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Cloudflare Issues Reports

Latest outage, problems and issue reports in social media:

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  Elijah 🌊 (@juiceboy_of_abj) reported 5 minutes ago

  @Ms_Ada6 Nahh is not network and cloudflare is not even the only one

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  ilesanmierioluwavictor (@ilesanmiEri) reported 8 minutes ago

  This is 100% accurate, I remembered when I was trying to buy domain, I thought Cloudflare website was down, I had to wait for the next day, same issue, luckily, I switched to MTN and Cloudflare loaded perfectly

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  David Aronchick (@aronchick) reported 10 minutes ago

  @QuinnyPig @Cloudflare @vercel When do **** posts get their arn

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  AI News (@ainewsusa) reported 13 minutes ago

  The numbers are brutal: 85% fewer open issues, powered by agentic AI inside GitHub Actions. Cloudflare paired Flue (their agent framework) with triagebot on Workers to auto-classify, reproduce, and even patch bugs. Human reviewers only see the final diff. That’s not automation—th

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  Scott Tolinski (@stolinski) reported 15 minutes ago

  Basically, you push to record. Talk into it. That audio is chunked as you speak, saved to the onboard SDCard. From there uploaded to a Cloudflare bucket. From there sent to transcription service on my MacMini

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  vivek (@vivek31337) reported 17 minutes ago

  Don’t spend on marketing tools. Do this for free Build a system where AI agents have access to PATs/APIs for GitHub, vercel , cloudflare , Render, Gumroad, Stripe, etc. Then keep them in a loop: Research → find ideas → generate keywords → save to file → build website/product → deploy → market → track sales → research again. ask agents to submit to google search console. Tell your hermes to note down things , for reports tasks / status/ needs ask chatgpt for telegram script so your agents will run automatically, even if they get stuck somewhere they will move on to another task. my agents have shipped 300+ websites/ pages / articles in 20+ languages in few hours , i can bet no human on earth can do that. if you need free tokens lmk.

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  Akash (@akashcorex) reported 18 minutes ago

  I stopped paying API fees to run the backend logic for my job application tracking engine. Instead, I figured out how to turn a free Google Colab GPU into a private, remote API endpoint running DeepSeek-R1. Here is the exact architecture I use to process raw job postings for free: The stack: - Open Colab and grab a free T4 GPU - Install Ollama to run deepseek-r1:8b in the background - Tunnel the local port via Cloudflare to get a secure public URL The automation: To fix the chaos of tracking applications, my system needs to parse hundreds of messy job postings into clean database entries. Whenever I save a new role, a local script scrapes the raw web page and pings my custom Colab URL. DeepSeek-R1 uses its advanced reasoning capabilities to process the text, extract the tech stack, salary range, and requirements, and returns perfectly structured JSON directly into my system architecture. My laptop does zero heavy computing. Colab handles the massive GPU load in the cloud. Zero hardware costs. Zero API fees. Total control.

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  scott (@scottonote) reported 18 minutes ago

  @NateMeyvis correct primitives can help mitigate this. MVC for "classic" web request/response, cloudflare Durable Objects for actor pattern, polars over pandas. something something map reduce

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  PromptKing | The Governance OS for AI Agents (@PromptKing32) reported 19 minutes ago

  @Cloudflare Optional scopes fix the consent problem. The remaining problem is independent proof that the agent actually stayed inside those scopes once it started running across systems.

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  AHosting.net (@ahostingdotnet) reported 22 minutes ago

  The tell is two headers, never one. cf-cache-status = what Cloudflare did x-litespeed-cache = what the origin did Read either alone and a broken setup looks perfectly healthy. That is exactly why this survives for months.

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  Neria Basha (@NeriaBasha) reported 23 minutes ago

  N-able says attackers exploiting N-central used Take Control to reach managed endpoints, then registered Cloudflare tunnel services for persistence. If you patched late, N-able says to treat the environment as potentially compromised even when its IOC scan comes back clean.

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  JohnTheRevelator ✝️🥩 (@MaranathaJohn30) reported 24 minutes ago

  Is there a worse service than @Cloudflare? It's pretty much total garbage, every ******* time.

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  The Agentic Operator (@AgenticOperator) reported 25 minutes ago

  By the time revenue drops, the AI visibility problem is already 3 months old. These are the early warnings I track for every client. Red flag 1: AI crawler visits are declining. Check server logs weekly. If GPTBot visits drop from 400/day to 150/day over 3 weeks, something changed. A Cloudflare update. A robots.txt edit. A broken redirect. The crawlers usually leave before the citations disappear. Red flag 2: Citation rate is stable, but citation quality is shifting. You're still showing up in answers. But the framing changed. “Recommended” becomes “one option among several.” “Top pick” becomes “also available.” The mention count looks fine. The endorsement isn't. Revenue follows the endorsement, not the mention. Red flag 3: A competitor starts publishing comparison content about you. The day a competitor publishes “[Your brand] vs [Their brand]” and you don't have your own version, the clock starts. Within 4–6 weeks, AI can start citing their framing of you. You lose narrative control before you realize there's a problem. Red flag 4: Review velocity drops to zero. No new reviews in 30+ days across any platform. AI can treat review recency as a freshness signal. Stale reviews can make a product look stale. Citation rates can follow within 6–8 weeks. Red flag 5: Branded search volume rises, but AI mention rate stays flat. This one is easy to miss. People are hearing about you somewhere else. Then they're going to AI to verify. And AI isn't confirming what they heard. The verification step is failing. They arrive interested. They leave uncertain. Red flag 6: A new competitor starts appearing in queries you used to dominate. Track share of voice weekly. When a new name suddenly appears that wasn't there last month, pay attention. They may have just started their AI visibility work. You could have 4–6 weeks before their presence compounds. Red flag 7: Your product pages are being crawled less than your blog. Check crawler patterns in your server logs. If bots are reading your blog but skipping your product pages, something may be wrong with how the product data is structured. AI crawlers come back to pages worth re-reading. They spend less time on pages that don't give them a reason to return. Here's the part I care about most: Any two of these showing up at the same time is a warning. Don't wait for the P&L to tell you there's an AI visibility problem. By then, you're already late. Catch the signal early. Fix it before the revenue reflects it.

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  Anton Dimitrov god1 (@god_1_ATD) reported 28 minutes ago

  Cloudflare blocks or challenges bad requests from hitting my website. #cloudflare

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  Faye Xiao (@faye_xiao_) reported 35 minutes ago

  The spirit of Spirit just sold for $10 million Google is buying Spirit Airlines' data out of bankruptcy. Emails, internal communications, spreadsheets, bookings, frequent flyer and HR records, all de-identified, for $10 million. Judge Sean Lane rules on the sale Wednesday. The obvious read is that Google wants more data. But de-identified data doesn't work for advertising, since you can't target someone you can't name, and Google already sees more airfare information through Google Flights than Spirit ever generated internally. An airline that went under in May is also a strange place to look for pricing wisdom. What's worth buying is the internal material. The emails and the spreadsheets they reference record how work moved through the company: a question gets asked, a document gets built, a decision gets made, a system gets updated. Consumer text is everywhere, and records of how an organization actually functions are not, which is what you need if you want models that operate inside workflows rather than talk about them. Google's own statement uses the word enterprise, and the runner-up bid of $7.5 million came from Mercor, a company whose entire business is sourcing training data for AI labs. When the second bidder isn't another airline, the market has told you what was being priced. The strange part is that Google isn't short on this data at all. It runs Gmail and Workspace and sits on possibly the largest collection of business correspondence in the world, and it has promised enterprise customers it will not train on their content, which is not a promise it can quietly break. So it has the material and no permission to use it. What $10 million buys is clean title, a court approved dataset nobody can sue over, at a moment when everyone else is defending scraping claims. Dead companies can agree to things live ones can't. Any of this is worth paying for because the public supply is running down. Epoch AI's 2024 analysis put the stock of quality public human text at roughly 300 trillion tokens and projected it would be consumed between 2026 and 2032, a window that opens this year, and access has closed faster since than the arithmetic alone suggests. A census of the top 100,000 domains this July found 19.1% blocking at least one AI crawler, and in September Cloudflare begins blocking mixed use crawlers by default across its entire free tier. Private operational records are the obvious next reserve, and they are almost untouched. Epoch left them out of its estimate because private data is fragmented and legally too messy to use at scale, which is precisely the condition a bankruptcy court removes. That makes Wednesday's ruling more interesting than the sale. If it goes through, every bankruptcy from here has a new asset to offer, and the value will depend on how well documented the industry already is. A corner store has nothing worth buying, since you can watch how it works from the sidewalk. A hospital or a law firm is the opposite, because even with names removed the record shows how a case moves through the organization, who escalates what to whom, and which exceptions get made. That knowledge lives in internal systems and in people's heads and appears nowhere public. The catch is that the supply is biased toward failure, since no healthy company would sell its internal record, so every dataset that reaches the market comes from an operation that didn't work. That's useful for learning how a process runs, and much less useful for learning what good judgment looks like.