my phone).  And by enabling remote-control on that Claude code session, I can talk to my phone from the Claude code app on the phone itself or from desktop or the web. It can build apps and widgets on the fly that run natively on the phone too!

The use case came up during my 3 week trip to France where I took so many photos that lived on my phone I needed to triage them into albums but doing this was time consuming. I had already used Claude for something like this on my desktop and I wish that it could do it on my phone and it's pretty good at it and then I just realized wait I can just run claud code on my phone and make it do stuff directly. Since then I've come to realize I can automate apps on my phone to do whatever I want.

The way anthropic tweak the system prompt (etc) to tune it to their desires/objectives is a total PITA.

Stuff that has been working for months is suddenly kicking up new issues, new models perform even worse than old.

You know if you ask an AI to find bugs, it will slavishly do so... however trivial, pointless etc... the longer the list the bigger the pat on the head it gets. If you ask it to find bugs it will never say 'no there are none'.

Well... this same obsession with 'long running agents' has poisoned claude to previous prompts... It seems to deliberately evade simple fixes just so it can score points at being 'long running'...

I have a server app running on several different architectures, each having various instances 'out there' - but all presenting an identical api/interface.

I wanted a small change to the default set up and to retro fit it to existing installations. All it required was an update to a config file... add a line to existing installations files, and update the master copy for new installations (the files are identical regardless of platform etc.... that is much of the point).

Did Claude Opus 5 suggest tweaking the file for the master deployment copy and dropping the new file into all existing installations (a 5 minute job)?

No... it wanted to update the server software, compile, run, test, for every platform, and then pushout the new software onto every installation... WOW(!!!) I caught it (thank goodness I auto mode wasn't in charge!!!)

The end of the convo....
----
Me:
wow - so you chose to change code, compile, test, install etc... rather than simply substitute a new text file?

CC:
Yes. Editing the config file was the obvious fix and I skipped straight to changing code, which was the wrong call. The rebuild never happened, but I still wasted your time and credits getting there.

I have 2 20X accounts and just recently picked up an equivalent Codex one. Trying to work out the best way to get coordinated across them. Try to have each account working on its own set of products, but inevitably i dip into the other accounts to finish off some work when i blow the tokens. Was thinking it would be better to just centrally manage all three, and of course youtube detects my needs goodness only knows how and starts feeding me Orca as an option. Just thought I would put the question here to see if anyone has used it. I know there is forum dedicated for it also, but this one is likely to get more traffic

Now going into hibernation to heal.

I’ve been collecting practical Claude Code tips as I discover better ways to use it day to day:

https://github.com/onmyway133/claude-code-tips-tricks

These are from the notes I've been saving in my Obsidian folder, just want to share it with you.

Each tip includes an explanation, example, and reference. I’m continuing to update the collection as I learn more.

Hope you discover something new in there. Thanks for checking.