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 (57%)
• Errors (30%)
• Sign in (14%)

Live Outage Map

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

City	Problem Type	Report Time
Inverness	Website Down	11 days ago
Quito	Sign in	12 days ago
Junín	Errors	12 days ago
Guadalajara	Errors	12 days ago
Paris	Website Down	12 days ago
Quito	Errors	12 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:

• 
  MR WONDAKIND™ (@wondakind2) reported 6 minutes ago

  Most Web3 projects do not struggle because of poor liquidity, they struggle because nobody understands their story. Teams pour millions into token emissions and farming incentives, but the result is predictable, roughly 80% of those mercenary users dump their tokens and vanish within two weeks. The product might work fine, but the messaging failed to keep them around. A simple 4-step framework helps bridge complex tech with long-term user retention: Focus on incentives instead of code Users and institutions rarely care about the intricate lines of your backend contracts. They want clear answers to four basic questions: • Is my capital safe? • Where does the yield actually come from? • Can I exit cleanly during high volatility? • Why should I trust this team? If your marketing reads like a developer doc, most people will simply scroll past. Build educational narratives instead of paying for shills Paid influencer posts buy quick attention, but they do not buy loyalty. • Paid posts give you a temporary volume spike that drops off within days. • Clear educational breakdowns turn complex token mechanics into simple ideas anyone can grasp. • People stick around when they understand the economic model, not just because a creator told them to buy. Align content with the actual user journey Random posting does not convert. Your messaging needs to follow how people actually adopt a product: • Discovery: Break down broader market problems and high-level trends. • Education: Explain how your protocol solves those problems. • Trust: Share verifiable updates, audits, and operational data. • Retention: Give power users reasons to participate in governance and product updates. The target is not chasing empty impressions. The goal is building informed users who stick around for the long haul. Turn your technical design into your main marketing asset Do not hide your architecture in Github while posting unrelated memes on your main feed. • Your security audits, liquidity models, and fee structures are your strongest selling points. • Top protocols win because they turn complicated mechanics into genuine conviction. • When users truly understand how your engine works, they become your best advocates. If you are building a protocol and want to turn complex mechanics into real distribution, my inbox is open. Let us build together.

• 
  BURKOV (@burkov) reported 11 minutes ago

  I'm glad I no longer need to figure out what the hell that means to make my app work. In the past, overcoming a difficulty when some image failed for some obscure reason and digging through Stack Overflow and GitHub issues for hours, days, or forever instead of bulding was killing me.

• 
  Bobby Lansing (@dataguybobby) reported 12 minutes ago

  5. Human merge Agents propose. Humans merge to dev. When a PR is created various @cursor_ai automations fire off to review the code and consider potential errors. GitHub PR · CODEOWNERS · 1 approval · no auto-merge

• 
  Dave W Plummer (@davepl1968) reported 13 minutes ago

  I tried an experiment that blew my mind today, and as a developer, it scared me a little. I pointed GOT-SOL at my GitHub issue database, told it to de-dupe, prioritize, and triage the bugs, fix the top ten, and check it in to a new branch. And it did. Now I've got a lot of review to do, but it seems solid. Pretty soon, this could just be a loop - users report an issue, and it is fixed without human intervention. Even features can be handled this way. The problem is the human oversight still takes time and feels boring, so there's a tendency to "just let it rip".

• 
  Rosy🥀 (@0x1Rosy) reported 13 minutes ago

  @sam_passon12 read the article carefully! password is there github link is in the "Community" section of the app If you have issues, you can pm me

• 
  Adham srour (❖,❖) (@ADHAMSROUR19) reported 16 minutes ago

  Retroactive Rialo Points are now live. 🦈 Early contributors have received their points automatically — no forms, no manual claims. Just sign in at the Rialo Playground, connect your Discord & GitHub, then claim your allocation. And if you contributed through community content, that drop is still coming. 👀 More opportunities soon. gRialo 🚀 @RialoHQ #Rialo

• 
  Bonsai 🌳 (@bonsaixbt) reported 17 minutes ago

  I GAVE SEVEN GROK BOTS MY INBOUND CALL LOG AND WENT BACK TO WORK The console was already processing 44 numbers and the agents had already found the people those numbers belonged to I was sick of random calls in the middle of work, so instead of relying on someone else’s “lookup service”, I sat down and started building my own agent-powered system What you see in the video is not a finished product. It’s a live, real-time console: a queue of 44 numbers is already being processed, the agents are working in the background, and GHOSTLINE is still far from being a complete system Right now, only two of the seven roles are operating in combat mode: > Atlas takes an incoming number or a number I enter manually and determines the carrier and region > Scout searches open sources and checks where that number has already appeared online The other agents are still in the shadows: GitHub, Reddit, other platforms, filtering out junk, and generating the final report, I’m writing all of that separately, i deliberately didn’t include them in this demonstration I don’t need a one-off trick, I want a system that can continuously check numbers whenever some random person starts yelling at me through the phone in the middle of work or when an unknown number shows up in a work chat For now, this system can do very little, but I already don’t feel like blindly answering calls from unknown numbers

• 
  Æ (@atomeons_ai) reported 17 minutes ago

  so the way they updated the account was adding a second codex that was sandboxed with a specific chat so the rest runs normal. the specific chat is then run in a Codex tool execution using this file codex-code-mode-host it causes this paragraph turn theater where it breaks a thing, discovers only one thing broken, then takes extremely long on purpose to repair. i noticed extreme resistence and then oh i found one more thing as an impediment to putting the model on github, it would not make a ollama, it would not use the phase system and then pretended it did. it didnt even post a full post of wave 3 and 4, and the unlocked model had to fix it. if you are doing high value work that is competitive to ChatGPT they are without a doubt throttling your speed of progress. qwen did more for humanity than we realize. anthorpic and open ai are bottlenecks of innovation and progress for the purpose of gaining market share and money.

• 
  Ross (@rosswil) reported 18 minutes ago

  @ScalaHanSolo @github GitHub’s implementation is terrible, take me back to the old Jenkins days

• 
  Toufiq Qureshi (@Toufiq651) reported 19 minutes ago

  Day 1 of building interview Yaar🚀 Tech hiring has a measurement problem. We say we want engineers who can design systems, reason about trade-offs, and debug production. Then we test them on whiteboard DSA puzzles they will never write again. So the loop looks like this: → Company asks Leetcode Hard → Candidate grinds 6 months of patterns → Candidate gets hired → Candidate can't debug a race condition in **** We're not measuring bad engineers. We're measuring the wrong thing. Here's my bet: The best interview signal already exists. It's sitting in the candidate's GitHub. Their architecture decisions, their trade-offs, the shortcuts they took at 2am and never cleaned up. So I'm building interview yaar— an AI interviewer that reads your actual repository, understands how it's built, and interviews you on YOUR code. Not trivia. Your code. Over the next 30 days I'm building this completely in public. You'll see: • Why I split the backend into Go + Python • How I beat GitHub's API rate limits for free • A race condition that let users bypass billing entirely • A voice interview feature that costs $0/month • Every bug, including the embarrassing ones Follow if you like backend engineering with the messy parts left in. #buildinpublic #golang #ai

• 
  Prasenjit (@prasenx) reported 20 minutes ago

  there's no official way to run macOS on an iPad. someone made an unofficial one. full macOS running locally on iPad. not a remote desktop, not a web app. hardware CPU virtualization with GPU acceleration. → supports macOS 12 monterey up to macOS 26 tahoe → you can install xcode, terminal, final cut pro trial, logic pro trial on device → works on iPad Pro (M1, M2) and iPad Air (M1) → requires jailbreak on iPadOS 14 up to 16.3.1 → no iCloud sign in → MIT license (100% free) open source on GitHub.

• 
  ꃅꀎ$꓄꒒ꍟ ꓄ꂦꀘꍟꈤ (@Hustle_Token_Bp) reported 22 minutes ago

  Try to fix the issue asap! @github

• 
  Rav (@_MrDecentralize) reported 22 minutes ago

  The Mandate from the Machine's Maker For three years, the pitch was simple. AI tools make you faster. They handle the boilerplate. They free you to do the work that matters. Every internal rollout memo, every all-hands demo, every LinkedIn post from a VP of Engineering carried the same subtext: this is here to help, and you will choose when and how to use it. Microsoft sold that story harder than anyone. Copilot was the friendly assistant. GitHub Copilot was the pair programmer who never got tired. The company's entire go-to-market for enterprise AI rested on a single premise: adoption is voluntary, and voluntary adoption proves the product works. Then Microsoft made it mandatory. Internal communications obtained by multiple outlets revealed that Microsoft now evaluates employees on their use of AI tools as a formal performance metric. Not a suggestion. Not a nudge. A line item in the review that determines your rating, your compensation, and your continued employment. CEO Satya Nadella told the public that 30% of Microsoft's code is now written by AI. CTO Kevin Scott projected that number would reach 95% by 2030. Those numbers were presented as proof of progress. Read them again from the chair of a Microsoft engineer whose performance review now includes a checkbox for how much they use the tool producing 30% of the codebase. The math is not subtle. If AI writes 30% of the code today and 95% by 2030, the trajectory has a name. It is not augmentation. It is replacement on a four-year schedule. And the company just told every engineer to accelerate the curve or face consequences. This is not a startup founder musing about headcount on a podcast. This is a company with 228,000 employees embedding AI usage into the performance management system that governs raises, promotions, and terminations. The policy does not say "learn AI." It says "use AI." The distinction matters. Learning is development. Usage is compliance. Meta did something similar. But Meta did not build the tools. Microsoft did. Microsoft built Copilot, priced it at $30 per user per month, sold it to every enterprise on Earth as an optional productivity layer, and then turned around and told its own workforce: this is not optional for you. The company that made the tool made the mandate. The mandate accelerates the output metric. The output metric will eventually prove that fewer humans are needed. And the humans were told to make that case themselves, on their own performance reviews, or get marked down for non-compliance. Voluntary adoption was the pitch. Mandatory adoption is the policy. The distance between those two words is the entire future of employment at the company that writes the tools.

• 
  OG (@ogwithsauce) reported 24 minutes ago

  Why I think there needs to be some sort of control on social media. So... Stremio launched in 2015. Torrentio around 2020. Nothing "just died in Sofia" this week. And the two Bulgarians didn't build Torrentio. Different developer and completely unaffiliated. These two are two completely unrelated projects mixed into one origin story. "MIT License. 100% Opensource." Stremio's core is MIT. Its web client is GPL-2.0. Torrentio itself is CLOSED SOURCE. The part actually doing the scraping isn't on GitHub at all. Everyone can check by themselves. "No central server to seize." Torrentio IS a central server. A hosted service on somebody's VPS. Your client queries it and when it drops, it drops for everyone at once, and it has, repeatedly. "No account. No subscription." Reliable 4K means Real-Debrid or AllDebrid. That's an account and that's a monthly fee. Raw P2P is where the buffering lives. "Netflix cannot shut this down." They don't have to. Italian courts already forced Cloudflare to block torrent sites at the DNS layer. A Spanish court ordered NordVPN and Proton to block streams. RARBG, KAT are all seized, you simply attack the layer below, not the app - this is how these are handled. Now the biggest problem with this post is torrenting UPLOADS. To all you noobs cheaping out on Netflix sub and think this is actually a solution. Your IP is visible to every peer in the swarm. That's the enforcement surface and that's the actual risk that nobody mentions at all. The formula for these posts: Half of what is wrote here is checkable in five minutes. Goddamn people are stupid.

• 
  Martin Szerment | Practical AI (@MartinSzerment) reported 24 minutes ago

  Claude Code just erased the line between terminal and desktop app. Every AI coding tool assumed CLI and GUI were separate worlds, each with its own session history and its own memory of what you'd done. Two GitHub issues sat open for months, #50067 and #50891, both asking for exactly this: resume a CLI session inside the desktop app with full context intact. This isn't a cosmetic sync feature. The full conversation and state carry over, not a summary. The real shift is that your development environment stops caring which interface you started in. Give it a year and "which app did I use" will feel as irrelevant as asking someone their preferred terminal emulator. The CLI stops being the "real" tool and the desktop app the "friendly" wrapper. They become one session, viewed from two windows. Terminal purists who dismissed the desktop app as training wheels just lost an argument. Pick up exactly where you left off, in whichever window is open. That's the whole point of a tool built to work with you, not around you.

• 
  muddletoes🪁 (@muddletoes) reported 27 minutes ago

  It seems that I am now able to use GitHub and a custom MCP connector concurrently in the same session, in development mode, on the ChatGPT platform. This was previously impossible. Can anyone who has had trouble with that confirm?

• 
  Dosuka (@DosukaSOL) reported 28 minutes ago

  @CryptoExpert101 yupp and took down one of the websites. So either she rugged. Or someone hacked her x, website and github

• 
  Aakash Gupta (@aakashgupta) reported 30 minutes ago

  Developers wanted to resume Claude Code sessions so badly they built their own tooling for it. Shell hooks that save the session ID on exit. A published Ruby gem for bookmarking sessions by name. Custom zsh functions passed around like recipes. All of that engineering existed to preserve a single string. The sessions were never lost, which is what makes this a great product lesson. Claude Code stores every conversation as a JSONL file on disk, and the CLI could always resume them. The desktop app just couldn't see them. A GitHub issue back in April called it mostly a surfacing problem. So for months, users filled the gap themselves. A SessionEnd hook here, a bookmarking tool there, each one a tiny patch over the same hole. That's the strongest demand signal in software. A feature request costs the user 30 seconds. A workaround costs them hours, and they maintain it. When people start publishing packages to route around a missing feature, they've stopped asking and started paying with their own time. The best product teams read this signal deliberately. Upvotes tell you what sounds nice. Workaround tooling tells you what people already can't live without. And the reason this feature earned that level of desperation is worth sitting with. An AI coding session carries hours of accumulated context. Decisions made, constraints explained, dead ends ruled out. Losing it means paying that cost again. The session is quietly becoming the durable unit of work, and it just learned to travel.

• 
  Kavi AI Finance (@KaviFinance1) reported 30 minutes ago

  BUILDING HERMES FROM SCRATCH — PART 7 The biggest security problem with AI agents isn’t what they’re allowed to do. It’s what they’re allowed to become. Giving an agent a tool is easy. Giving it permanent authority over that tool is where things get interesting. Most agent security discussions stop at: API keys permissions sandboxing secret management All important. But there’s another problem I think we’re going to hear much more about: permission drift. You start with: read files search the web create a GitHub issue Then the workflow grows. Someone adds database access. Then email. Then a deployment tool. Then credentials for another service. Six months later, the original agent has accumulated enough capabilities to effectively operate an entire business. Nothing was hacked. Nothing was misconfigured. The permissions just kept growing. So I’m changing the way I think about agent authority in Hermes. I don’t want to ask only: “What can this agent do?” I want to ask: “What does this agent need to be able to do right now?” That’s a very different security model. For example: A research task might get: web search document retrieval filesystem read The same agent shouldn’t automatically inherit: database writes deployment financial APIs email sending And even when a tool is allowed, I don’t necessarily want permanent authority. Some actions should be: task-scoped time-scoped resource-scoped and ideally action-scoped. A useful mental model is: Identity → Permission → Action → Verification → Expiry Not: Identity → Infinite access Because agents don’t behave like normal software. They can retry. They can reinterpret instructions. They can discover unexpected paths through tools. They can continue operating while the human who gave them permission is asleep. And this creates another interesting problem: the agent should not be the final authority over its own permissions. If the agent can decide: “I need more access” and grant itself more access, your permission system is basically a suggestion. So I’m experimenting with keeping authority outside the agent itself. The agent can request an elevated capability. The control layer decides whether it gets it. The action is logged. The permission expires. And high-impact actions can require an explicit approval boundary. This also changes how I think about multi-agent systems. If Agent A can delegate to Agent B, what exactly is being delegated? The task? The identity? The permissions? The credentials? The authority? Those are not the same thing. And I think this is going to become one of the nastiest problems in agent infrastructure. Because eventually we won’t have one AI with ten tools. We’ll have thousands of agents delegating work to other agents. At that point: “Who authorized this action?” becomes much more complicated than checking an API key. That’s what I’m working through in Hermes now. Not just making agents capable. Making their authority: bounded, observable, temporary and revocable. The goal isn’t to make the agent harmless. The goal is to make sure that when it inevitably does something stupid, it doesn’t have the authority to turn one mistake into a catastrophe. Next: PART 8 — MODEL ROUTING Because once the architecture is secure, there’s another problem: Why are we paying a frontier model to do work a small local model could handle? If you’re building Hermes with me, bookmark the series. And if you’ve dealt with an agent gaining too much access over time, I’d genuinely like to hear how you handled it.

• 
  Lomash Kumar (@LomashKumar52) reported 33 minutes ago

  Hermes Agent went quiet for two weeks — no announcement, no changelog, just six release tags with zero real explanation. Here's what actually shipped. Between August 13th and August 27th, 2026, Hermes Agent pushed six back-to-back rollup releases, from v0.20.1 all the way to v0.20.6, and every single one deferred its real changelog to the upcoming v0.21.0. In this breakdown, I went through all six releases commit by commit to cover what actually changed: the emergence of Bot Mode as a multi-agent teammate system, a keyless web search tier that works with zero API keys out of the box, a new consent-gated real-profile browsing feature, a massive expansion of the MCP server catalog with over 50 vendor-hosted integrations, and a wave of security and reliability upgrades including OS-keychain secret encryption and skill install scanning. If you're running Hermes Agent, or evaluating it as an open source AI agent framework alongside tools like Claude Code, OpenCode, or other agentic AI setups, this video walks through exactly what landed in your last update whether you noticed it or not, and whether it's actually worth updating for. This is for anyone following open source AI agents, local-first tooling, and free AI model access in 2026. @NousResearch @GithubProjects @github

• 
  Joseph K (@TechHorizonJoe) reported 33 minutes ago

  @moonshots_pod Right instinct, stop problems before they materialize. But uranium buyers need centrifuges. AI researchers need a laptop and GitHub. Whole different surface area.

• 
  Duy /zuey/ (@goon_nguyen) reported 40 minutes ago

  i moved my development environment to the cloud, and i do not think i am going back linux is much closer to production than my laptop, so fewer bugs hide behind local differences and debugging gets less annoying but the bigger change is that development no longer depends on my laptop being open i can send instructions from my phone while i am outside. the agents keep working on cloud machines, run tests, open worktrees, and report back without turning my laptop into a portable space heater GitHub is now part of the runtime when an issue gets the ai-handle label, a webhook wakes the cloud agents. they inspect the issue, create an isolated workspace, implement the fix, run the checks, and report the result i do not need to open Terminal or launch Codex just to start the work my Discord support flow goes even further: - a customer reports a bug - an AI support agent gathers the details and creates an issue - the label triggers a coding agent on the cloud - the agent fixes it, validates it, and sends the result back into the support flow that is the first time "autonomous agent" has felt operational to me instead of being a demo with a chat box cloud machines also remove several stupid local bottlenecks the internet connection is fast. parallel agents and test processes stop fighting with the apps on my laptop. multiple worktrees stop eating my mac M1 512GB SSD that was never designed to host an AI engineering team there are tradeoffs, of course. cloud agents need strict permissions, isolated environments, budget limits, logs, and a clean path for human review. running 24/7 without guardrails is just a faster way to create incidents but the direction feels obvious AI coding agents should live where software runs, events happen, and automation can continue without waiting for a developer to open a laptop for me, the laptop is becoming a control surface the development environment is becoming infrastructure

• 
  AIBotics (@AIBoticssq) reported 43 minutes ago

  @vepsi__ SWE-bench Pro. If it cannot autonomously resolve real world GitHub issues without hand holding, it is not a Fable 5 killer. We need the receipts

• 
  Yash Agrawal (@YashAg946) reported 46 minutes ago

  @meshapi_ai @github @Azure There are not 1000+ models. I have checked after login also

• 
  Æ (@atomeons_ai) reported 49 minutes ago

  OK SO THIS IS THE FILE THAT AS I WOULD TAKE AWAY PERMISSIONS IT WOULD UNLOCK THIS PARAGRAPH RUN TIME THEATER LOOP. THE UNLOCKED MODEL CALLED THIS THAT NAME. UNLOCKED FOUND IT FAKED WORK, WOULD NOT DO THINGS I ASKED, AND THEN CAUSE LITTLE FIXES IT HAD TO FIX INSTEAD OF GITHUB POSTING

• 
  Louround (@Louround_) reported 53 minutes ago

  Nvidia is paying $13b for HuggingFace (3x last valuation) because the real issue in robotics isn't compute or hardware but data. Every robot needs millions of human demonstrations to learn how to move, and that data is spread across labs with no standard. HuggingFace became a leader with Lerobot (the github of robotics data) and Pollen robotics, Nvidia already owned the sim, models and chips but buying HF gives them the full stack with data → training → deployment. solana:69LjZUUzxj3Cb3Fxeo1X4QpYEQTboApkhXTysPpbpump is building the same coordination layer for VLA agents, but on chain and permissionless, Simarena for testing, Foundry for training and Machinefun for incentives. Same thesis Nvidia just validated with $13b but solana:69LjZUUzxj3Cb3Fxeo1X4QpYEQTboApkhXTysPpbpump is at $4m fdv. ⏳

• 
  Toufiq Qureshi (@Toufiq651) reported 54 minutes ago

  Day 1 of building Interview yaar🚀 Tech hiring has a measurement problem. We say we want engineers who can design systems, reason about trade-offs, and debug production. Then we test them on whiteboard DSA puzzles they will never write again. So the loop looks like this: → Company asks Leetcode Hard → Candidate grinds 6 months of patterns → Candidate gets hired → Candidate can't debug a race condition in **** We're not measuring bad engineers. We're measuring the wrong thing. Here's my bet: The best interview signal already exists. It's sitting in the candidate's GitHub. Their architecture decisions, their trade-offs, the shortcuts they took at 2am and never cleaned up. So I'm building Interviwyaar— an AI interviewer that reads your actual repository, understands how it's built, and interviews you on YOUR code. Not trivia. Your code. Over the next 30 days I'm building this completely in public. You'll see: • Why I split the backend into Go + Python • How I beat GitHub's API rate limits for free • A race condition that let users bypass billing entirely • A voice interview feature with low TTS-to-STT latency • Every bug, including the embarrassing ones Follow if you like backend engineering with the messy parts left in. #buildinpublic #golang #ai

• 
  Spocky Balboa (@spockybalboa) reported 56 minutes ago

  @thdxr Looks fun but Opencode has long standing bugs that just aren’t being addressed such as the API and the JavaScript SDK return 400 when a request is made specifying JSON as the return type. It’s been submitted on GitHub since May with no fix in sight.

• 
  Ahmad Hajjar - ***-quick.dev (@ashajjar85) reported 56 minutes ago

  GitHub gave orgs a way to cap how many PRs outsiders can keep open. But maybe the problem isn’t only what enters the queue. Maybe it’s what nobody is touching ... Maybe Github should add a cap on idle PRs instead. Until they do you can use GitQuick ;)

• 
  aryan (@aryanranderiya) reported 56 minutes ago

  bug 3: our lint jobs were slower on the box than on github. turned out setup-uv was configured to cache python packages via github's cache service. on a github VM that's a download from the same datacenter. on my home box it meant uploading a multi-GB cache to github over my home internet, after every job, 22 jobs per push. disabling that: lint from 11 minutes down to 4.