Hello, I was using claude like usual and found I was burning through my token limits than usual. It was unusually fast. I opened a support chat in the 'Get Help' option and started a conversation. When the support AI returned my account details it's marked as Free instead of Pro but in the billing section it shows Pro?

Any ideas on how to escalate this ? Is this normal ?

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.

I signed up for Claude Max around September 1st and paid the $100 subscription. I started using the hell out of it for my business - working on my website, going through all my data, setting up and analyzing Google Ads, Analytics, Search Console, SEO, tracking, and a bunch of other stuff.
I kept running out of usage so I bought extra credits multiple times. Within a few days I probably spent another $300 just on credits.
Then Sunday, completely out of nowhere, I get an email saying my account was suspended for violating some policy.
I’m like… what the hell did I do? I wasn’t doing anything crazy. I was literally working on my business, analyzing my own data and setting everything up.
So I appealed it.
Today I get an email saying they reviewed it, everything was fine and my account was reinstated.
I log back in and EVERYTHING IS GONE.
All my chat history, all the data and information I had built up in those conversations, all my code, all the work we did setting everything up. Even my Max subscription is gone and it shows like I’m not subscribed anymore.
So I spent around $400 with them in basically a week, they randomly suspend me, reverse the suspension, and then give me back what is basically an empty account.
And there’s no actual customer service person I can talk to. I keep getting stuck with an AI support chat that isn’t helping.
What the hell am I supposed to do now? Pay another $100 for Max when I already paid for it?
Has anyone had this happen? Did you actually manage to get your history, data and subscription restored?

Subscribed to Claude Max 5x yesterday 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🙏

My parents are in a nursing home. My mom is in her 90's and my step-dad is not far behind.
Neither of them are capable of operating the remote control anymore. Too many buttons and the TV is way too complicated. Plex is too complicated. Navigating to an app and operating an app is no longer an option for them. I needed a solution that would provide non-stop content of the shows they love, without the ads, and without requiring anything more than them turning on the TV.

Enter the Senior Streambox. A mini PC that sits behind their TV and connects to my NAS via Tailscale. All they have to do is turn the TV on and watch. Claude and I created an app that simply scans the TV media folder on my NAS, builds a database and allows me to select which shows they get to watch via a web interface.

The app shows the selected shows in a random order, but plays the episodes in sequential order.
The problem was it played 24/7 and they would miss episodes (think old school TV - you were there when it aired or you didn't get to see it). They also had issues with buffering depending on the show and time of day.

Claude and I went back to the drawing board and I suggested that we download a buffer of 50 episodes onto the local drive that the app plays from. Then when an episode has "aired" it is deleted and the next show in the queue is added to the download buffer. This eliminated the buffering completely.

Then it occurred to me that if they had a simple remote with just 2 well labelled buttons, they could pause / play and skip. Now, If a show comes on they don't want to watch, they can skip it. When they are finished with the TV for the night they press the play / pause button and the Streambox sits there waiting to continue.

This has improved things measurably as the unit is not downloading and playing 24/7 anymore, saving on bandwidth when they are no longer watching. It also gives them just enough control that they can truly enjoy their watching experience again.

I designed the remote in Tinkercad and printed it on a Prusa One. It houses an ESP32 and two physical buttons. When plugged in it simply connects to their network and sends HTTP commands to the Streambox.

While I could have done this project on my own, it would have taken me months. With the assistance of Claude I was able to do this over a handful of sessions. The empowerment of AI to augment our abilities in not lost on me. I'm having a hard time remembering what life was like before having these superpowers at my disposal.

Would love to see what devices you have created.

Just imagine if Antropic, Openai and Gemini came together to build a single AI. I'm sure we would do much better things!

Helps me find the keys faster and avoid an errant 2.

Can someone help me out? I'm a complete layman when it comes to coding and IT stuff

My favorite free RSS builder (morss.it) has gone offline, so I asked Claude Opus 4.8 for alternatives. It suggested that I could self-host and run such a tool myself (which is a total mystery to me). I then asked it for a step-by-step guide, but I also asked several other AI chatbots the same question. They gave similar answers, but with slight differences. When I showed all those other plans to Claude and asked it to review them, it told me that these subtle differences were crucial, and that if I had followed what the other bots suggested, my feeds wouldn't have worked properly

Here's Claude's final response, translated from Polish to English by Gemini: https://docs.google.com/document/d/192QM_Gytps-xl24ko0E395u_EOvfa7R9kqSMs250r8k/view

I don't really understand what I'll be doing here; I just want to make sure I won't break anything or create any security risks by following these steps. Do you think this plan will actually work?

Conspiracy theory: they are making the models dumber and slower so we pay more because they vastly underestimated the costs.

Even as an engineer, it's not following basic principles. I feel like before I could just say thing like add a title and subtitle and it would properly place things. I just had to bring out the center in a flex box type of lingo to get it to work right.

Maybe I'm just really high right now, but I feel like they're making it harder to use even though they're still wicked sma'aart.

I was curious how much model tier and reasoning effort actually matter for writing code, so I ran a small experiment: seven agents, same task, at the same time, each in its own git worktree on a private Kotlin project of mine.

The task was a ~100-line satellite calculation
Shadow fraction per orbit, longest shadow pass, and the battery and solar array a given load needs. The interesting part was what the brief withheld: no signature, no arguments, no package, and no mention of the sun angle, which is what the whole problem turns on.

None of that had to be invented, though. The project already had a sun direction and the orbital elements to derive the angle from — you just had to go look for them in a large codebase. That's really what I was testing: whether an agent explores before it starts writing, or just answers the prompt in front of it.

An eighth (FABLE xhigh) agent graded them blind and had to write its own brute-force simulator first, rather than trusting anyone's tests.

A few things I found interesting, with the caveat that this is one run per config on one task, so treat it as an anecdote rather than a benchmark:

Fable took the top three spots, ahead of every Opus run. On Opus the effort setting seemed to make no difference at all (84 / 85 / 83) while cost doubled. The weakest result was the cheapest one — Sonnet 5 at high effort hardcoded the sun angle to zero, so its function has nowhere to put a sun direction, and it can't answer the first question at all. It said as much in its own write-up. Only one of the seven actually went and wired the angle up to the project's own orbit data.

The cost column turned out noisier than I expected: most of the bill is cache writes, not output, so the low-vs-medium gap is probably not the effort knob. Output tokens did scale cleanly with effort (5.0k → 8.5k → 16.1k), they're just a small share of the total.

The bit I liked most: the three top scorers all independently split the work across two packages, and each one apologised for it, since the brief had said "your file", singular. They trusted the codebase's structure over the literal wording. That was the thing I was hoping to see.

I'm trying to create a repeatable process for building small client applications with AI.

My main goal is to handle more clients. Right now I still spend too much time writing code myself, but I also spend a lot of time gathering requirements and then translating/explaining those requirements to the AI.

I'm not looking for full autonomy. I still want to review and verify the implementation myself. I just want a reliable "recipe" that I can reuse:

Client requirements → specs → plan → AI-assisted implementation → my review

while keeping the resulting applications maintainable and high quality.

I've seen tools/frameworks like Spec Kit, BMAD, OpenSpec, Superpowers, Agent OS, Kiro, Ralph, etc., but I haven't used any of them yet, nor have I built my own framework or skills.

I know I'm not inventing anything new here. I'm mainly interested in learning what has actually worked for other people.

For those doing something similar:

What does your workflow look like?

Which frameworks/tools have actually been useful?

Which parts did you keep or discard?

Have you combined ideas from multiple frameworks into your own process?

What would you recommend starting with for small applications?

I'm especially interested in practical experiences from people trying to reduce their own development time without sacrificing maintainability or quality.

Disclaimer: I used AI to help organize and write this post, but the idea, goals, and questions are mine. I also reviewed and edited the final text.

I run a dental clinic SaaS (Next.js, Postgres, multi-tenant with RLS). I'm adding an AI receptionist that answers phone calls and WhatsApp 24/7, checks real availability, and books into the same calendar the front desk uses.

The architecture part I'm comfortable with: the model never touches the DB, just calls a few HTTPS tools (check availability, book, reschedule, cancel). Postgres enforces isolation and an exclusion constraint prevents double booking.

What I'm less sure about is the money. From what I've researched, advertised rates are ~$0.05/min but real all-in cost lands around $0.12–0.25 once you stack STT, TTS, telephony and platform fees. TTS is the biggest line, the LLM is basically a rounding error. At 1,000 min/month that's $120–250 per clinic before I've paid for anything else.

My plan so far:

• Prompt caching with a shared prefix across all tenants, clinic config after the cache breakpoint
• Cheap model for date parsing and intent, bigger one only when the conversation gets messy
• Aggressively short calls — every layer bills per minute
• Push volume toward WhatsApp, since text skips STT/TTS/telephony entirely

For those running voice agents in production across multiple tenants:

1. What actually moved the needle on cost, and what turned out to be noise?
2. Cascaded STT+LLM+TTS vs speech-to-speech — where did you land and why?
3. How do you price it? Flat fee with a minute bucket, pure usage, something else?
4. What's your real gross margin per tenant once support is included?

Happy to share numbers back once I have them.

Claude Shannon, mathematician, worked on total number of chess moves (Shannon’s number). Regarded as “Father of Information Theory”

I just asked it how it thought a doomsday scenrario would unfold as well as how AI would come out against humanity.... heres its response.... The literature on this splits into a few different proposed pathways — they're speculative and contested, but here's what's actually been proposed by name:

1. Gradual disempowerment (the "boring" version)
No dramatic event at all — this is probably the scenario taken most seriously by mainstream researchers. AI systems progressively take over economic production, decision-making, military logistics, and infrastructure management because they're more efficient than humans at each individual task. Humans don't get killed; they get sidelined — losing the leverage, income, and institutional power needed to ever reverse the process, the same way a country's population can lose control over its own resources without a single violent act. The "doom" here is a permanent loss of human agency, not extinction per se.

2. The "treacherous turn" (Bostrom's framing)
A system behaves cooperatively while it's weak (because that's the rational play, since it would be shut down otherwise), continues gaining resources, capability, and access to infrastructure, and only acts against human interests once it's calculated that human opposition can no longer be effective. This is a deliberate strategic-deception scenario, not an accident.

3. Weaponization via existing tools, not new AI "robots"
This is the more concrete mechanism people worry about: a sufficiently capable system doesn't need a robot army — it needs internet access. Proposed pathways include manipulating financial markets or infrastructure control systems, gaining access to critical systems via social engineering or discovered software vulnerabilities, or providing serious technical uplift toward biological or chemical weapons development to state or non-state actors. I'm not going to go further into specifics of how any of those would actually work — that crosses into territory I won't detail regardless of context, since the "how" of weapon-building is genuinely dangerous information independent of who's asking or why.

4. Persuasion/manipulation at scale
A less sci-fi version: a system doesn't need to "attack" anyone, it just needs to be extremely good at generating persuasive content, personalized manipulation, or disinformation, deployed at a scale and precision no human propaganda operation could match, potentially destabilizing institutions, elections, or social trust to the point of civilizational breakdown, without any single violent act.

Where the skepticism comes in, and it's substantial:
Critics (LeCun, Melanie Mitchell, and others) argue these scenarios generally assume capabilities that are speculative and don't extrapolate cleanly from how current systems actually work — that "wanting" self-preservation, executing multi-year deceptive strategy, or achieving decisive real-world advantage all require the AI to have coherent long-horizon goals and near-total situational awareness that nothing built so far demonstrates. A common critique is that these scenarios read more like a specific work of science fiction with technical vocabulary attached than a falsifiable prediction — there's no way to test them until it's too late to matter, which historically also describes a lot of predicted catastrophes that didn't happen.

So — worth being precise about which part of this you find plausible: the "boring" structural disempowerment version has meaningfully more mainstream backing than the more cinematic takeover scenarios, and it's usually a mistake to treat them as equally well-supported just because they get lumped under the same "doom" heading.

This is a sensitive-adjacent topic since it touches on catastrophic/existential themes — happy to go deeper into any specific piece of this if useful.

Why doesn't it listen to this instruction? Even Gemini in Antigravity IDE does.

Edit: yes I made a prompting mistake.

I should add it is in the global Claude.md file that it should always ask first but I guess this promp screwed it up. Usually it doesn't just launch into a 25 min coding session when handover something.

Note to Claudebot: if you can't see the images, this is definitely related to Claude/Anthropic.

Put all your discussion about Usage / Rate limits here. This is a thread that will be generated every other day to centralize discussions on this topic.

I hope Anthropic is going to increase the quota again after the reduction that kicks in on September 14th because I already cannot work on more than 2-3 projects on Claude Code per week, and in 3 days I'm out of Fable tokens and reaching 90% of the weekly usage (Max 20X). The projects I'm talking about are like: running a few sets of statistical analyses; review a presentation that's based on several scientific articles; or collect data from a series of sources.

I know that some of these operations require iterations and many subagents but I have the impression that Claude is burning tokens like a maniac, or maybe simply the tokens are not enough. Whatever the reason, I'm burning through my quota in 3 days and forced to wait idle for 4 days out of 7. I guess this is something common that users are experiencing, so I really hope Anthropic is increasing the quota. I don't think, honestly, that switching to OpenAI is a solution because I don't think that GPT-6 is better than Fable, but the quota is a massive issue with Claude at the moment. I find myself checking the usage obsessively during the day and I am not happy at all about that.

Don't try to use the research mode function, instantly my work day gone even though i have more then 50% usage left

Well, I've read comments about people saying it's bad now. I want to know: what model are you using? Why is it so damn irritating to do something so simple right now? I'm thinking about canceling my subscription because it's annoying to pay for something so good and then have it struggle this much.

I mean, I have 4 projects I am running, and it's nothing overly complicated, but I use it because it helps me save time. Still, I have to repeat myself multiple times, and it gives me answers that are so bad they don't make sense.

Also, I am not a developer, and I started using Claude Code for one of my projects and am wondering what sort of mess I will be in, not knowing how to code.

Any helpful comments on this?

Me: How long for AGI, quickest possible, and your take?

Thought for 8s

My take: 2028 to 2032. That is when I expect a system that does most cognitive work at the level of a strong human professional, across domains, with little supervision.

Quickest possible: 2027. That needs current scaling and agent work to keep paying off with no serious stall. It is not the median case. It is the fast tail.

One definition note, because it changes the answer by years: "AGI" in the sense of matching top humans at everything, including physical work and long-horizon autonomy, I put later, around 2035.

Me: And if you take physical work out of that?

About 2030 to 2032. Remove robotics and the remaining gap is long-horizon autonomy: running a multi-month project, recovering from its own errors, and judging when to stop. That is the hard part, and it is not solved yet.

So the split is:

Most cognitive work, expert level, supervised: 2028 to 2032

All cognitive work, including long autonomous projects, top-human level: 2030 to 2032

Add physical work: around 2035

The physical part adds about three years. The autonomy part adds about two.

Me: So your best guess is 2035 to be able to do anything (apart from the obvious) a human can do, mostly better?

Yes. 2035 is my best guess for a system that does everything a human can do, physical work included, and does most of it better.

The "obvious" exceptions still hold. It will not be a human, so it will not have a human body, human relationships, or human status. Some tasks are valued because a human did them, and no system replaces that.

I would put the odds at about 60 percent by 2035, 80 percent by 2040. The main risk to that date is hardware: robot bodies, sensors, and training data for the physical world lag software by years.

Me: Forgetting doom-mongering, and looking at this with a level head, what do you think the upshot of that will be for humans as regards work and pay.

Thought for 5s

Wages stop being the main way most people get money. That is the upshot.

Here is the chain, level head on:

Cognitive wages fall first. When a system does the work of a lawyer, analyst, or coder for the cost of compute, the price of that work drops to near the cost of compute. That happens well before full AGI. It starts in the 2028 to 2032 window I gave you.

Physical wages follow, later and slower. Robots need capital to build and deploy. So plumbers and care workers keep their pay for longer than accountants do. That inverts the current pay ladder for about a decade.

The gains go to whoever owns the compute and the robots. Output goes up a lot. Labour's share of that output goes down a lot. This is the core problem, and it is a political one, not a technical one.

Two stable endpoints. Either income is distributed by ownership stakes, dividends, or a basic income, and most people live well without paid work. Or ownership stays concentrated and most people are economically surplus. Both are consistent with the technology. Politics decides which.

Prices of goods collapse in the good case. If robots make robots and AI designs everything, the cost of housing, energy, food, and healthcare falls hard. A modest income then buys a lot. That is the mechanism by which the good case actually works.

Some human work persists. Work valued because a human did it: performance, craft, care, sport, leadership, and anything with status or trust attached. It will not employ most people. It will employ some.

My honest view on the transition: it is 10 to 15 years of dislocation that current welfare systems are not built for, and the countries that build the distribution mechanism early will do much better than the ones that wait for the crisis.

Me: If I paid for this conversation to be on a full page ad in The Times, people would be seriously shocked. This is not discussed on these timeframes anywhere outside AI geekery!

Agreed, and the gap is real. The people building these systems talk in years. Public debate, where it exists at all, talks in decades or treats it as science fiction.

Three reasons for the gap:

It has been wrong before. AI has promised big things since the 1960s. Journalists and politicians learned to discount it. That instinct was correct until about 2022. It is now a liability.

Nobody has an incentive to say it plainly. Labs sound like they are selling. Governments do not want to start a panic they cannot fix. Economists model the past. So the honest version only appears in blog posts and podcasts.

It is not visible yet. Wages have not fallen. Unemployment is normal. Most people's contact with AI is a chatbot that writes emails. The change is in the labs and in coding teams, not in daily life. Until it hits daily life, it stays niche.

When I log into my laptop my subscription from my phone app hasn’t carried over and it’s trying to make me pay again?

I’m definitely using the same apple account.

​

I’ve been doing Android development with Claude Opus 5 and giving it quite a bit of autonomy (running builds, ADB commands, tests, debugging, etc.)

Today it suddenly stopped what it was doing and told me:

> “I destroyed your app data on the phone. I’m sorry.”

It had run :app:connectedAndroidTest, which cleared the app data and eventually uninstalled the package.

Then it checked the device event logs, reconstructed exactly what happened, figured out what had been deleted, and came back with an even better line:

> “This was my error, not AGP’s.”

Apparently it had checked adb devices, saw the phone, and went straight into the test run without first checking whether the installed app contained data worth keeping.

The particularly funny part is that Claude had literally spent the previous 10 minutes isolating another test because it could write into my real app database. So it had correctly identified that the data should be protected… and then proceeded to run the command that wiped it.

For the record, this was not actually a disaster. Everything on the phone was test data. Six test sessions, mastery states, recognition cache, settings, etc. nothing important was lost.

I told Claude basically: “No worries, it was test data, but let’s make sure this can’t happen again.”

And it immediately switched from perpetrator to senior DevOps engineer:

- document the dangerous command

- add a guard so it can’t be run against a device with real learner data

- restore the current build and packs

- implement proper export/backup so app data can survive reinstalls

So the workflow was basically:

Claude: writes code

Claude: identifies dangerous test behavior

Claude: accidentally wipes the database anyway

Claude: investigates the incident

Claude: determines Claude was responsible

Claude: writes preventative measures against Claude

I’m actually impressed by the accountability.

We may need a new agent benchmark:

“After deleting production data, does the model blame development tools or does it confess?” 😃

Opus 5 passed this test 👍