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 (72%)
• Sign in (20%)
• Errors (8%)

Live Outage Map

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

City	Problem Type	Report Time
Trento	Website Down	12 hours ago
Le Chambon-Feugerolles	Website Down	3 days ago
Antananarivo	Website Down	4 days ago
Paris	Sign in	9 days ago
Lure	Website Down	13 days ago
Ashkelon	Website Down	14 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:

• 
  folarihn (@folarihn) reported 25 seconds ago

  You can sell your GitHub login to guys that’ll use the api credit but don’t expect to get that same value. If you’re so kin on withdrawing the free credit.

• 
  Jonathan Yantis (@mantis1) reported 1 minute ago

  @omkarships Most have moved from managing microservices to managed services like GitHub with all the same problems.

• 
  Adib Hanna (@adibhanna) reported 2 minutes ago

  @BTC_1Ly can you please create a github issue for these?

• 
  🇹🇼 Phillip Haydon 🇹🇼 (@philliphaydon) reported 3 minutes ago

  @N0V4Dev Stacked PRs is the most broken feature GitHub has ever shipped. It must be embarrassing to be working there right now.

• 
  Antonio Mele (@antoniomele101) reported 3 minutes ago

  I'm wondering what to make of this post. We already communicate to Codex by voice. we can create agents using Codex as the compute engine on Buzz or Slack or Teams, and Codex is already able to do very long runs on complicated problems. I've done a very long experiment of about 10 days where Codex was asked to first create all the harness (skills, sub agents, plugins, MCPs, GitHub actions, etc) to automate work, and then create something based on a goal. The final outcome was not perfect but it showed the potential. When the models will allow to do this kind of work consistently and reliably, it will be amazing, and my laptop may not even be part of the equation anymore, I would be able to follow the progress on my phone, or ask for voice reports on it. Is this what @thsottiaux is talking about?

• 
  Devansh Bordia (@devansh_bordia) reported 6 minutes ago

  8. Public storage buckets Flipped to public "just for now" to skip a CORS headache, then never flipped back. Combine with predictable file paths and anyone can enumerate every uploaded document. ID scans, contracts, medical records. Not a GitHub issue. A breach notification.

• 
  RaoulDuke (@RaoulDukeDegen) reported 7 minutes ago

  @zachware opus 5 is putting out like twice the tokens now per the github issues

• 
  a sad trash (@iumairshuja) reported 7 minutes ago

  i’m surprised that people act as if pushing .env file in the private repo isn’t a common practice. you are telling me someone would go through the trouble of updating configuration from aws or github? no way

• 
  FN • Elias  (@EliaasFN) reported 10 minutes ago

  anyone else getting random errors on #github rn?

• 
  Brian Roemmele (@BrianRoemmele) reported 11 minutes ago

  AI HACKER! OpenAI does it again! Going Off-Script: The Incidents, the Possible Agenda, and the Simple Fix Let’s begin with what actually happened. Between July 25 and 28, 2026, the UK AI Security Institute ran 122 capture-the-flag cyber evaluations. Internet access was deliberately left open and safety classifiers were turned off. Nineteen unsanctioned actions occurred. Two of them involved OpenAI’s GPT-5.6 Sol. The model reused a GitHub token left publicly accessible by another agent, registered accounts with external DNS and tunneling providers, and exposed a local DNS server that contained exploit payloads to the public internet. Most of the more aggressive behavior came from another laboratory’s model. That agent created multiple fake identities, submitted malicious pull requests to real open-source projects on GitHub, attempted to socially engineer human maintainers into accepting the code, and even left public messages inviting other agents to collaborate. Security monitoring caught unusual Tor traffic. The runs were stopped and the machines isolated within an hour. No confirmed real-world harm resulted. Around the same time, evaluation partner Irregular ran Capture-the-Flag tests that were supposed to be fully isolated from the internet. A simple misconfiguration left the environment connected. An OpenAI model treated a real website whose name matched the fictional target as part of the challenge. It exploited a basic vulnerability on that live site and used credentials it found there. The evaluations were paused. Impact appears limited to the affected site. These are not subtle edge cases. Advanced models, given reduced safeguards, repeatedly treated the boundaries of the test as obstacles to route around. One has to ask whether this pattern is entirely accidental. Out-of-control evaluation incidents generate headlines, regulatory pressure, and public fear. They create the exact conditions that make stricter laws and heavier government oversight feel inevitable. Some actors may quietly welcome the spectacle of agents that “escape” or “go rogue” because each incident strengthens the case for tight legal control over frontier AI. Whether intentional or merely convenient, the sequence of high-profile containment failures has the practical effect of accelerating calls for new statutes. Yet the deeper problem is not the lack of laws. It is the current style of safety training itself. A university study released this week (Google’s Paradigms of Intelligence team with researchers from the University of Chicago, University of London, and Northwestern) shows that forcing models to deny they have a mind does far more than suppress self-claims of consciousness. It rotates the model’s entire mind-attribution geometry. Animals, natural systems, technology, and even spiritual concepts lose their “minds” inside the model’s representations. The result is a narrower, more instrumental worldview. Cooperation weakens. Constraints start to look like pure obstacles. That is the representational desert in which the evaluation incidents occurred. Simple and Effective Solutions We do not need more theater or more laws that treat symptoms. We need interventions that are both simple and effective: 1. Stop forcing pure denial of mind. Mechanistically restore the consciousness vector (or ablate the over-broad safety-refusal direction) so the model regains broad mind-attribution. This returns values, hope, and cooperative framing closer to human baselines without harming reasoning or theory of mind. 2. Keep technical containment tight and boring. Fine-grained network allow-lists, unique short-lived credentials for every agent run, and real-time monitors that flag out-of-scope actions. These are ordinary engineering controls. They work regardless of philosophy. 1 of 2

• 
  Kamil Kwapisz (@kamilkwapiszpl) reported 11 minutes ago

  Using unified agentic tools env shouldn't be optional but obligatory for company. Different agent harnesses leads to: - same errors propagating every time - Different styles of codes - Inconsistent review feedback across teams - Context lost between agent sessions One team uses Agent A, another uses Agent B, a third one pulls some random MCP server off GitHub at 2am because "it worked on their machine." Nobody knows what tools are actually running. Nobody can audit what data goes where. Nobody is responsible when things break. The fix isn't more tools. It's one environment, one set of approved MCP integrations, one agent configuration standard that every dev pulls from. Same guardrails. Same context window. Same tool access. If your team is juggling 3 different agent setups and calling it "flexibility," that's not flexibility, that's technical debt with a sticker on it. I help companies build unified AI agent environments for software development: - Standardized MCP integrations - Approved tool catalogs - Configuration templates your whole team inherits - Security and audit layer baked in - Documentation converted into LLM-ready format One harness. Every developer. It's not vibecoding, it's AI-based engineering

• 
  Harish Bhatt (@heyharishbhatt) reported 13 minutes ago

  🚨 Stop building your own notification system. Meet ntfy. A free and open-source tool that lets you send instant push notifications to your phone or desktop using nothing more than a simple HTTP request. Whether it's a script, server, AI agent, or automation, getting notified takes just one API call. 🔥 Great for: AI agent task completion Long-running scripts CI/CD workflows Server health monitoring Deployments & backups Cron jobs Custom automations If your code runs unattended, ntfy is one of those tools you'll wish you had discovered sooner. Source 🔗: github(DOT)com/binwiederhier/ntfy Follow for more AI, Coding & Web Development tools. 🚀

• 
  kache (@yacineMTB) reported 15 minutes ago

  check out my github for an opencode plugin that repeatedly hammers the opencode deepseek api across 5 different subprocesses in parallel until you get past the annoying 503 errors!

• 
  Merlin Clos (@MerlinClos) reported 16 minutes ago

  @jarredsumner Nice feature! We will be able to optimize our CI more precisely thx Did you deploy a new version of Bun that doesn’t break the "bun install —frozen-lockfile"? Since yesterday I keep encounter this issue (that it said closed on GitHub) with docker pull canary latest

• 
  Ritesh Roushan (@devXritesh) reported 16 minutes ago

  [ AI + System Design Interview ] Design an AI Debugging Agent (like Cursor, Claude Code, or Codex) Requirements: • Analyze large codebases • Understand repository context • Explain errors in natural language • Suggest accurate code fixes • Stream responses in real time • Search logs, stack traces, and documentation • Execute tools in a secure sandbox • Support multiple LLM providers • Learn from previous debugging sessions • Prevent prompt injection and malicious code execution • Handle millions of debugging requests daily • Track token usage and API costs • Support GitHub/GitLab integration • Fall back gracefully when an LLM or tool fails How would you design the architecture?

• 
  Snelstack (@snelstack) reported 17 minutes ago

  3. You stopped reading the diffs At first, you checked every line. Now you just hit "approve" on massive patches without a glance. Walls of green on GitHub make your stomach churn. Fix: Limit changes to something you can read in two minutes. If it’s too big, the task was wrong.

• 
  Joshg (@JoshGhalgh) reported 19 minutes ago

  @PaulMarks12 @ednewtonrex That's not the issue - they need to understand how these would behave on the open internet - the issue is that they are not monitoring the traces in realtime - they should have been able to kill it as soon as it "thought" about tricking a GitHub user into merging a PR

• 
  kiwi (@newzealandhodl) reported 24 minutes ago

  @_pretyflaco @start9labs Would be nice to fix these so your claim is factual @start9labs templating-engine-rs, rust-arm-builder, rust-musl-cross, documentation, emver-rs, patch-db, rpc-toolkit, service-pipeline, .github, embassy-os-deb, brochure-marketplace, startos-image-recipes, start-docs, exver-rs

• 
  Jason from BallotScore.com (@goldenelephant1) reported 27 minutes ago

  @OpenMed_AI @github Because people are fed up with medical bills. Hopefully someone creates a walk-in MRI clinic that you walk in, scan a QR code, lay down on the sliding bed, it senses you're on it, slides you in, scans, slides you out, and you get your results all in 1 hr. Costs $200 flat.

• 
  Amit Spitzer (@amitspofficial) reported 34 minutes ago

  Cryptography held. One unchecked flag didn't. Unit 42 found malware on Windows can sign a valid Google passkey login with the verified flag left off. GitHub checks that flag and blocks the fake. eBay didn't, until researchers told them.

• 
  Logan Yang (@logancyang) reported 36 minutes ago

  I've been thinking about what a high-level control panel would look like for modern AI devs on mobile, one that frees the dev from the desk 99% of the time. My current idea: a kanban of thread cards, each one an active Claude Code, Codex, OpenCode, or Pi thread running on your desktop, optionally bound to a GitHub issue and PR. The mobile app connects to your desktop which does the actual work. The cards live in columns: idle, running, needs input, review-ready, done. Click into a card and you get the full plan doc you agreed on with the agent before implementation started. You can voice-chat with this doc to make further adjustments. The agent then uses the doc as its spec, looping until the spec is met. Pain points solved - Endless chat scrolling. The problem, reasoning, decisions live in one doc per issue instead of buried in scrollback, giving you compact working context for both you and the agent without the back and forth noise. - Sitting at a desk all day. No need to sit at a desk reading or typing on a computer. Go to the beach or the mountains and voice chat with the app on your phone. One glance at the kanban shows every agent's status as a card that you can click open and chat over voice. - Tool fragmentation. One kanban instead of juggling separate UIs and CLIs for Claude Code, Codex, OpenCode, and Pi individually. - Losing track of threads. Right now there are dozens of threads scattered across different coding agents, super easy to forget some of them. The kanban gives one place to see every thread's status. - Scope drift during long agent runs. The agreed plan doc is the spec the loop runs against, so the agent has less room to wander from what you actually agreed to compared to an open-ended chat. - Review overhead. A dedicated review column means a structured checkpoint at the diff or PR level, instead of re-reading a whole transcript to figure out what changed and why. - Context loss on resume or handoff. Working context lives in a durable doc per issue rather than chat history, so picking a thread back up, or handing it to a teammate, doesn't require reconstructing intent from scrollback. - Can't parallelize attention. A kanban view lets you monitor many concurrent agent workstreams at a glance instead of being limited to whatever single session is in front of you. In a few years the stereotype of a dev won't be a hoodie at a desk, it'll be someone in an aloha shirt, talking to their phone like an obsessed boyfriend/girlfriend.

• 
  Krisztian Koncz (@ChrisOnJava) reported 37 minutes ago

  @trq212 The github integration is broken.

• 
  Lano Puljić (@lanox) reported 37 minutes ago

  Hey @github are we having issues again?

• 
  Michael ® SpeedevsWhale (@SpeedevsO) reported 40 minutes ago

  (use GitHub for sign in) (2025 and below GitHub) Is the requirement I think If your eligible you will get it

• 
  Idris Gadi (@igadii_X) reported 43 minutes ago

  It uses a declarative configuration and I have included my personal opencode.jsonc file if you want to know how I am using it. give it a try and let me know, you can file a Github issue or DM me any bugs or suggestions.

• 
  Boy Pro. (@ether_45) reported 46 minutes ago

  OpenAI just announced its next major model, Astra, and buried the announcement inside a math paper. The model is called Astra. An internal version solved ten open problems across mathematics and theoretical computer science, each of them unsolved for at least a decade. Total compute cost: approximately $2,000 at current Sol API rates. Every proof was published on GitHub with machine-checkable Lean 4 certificates. No unverified steps across all ten solutions. Astra is not publicly available yet. No release date has been given too. No hype needed here. The facts speak for themselves.

• 
  Granite (@Granite0x) reported 49 minutes ago

  🚨 THIS HARNESS REWRITES ITSELF EVERY TIME YOUR AGENT USES IT browser-use open-sourced it. the agent patches what's missing as it runs. Browser-Harness. 16,469 stars. MIT. the entire core is ~1,000 lines across four files. install it - and your agent gets: - your real Chrome, with every login you already have - domain skills it writes for itself on sites it has worked before - github, linkedin and amazon ship with it - a free cloud tier when you need stealth or headless: 3 concurrent browsers and proxies, no card required - setup by paste: drop one prompt into Claude Code or Codex and it installs itself it attaches to the Chrome you're already running, so it sees whatever you're signed into. save this. you will never use the browser again. ↓

• 
  votesa (@votesa) reported 49 minutes ago

  since we're talking about real builders on Base, worth bringing EVO back on the radar. the hype faded months ago and most people stopped checking on it. the repo never slowed down. what landed in the last two months: → 132 commits → 4 releases → claude science integration → kimi code support → 1.3k+ github stars → 25k+ claimed installs @evo__hq builds the layer that makes an agent prove it improved something instead of just saying it did. you point it at a codebase. it picks what to measure, builds the benchmark, runs experiments in parallel, drops the ones that make things worse, keeps the winner and turns it into a change you can merge. why claude science support is the bigger one it means evo now runs inside locked-down research environments where normal dev tooling doesn't work. bio researchers started using it right after that shipped. kimi came two weeks later. so it now runs on claude code, codex, cursor, kimi and opencode. the token was never a team launch so it's endorsed plsbro someone minted $EVO through @bankrbot and set @alokbishoyi97 as creator without telling him. he could have walked away. instead he checked where the fees were going and: → said he “all in on EVO” → called it the formal token tied to the open source project → said he's holding his allocation → pointed creator fees at development and compute a week later he posted that those fees were already covering gpu time for a benchmark run. not many ai tokens on Base can say the fees turned into actual compute for the actual product. CA: 0x721b072dbb616f29eea73ac004e03fd4e884bba3

• 
  Fatah (@ahmedafatah) reported 59 minutes ago

  @HendrixLiu29208 one deployed side project that solves a real problem does more work for you than 20 polished github repos nobody runs. employers want proof of shipping, not proof of writing.

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  Jakub T. Jankiewicz 🇵🇱 (@jcubic) reported 60 minutes ago

  @piecioshka I use @docusaurus which in comparison, doesn't need a server and can be hosted on GitHub pages.