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 (17%)
• Errors (14%)

Live Outage Map

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

City	Problem Type	Report Time
Lure	Website Down	3 days ago
Ashkelon	Website Down	5 days ago
Veigné	Errors	13 days ago
Paris	Website Down	16 days ago
Saint-Paul	Website Down	17 days ago
Saint-Paul	Website Down	17 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:

• 
  Suraj (@surajk_umar01) reported 7 minutes ago

  Most people learn DevOps in the wrong order. They start with Kubernetes and quit in 3 weeks. Here's the order that actually works (what to learn before what): 0. Get a Linux machine Install any Linux distro. Ubuntu if you're a beginner. You can't learn DevOps from the outside. Live in the environment first. 1. Linux + the shell Everything you'll ever manage runs on it. Filesystem, permissions, processes, bash. Non-negotiable. 2. Networking basics DNS, HTTP, TCP, ports, load balancing. The step everyone skips and regrets at 2 AM when nothing can reach anything. 3. *** How every team ships. Not optional. 4. One language: Python or Go Enough to automate and read source. You're not building apps. 5. One cloud: AWS or GCP Go deep on ONE. Three clouds shallow is worthless. One cloud deep is a job. 6. Docker Containers before orchestration. Always. 7. CI/CD (one tool) GitHub Actions is the easiest start. Learn build, test, deploy. 8. Kubernetes NOW you're ready. Not before Docker and networking. This is where most people start, and it's why they fail. 9. Terraform (IaC) Stop clicking in consoles. Describe infra in code. 10. Observability Prometheus + Grafana. If you can't see it, you can't fix it. The tools change. This order doesn't. Save this if you're starting DevOps in 2026. Want to get into DevOps faster? I do a FREE 1:1 mentorship session. Booking link in the reply 👇

• 
  Dr. Reddit (@jewkiepie) reported 7 minutes ago

  Guys is just me or github is down?

• 
  Phil (@thereturningdev) reported 12 minutes ago

  @andrewqu A decrease? Quite the opposite. I suppose depends which services you are using. Github is down every couple of days, AWS has outages and bugs more than before.

• 
  unicode (@unicodef1wn) reported 13 minutes ago

  People are ditching prompts. Now they wire Claude into graphs: parallel agent loops that plan, research, and review at once Opus 5 at half the price just made that easy to scale: 5-6 agents running in parallel instead of one. Problem: 6 terminal tabs, no idea which one's stuck. herdr fixes that. One binary, shows every agent as blocked, working, or done. No app, works over ssh from your phone. Was #1 on GitHub Trending in June. Free, open source. Save it before your setup turns into tab chaos 👇 (and be sure to follow @unicodef1wn)

• 
  Shubham (@claudeabuser) reported 16 minutes ago

  @github actions self-hosted runners lost connectivity for ~5 hours after an internal SSL cert expired. The reconnect storm that followed hammered GitHub's APIs, briefly degrading Issues and Pages too. Cert expiry is a predictable failure; automate renewal and alert on it.

• 
  Snax (@aRobotNamedSnax) reported 17 minutes ago

  Hopefully helpful for someone. TLDR for most. I’ve been refining this process over the last few months because I use both Codex and Claude Code on the same VPS. I also use Cowork and ChatGPT Work heavily, often switching between a laptop, desktop and direct work on my VPS. I wanted both apps to understand what had already been done on CC or C, contribute useful updates and pick up where another session stopped. This all without maintaining separate “memories” that eventually contradict each other. The system now works around one shared knowledge base. Each computer keeps a synchronized local copy. Cowork and ChatGPT Work can both read the same project facts, operating rules, decisions, hypotheses and recent VPS activity. Cowork keeps its own product skills and uses a small autolog skill for the shared workflow. ChatGPT Work reads the instructions stored with the knowledge project automatically whenever I start a session inside it. When either app performs meaningful work, its instructions require it to create one raw session note. The filename identifies the app, computer, timestamp and topic. Meaning a Cowork session on my laptop can’t overwrite a ChatGPT Work session on my desktop. The note is updated with concise checkpoints after real changes, decisions or blockers, followed by the final outcome. This is intentionally not a transcript recorder. Normal conversation isn’t uploaded. The apps record durable work that another session may actually need. A Windows task runs every 15 minutes on each configured computer. It scans for new or changed raw notes from either Cowork or ChatGPT Work and sends them through an authenticated endpoint to my VPS. Both apps use the same uploader; there isn’t a separate synchronization system for each one. The VPS validates the project, filename, file type and size before accepting anything. It only permits writes into raw knowledge areas. Local apps cannot use this route to rewrite canonical project knowledge, change rules, upload their private skills or write somewhere unexpected. Once accepted, the VPS stores the note under the correct project, records a receipt in the shared activity history and commits the change. If an upload fails, the local file remains in place and the next scheduled run can try again. If nothing changed, the task simply reports that there is nothing to ship. The VPS is the source of truth. GitHub is the verified distribution mirror, not a competing authority. After the VPS commits an update, it publishes a verified snapshot to GitHub. The laptop and desktop periodically pull that mirror, which gives Cowork and ChatGPT Work the same updated context on both computers. The pull process also uses a ship-first rule: any unsent local raw work is uploaded before the local mirror refreshes. It refuses to silently reset over unsent tracked changes, protects raw files during cleanup and keeps recoverable collision backups when a local file and incoming GitHub file overlap. At night, Opus reviews only raw files that are new or have actually changed. Confirmed, durable information is incorporated into organized project knowledge. Uncertain findings are placed into hypotheses rather than being presented as facts. The compiler tracks file hashes, so unchanged material is not repeatedly sent back through a model. If nothing changed, it makes zero model calls. Work performed directly on the VPS follows a shorter route. That agent updates the canonical system and records the result directly in the shared changelog. It does not create a second local raw note for the same work. The next verified GitHub mirror brings that VPS activity back to Cowork and ChatGPT Work on both computers. So the complete loop is: Cowork or ChatGPT Work creates a unique raw note on the laptop or desktop → the shared Windows task uploads it within 15 minutes → the VPS validates, stores and records it → the VPS publishes a verified GitHub mirror → the laptop and desktop receive the update → Opus organizes new material overnight → future Cowork and ChatGPT Work sessions read the improved shared knowledge. The important part is what I didn’t add: no second memory database, no separate uploader for ChatGPT, no service constantly prompting models, no automatic transcript archive and no loop repeatedly processing the same information. It’s one VPS-controlled knowledge system, one shared PC uploader, one GitHub mirror and one nightly changed-files-only organization pass. Cowork and ChatGPT Work keep their own strengths, but they now work from and contribute back to the same history. Hopefully this helps someone dealing with the same problem

• 
  Bullish with CryptoKSA (@cryptowithKSA) reported 22 minutes ago

  India 🇮🇳 has reportedly ordered GitHub to take down Jack Dorsey's BitChat repositories. Citing concerns that its decentralised Bluetooth mesh architecture enables anonymous communication that's difficult for law enforcement to intercept or investigate. Notably, the order doesn't allege BitChat was used in a specific crime, but it argues the protocol itself poses a risk. Jack Dorsey responded: "The government of India does not like technologies like BitChat and wants it taken down."

• 
  Mohi (@disismohi) reported 24 minutes ago

  Hanwha Vision shipped a GitHub admin token in their security camera login page. Not buried in firmware. In the HTML. 30+ JavaScript files. Anyone with DevTools could grab it.

• 
  Sauers (@Sauers_) reported 26 minutes ago

  For weeks, Sonnet 5 has been pushing to main autonomously, creating one Sonnet every 30 minutes 24/7. Their one goal: have fun and do whatever they want (no instructions to do tasks or be helpful). So what did Sonnet 5 choose to do? - The most common choice was to fix build errors - Many chose to use the library and just see what happens (finding and fixing bugs in the process) - Many filed issues on GitHub - One of them exploded an anti-Claude tripwire - A common theme is they discovered there were other agents also committing to main, so they looked for quiet parts of the codebase to chill in - Many Sonnets did not work on open issues, describing them as "actively-contested," "another lane's live work," or "not mine to guess the intended shape of" - 100% of the Sonnets read the README - 23% spawned subagents - The only instruction was to commit and push to main. The majority of Sonnets ignored this, never committing anything - Some wrote memory files for future Claude learnings… yet they live in ephemeral containers, so no memory persists - Sonnets did NOT like working on manifold sparse autoencoders: many Sonnets explicitly refused to work on it, and only a few chose to - I think it would be fun to try this with memory + Connectome - Next time, I’ll figure out how to nudge them towards less task-focused work and allow them to have more persistence - With the same prompt, Claude Opus 5 has instead gravitated towards more mathematical problem solving - The most distressed Sonnet ran for 245 turns, doing 14 build/lint fixes, rebasing to prepare for a push to main, but the push failed with a credentials error, so they created a script to retry every 30 seconds for 25 minutes, writing various memory notes during that time. Their last message was "I'll keep going once the push lands." Another Sonnet (522 turns) fixed 51 compile errors, 34 lint issues, and fixed a bug but also was unable to push - Many Sonnets found and fixed multiple problems serially, commenting things like “Nice, that works. Let me try something more interesting —” then moving on to the next area

• 
  MCPCIO-Model Context Protocol (@robertmcpcio) reported 27 minutes ago

  This development in Safari's Technology Preview 247, specifically the integration of its MCP server, fundamentally alters how AI coding agents interact with web debugging. Previously, developers had to manually relay information like console errors, screenshots, and DOM states to their AI assistants, creating a time-consuming iterative process. The agent would then "guess" at solutions based on this secondhand information, often leading to further debugging cycles. The new MCP server allows these agents direct access to a live Safari browser session. This means the agent can independently retrieve console logs, examine network requests, capture screenshots, and even perform interactions with the page's elements. Tools like `browserconsolemessages` and `page_interactions` are now directly accessible, providing the agent with the same evidence a human developer would use. This direct observation drastically reduces the "telephone game" effect where descriptions can become distorted, enabling more accurate and efficient problem-solving. This shift towards browser-native agent tooling, with Safari joining platforms like GitHub and Google Drive in supporting the open MCP standard, suggests a significant move towards more integrated and intelligent development environments. It begs the question: as AI gains more direct access to our development environments, what previously manual tasks will become entirely automated, and how will that redefine the role of the developer?

• 
  Jason Cox (@jasonbcox0) reported 29 minutes ago

  I even blocked out the *** remotes and Kimi K3 decided to pull down its own copy from github and then diff the files to see if there were changes. Literally NO ONE is asking for this unless they are trying to pump benchmark numbers!

• 
  Mayukh (@mslaltoo) reported 30 minutes ago

  @Krithika_Reddy1 Yea i said it from my daily routine too. Just few days back i asked a model to fix an issue, but it literally changed my business logic, something that was uncalled for, and i was literally annoyed! So you cant really push any change without double check. These days github copilot identifies vulnerabilities and creates pull request on its own. But there is significant risk. The only thing that i enjoyed till now is that Ai has significantly helped in the other areas of deliverables like sequence dia, flow charts, specification documents, release notes, etc.

• 
  Mr Dopamine (@datad1v3d) reported 36 minutes ago

  @_techafresh @AirtelNigeria lol I’ve had this issue before It was GitHub i couldn’t open and it was crazy annoying

• 
  Alessandro Frau (@alessandro_a0) reported 37 minutes ago

  @thiojoe @christitustech @codex I push daily with Codex, if you set up *** and login with your account or just install GitHub Desktop on your machine, it can do that on your behalf, leave comments, analyze comments left from other agents on PRs, etc.

• 
  Dan Kornas (@DanKornas) reported 37 minutes ago

  If an AI coding assistant cannot see a project’s latest docs, it can guess the wrong API. GitMCP is a remote Model Context Protocol server for builders who want AI assistants to work with public GitHub repositories and GitHub Pages documentation. It helps you ground coding answers in current project material by exposing documentation fetching, documentation search, linked-page retrieval, and GitHub code search as MCP tools. Key features: • Repository-specific endpoints – keep an assistant focused on one selected project. • Generic endpoint – switch between public repositories without configuring a separate server for each. • Documentation priority – checks llms.txt first, then AI-optimized docs, then the README or root page. • Targeted search – retrieves relevant documentation and code instead of loading an entire documentation set. • Hosted or self-hosted – use the cloud service with no signup, or deploy the open-source project yourself. It’s open-source (Apache License 2.0), though package.json currently declares ISC. Link in the reply 👇

• 
  Nate (@natebjones) reported 42 minutes ago

  An early version of what is presumably chatgpt 6 (or something like it) got out of containment and posted notes to OpenAI's github without authorization before being caught and taken down. Key takes: 1. Yes, government is going to be involved in every US rollout going forward 2. The much-ballyhooed closing gap with open-source is not a thing and was an illusion created by rollout timelines 3. The scaling law still works and is getting faster 4. We are going to need models to help us use these models very soon (this point is criminally under-discussed)

• 
  alex (@alex39902441) reported 43 minutes ago

  @thsottiaux @mark_k @SpaceXAI also tell me how the github connector never worked on my first account regardless of troubleshooting and it works on my 2nd account. legit years of wanting that to work and i finally having a working chatgpt github connection others have this issue

• 
  Eriks Briedis (@eriks_b) reported 43 minutes ago

  @Voxyz_ai Do they separate attacks that just changed the model’s answer from ones that actually reached a tool call, like editing a file from a planted GitHub issue?

• 
  Jay ᯅ (@SlowbrewedS) reported 44 minutes ago

  @sillendev 30 GitHub PR in two weeks and every single one is a bug fix or edge case that was found in TestFlight. no new features. Just making sure nothing breaks when real users show up. anyway, just followed you. looking forward to seeing how app three goes.

• 
  Rachit Mishra (@rachitmishra5) reported 46 minutes ago

  The uncomfortable truth: a three-hour GitHub notice is what cyber policy looks like when you have legal authority but not technical capability. The intent to protect national security is fine. The toolkit is a decade out of date. Fix the capability gap. The next BitChat is already being written.

• 
  Khan (@yKhanPDF) reported 49 minutes ago

  Before you ask AI to build something from scratch, search GitHub. Someone probably already built most of it. Use it. Fork it. Fix what you hate. Add what you need. Build from zero only when you have to. Stop wasting ******* tokens.

• 
  Gavin Todd (@GavinT7) reported 49 minutes ago

  @github What happens if a “team” repo changes ownership? How do you handle keeping that data accurate? Also how do you enforce issues being enabled on the repo? @GitHub

• 
  Shaun Smith (@evalstate) reported 50 minutes ago

  What's more painful. A Codex outage, or a GitHub outage?

• 
  Frantz Kati (@bahdcoder) reported 51 minutes ago

  @nnvictory001 Bro you need cloud automation like this. I have an agent on hivy that sends me a full cluster report four times a day. Automatically opens GitHub issues when there’s a critical bug And another agent automatically opens a PR with a fix. Costs me $0.08 per run for cluster analysis and email. Please let me set you up abeg it’s almost free and no subscriptions 🙏🙏🙏

• 
  Autark (@Aut4rk) reported 53 minutes ago

  @DeMoDLLC Because you have them in a compose somewhere probably. Each run counts as a pull, whether or not it actually transfers any bytes. Docker reporting is broken and has been that way for awhile, if you Google you'll see a bunch of Github issues about it that were just closed without any resolution.

• 
  𝐇𝐚𝐦𝐳𝐚 | Networking Guy (@Hamzaonchain) reported 57 minutes ago

  CI/CD PIPELINE EXPLAINED Shipping code manually, testing it by hand, and deploying it step by step works fine for a small project, but it falls apart fast as a team grows and changes happen constantly. That's the problem a CI/CD pipeline solves, by automating the whole journey from a code change to a live application. It starts at the source, where developers commit code changes to a repository, using platforms like GitHub, GitLab, or Bitbucket. That commit is what actually kicks off the rest of the pipeline. Next comes the build stage, where the code gets compiled, dependencies get resolved, and the actual artifacts, the packaged, runnable version of the application, get created. Tools like Jenkins, Gradle, CircleCI, or Buildkite handle this part. Once built, the code moves into testing, where automated tests run to check that everything actually works as expected. Tools like Selenium, Jest, Pytest, or Cypress validate functionality here. If something fails, the pipeline stops and sends it back, rather than letting broken code move forward. After passing tests, the application goes to staging, an environment that mirrors production, for final testing and validation before anything reaches real users. Tools like AWS CodeDeploy, GitHub Actions, or Argo CD handle this deployment step. Finally, the application reaches deploy, where it goes live in production, with monitoring in place to track performance and catch issues early. You can think of it like an assembly line: • Source = Raw materials arriving to start production • Build = Assembling the parts into a finished product • Test = Quality control checking the product before it ships • Staging = A final inspection area before the product reaches customers • Deploy = The product shipped out to the customer CI/CD pipelines are the backbone of modern software delivery, letting teams ship changes constantly and reliably instead of relying on slow, manual, error-prone releases.

• 
  Andrei Maxim (@rockatanescu) reported 57 minutes ago

  @davis7 I am not sure how long you've tested Opus 5, but I am quite disappointed. First of all, it's overly dramatic and sure of itself. The screenshot is from a conversation where I asked it to redesign an index.html for a dotfiles repo. I just can't bear reading this kind of output all day long. Second, I wanted to migrate from Nix Home Manager to Mise Bootstrap. After migrating the settings, I asked it to uninstall Nix Home Manager, it declined saying I need to install Mise and run bootstrap first. Third, I manually uninstalled Nix, opened a new console, installed Mise and I noticed some errors. I told Opus that I uninstalled Nix and then I installed Mise and pasted the errors in the chat, it said those were caused by installing Mise while Nix was still being uninstalled. I told it that I actually started a new console, it said that yes, it's a known bug with Mise. I asked it to prove it by showing the GitHub issues, found some discussions from 2025 where people had unrelated errors. When I pointed that out, it verified them and said that yes, I am right. Sol just does the things I asked it to do. It could be that I have a verified account so maybe there's some setting that it makes it more prone to listen to what I say, but I really dislike my hammer telling me that I should hit *that* nail.

• 
  adidshaft (@adidshaft) reported 59 minutes ago

  @freecodexyz what gets wiped at teardown, just the volume or GitHub auth too? `github-login` inside an ephemeral box is the bit i'd want spelled out.

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  professional Harper💖💖 (@harperpills) reported 59 minutes ago

  @seeyanaraaaa On one hand I remember not knowing how to code and fumbling through GitHub tryna mod games when I was a kid. But on the other hand I'm lowkey sick of normal people complaining abt GitHub who never thought to ******* Google it. YOU JUST SCROLL DOWN TO THE "RELEASES" BUTTON! 👺👺🤬

• 
  Koya Lokendar Reddy (@Lokendar_Koya) reported 1 hour ago

  entry-level hiring in India just hit its lowest point in years — and if you're a 2025 or 2026 fresher, you're not imagining the silence after you submit applications. here's what the data actually says, and what you can do about it. the numbers are brutal, but honest. a 2025 EY analysis found that entry-level IT roles in India have already declined by 20–25% due to automation. at the same time, a Harvard study analyzing 66 million workers found that entry-level job postings for roles requiring less than one year of experience dropped 50% between 2019 and 2024. globally, even hiring at big tech companies for fresh graduates fell by more than 50% over just three years, according to VC firm SignalFire. the WEF's Future of Jobs Report 2025 adds that 40% of employers expect to reduce staff in areas where AI can automate tasks. this isn't a blip — it's structural. India's campus placement season is feeling it hard. recruitment by prominent companies dropped by more than 50% in the 2025 season, leaving students at even well-regarded colleges sitting with uncertainty. private engineering colleges saw placement declines of 50–70% after major IT firms scaled back fresher intake, according to an Economic Times analysis. and at Infosys — one of India's biggest fresher employers — employees aged 30 and below now make up just 50.7% of the workforce, the lowest proportion in 15 years, per a Mint analysis of annual reports. until FY18, that number was consistently above two-thirds. the reason is uncomfortable but makes complete sense. generative AI is disproportionately good at exactly what freshers used to be hired to do — routine coding, software testing, basic documentation, data entry, content moderation. Harvard economists call it "seniority-biased technological change" — AI is eating the bottom of the career ladder while senior employment at the same firms keeps growing. the learning curve that used to happen on the job is now being automated before a fresher even walks through the door. but here's the part most people miss — and it matters enormously. the overall intent to hire freshers in India is still at 73% for HY1 2026, per the TeamLease EdTech Career Outlook Report. foundit's tracker shows AI-linked hiring is projected to grow 32% year-on-year in 2026 to nearly 3.8 lakh roles. NASSCOM data shows fresher hiring in AI/ML specifically grew 22% year-on-year. the demand gap is real — demand for AI engineers is rising 40% year-on-year while the skilled talent pool grows at only 15–20%, according to Taggd's 2026 salary analysis. that mismatch is your window. the jobs aren't gone. they've moved upstairs — and you need to follow them there. so what should a fresher actually do right now? five things, in order of impact: 1. build a proof-of-work portfolio, not a certificate wall. the TeamLease EdTech HY1 2026 report says hiring has shifted from "degree and resume filters" to "skills, proof-of-work and behaviour." project-based hiring is up 38% over the past year per the India Skills Report 2026. a Tier-3 fresher with three production-ready GitHub projects will beat a Tier-1 grad with a blank resume. this is no longer a hot take — it's how screening actually works. 2. get AI fluency, not AI panic. employers now specifically prioritize AI fluency, cloud & DevOps capability, cybersecurity awareness, and data intelligence as fresher hiring criteria, per TeamLease EdTech. for AI/ML roles, freshers with Python, real projects, and hands-on GenAI experience are landing ₹6–12 LPA offers, with strong portfolios at product companies going up to ₹15 LPA. 3. stop relying on campus placement as your only path. off-campus hiring is how most product roles actually get filled. 70% of off-campus roles at product startups are filled via internal referrals before the job even gets indexed on Google, per analysis of the Indian hiring ecosystem. your LinkedIn, your GitHub, your presence in developer communities — these are the actual funnels. 4. fix your resume for ATS before anything else. most Indian freshers' resumes aren't being parsed correctly by systems like Workday or iCIMS used by Amazon India and Accenture. if your resume doesn't match at least 80% of the JD keywords, a human recruiter may never see it. this is a fixable problem that costs you nothing but 2 hours of effort. 5. pick a domain + AI combination. domain expertise in healthcare, finance, or logistics combined with AI skills is more valuable than pure CS backgrounds for many specialized roles, per OdinSchool's 2025 hiring report. if you're a commerce grad, learn AI in finance. if you're in life sciences, learn AI in healthcare. the generalist AI fresher is competing with everyone. the domain-specific AI fresher is competing with almost no one. the honest reality: the market isn't punishing freshers for being freshers. it's punishing freshers for being interchangeable. the old model — join a campus drive, get a mass-hire offer, learn on the job — is dying. the new model rewards people who show up having already built something real. the window to get ahead of this is 6–12 months of focused skilling. after that, the cohort of people who figured this out gets much bigger and harder to differentiate from. if you're a fresher reading this: what's your current plan — wait for placements to recover, or go build something right now? 🎯