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 (55%)
• Errors (30%)
• Sign in (15%)

Live Outage Map

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

City	Problem Type	Report Time
Delme	Sign in	5 hours ago
Lyaud	Website Down	5 hours ago
Catania	Errors	3 days ago
Inverness	Website Down	15 days ago
Quito	Sign in	16 days ago
Junín	Errors	16 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:

• 
  Павел Гансон (@PashaGanson) reported 43 seconds ago

  @colinsolvely @openclaw Hi! Could I ask for your advice on OpenClaw architecture? I’m just getting started, so some of these questions may be basic, but I want to build the system properly from the beginning. My current setup: * Two VPS servers running OpenClaw 2.0, with roughly 10 agents on each. * The same VPS servers also run several small business applications I vibe-coded. Some support client-facing processes. * Previously, the agents developed and modified code directly on the production servers. * I also have a MacBook and an always-on Windows PC, both running Codex. I’m trying to understand how to organize the development lifecycle without unnecessary complexity: * Should the OpenClaw agents stay on the same servers as the production applications, or should I move them to separate machines or VPS servers? * Where should code be written, built, and tested: Windows/WSL, the Mac, a dedicated development server, or close to production? * Should I have one central coding agent or a separate coding agent for each OpenClaw instance? * What is the best way to connect Codex, OpenClaw, GitHub, and the servers? * What should the simplest reliable workflow look like: a product agent discovers a problem → development → review/testing → deployment → production verification? * How many OpenClaw instances and machines do I actually need? This isn’t a large SaaS product: most applications are used by only a few employees, although some client-facing processes should remain reliable. If you were designing this setup from scratch, what would be the simplest reliable architecture you would choose?🙏

• 
  Zag𐤊 (@ZigZag1278770) reported 3 minutes ago

  DAGKnight Public Testnet preparation is officially ON. 🚦 GitHub issue DK-406 (M4 - Public testnet) assigned for Testnet-13 activation. Nakamoto Consensus evolved: • Silverscript L1 covenants ➔ Live RC • DAGKnight parameter testing ➔ Public Testnet loading Ignore the chart

• 
  Sean Timm (@timmsc) reported 4 minutes ago

  Yesterday there was a major Outlook outage, today Mermaid diagrams are failing to render on GitHub. What is going on at Microsoft?

• 
  Andre Infante (@AndreTI) reported 4 minutes ago

  @reconfigurthing Yeah, I think this is a good sign. Although the Mythos github malware / social engineering incident was bad enough that I think we can basically say that this level of care is not sufficient to resolve the issue.

• 
  Asterix (@Asterix54907294) reported 4 minutes ago

  end-of-summer snapshot for @QFEX : -~$222M in open interest -CLI v0.3.12 shipped in August with improved installation docs and a go.mod fix -GitHub activity continued through late August not a flashy launch recap, just a quick look at how the exchange is closing out the summer: more markets, meaningful liquidity, and active work on the tooling side still early, but the infrastructure is clearly moving

• 
  T (@T54321X) reported 7 minutes ago

  @ContaboCom Please make Contabo available for Automatic push for GitHub. We need to fix this and UI.

• 
  Bhagya Mudgal (@BhagyaMudgal) reported 8 minutes ago

  @pcshipp can use github issues, agent work well with gh cli

• 
  RUX (@0xrux) reported 8 minutes ago

  This man turned Grok Bot into his CTO. He handed the bot his GitHub repo and told it to run the show. It spins up cloud agents, follows PRs, and uses pstack/poteto mode when a task actually needs deeper reasoning. Then he gave it a second prompt: “You’re overloaded. Hire child bots and delegate the work.” From there, the bots started talking to each other instead of constantly talking to him. One handles PRs. One works on the Convex backend. One owns auth. He just watches the threads in view-only mode. But there are two things worth knowing before copying the setup: 1. It burns tokens. He went past 2 billion tokens in a single day after telling the bot to use pstack for everything. The fix? Save pstack for the genuinely hard tasks. 2. It doesn’t replace engineers. It simply takes him out of the coordination seat. And honestly, that’s a much more interesting use of AI. Timestamps: 00:00 — The Grok Bot CTO Workflow 01:23 — Direct Responsible Agent Prompt 04:20 — pstack Plugin & poteto Mode 08:46 — Token Consumption Warning 11:49 — Spinning Up a Team of Bots 17:20 — Advanced Grok Bot Use Cases

• 
  Farley (@FarleySchaefer) reported 8 minutes ago

  @github Hope this doesn't bring down GH

• 
  Martin Tobias (Pre-Seed VC) (@MartinGTobias) reported 11 minutes ago

  if you know any founders who are winding down, I may have a buyer of their github repos. DMs open.

• 
  deezzex (@deezzex) reported 12 minutes ago

  6 GROK BOT AGENTS WHICH RUN an ENTIRE SEO COMPANY STARTING FROM ONE PROMPT MAKE me $10k per MONTH spent a week and built a prompt that spins up the team of agents with manager, each has its own responsibilities, all together work as a team, see details, you can build your own one: six roles. one prompt spins all of them up. here is the split and what breaks when you skip one. - MANAGER. holds the queue, assigns, kills. the only agent with the client list. without it the workers all pick the highest-value keyword at once and you get 5 drafts of the same page. - SCOUT. serp gap only. pulls what ranks, what the top 10 all forgot to cover. give scout the drafting tool too and it stops researching and starts writing at line 3. - BRIEF. turns the gap into an outline with the angle LOCKED. skip this and your writer regresses to generic listicle every single time. this is the agent everybody deletes first and regrets. - WRITER. draft only. no publish access. no analytics access. one job. - AUDITOR. checks every claim and every stat against a source. it can REJECT and send back. an SEO stack without a rejecting agent is a spam factory with better grammar. - SHIPPER. publish, internal links, schema. runs LAST or your internal links point at pages that do not exist yet. second business i have rebuilt this way and it went the same direction both times. going to show on this account other ways how to start a passive business using AI so you know what to do, stay tuned the framework prompt is in my github, you should try it yourself fewer, audited, slow. or a week and let the index decide. which one are you actually running?

• 
  Poonam Soni (@CodeByPoonam) reported 21 minutes ago

  You don't fix this by migrating to a new app. You fix it by making Slack actually work. → Connect Slack or Teams. Whole team is AI-native by the next morning. → Plug in the rest in 15 minutes: calendar, CRM, finance, storage, GitHub. 3,000+ integrations. You approve what she reads.

• 
  Dr Milan Milanović (@milan_milanovic) reported 22 minutes ago

  How Cursor made *** scalable The thing with *** is that it never was designed to be scalable. Your repo lives on the disk, and *** client expect every read to be consistent. This was a problem on GitHub, where shared filesystems and replicated storage failed before 2013. The GitHub built 𝗦𝗽𝗼𝗸𝗲𝘀, and it became the industry standard. This means that every repo is stored as three full copies on three servers, and every push runs a vote (three phase commit). A majority of servers must confirm before it exists. This works, but with high cost, because every push is slow as the slowest server. When we add new servers, it makes it even slower. Now Cursor took some opposite direction with 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗶𝘁𝘆. The repo history is now written as a log in S3, and this is only source of truth. Any push counts only if it is located in the log. The servers don't need to keep anything important, they are just cache. Any server can take a push, and idle repos are dropped from disk and rebuilt from the log when it is needed. This resulted in 120 pushes per second on standard S3, and over 300 on S3 Express. Their tests have shown that read capacity grew linearly up to 100 replicas. Why is this important now? Because of AI agents mostly. We now have more code, PRs, CI runs and many small repos. All of these repos would need three full copies in the old model. This means that we achieve scale by removing parts, not adding them.

• 
  Praetor (@FourVork) reported 24 minutes ago

  It's crazy how everyone is praising Grok Bot, but let me be the annoying person who tells you why it still kinda sucks. 1. The actual capabilities of agents are not novel. Most of the demos I've seen can already be done with Hermes and similar setups, often with smarter models and more control. The difference is that Grok Bot gives you everything out of the box ,without setup and wiring things together. 2. The agent itself is still pretty weak. The UX makes you want to delegate ambitious tasks to it, but the underlying model/agent doesn't always live up to that promise. It takes dumb paths, burns through context/tokens surprisingly fast, and needs more babysitting than the "AI employee" framing suggests. Also, there's no native X search which is especially funny for a product coming from (you have x_search on @grok but not on @bot which needs to use x connection and burn credits). 3. The security model deserves way more attention. All your Bots share the same computer, including files, cookies, browser sessions, and logins. So once you start logging into Gmail, GitHub, Stripe, internal dashboards, etc., a prompt injection or compromised webpage suddenly has a much bigger blast radius. Well, these were the main issues I had with grok bot (apart from smaller issues like absence of read aloud) and why I stick with hermes. But on the other hand, the product is genuinely good, and this is why.

• 
  Jayesh Betala (@jbetala7) reported 27 minutes ago

  @github Exactly how issue issue comments should handle local media files

• 
  Syn (spirit/acc) (@Synxneuos) reported 29 minutes ago

  GM guys. Don’t assume that because the token price is down, I’ve stopped working on development. If you want to see what’s actually being built, check the GitHub. I’ll keep posting updates here as well, but today was a little hectic for me, so I couldn’t post as much as I wanted to. Development is still going on. More updates soon.

• 
  pratwwk (@pratwwk) reported 29 minutes ago

  @dhh please fix github

• 
  Speen Bhai (@Speenbhai) reported 30 minutes ago

  @johnternus Hi John. Congrats Let us see what new you bring with you. Affordability and intelligence. You have source code or an AI and can get it from GitHub. Why not turn 234 million iPhones to a massive distributed server infrastructure with zero power consumption

• 
  Jake Liddell (@Jakeliddell) reported 31 minutes ago

  I run my task app's AI dev team entirely on GitHub. Issues are its inbox, labels are its mutexes, comments are its audit trail, Actions is its nervous system. GitHub made that possible, and given what I knew when I chose it, I still think it was the right choice. But... This last month, GitHub itself has been the least reliable part of the system. Not the AI. The plumbing underneath it. Examples, all from the last fortnight: For 56 minutes one afternoon, Actions ran nothing at all, repo-wide. Every workflow, instant startup_failure. It recovered on its own. Nothing on the status page covered it. One branch went completely silent. Three different webhook trigger types produced zero CI runs, while a sibling branch minutes earlier worked perfectly. I merged with a bypass and wrote the justification up by hand. Scheduled workflows are the big one. My every-15-minutes sweep delivered 3 runs in a day. The nightly cleanup job - the safety net that unsticks dead agent runs - simply didn't fire, two nights running. One of those runs eventually turned up 11 hours late. The docs do say schedules are best-effort under load. Nobody reads that sentence expecting "not at all". So last week I added a watchdog on a different company's scheduler, whose only job is to check GitHub's scheduler did its job, and to fire the workflow itself when it didn't. I built it after the manual version - me noticing over breakfast and pressing the button myself - had been needed two mornings straight. Then the bill. 3,000 included Actions minutes didn't come close to covering the month, and the meter is running at about $50 - a decent chunk of it CI runs and preview environments spun up for documentation-only changes. The noise costs actual money. And on a different repo: I hit the Actions artifact storage quota. There's no proper breakdown of what's using it, no way to extend it, and when you delete the artifacts, the quota number doesn't move - and no way to force it to update. The docs say it will clear in 6-12 hours. It took a lot longer than that. Search the community forums for "storage quota not updating". It's not just me. <sigh> That's the shiny bit of the setup and the messy bit, side by side. And none of it has changed where the code lives - GitHub is the market-leading repo system for a reason, and the network effects are real. But I've stopped thinking of it as infrastructure and started thinking of it as weather. You don't rely on weather. You check it, you plan around it, and you keep a coat in the car. And I've started watching the alternatives properly. Cursor launched Origin last month, pitched as agent-first *** hosting. The one review I've heard couldn't find anything it does that GitHub doesn't. Fair enough - but the list above is what agent-first means to me: delivery you can trust, schedules that fire, quotas you can see. That's the scorecard I'll be marking the newcomers against. And it makes me tempted to give Origin a spin. If you're building agents on GitHub: assume any webhook can fail silently, treat every cron as optional, verify everything happened rather than trusting that it did, and put your safety net's safety net somewhere else entirely.

• 
  Landon (@LanCowawa) reported 35 minutes ago

  @slingoorio You ***** my last $12 on $Mona The Github mascot? the one you said you were leaving a moon bag and then sold it. **** was slow cooking til you came in and crashed the party. BGGYNGsnouXfi4nYo9JvNbQbaVdnaVby6g9FeZMYpump

• 
  Sam Lambert (@samlambert) reported 38 minutes ago

  @saltjsx I mean this was in 2014 when GitHub didn't have these problems. You have never and will never build anything as good as GitHub, so you probably should take some advice.

• 
  Peter (@NoDataSold) reported 44 minutes ago

  @thsottiaux For GPT-5.6 Sol specifically, I’d push beyond “more context / more agents / think harder” and focus on making all that intelligence compound over long-running work. A few upgrades I’d love to see: • Durable cognitive state Not just memory of facts or chats. Maintain a structured evolving state of the problem: goals, decisions, hypotheses, evidence, uncertainties, dependencies, unresolved questions, rejected approaches and why. I should be able to return weeks later and have Sol understand where the thinking reached, not merely retrieve things we once said. • Epistemic retrieval Make retrieval part of reasoning. Instead of mostly finding semantically similar context, deliberately search for: – contradictory evidence – failed approaches – structurally different precedents – high-surprise observations – information likely to change the conclusion Retrieval should reduce uncertainty, not reinforce whichever explanation Sol already has. • Verifier invention Move beyond generic self-review. When correctness matters, Sol should invent an appropriate falsification mechanism: What experiment could break this? What counterexample disproves it? What independent source should disagree if I’m wrong? What test should I construct? Would an independent agent reach the same conclusion? Separate discovering an answer from certifying it. • Adaptive compute allocation Reasoning effort should become internally dynamic rather than mainly determined by one global setting. Sol should estimate where uncertainty and consequence sit, then allocate searches, reasoning, agents, tools and verification accordingly. Most of a task might need little thought while one assumption deserves 80% of the compute. Spend intelligence where another unit has the highest expected value. • Persistent world-state modelling When Sol interacts with GitHub, browsers, terminals, Drive, apps, APIs, etc., maintain an explicit model: What state existed before? What did this action change? What evidence confirms it? What could invalidate that belief? What may have changed externally? Tool use becomes reasoning over state transitions rather than disconnected calls. • Counterfactual execution planning For ambiguous problems, preserve multiple materially different strategies long enough to test them. Branch when uncertainty warrants it. Run cheap experiments. Kill losing branches when evidence arrives. Merge useful discoveries. Replan when the problem representation is wrong. Multi-agent becomes exploration and falsification, not simply parallel labour. • Native continuity across ChatGPT → Work → Codex One durable task state that moves between interaction modes without hauling an entire conversation behind it. Carry forward: – objective – current state – decisions – evidence/provenance – unresolved questions – artifacts – permissions/constraints – exact restart point The interface can change without giving the intelligence amnesia. • Context observability Without exposing private chain-of-thought, let users inspect the information shaping the task: Which memories were retrieved? Which project files are active? Which chats/sources influenced the state? What was omitted? What is stale? Where do sources conflict? What assumptions lack evidence? A million-token intelligent system is easier to trust when its epistemic inputs are observable. The common theme: I don’t particularly want Sol to just “think longer.” I want it to maintain a coherent, falsifiable, evidence-grounded understanding over time — while deciding what to remember, retrieve, test, delegate, revisit and discard. That feels like a much more interesting frontier for Sol.

• 
  Bash (@bashirbuilds) reported 48 minutes ago

  Your Stripe account can be healthy while your checkout is broken. OpenAI can be operational while your AI feature is failing. GitHub can be up while your deployment workflow is stuck. That’s the problem I’m building Reeno around. Dependency uptime is not the same as product health. Your monitoring should tell you when the thing your customers actually use stops working.

• 
  Donnie Danko // CHΛOS (🐦‍⬛, 🏴‍☠️) (@daanisharif) reported 49 minutes ago

  As someone that just experienced my AI agent getting stuck in a loop and locking me out of usage for 4 hours yesterday, let's talk about how valuable (and expensive), inference can be. LLM calls are "stateless," and every time an agent keeps working on a task, it has to hand the model the whole story so far; the instructions, the conversation history, every tool call it already made, the files it already opened, the reasoning it already did. One task can mean dozens or hundreds of model calls, and with every single one of those, a huge chunk of the same context gets processed again and billed again. That's what I'd call the agent token tax. Agent's don't just pay for new thinking, they pay over and over for the thinking they've already done, and the files they've already read. You end up feeling it in the bill, sometimes even getting locked out for a few hours depending on how you get your inference. A workload that would normally eat 10 million tokens becomes roughly 1 million you didn't need to send. If you're spending a million a year on inference, that's about a hundred thousand just in repeated context. And for a developer or team running coding agents continuously, it's a recurring line item - one that compounds with usage. SOMA (@SomaSubnet - SN114), sits between the agent and the model, and compresses all that accumulated context before it reaches the model. Same agent, same model, same workflow - but fewer tokens to pay for. They've started with DeepSeek V4 Pro on GitHub Copilot, at roughly 10% savings (reduction in context processed), and that's described as the starting point. Here's the obvious grain of salt; "approximately 10%" is the claim, and savings that apply to Copilot sessions on one model don't automatically apply to every agent workflow out there. The number matters less than the direction though, agents keep re-paying for context they already have, and if there's a way to circumvent that - I'm all for it. TL;DR: Keep your current agent(s), spend fewer tokens. That's the idea. I'm down, let's go.

• 
  Rashi Umapathi (@rashiumapathi) reported 51 minutes ago

  A founder I worked with spent 4 months on the wrong channel. Posting on LinkedIn daily. Getting engagement. Feeling productive. Zero customers. Her best customers? They weren't on LinkedIn. They were on GitHub + Reddit + indie hacker communities. She was optimizing for visibility on the wrong stage. The problem: Founders pick channels based on: - "Everyone says this is important" - "That's where I'm most comfortable" - "This is where I see other founders" NOT based on: "Where are my actual customers?" The fix: 30 days to diagnose the right channel + build ONE system. By day 30, she had proof: community participation > LinkedIn posting. Now she focuses there. Revenue is growing. The insight: Most founders' growth isn't broken. Their channel choice is. If you're stuck, it's probably not that you need to work harder on the channel you picked. It's that you picked the wrong channel. Where are you actually getting customers from right now?

• 
  catman (@catmanyau) reported 52 minutes ago

  @CricTalk29 for me, losing Cursor would hurt most because it sits directly in the editing loop. would the vote change if github outages were limited to code hosting but issues and reviews stayed available?

• 
  Smakosh (@smakosh) reported 55 minutes ago

  @nainia_ayoub @LLMAPI100 They got DMCA taken down and are still trying to trick @github while having the stolen code in some private repo or so breaching both licenses

• 
  John Zhong | AI Growth Systems (@John_zhong324) reported 55 minutes ago

  @github A repeatable --attach flag turns CLI reports into reproductions: inline screenshots in issues mean a bug gets fixed in one pass instead of two round-trips for context.

• 
  small_j (@a_small_j) reported 56 minutes ago

  SmallDocs recently crossed 200 stars on GitHub and 20 forks. SmallDocs is the first open source project I've managed. Handling other people's pull requests is not easy (and I need to improve). They implement features you're not considering and fix bugs you didn't know you had. Extremely useful, but if you're squeezed for time and trying to develop core functionality, it's hard to manage both things well.

• 
  Tae Park (@taepark_gp) reported 59 minutes ago

  The fake Claude desktop app is not a glitch. It is a feature of an ecosystem that rewards speed over verification. RevStealer waits for specific hardware signatures before decrypting its payload, checking core counts and memory to avoid sandbox detection. This is not script-kiddie work. It is industrialized theft designed to bypass the lazy security habits of developers who download free tools from unverified GitHub repos. We keep talking about institutional adoption while the retail on-ramp is mined for credentials. If your due diligence stops at the whitepaper, you are the exit liquidity for these operators. Security is not a product feature. It is the only moat that matters.