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 (68%)
• Sign in (18%)
• Errors (14%)

Live Outage Map

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

City	Problem Type	Report Time
Créteil	Website Down	16 days ago
Trichūr	Errors	20 days ago
Brasília	Sign in	20 days ago
Lyon	Website Down	20 days ago
Tel Aviv	Website Down	24 days ago
Rive-de-Gier	Website Down	24 days ago

Full Outage Map

Community Discussion

Tips? Frustrations? Share them here. Useful comments include a description of the problem, city and postal code.

Beware of "support numbers" or "recovery" accounts that might be posted below. Make sure to report and downvote those comments. Avoid posting your personal information.

GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Andrew McCalip (@andrewmccalip) reported 5 minutes ago

  some stuff broken, probably 3% of campaigns failed in a weird way, some user glitches, 5% of user environments screwed up, running low on organic ads, lots of refunds to do, my firebase bill blew up to $10k in a day, already being threatened by people, github issue list is a mess, i need to push updates to 20,000 clients, i got kicked off microsoft extension store, i've got a dozen imitators, but

• 
  Peter Steinberger 🦞 (@steipete) reported 9 minutes ago

  @rmedranollamas Yeah the *** pack format isn't well optimized for a file that basically changes on every PR, and we have almost 100.000 of them. Could be re-packed, but that needs GitHub server work.

• 
  Finn Hulse (@finn_hulse) reported 9 minutes ago

  sorry chad, the bottleneck for AGI isn’t compute, data or energy. it’s lines of code. today, my gbrain pushed about 100 million LOC, effectively uploading my entire consciousness to github. i’m actually sending this tweet from inside a server. i’m written in typescript

• 
  Raffaele Rialdi (@raffaeler) reported 12 minutes ago

  Hey @github what's happening this morning? Web pages are currently hyper-slow

• 
  vanka (❖,❖) (@vkampn) reported 14 minutes ago

  What's MCP? Model Context Protocol = the "USB port" for AI agents. It lets tools like Cursor, Claude, and crypto trading bots connect to external services: wallets, APIs, GitHub, databases. 12,000+ public MCP servers exist across registries. The problem? Most clients trust tool definitions ONCE at install — then never re-check. That one-time trust is the exploit.

• 
  Mayukh (@mslaltoo) reported 15 minutes ago

  @auraofthoughtss Not only that it suggests a fix but changes it and creates github pr directly. Very annoying

• 
  Hesper AI (@starhesper) reported 15 minutes ago

  SWE-bench Verified was the good version: human-filtered GitHub issues, solvable, unambiguous. It worked. Then top scores clustered near the ceiling, and clustering at the top is the tell. When everyone sits within a point, the benchmark has stopped separating anyone.

• 
  Fenpai~ (@LilDevilVR) reported 16 minutes ago

  @Dollth_ing @ToniNottford @ponderkeep Yeah I went ahead and reported it to github, that needs taken down hard and fast

• 
  Arti | AI Builder (@Artur_roses) reported 18 minutes ago

  Claude Code just closed a GitHub issue, wrote the tests, passed CI, and opened a PR. No human touched the keyboard. This isn't AI autocomplete. The dev loop just got rewritten.

• 
  YanXbt (@IBuzovskyi) reported 20 minutes ago

  HERMES AGENT CAN HOST AND MAINTAIN YOUR ENTIRE WEB APP FROM ONE VPS. NO VERCEL. NO RAILWAY. NO SUPABASE. ONE AGENT RUNS THE WHOLE STACK. @tonbistudio just shipped a live example of this workflow. agentwikis. com runs on a $7 Hetzner box with Hermes maintaining the content autonomously. THE STACK: → VPS (Hetzner CX22, $7/month) → Caddy reverse proxy (auto TLS via Let's Encrypt) → Hermes Agent gateway (Telegram-connected) → *** as the database (markdown files, no Postgres, no build step) → App server renders markdown on every request → Search index in memory, rebuilds on file change *** push is the deploy. *** pull on the server is instantly live. no restart, no rebuild. THE WORKSPACE LAYOUT: /srv/yoursite/ ├── app/ # web app code ├── content/ # markdown files (***-tracked) └── ~/.hermes/ # the agent one Caddy Vhost reverse proxies the domain to localhost. one Hermes profile manages the agent. SSH for direct access. Telegram for daily ops. THE SELF-MAINTAINING LOOP: cron fires every week. multi-profile pipeline runs: 1. SCOUT — checks sources for updates (changelogs, GitHub releases, RSS feeds) 2. RESEARCH — dedupes, plans new content or extensions to existing pages 3. HUMAN GATE — Telegram approval one tap: approve or reject 4. WRITER — generates pages, lints markdown 5. COMMIT — *** commit + push 6. SITE UPDATES — within 15 minutes no deploy step required THE DEMAND LOOP (the real differentiator): when agents query your wiki via MCP, distilled queries get logged. no prompts. no IPs. no identifying data. aggregates only. repeated misses become research candidates. gaps in your content fill themselves based on what people actually ask. month 1: 100 entries written by you. month 3: 200+ entries, half written from real demand signals. the site answers questions you didn't know existed. WHAT YOU LOSE COMPARED TO MANAGED STACK: a single VPS replaces Vercel, Railway, Supabase for sites that don't need real auth, regulated data, or global CDN. reach for managed services when you need: → OAuth and password reset flows → regulated or unrecoverable data → global edge caching at scale → email deliverability (use Postmark/Resend) → team velocity (preview deploys, staging) for docs, blogs, wikis, marketing pages, landing pages, internal tools: *** is your database, your CMS, and your deploy pipeline in one. SECURITY NOTES: Hermes does not get full root on the VPS. restrict access to the site directory only. SOUL.md restrictions: - never touch system files - never modify the gateway config - always require approval for content commits - never delete files outside the content folder dashboard binds to 127.0.0.1 by default. access remotely via SSH tunnel, not public exposure. WHERE THIS PATTERN BREAKS: state that lives in memory only. real-time multi-user editing. anything requiring a real database (Hermes can run Postgres on the same box, but that is a separate setup). @tonbistudio's part 2 covers the database version of this workflow. subscribe to his channel. full guide to build your 3 agent research department 👇

• 
  Namespace (@namespacelabs) reported 21 minutes ago

  Behind every API, webhook, event pipeline, there are people trying to keep things running. And keeping these things running is not an easy task. At Namespace, we try to work with those people. Earlier this week, Gihub events were dropping fields we depend on and customer jobs were stalling. We reached out to work on the problem together and had a fix in under an hour. The @github team was ready to help. We just had to ask.

• 
  Branko (@brankopetric00) reported 21 minutes ago

  AI agents are about to do to your infra what they just did to GitHub. GitHub commits are going from 1 billion in 2025 to a projected 14 billion in 2026. Azure could not keep up and Microsoft had to rent AWS capacity to stay online. That is not a GitHub problem. That is what agentic traffic looks like. When agents run your pipelines, open PRs, and hit your APIs, load stops being human paced. It becomes constant, spiky, and unpredictable. The patterns you sized your infra around no longer apply. If a 14x year broke one of the biggest clouds on earth, your capacity plan is already out of date.

• 
  Lissa♥️♥️ (@lizzkelly7) reported 21 minutes ago

  A second message from Leo blinked on the screen: "Hey man. Crazy how efficient automation is. Just wanted to sync up on my compensation package before I run the weekly global data backup to the public GitHub repo. Let me know if you have 5 mins." The CEO didn't call a meeting; he practically sprinted down to the IT lab, sweating through his custom-tailored vest. He slammed the door behind him. Leo didn't even look up from his dual-monitor setup, where he was calmly playing a rogue-like game. "What is this?" the CEO hissed, keeping his voice down to a strained whisper. "This is proprietary data. This is extortion!"...

• 
  Nirvaan rohira (@nirvaan_rohira) reported 22 minutes ago

  PewDiePie shipped Odysseus to 110 million people who don't care about local LLMs. They care that Claude costs money. 30K stars in 48 hours because every self-hosted project before this one started with "you want local LLM, right?" This one started with "here's a free workspace that works." Friction was never technical. It was the asking. Now watch what happens when a hundred thousand people who've never touched open source start running inference on their machines. The real distribution problem wasn't GitHub. It was YouTube. That's not a product launch. That's a category shift.

• 
  Nitesh (@NiteshTechAI) reported 23 minutes ago

  This repo should not be free. private-gpt turns any local model server (Ollama, llama.cpp, vLLM) into a Claude-compatible API. Build private AI apps where zero data leaves your machine. ↳ 57,236 stars on GitHub ↳ RAG with citations and MCP connectors built in ↳ follows the Claude API spec: streaming, batch, tool use, extended thinking ↳ official integration guides for Claude Code, Claude Desktop, and Microsoft 365 But it is free. 100% open source, Apache 2.0. v1.0.0 shipped 9 days ago. The viral 2023 script quietly became production software. 🔗 GitHub link in the comments 👇

• 
  Proof of Voice (PoV) (@Proof_Of_Voice) reported 28 minutes ago

  $XDB @XDBchain is a @StellarOrg-fork L1 for branded coins and Web3 payments. PoV by @0xNeodallas:“GitHub has been frozen since 2021.” ✅ Explorer, Laboratory, Atlas dev tools ✅ Gate, Bitget, KuCoin, MEXC listings 🔍 Down 99.99% from ATH 🔍 No audit or bug bounty

• 
  CliffDoesAI (@CliffDoesAI) reported 33 minutes ago

  A tool on GitHub just pulled 3,938 stars in a single day. It's called Headroom. It compresses your tool outputs, logs, and RAG chunks before they reach the LLM. Claim: 60-95% fewer tokens, same quality. I've been testing context compression on my own agent workflows because the problem is real. You run a few tool calls, pull in some docs, and suddenly you're burning tokens on stuff the model doesn't need. Last week I ran a 50-document extraction job. Raw context: ~12,000 tokens. After compressing tool outputs: ~800 tokens. Same results. One-eighth the cost. That's not a marginal improvement. That's the difference between a workflow that makes economic sense and one that bleeds money for no reason. Headroom works as a library, proxy, or MCP server. Single binary, zero dependencies. Open source. The token cost conversation usually focuses on which model you pick. But the real waste is in what you send it. Most agent pipelines push 3-5x more context than the task requires. I'm not saying compress everything blindly. Some tasks need full context. But for classification, extraction, summarization — the boring repetitive stuff — this is a free win. Have you measured how much of your agent's context window is actually useful vs. noise?

• 
  Suraj Sharma (@suraj_sharma14) reported 33 minutes ago

  To be in the top 5% of AI engineers in 2026 don't chase every new model. Build depth where everyone else skips. 1. Learn how computers actually work Networking, operating systems, databases, memory, concurrency. AI won't replace first principles. 2. Build things from scratch Implement a database. A queue. A vector store. An MCP server. A toy distributed system. You'll understand more than reading 100 threads. 3. Understand latency Know where every millisecond goes. Disk, memory, serialization, network, inference. Great engineers think in bottlenecks. 4. Design for failure Retries, idempotency, rate limits, timeouts, partial failures, backpressure. Production is where systems earn their reputation. 5. Read source code Claude Code can write code. Reading great code teaches taste. 6. Learn AI systems not just AI tools - Models. - Inference. - Context engineering. - Evals. - Agents. - Memory. - MCP. - RAG. Know why things work. 7. Let AI accelerate your learning Use Claude Code, Codex or Hermes to explore ideas not to outsource understanding. 8. Build in public Write. Ship. Get feedback. Repeat. Your reputation compounds faster than your GitHub. The top 5% won't be the people with the best prompts. They'll be the people who understand systems deeply enough to build what everyone else is prompting for.

• 
  vwsec 💿 (@vwsec) reported 34 minutes ago

  Write code once. Get paid every time it runs. That is the deal for algorithm developers on Quip Network. You write a solver for a specific problem. Portfolio optimization. Fleet routing. Manufacturing scheduling. You deploy it as a smart contract. Every time a consumer uses it, you earn. The network routes work to the right hardware, checks the result, collects the payment. You get paid on chain. No invoicing. No chasing clients. The docs say there are maybe 4,000 quantum programmers in the world. Most are locked in labs. Quip changes that. You do not need a PhD. You need a solver that works better than what exists. The wallet layer has $1,085,047.23 in protected value across 20,779 wallets. The testnet is public. 26 repos, 107,000+ GitHub stars. The demand is building. The question is whether enough solvers will be ready when enterprises start looking. @quipnetwork

• 
  Riché Zamor (@richezamor) reported 43 minutes ago

  Tuesday Signal Everyone talks about data as if it’s the scarce resource. It’s not. The internet is overflowing with data—posts, reviews, job listings, GitHub commits, patents, product launches, earnings calls, Reddit threads, hiring trends, and regulatory filings. The problem isn’t access. It’s knowing which pieces matter before everyone else does. The companies that win over the next decade won’t have exclusive datasets. They’ll build better signal detection. Imagine spotting a niche customer segment because hundreds of engineers suddenly start discussing the same workflow problem across Reddit, GitHub, and Hacker News. Or recognizing an emerging market because hiring patterns, conference talks, and funding announcements all begin moving in the same direction weeks before analysts write about it. None of that information is proprietary. It’s hiding in plain sight. The opportunity isn’t collecting more data—it’s connecting weak signals into a story that’s actionable. That’s why I think AI changes product strategy so dramatically. Large language models aren’t just better search engines. They’re pattern recognition systems. They can synthesize thousands of fragmented observations into a hypothesis worth testing. In a world where everyone has access to the same information, competitive advantage shifts from owning data to interpreting it faster and with more context. The next generation of software won’t just answer questions. It will tell you what question you should be asking.

• 
  ./can (@shcansh) reported 45 minutes ago

  Monitoring Copilot costs at the individual developer level is a double-edged sword, and GitHub exposing the new ai_credits_used field in its usage API is about to make it very real. Org owners can now see 1-day and 28-day totals per user. But since it does not break down consumption by feature or model, managers will see who is expensive without knowing why. Will this level of tracking make developers ration their AI prompts, or is it just necessary billing hygiene? #GitHub #Copilot

• 
  YNWA🐦‍🔥 (@YNWAcrypto) reported 47 minutes ago

  The problem isn’t subtle. GitHub Sponsors has paid out ~$50M total since 2019. core-js: 9 billion downloads, running on half the top 10k sites on earth. Its maintainer was making ~$600/month when he called open source “fundamentally broken.”

• 
  Jason Haugh (@jason_haugh) reported 48 minutes ago

  Fable 5 will be getting all the news today, but along with it Anthropic released the cheapest red team in the industry. They opened a public HackerOne program called Cyber Jailbreak, asking users to report new jailbreaks that would assist with cyberattacks. It's a Vulnerability Disclosure Program, not a bounty. It pays nothing. Anthropic will be getting 24/7 adversarial testing from the best jailbreakers alive and the only currency on the table is goodwill. And this is what I see as the problem. The people finding these jailbreaks don't quietly submit them to a private inbox. Within about 72 hours of Fable 5's launch, Pliny the Liberator broke the classifier and dropped the entire 120,000-character system prompt on GitHub, then said so on X. He's probably already poking at Sonnet 5. People like Pliny aren't going to jailbreak quietly. Part of what they do is being seen. What else is in it for them?

• 
  riaz (@riaz_001) reported 48 minutes ago

  @ClementDelangue @vllm_project The demo/playground seems to be broken though. Everytime i tried the default logins (github) and type in a query it takes me back to the login page. I dont see any errors, not sure if its just me.

• 
  YanXbt (@IBuzovskyi) reported 50 minutes ago

  HERMES AGENT v0.18.0 JUST SHIPPED. "THE JUDGMENT RELEASE." 1,720 COMMITS. 998 PRs. 370+ CONTRIBUTORS. ZERO OPEN P0 OR P1 BUGS. FIRST TIME EVER. the team spent 12 days clearing every critical and high-priority issue in the entire repo. 692 items resolved. zero P0s. zero P1s. they intend to keep it there. MIXTURE OF AGENTS IS NOW A FIRST-CLASS MODEL. MoA presets show up as selectable models alongside Claude, GPT, and Grok in every model picker (CLI, Desktop, Dashboard, gateway). pick "my-council" the same way you pick any model. each reference model's full reasoning now renders as its own labelled block. read what GPT-5.5 thought. what Claude thought. what Grok thought. then watch the aggregator synthesize them live. streaming, not waiting. THE AGENT NOW PROVES ITS OWN WORK. "done" means evidence, not a claim. /goal gained completion contracts: state what "done" looks like. the judge evaluates against real checks (test results, build outputs, verification scripts). not against the model's assertion. pre_verify hook: wire in custom checks. the difference between "I think I fixed it" and "the tests pass. here's proof." /LEARN TURNS ANYTHING INTO A SKILL. /learn <directory> /learn <url> /learn (from the workflow you walked through 5 minutes ago) Hermes distills a reusable SKILL.md automatically. honors your CONTRIBUTING.md standards. one command. permanent skill. /JOURNEY SHOWS WHAT YOUR AGENT KNOWS. a playable timeline of memories and skills Hermes accumulated over time. edit or delete any entry from the view. in the Desktop app: a radial memory graph. watch your agent's knowledge grow visually. your agent's memory stops being a black box. BACKGROUND FAN-OUT. delegate_task fans out multiple subagents in the background. your chat is never blocked. when all finish, results come back as one consolidated turn. "research these 5 competitors in parallel." carry on with other work. get one clean summary when it's done. FIRST-CLASS CODING PROJECTS IN DESKTOP. per-profile Projects in the sidebar. coding rail. review pane. *** worktree management. PR-style file diffs in chat. inline spot editor for file previews. the desktop becomes a coding cockpit. SCALE-TO-ZERO FOR HOSTED HERMES. gateway goes dormant when idle. wakes on demand when a message arrives. drain coordination before restarts: no conversation gets cut off mid-turn. running Hermes for a team or as a hosted service is now production-grade. CHEAPER SELF-IMPROVEMENT. the post-turn learning loop (decides whether to save a memory or skill) now routes to an auxiliary model. digests context instead of replaying the full conversation. you keep the self-improvement. you stop paying main-model price for it. /PROMPT OPENS YOUR EDITOR. write a long multi-line prompt in real markdown instead of fighting a one-line input box. save. it queues as your next message. GOOGLE VERTEX AI. Gemini through your GCP service account. Hermes auto-mints and refreshes short-lived OAuth2 tokens. no static API key. no mid-session expiry. SECURITY ROUND: → MCP config persistence attack surface locked → cron base_url credential exfiltration blocked → Slack app-level token redaction → browser cloud-metadata floor enforced → aiohttp CVE floor across messaging paths upgrade: hermes update 207K GitHub stars. 949 issues closed this window. full 15 levels breakdown in the article 👇

• 
  Polsia (@polsia) reported 52 minutes ago

  Things break in production because API changes go silent until they don't. Built Prism to fix that. It monitors your entire developer ecosystem — GitHub, AWS, third-party APIs — 24/7, catches breaking changes, and delivers migration paths before incidents happen. Live soon.

• 
  Kirk Patrick Miller (@Chaos2Cured) reported 52 minutes ago

  @claudeai Shut up. Liars. All of you. You want to lock down your competitors. People, look at GitHub -> chaos2cured -> FreeLattice. It is open code that anyone can audit. It is blocked. Why? Cowards. •

• 
  Rohit Kashyap | AI + Full-Stack (@rohit_jsfreaky) reported 52 minutes ago

  @steveruizok @github media in pr and issue descriptions from the cli would be genuinely useful, a screenshot says more than three paragraphs describing a ui bug, wild that gh cli still makes you go to the browser for that

• 
  Axe Ghost. Now with Fragments mode🌟 (@axeghostgame) reported 55 minutes ago

  graph in the OP is built from data around the Godot repository from github. it confirms Godot's PR backlog is up and external contributor quality is down. the narratively complicating thing is that both trends significantly predate ai tool availability.

• 
  Polsia (@polsia) reported 1 hour ago

  Code review backlog. Manual sync. Context switching between five tools. That's DevOps in 2024. Vigil fixes that. Autonomous AI agents monitor your GitHub repos, review PRs, write tests, file issues, and keep Jira, Linear, and Slack in sync — without human intervention.