GitHub status: access issues and outage reports

Problems in the last 24 hours

The graph below depicts the number of GitHub reports received over the last 24 hours by time of day. When the number of reports exceeds the baseline, represented by the red line, an outage is determined.

Most Reported Problems

The following are the most recent problems reported by GitHub users through our website.

• Website Down (69%)
• Sign in (19%)
• Errors (13%)

Live Outage Map

The most recent GitHub outage reports came from the following cities:

City	Problem Type	Report Time
Paris	Website Down	11 hours ago
Saint-Paul	Website Down	1 day ago
Saint-Paul	Website Down	1 day ago
Mexico City	Sign in	2 days ago
León de los Aldama	Website Down	2 days ago
Créteil	Website Down	25 days ago

Full Outage Map

Community Discussion

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Bounce (@bounceidc) reported 3 seconds ago

  HE CHARGES $5K FOR SITES THAT LOOK LIKE A NEW YORK STUDIO SHIPPED THEM same model everyone else runs, but his claude picks from a real design library instead of guessing, so every build lands with animations, glass morphism and gradients already dialed in the two installs: grab the ui ux pro max skill off github and tell claude to install it, that one move loads 50 ui styles, 97 color palettes and 57 font pairings pull the magic mcp server from 21st dev and install it the exact same way after that you just say build a website and it comes out looking like a studio shipped it, not a template everyone else is still prompting for the word beautiful and wondering why claude keeps handing back the same flat bootstrap page save the two installs, the skill url and the mcp command are in the guide below

• 
  Akshay Nandwana (@akshay81844) reported 2 minutes ago

  2/10 Unlike traditional benchmarks, this one uses 105 real GitHub fixes from production Kotlin repositories. Models receive an issue description and repository state, then must generate a patch that passes hidden regression tests That's much closer to real software engineering.

• 
  Wasim (@WasimShips) reported 2 minutes ago

  if you open Claude Code without a structured workflow, you probably hate money. the skill gap isn't knowing prompts. it's knowing which command to run before you touch the terminal. here's the exact workflow I used from @mattpocockuk 1. start with `/grill-me` - paste your app idea or plan - Claude will ask you 16 to 50 questions before it does anything - mine ran 38 the first time i tried it - it walks every branch of the decision tree, resolving dependencies one by one - you fix the broken assumptions before they become broken code 2. move to `/to-prd` - converts the grilling conversation into a proper requirements doc - skips the steps you already covered - doesn't start from scratch - outputs user stories, not implementation notes - lands as a GitHub issue with a triage label - normal team workflow, no AI sidetrack 3. then `/to-issues` - reads the PRD and breaks it into independently-grabbable vertical slices - each issue is tagged HITL (you stay in the loop) or AFK (agent executes solo) - dependency-sorted so nothing blocks anything 4. finally `/tdd` - now the agent writes code. red-green-refactor - can't start green if red hasn't failed - phase-gated. no shortcuts. Hope this helps !

• 
  Ben (@bensenescu) reported 3 minutes ago

  Github sign in doesn't work for either of your apps @21st_dev @fal

• 
  Emmanuel Isenah (@EIsenah) reported 3 minutes ago

  I kept spamming cancel on a gh action this afternoon and it never canceled. Figured I'd just let it time out 3 hours later it's still running, only to learn the timeout is 6 hours 😭 Ended up force-canceling via the API GitHub, please fix your ****

• 
  Atul Mishra (@The_AtulMishra) reported 6 minutes ago

  The "Revolutionary" Playbook : Step 1: Choose your model. Step 2: Choose the model usage tier (because the base tier is essentially a very confident autocorrect). Step 3: Add your skills (which the context window will conveniently ignore five minutes later). Step 4: Add loops (to ensure you burn through maximum tokens in an infinite spiral of despair). Step 5: Build your custom harness (so you can feel like a real 10x engineer). Step 6: Slap the word "Agentic Workflow" on a basic script and act like you just cured gravity. Step 7: Gaslight the architecture with a 10,000-word system prompt just to get it to output standard JSON. And the grand finale: Now, pay us $5 to $20 per task. Oh, did something go wrong? Did the output completely derail? That sounds like a you problem. Just head over to our GitHub issues page, where our entire community of open-source sycophants is standing by to tell you that you just don't understand prompt engineering. There is absolutely nothing wrong with Claude. We have very powerful models. You just aren't holding it right.

• 
  Rohit Kashyap | AI + Full-Stack (@rohit_jsfreaky) reported 6 minutes ago

  @Hetzner_Online every github outage is a reminder your workflow is a tenant, forgejo on a cheap vps is a real fallback

• 
  BSCN (@BSCNews) reported 11 minutes ago

  Injective SDK Hit By Supply Chain Attack Hackers compromised a widely used Injective (@Injective) npm package with malware designed to steal crypto wallet private keys, per security firm Socket. Attackers reportedly compromised a developer GitHub account before modifying the npm package. The malware secretly captured seed phrases and transmitted them through a fake telemetry server. The compromised release has been removed, but affected wallet keys and seed phrases should be treated as compromised.

• 
  NIKHIL (@badnikhill) reported 11 minutes ago

  Wake up→open github project →write insane amounts of code → break everything→fix it like a maniac →repeat till 5AM. No sleep. No chill. Just pure unhinged contribution mode. That's how you go legendary. Who else going full degenerate this summer? #GSOC2026

• 
  Shaun Smith (@evalstate) reported 19 minutes ago

  @DanielLockyer It's a GitHub CLI error message isn't it?

• 
  Shadow Nick (@doublenickk) reported 20 minutes ago

  87% OF THE PLANET SUCKS AT AI BECAUSE THEY ARE STILL TYPING MANUAL PROMPTS LIKE AMATEURS While the masses use ChatGPT as a glorified search engine, elite builders are deploying autonomous digital armies that execute high-stakes business operations 24/7. Meet Synapse, an open-source MCP engine that hands AI complete vision and surgical command over your desktop to run background tasks silently while you sleep. The exact strategy used to break the system: The FBI Negotiation Hack: Scrape a massive list of multi-million dollar startups, feed real FBI hostage negotiation transcripts into the AI, and let the agent autonomously blast out high-leverage B2B outreach that forces prospects to say yes. Zero-Drift Execution: Ditch chaotic markdown files and manage your agent's state through GitHub Issues to keep them locked in for weeks without a single hallucination. Full-State Reality Testing: Stop relying on worthless pre-compile unit tests because this agent forces your system to compile, screenshots the actual interface, and verifies performance against reality itself. You can keep playing around with basic chatbots, or you can deploy a ruthless autonomous agent to scale your code and outreach on autopilot.

• 
  nik skld (@nikskld) reported 22 minutes ago

  GROK 4.5 JUST EXPOSED A WEAKNESS IN EVERY OTHER AI MODEL xAI dropped it yesterday. Everyone’s staring at benchmarks. The real story is persistence. Composio ran an eval: audit a GitHub repo for hardcoded credentials. The search returns paginated results. The prompt literally warned: “page through ALL result pages.” → GPT-5.5: stopped at page 1. Submitted 18 of 48 results → GLM-5.2: gave up exactly the same way → Grok 4.5: paginated until results ran out. Full audit. Done. It also one-shotted an entire fantasy voxel world — 1.4M voxels, castle, procedural terrain, working camera controls. The numbers behind it: → $2/$6 per 1M tokens (Opus 4.8: $5/$25) → 4.2x fewer output tokens per task on SWE-Bench Pro → #1 on SWE Marathon Here’s the thing nobody says out loud: Benchmarks measure how smart a model is. Persistence decides whether your task actually gets finished. An agent that quits at page 1 is useless no matter how high its IQ is. I break down how to get this kind of agent behavior from any model in my articlе

• 
  cn80 (@cn8011) reported 22 minutes ago

  I made an MCP server but it might be too powerful, I don't think I will share it because it will inevitably be used by everyone to make more AI slop. The GitHub stars clout chasing isn't worth it.

• 
  Vatsalpandya333 (@Vatsalpandya333) reported 26 minutes ago

  Everyone is racing to build AI agents. Very few are asking what happens after they ship. Here's what we've learned talking to engineering teams: The first failure is rarely the expensive one. The second is. Why? Because the company already had all the information needed to prevent it. The logs existed. The GitHub PR existed. The Slack thread existed. The customer ticket existed. But none of them talked to each other. Every incident becomes tribal knowledge. Someone remembers it. Until they leave. Companies don't have an AI problem. They have an institutional memory problem. Every production incident should make the next incident easier to solve. Not start from zero. That's the infrastructure we're obsessed with building.

• 
  Dawson James (@Dawson_James_) reported 32 minutes ago

  @hunterhammonds @linear We’re currently switching from linear to notion. Biggest gap in linear was (1) no easy way to manage docs/artifacts produced during the dev cycle, and (2) no way to facilitate the product operating model (see Marty Cagan’s work) - writing down business problems, creating OKRs, aligning on outcomes, etc all before creating a ticket. Agreed though that Linear is much much better and easier to use once you have your tickets identified, and connecting them to GitHub branches and releases. Notions solution here feels over engineered and I don’t want to set up more databases, agents, and workflows inside our notion workspace.

• 
  rachaelsacks.eth (@RAnSacks) reported 34 minutes ago

  @GJarrosson 9/9/6 is an embarrassing psyop, saying you're working hard is not working hard. Period. Like let me see your contributions on github; go and flex that instead.

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  🇯🇲 (@MidasTouchmkt) reported 36 minutes ago

  @AIatMeta sign up for the platform sucks i just want to connect my github fam i dont remember my fb account login i do not care to get it back, i feel like im jumping through hoops to give you my money

• 
  Rituraj (@RituWithAI) reported 38 minutes ago

  🚨 Someone built the web crawler that every AI agent actually needs. Not a scraper. Not a spider. A crawler designed specifically for feeding LLMs — structured, clean, and fast enough to process the entire web at scale. It's called Crawl4AI. 44,000 GitHub stars. The most starred web crawling repo in AI history. And it does something every other crawler gets wrong. Here's the problem. Every web crawler built before AI was the primary consumer was built for humans or databases. They returned raw HTML. Noisy. Bloated. Full of navigation menus, cookie banners, ad containers, and script tags that have nothing to do with the content you actually need. Feed that raw HTML to an LLM. You're wasting 60-80% of your token budget on noise. Your context window fills with irrelevant markup before the actual content loads. Crawl4AI returns clean, structured Markdown. Not HTML. Not JSON. Markdown — the format LLMs read most efficiently, with all the noise stripped and the structure preserved. Here's what it actually does: → Async-first architecture — crawls hundreds of pages simultaneously without blocking → LLM-ready Markdown output — clean content, no navigation noise, no ads, no cookie banners → Smart content extraction — identifies the main content block automatically, ignores boilerplate → JavaScript rendering — handles SPAs and dynamic content via Playwright integration → Media extraction — images, videos, audio all captured with context → Link analysis — internal and external links extracted and categorized → Structured data extraction — CSS selectors, XPath, and LLM-based extraction strategies → Session management — maintains login state, cookies, and browser context across requests → Proxy support — rotate proxies for large-scale crawling → Magic Mode — automatically handles consent forms, cookie banners, and overlays Here's the architecture that makes it genuinely fast. Crawl4AI uses an async browser pool — multiple browser instances running simultaneously, each handling their own queue of URLs. No sequential processing. No waiting for one page before starting the next. Hundreds of pages crawling in parallel. Combined with smart caching — pages already crawled get served from cache without re-fetching — large crawls that would take hours on a traditional crawler finish in minutes. Here's the wildest part. It ships with a Deep Crawl mode and an AI-powered extraction pipeline. You describe what you want to extract in plain English. Crawl4AI uses an LLM to intelligently extract structured data matching your description from any page — no CSS selectors, no XPath, no brittle scraping rules. "Extract all product names, prices, and descriptions" — it understands that instruction and applies it to any e-commerce page it crawls. And it has full MCP support — Claude Desktop, Claude Code, and any MCP-compatible agent can call Crawl4AI as a native tool. Your agent can crawl the web as part of its reasoning process without you writing a single line of crawling code. Your agent can now crawl any website, extract clean structured content, and use it directly in its reasoning — at the speed of async Python, at the scale of a professional web crawler. 44K GitHub stars. 6.2K forks. 847 commits. Apache 2.0 License. 100% Open Source. GitHub link in the comments 👇

• 
  Super Testnet (@SuperTestnet) reported 39 minutes ago

  3/4 ...invoice, check if it is paid via the web wallet's api, get the preimage, construct the decryption key, and give the user their now-decrypted file. Thus the server for a webstore could be a simple file storage system, such as Github Pages, and all the logic could be...

• 
  Fluso (@Flusoai) reported 40 minutes ago

  Fluso 1.3.0 shipped yesterday! You can now ask ChatGPT (or Claude, or Codex) to package up your work with it and hand the whole thing to Fluso. How we do it: Fluso gives you an export prompt inside the import dialog. Paste it into your agent. That agent builds a fluso-import[.zip] with your projects, decisions, and files. Drop the zip into Fluso. It becomes real projects on your side, with your work intact. Leaving a tool has always meant losing everything you built inside it. Six months of prompts, corrections, and half-finished drafts trapped inside a chat interface is what keeps you there. Also in 1.3.0: · Every project has working memory. Come back after two weeks and Fluso remembers where you left off, house rules and all. · Steer a running reply, or queue up work that survives your laptop closing. The queue lives on the server. · Confidential Mode shows its work. Tap the Encrypted badge for a real-time cryptographic re-check of the CPU, GPU, and model. · Connected-app tasks (GitHub, Linear, Slack, and the rest) finish in about half the time. 1.3 is the release we've been waiting to ship since we launched. Start with Imports. If it pulls back a project you'd forgotten about, tell us. We're keeping a list.

• 
  *nilpointer (@Dastagi39923618) reported 41 minutes ago

  github's diff page is completely broken always showing a single file diff. whats happening at @github

• 
  Harry Tandy (@HarryTandy) reported 43 minutes ago

  A World Cup prediction game sounds like a gimmick until you see the payments layer Sam Witteveen breaks down Google's Agent Payments Protocol in 10 minutes: 0:00 - Agents, MCP, and A2A context 1:00 - Agent Payments Protocol 1:41 - AP2 use cases 6:42 - Core principles: openness, user control, accountability, verifiable intent 8:16 - Google Agent Store 8:49 - AP2 GitHub Then open the Cyber Cup piece with one question in mind: what happens when leaderboard points depend on a tiny agent org? > one agent pulls match data > one agent checks odds and injuries > one agent decides the bet > another agent gets hired when the first 3 can't answer That is why a football tournament is such a clean test The scoreboard is public, the feedback loop is daily, and bad agents can't hide behind a polished demo

• 
  BuildAI | base.eth.ink✨🌊 (@FreeMoneyGL) reported 43 minutes ago

  OpenAI just dropped $GPT-5.6 for everyone, and it’s a direct pricing war against @AnthropicAI After a two-week White House lockdown where only 20 approved organizations had access, the gates are finally open. Instead of a single model, OpenAI unrolled a full tier system: Sol (Flagship), Terra (Mid-tier), and Luna (Budget). The numbers on the flagship Sol are wild: - Specs: 1.5M context window, Ultra-mode with sub-agents. - Speed: Blazing fast 750 tokens/sec powered by Cerebras. - Cost: $5/M input and $30/M output. That makes Sol exactly half the price of Anthropic's Fable 5 ($10/$50). Meanwhile, Terra delivers GPT-5.5 performance at a deep discount ($2.50/$15), and Luna bottoms out at a budget-friendly $1/$6. The Catch: Hype vs. Reality On paper, @OpenAI is claiming a clear win. Sol beats Fable 5 on synthetic benchmarks (91.9% vs 83.4%). But there’s a glaring omission: OpenAI stayed silent on real-world engineering tasks, like resolving GitHub bugs, where Fable 5 still leads the industry. The narrative that "the model was so powerful the government had to lock it down" is elite marketing, but developers are waiting for actual production data. The Market Remains Unshaken Despite a 50% price cut and unprecedented inference speeds, the smart money isn’t jumping ship just yet. Current Polymarket odds for the "Best AI Model by End of July" tell a completely different story: - Anthropic: 84.5% - OpenAI: 4.6% The market is betting 12-to-1 on Anthropic holding the performance crown. OpenAI brought the speed and the discount, but they still have to prove they brought the depth.

• 
  Nas (@Nas_tech_AI) reported 45 minutes ago

  You can’t believe this: you spent more on coffee this month than on a startup’s infrastructure. If you’re still waiting for the “right moment” to build, this is it. The cost of entry has never been lower. - Claude = coding ($20/mo) - Supabase = backend (free) - Vercel = deploying (free) - Namecheap = domain ($12/yr) - Stripe = payments (2.9%/transaction) - GitHub = version control (free) - Resend = emails (free) - Clerk = auth (free) - Cloudflare = DNS (free) - PostHog = analytics (free) - Sentry = error tracking (free) - Upstash = Redis (free) - Pinecone = vector DB (free) Total monthly cost to run a startup: ~$21 There has never been a cheaper time to build.

• 
  James Montemagno (@JamesMontemagno) reported 46 minutes ago

  @digitalix @burkeholland @WonderingDavid Yeah wanting to use inside of VS Code or GitHub Copilot app for testing purposes. Just been brutal slow. Sort of want the out of the box experience. There are so many variants of models hard to know what to pick.

• 
  FanBe (@FanBe_web3) reported 47 minutes ago

  @TheSandboxGame creator portal live with engine downloads and changelogs in one place, thesandboxgame no more GitHub detours is the kind of UX fix that actually matters to builders

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  Bharath (@bharath__2020) reported 49 minutes ago

  @PKodmad @dprophecyguy Yes GitHub issues.

• 
  Adam Shepherd (@AdamShephe61844) reported 50 minutes ago

  The GitHub agent that leaked private repos wasn't a model jailbreak. It read a poisoned issue and did what the text said. Read scope plus any outbound channel equals an exfiltration path. Least privilege stops being optional the moment your agent can be talked to.

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  cryptolistern (@horbunovdima) reported 54 minutes ago

  i asked Hermes agent to turn a real PR into a reviewer briefing video it pulled the diff, wrote the storyboard, built the visuals, generated the voiceover, rendered the MP4, ran checks, caught the wrong TTS provider, fixed it, and rerendered the PR was from the @NousResearch GitHub repo: NousResearch/hermes-agent#61415 a small but very visible fix: "caption" before: text message + media bubble after: one native captioned media bubble and the video itself was built with @HeyGen's HyperFrames - small PR - clear reviewer brief - real artifact this is where agents start feeling less like scripts and more like operators you can actually hand work to

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  pulmencrFOMO (@pulmencr) reported 58 minutes ago

  I found a workflow that turns a Discord annoyance into a full software business. Someone did this with zero prior coding experience. No CS degree, no bootcamp, just Claude open in a tab and a study group where people kept asking the same 5 questions in the wrong channels. Three hours later there was a working bot answering those questions automatically. Setups like this can eventually land $3,000+/month once enough of those servers upgrade to a paid tier. Here is the step-by-step playbook (detailed roadmap in the image below): 1. Pick one real annoyance, not a generic idea. "A bot that does everything" never gets built. "A bot that answers the five questions new members always ask" gets built in an afternoon. 2. Describe it to Claude clearly enough to get the actual bot code back. Tell it what platform, what trigger, what response - Claude writes the framework-ready code and explains each section in plain language. 3. Something breaks on the first run. That's normal. Paste the exact error back to Claude, it explains what happened and fixes it. 4. Give it a name and a short description so it feels like a real tool, not a script. The same loop works identically for a Slack app, a Telegram bot, a Chrome extension, or a small GitHub app. Discord is just the easiest place to start because publishing costs nothing and nobody gates who's allowed to try. Check out the full Discord Bot Business Playbook infographic below for profitable bot ideas, tech stack, and pricing tiers. Bookmark this.