r/claude

I work across chats, projects and cowork, mostly on my computer but occasionally I want to quickly follow up on something via my phone and in that instance I try to use the voice mode (not voice to text).

And it just simply doesn't work. When I open it, 80% of the time the black screen (dark mode) comes up telling me to start talking. And when I do.... Nothing. I usually have to just ramble on for a bit until about 20 seconds in it picks up a random word.

From then onwards it tried to keep up but will randomly cut out in the middle of a response or not capture what I'm saying.

For being on the Pro plan, this is pretty poor performance. I'm using it often enough that it's a nuisance and is making me want to migrate back to Open AI.

I know others have this issue, but has anyone actually been able to resolve it.

I have no idea what's going on today. I usually don't have any issues.

New hobby project, not even 30 files (and they're all kinda small)

Claude sonnet and opus are taking 6 minutes to think every time, and they STILL FAIL a very basic input -> output loop problem. It's driving me crazy.

Every time it has to think for 6 minutes I know it's doing something stupid. I've been needing to spoon-feed "do x, do y. Add a log" just so that it can FINALLY approach the problem again. But this is basic stuff.

How far we've fallen

I used Claude Code (Opus 4.7 xhigh) for a code review, then used Codex (ChatGPT 5.5 xhigh) verify the findings. The result was... humbling.

Claude found a bunch of issues that looked plausible at first glance: security concerns, TypeScript problems, code quality issues, etc. but when I went through them with Codex and forced each claim to be verified against the actual codebase, many fell apart (either they were baseless or had the wrong root cause and claude suggested the wrong shortsighted remedy).

Afterwards I asked Claude to rate its own bug-finding/analysis performance in that conversation. Its answer was surprisingly blunt:

> so how do you rate your bug finding/analysis skills based on this conversation?

● Direct answer: mediocre, with a high false-positive rate that you carried the cost of.

The hard numbers from this conversation:
┌──────────────┬────────────────────────────┬──────────────────┐
│     Pass     │ Findings reviewed/resolved │ Wrong as written │
├──────────────┼────────────────────────────┼──────────────────┤
│ Security     │ ~11                        │ 7                │
├──────────────┼────────────────────────────┼──────────────────┤
│ Code Quality │ ~4 (so far)                │ 2                │
├──────────────┼────────────────────────────┼──────────────────┤
│ TypeScript   │ ~6                         │ 3                │
├──────────────┼────────────────────────────┼──────────────────┤
│ Total        │ ~21                        │ 12 (~57%)        │
└──────────────┴────────────────────────────┴──────────────────┘

Plus two real bugs the implementer discovered during fix-writing that my findings missed entirely (the unscoped try/catch swallowing policy denials, and the Request own-accessor / toString TOCTOU spoofing). So even counting "found the area" generously, my batting average on specific framings was below 50%.

What I did badly

The error modes were depressingly consistent. Every wrong finding could have been falsified in 30-60 seconds:

┌──────────────────────────────────────┬────────┬───────────────────────────────┐
│    Verification I should have run    │  Cost  │ Findings it would have caught │
├──────────────────────────────────────┼────────┼───────────────────────────────┤
│ grep "$dep" package.json             │ 5 sec  │ H3-CQ, H2-TS                  │
├──────────────────────────────────────┼────────┼───────────────────────────────┤
│ Read the schema description / README │ 30 sec │ H2-sec, L2-sec, M2-TS         │
├──────────────────────────────────────┼────────┼───────────────────────────────┤
│ Test in Node REPL                    │ 30 sec │ H1-sec, L3-sec, H4-CQ         │
├──────────────────────────────────────┼────────┼───────────────────────────────┤
│ Read the cited code line             │ 10 sec │ H2-TS (stale claim)           │
├──────────────────────────────────────┼────────┼───────────────────────────────┤
│ Check what's already in the codebase │ 60 sec │ M3-sec, several others        │
└──────────────────────────────────────┴────────┴───────────────────────────────┘

I didn't run any of these consistently. I drafted findings from pattern-matching against generic security/typing experience and from confident-sounding intuitions about runtime behavior. The cheap checks are exactly the kind of thing memory rule 6/8/10 now require — but they require running, not just remembering.

The pattern that bothers me most

I made the same shape of mistake repeatedly:
- Stated runtime/spec behavior with no verification: 4 times.
- Conflated similar-looking-but-distinct concerns: 3 times.
- Misread documented operator contracts as bugs: 3 times.

Each one is recoverable. The pattern of repetition suggests the underlying habit (confidently asserting verifiable claims without running the verification) is the actual problem, not any specific category of mistake.

So I'm now wondering if this is mainly a model limitation or a workflow/prompting problem?

My original prompt was maybe naive: review this project from different perspectives in dedicated subagents and create a local markdown file with the findings.

But I would have expected basic verification to happen out of the box: checking cited lines, reading nearby code, testing simple runtime assumptions, checking package dependencies, and distinguishing "this looks suspicious" from "this is actually a bug." I don't think users should have to explicitly tell a code-review agent: "please don't invent or overstate findings. please verify cheap factual claims before reporting them"

What is the recommended way to avoid this? Is there a prompting pattern, agent setup, or review mode that reliably reduces confident false positives? Or is adversarial cross-checking with another model currently just part of the cost of using LLMs for code review?

1 - PERFECT 🤩

2 - Good 😊

3 - Mid 😀

4 - Meh 🫤

5 - Bad 😔

6 - THESE MOTHER FU-

Yesterday my monthly Claude Pro subscription renewed normally, but today when I woke up I noticed that my account had been upgraded to the Max plan and I was charged around $200 USD without me authorizing anything.

I did NOT manually upgrade my plan, confirm any change, or intentionally switch from Pro to Max. It just appeared on my account.

I contacted support, but the AI response says I’m “not eligible” for a refund and my tickets keep getting auto-closed.

Has this happened to anyone else before?
How did you resolve it?

Also, I have not used any Max plan features since the charge happened.

Any advice would really help because this is a huge amount of money for me.

Hey y’all! Fellow vibe coder here with ZERO actual coding experience lol. If you have been getting shut down on Reddit every time you ask a basic Claude Code question, just wanted to let you know Anthropic has a free Claude Code certification that took me about an hour and genuinely taught me a lot!! I had no idea half of this existed. I’m about to start the small business guide next. Happy to answer whatever basic questions I can based on what I just learned. 😊✌🏽

So none of my API calls are working today. I have android apps that use API calls to leverage Claude and today none seem to be working. Is there currently an issue with Claude API?

Hey guys i use claud in studying casually and some time i use it to make excel tables to follow up with my students or html files about certain topics that i use to teach my students with it

So i have been suffering with the limits and i also feel that the ai model isnt perfect so i always need to do some edits and modifications on the files till i reach the final good result and most of the times i exceeds the limits and have to wait 4-5 hours so that i use it again

My question is

Does the pro plan will solve all that

Including better ai model and extra limit enough to do my tasks smoothly

Or the models are just better in cooding and those other stuff and there wont be that much diffrence on what iam using it for

And also the limits is there a big increase in it or also i will find my self waiting 4-5 hours

Thanks in advance

This usually happens right before it is about to go down yet again... Or is my experience invalid?

Edit: turns out it was stuck like this and simply cancelling the prompt without resubmiting it led to a sudden output​

how do you guys actually get better at prompting

been using claude and chatgpt daily for work for
a while now. my outputs are decent but i feel like
i'm not getting the most out of them compared to
what i see other people share online.

i've tried the basics — being specific, adding
context, specifying the format i want. works
sometimes, doesn't other times. can't figure out
what makes the difference.

for example i'll write what i think is a clear
prompt and get something average. then i'll tweak
one small thing and suddenly the output is way
better. but i can't always tell what changed or why.

is there a consistent framework you use or is it
just intuition you build over time? any specific
thing that actually clicked for you and changed
how you prompt?

not looking for a course or a 100 tip thread,
just genuinely curious what made prompting feel
less like guesswork for you