ClaudeDown.com

Is Claude being dumb right now?

No.

Everything looks normal.

Claude Code v2.1.260

rate limitsusage quotasunusable limits

AI-generated summary

Complaint Timeline

What share of all Claude mentions on X are complaints? Higher means more people are having problems. All times UTC.

Complaint rate
Elevated (>3%)
Critical (>5%)
In progress

Want to know when things go wrong?

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Recent Tweets

Sample of recent Claude tweets (UTC).

kimmonismus@kimmonismus
09/01

Literally unusable. The rate limits are absurd. Oh, and by the way, Fable’s automatic continuation is bugged and doesn’t even work. I honestly don’t know why I still bother using Claude at this point. 5.6 is simply better overall anyway. Give me GPT-Astra and im fine. its so fr... show more

620 likes22 reposts109 replies
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TheAhmadOsman@TheAhmadOsman
08/28

Wanna know why Anthropic hates Opensource AI? Models like GLM 5.3 being free and available to download makes their $1 Trillion Dollars valuation sound crazy stupid https://t.co/7ZFA0M3HH9

374 likes20 reposts33 replies
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synthwavedd@synthwavedd
08/31

Fable 5.1 has been staged on the Amazon Bedrock API, with the slug now returning a 404 "Model not found" instead of a 400 "The provided model identifier is invalid" (like Opus 5.1 and garbage slugs like "us.anthropic.banana" do) This usually happens in the days before release

299 likes16 reposts20 replies
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NeelNanda5@NeelNanda5
09/03

A concerningly common take seems to be that keeping Chain of Thought monitorable doesn't matter because interpretability will save us, or it's already useless This is total bullshit. CoT is our best current tool for safety & interpretability, losing it would be a major trag... show more

262 likes22 reposts16 replies
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About ClaudeDown

Read the launch post

Built by Prabal, Saransh, Sarthak

Have you ever wondered if Claude is actually getting worse, or if it's just you? ClaudeDown tracks public complaints about Claude AI across Twitter/X in real time, so you can see whether other people are experiencing the same issues.

Every hour, we collect complaint tweets, compare the volume against historical baselines for the same time of day, and flag when something looks off. The complaint-to-mention ratio helps separate genuine regressions from normal background noise.

This is a community project. The data is estimated, not exhaustive. But when hundreds of people independently start complaining about the same thing at the same time, that signal is hard to ignore.

Complaint volumes and baselines are estimates based on sampled Twitter data. Spikes are detected algorithmically and may include false positives.