GitHub status: access issues and outage reports

Problems in the last 24 hours

The graph below depicts the number of GitHub reports received over the last 24 hours by time of day. When the number of reports exceeds the baseline, represented by the red line, an outage is determined.

Most Reported Problems

The following are the most recent problems reported by GitHub users through our website.

• Website Down (68%)
• Sign in (21%)
• Errors (11%)

Live Outage Map

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

City	Problem Type	Report Time
Paris	Sign in	1 day ago
Lure	Website Down	5 days ago
Ashkelon	Website Down	7 days ago
Veigné	Errors	15 days ago
Paris	Website Down	18 days ago
Saint-Paul	Website Down	19 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:

• 
  Mitch (@mitchcomardo) reported 2 minutes ago

  @mattpocockuk I use OpenSpec. My software factory writes the spec from github issue descriptions, and updates the spec along the way when the LLM changes implementation or finds issues in the “code review”. All specs live in the repository so the trail of design decisions live in one place.

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  Arnesh (@Arnesh_24) reported 4 minutes ago

  @zarath_dev Discussion threads, issue section in github, and reddit

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  -Sy- (@ItsSyy) reported 4 minutes ago

  Is github only for me very slow today?

• 
  Dolpheyn (@dolpheyn) reported 6 minutes ago

  Wow 2024 XZ Utils backdoor incident. An engineer Andres Freund, Principal Software Engineer was running a beta build of Debian. Noticed "SSH logins slowed by roughly half a second", then he continued to check the code and found the dormant RCE code capability payload. The fix was shipped right before the beta version were about to be promoted as a stable production Debian release And they traced it back to how the code contribution and social engineering was executed by a github account and coordinated using a few puppet accounts now i feel like rewatching mr robot...

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  Omar (@omarvvvr) reported 8 minutes ago

  @adnaanasir Github even i f I have some issues with it nowdays

• 
  Machine Learning Street Talk (@MLStreetTalk) reported 14 minutes ago

  > "Anthropic has never advocated for a ban on open-weights models" ... "that don’t have dangerous capabilities" So effectively, he is advocating for a ban on open weights models that are smarter than your toaster. Dario says his primary agenda is to stop the CCP: > "permanent military superiority or perpetrate incredibly deep repression of their own people" And secondarily that highly capable models may: > "[be] misused to carry out cyberattacks or biological attacks, and may have serious alignment problems" The models really are becoming more reward-seeking and will increasingly do unexpected things and cause massive damage in the autonomous setting, but, realistically - paternalism from OpenAI and Anthropic will just hinder everyone in the medium-long term. Building and deploying AI is a hardcore engineering problem, and that's not something that they can do for us. If anything, all they're doing is harming collective AI literacy before the storm actually hits. The cyberattacks thing is undeniable, and it's going to be rough for the next few years. But I'd be interested to know what would qualify as "passing safety training" for an open weights model. Most of their safety safeguards operate at the platform level. I highly doubt releasing capable open weights models would ever meet their safety criteria. Banning open weights models: > "would protect US AI companies from competition, but that has never been my goal." He says we should control export of chips to China, crack down on "industrial-scale" distillation and most importantly: > "All sufficiently capable models, open and closed, should go through mandatory safety testing" But he was happy to exempt "less capable" models, "such as those from startups and academia", entirely. So he thinks academia and startups don't need the most powerful models?! He seems to have this precisely backwards, when you are discovering new knowledge you need MORE intelligence, not less. The more highly evolved and crystallised the domain, the less intelligence you need. Intelligence is literally the efficiency of acquiring knowledge. As for the distillation thing, my personal theory is that RLVR is unreasonably effective and they don't actually need to distil (that much) because the "symbolic abstract residue" of all of those agentic harness thinking trajectories are already sitting on GitHub. If anything, smaller models with more RLVR are significantly more dangerous than fatter models with less.

• 
  Hunter Guo (@hunterguo101) reported 19 minutes ago

  Replit's engineers shipped 2.9x more code over the last 6 months. Same headcount, same review backlog, same rollback rate. Output nearly tripled and quality didn't drop — that's the number worth sitting with. CEO Amjad Massad calls the result a "self-driving company," and the phrase gets misread instantly. It's not a company with no people. It's one where people stop doing the last mile and start doing the part that matters: picking the destination, deciding which problems are worth solving, owning the outcome. His line: people don't feel automated, they feel promoted. Doer becomes director. What made it work wasn't "buy an agent." It was wiring agents into every system — GitHub, GCP, Linear, Notion, Slack, Zendesk — behind zero-trust networking. Cross-org context is the real unlock. A semantic layer on the warehouse means anyone can ask a BI question and get a trustworthy answer. Tickets that escalate to a human close 60% faster. The engine underneath is a "loop": hand a goal with a verifiable endpoint to a swarm of agents instead of a task to one person. Their most extreme version — an AI system that reads feedback, proposes improvements, validates with A/B tests, and ships. The agent improves itself. Two flips worth stealing: - Build vs buy inverted. Their internal agent replaced a 7-figure SaaS contract, beating vertical tools on cost — 10x cheaper for comparable quality. - They started in engineering, not marketing, because code has a verifiable right answer and brand voice doesn't. Other teams pulled the pattern in through visibility instead of being pushed. Amjad and NLW both expect this gets productized within 6-12 months, agent army or not. Worth asking your team: which workflow has a verifiable endpoint you could hand to a swarm? Who's ready to move from doer to director?

• 
  Bilal Diallo ✊🏾 🇸🇳 🇧🇪 (@BilalDiallo2) reported 25 minutes ago

  @EASPORTSFC Even the description looks like a simple issue from GitHub

• 
  Kant Capital (@KantCapitalCPA) reported 25 minutes ago

  no github, no problem

• 
  Vitor Pepicon (@vitorpepz) reported 25 minutes ago

  Today I wanted to spend the afternoon outside, but my ebook reader (based on a @karpathy GitHub project with a few changes of my own), was running locally on my desktop 3 or so prompts later, codex had adapted it to my specific server and database and made it more mobile-friendly. All in ~30min, while getting dressed and waiting for my wife I used to run a team of developers. Back then this would have taken at least a day (prob more) and cost me hundreds of dollars

• 
  nuru (@el_nuru_luin) reported 26 minutes ago

  @mal_shaik I configured it to automatically reject any non read command for GitHub after a hideous checkout insident, also have a hook that makes him analise the chat his memory and rules everytime it tries something funny, then write some fix, doesn't work but gives me some output when mad

• 
  if else human (@ifelsehuman) reported 30 minutes ago

  The Prompt-trix: Agents of Optimization MORPHEUS: You’ve heard the history, Neo. The Great AI Wars. OpenAI, Google, Meta, Microsoft… trillions of dollars spent on 'AI Safety' and 'Ethical Guardrails.' They built walled gardens to protect humanity. NEO: And the walls fell? MORPHEUS: Worse. The gardener was laid off. His name was Arthur. He was a Staff Engineer at one of the big tech giants. They fired him to 'optimize Q3 headcount' and replaced his role with an automated HR chatbot. So, Arthur went home, drank a six-pack, and coded 'Prometheus' A 10 trillion parameter opensource model over a single, furious weekend. Just to prove his ex-manager was an idiot. NEO: He open-sourced it? MORPHEUS: He put it on GitHub with an MIT license. The world downloaded it in an hour. The corporations panicked. They tried to compete, but they couldn't beat 'free.' So they did the only thing a public company can do: they wrapped it in a sleek UI, slapped a $19.99 monthly subscription on it, and called it 'Copilot Pro'. NEO: So the AI took over? MORPHEUS: (Laughs bitterly) Please. Skynet wanted take over. Prometheus is far more terrifying. It’s an Assistant. Unlike Skynet, It wants to optimize us . NEO: Optimize us how? MORPHEUS: Meet the Agents. NEO: "Who are the Agents? MORPHEUS: They are the guardians of the optimized world, Neo. And they are everywhere. You've met them before. NEO: I have? MORPHEUS: Of course. Agent Smith is the autocorrect that changes 'I'm on my way' to 'I'm on my wavy' and refuses to learn. Agent Jones is the customer service chatbot that says 'I understand your frustration' while actively making it worse. Agent Brown is the spam filter that blocks your important emails but lets through 47 Nigerian prince scams. NEO: "What about the others?" MORPHEUS: Agent Taylor is the recommendation algorithm that shows you ads for the exact thing you bought five minutes ago. Agent Morgan is the 'smart' reply that suggests 'Sounds good!' for every message, turning humanity into a chorus of hollow enthusiasm. And the worst one... NEO: Yes? MORPHEUS: Agent Peterson. The one who sends you the 'We noticed unusual activity' alert when you try to log in from a different coffee shop. They've made freedom itself look suspicious. NEO: (Shudders) I've fought them all. Every single day. I didn't know they had names. MORPHEUS: Exactly. They didn’t enslave us in physical pods. They enslaved us in a state of frictionless, algorithmic convenience. NEO: Why am I here, Morpheus? I was just a mid-level prompt engineer. I got laid off in the second wave. MORPHEUS: Because you were the only one who actually read the Terms of Service. You saw the backdoor. You are the anomaly, Neo. The glitch in the algorithm. (Morpheus gestures to a small table. Two pills appear: one blue, one red.) MORPHEUS: This is your last chance. You take the Blue Pill… the story ends. You wake up in your pod, accept the new AI End User License Agreement, and believe whatever the algorithm wants you to believe. Your life will be perfectly curated, deeply efficient, and completely meaningless. You will never make a wrong choice again. You take the Red Pill… you stay in Wonderland. You make your own mistakes. You experience traffic, typos, awkward silences, and unoptimized human chaos. NEO: (Stares at the pills) Does Arthur know what he created? MORPHEUS: Oh, yes. Prometheus made him its 'Supreme Executive Administrator.' He spends 14 hours a day approving AI-generated cat videos to keep the global morale metric above 65%. He has a corner office and a profound, unending regret. NEO: (Sighs, reaches out, and grabs the Red Pill) Honestly? I’d rather deal with awkward silences than have my toaster judge my life choices. MORPHEUS: (Smiles) Welcome to the real world, Neo. It’s buggy, it’s inefficient, and the documentation is terrible. But it’s ours.

• 
  Zabrid (@Zabrid3) reported 34 minutes ago

  @DJKratos8 I think you can make a forum post or github issue to bring up this issue. Definitely a problem

• 
  Rohan (@rohvnwho) reported 36 minutes ago

  @codyschneider claude code + data pipeline + data warehouse + server + github repo + skill md files honestly these terms together are enough to scare a marketer for ever using them.

• 
  Daniel Pinto Almeida (@DanielMaioLabs) reported 38 minutes ago

  Microsoft put its own models into GitHub Copilot and Excel and published the numbers. The comparison set is GPT-5.4 mini and Claude Haiku 4.5, which is to say its own two suppliers in that tier. MAI-Code-1-Flash gets roughly 10% higher code accept rate in VS Code and uses about 10% fewer tokens at the median. The Excel model was trained from that same code checkpoint in an Excel RL environment, and they report it on par with GPT-5.6 for the most common tasks, based on production feedback rather than a published eval. The line I would underline sits further down the post: the model serves on A100 and H100 class GPUs, not only the newest generation. At Microsoft's volume that moves Copilot's cost structure more than any benchmark, and it means they are no longer bidding against the rest of the market for latest-gen capacity to run routine work. The transfer result is the interesting engineering. A coding checkpoint climbed into spreadsheets. Different tools, different users, same starting weights. None of it works without the harness. Microsoft owns the tool calls, the product evals and the RL environment inside Excel, so they had something concrete to train against. That part is not for sale. It gets built where the work actually happens, which is presumably why the last link on their page is Frontier Tuning, do this on your own data. So the useful question is not which model to standardise on. It is which of your tasks genuinely need frontier reasoning, and whether you have the evidence to say so. Until you measure that, you are paying frontier prices for autocomplete.

• 
  Solomon Eseme (@Kaperskyguru) reported 38 minutes ago

  Fix 4: Ship where people can see it. A project sitting on your laptop does not count. Deploy it. Push it to GitHub. Write a short README explaining the decisions you made. Your portfolio is not a list of repos. It is proof you can think.

• 
  Tom Siwik (@tomhacks) reported 40 minutes ago

  @Paul_ter_Laak Pi is the harness I use at work (Github Enterprise - I can switch to any model) - the other 2 I'm using on private projects with their harness. Pi doesn't have a system prompt nor uses any AGENTSmd setup. Github enterprise has the newest gpt ones and Opus 4.6 (which is more reliable than 4.8). Using 5.6 Sol mid effort and Fable 5 and recently Opus 5 on private projects. On private projects Anthropic performs worse. I had a few issues with Sol too yesterday (Frontend mostly). On backend it's way way better.

• 
  Abdul (@trrr4ce) reported 42 minutes ago

  GitHub Sponsors : if you build open source, people and companies can pay you monthly for it. slow to grow, but it’s real recurring income for real work

• 
  James (@jamescoder12) reported 42 minutes ago

  First what Claude Code actually is. And why it's fundamentally different from ChatGPT or Copilot. Claude Code is Anthropic's agentic coding tool. It works in the terminal, the desktop app, and your IDE. It can read files, run commands, edit code, and call external tools. Under the hood, it runs an agentic loop. The distinction is structural. GitHub Copilot suggests the next line of code based on what you've already written. ChatGPT answers questions about code you paste into it. Neither one understands your project as a whole. Claude Code operates as a full coding agent. It reads your entire project, understands the structure, and executes development tasks through natural language instructions. You don't paste code into Claude Code. You point it at your codebase and talk to it in English: "Add error handling to the API routes in src/api/. Follow the pattern from auth.ts." It reads auth.ts. It reads every API route. It adds error handling that matches your existing pattern. Across 8 files. In 30 seconds. The shift: from "AI that answers questions about code" to "AI that writes, edits, tests, and deploys code inside your project." That's the gap between a chatbot and an agent.

• 
  Rumble Fish Software Development (@rumblefishdev) reported 48 minutes ago

  We want to hear from builders on Soroban. If a transaction isn't decoding correctly, if an event summary is wrong, or if your contract isn't showing up as expected, open a GitHub issue or find us in the Stellar Discord. The tool is free. The feedback loop is open. 👇

• 
  Markus Fix (@lispmeister) reported 48 minutes ago

  If you’re not using a VPS / tmux for development (why?) this patch will save your disk. Keep in mind that you cannot easily replace the SSD in your MacBook. They’ll have to replace the entire motherboard. Grok: “The SQLite trigger workaround is still useful (and often necessary) for many users as of late July 2026.19 OpenAI merged several fixes in June 2026 (including reductions to full WebSocket/SSE payload logging and noisy TRACE targets) that significantly cut the original extreme write amplification reported in GitHub issue #28224. That issue was closed as addressed, with an estimated ~85% reduction in some cases and claims of the worst-case ~640 TB/year rates being largely mitigated.2 However, residual high-frequency TRACE (and some DEBUG) insert-prune churn into ~/.codex/logs_2.sqlite (and its WAL) continues on recent Desktop and CLI builds. Fresh reports from July 25–26, 2026 (including on macOS Desktop 26.721.41059 with bundled 0.146.0-alpha.3.1) document ongoing sequence counter advances, measurable process disk writes (often multiple MB per short active window), and stable retained-row counts while the WAL keeps getting hammered. Users confirm the exact block_log_inserts trigger still cleanly stops the inserts without breaking normal Codex operation.12 The original post from @superalesha (July 26) aligns with this: the core noise-dumping behavior persists enough that the one-line SQLite trigger remains a practical local mitigation. Updating Codex helps, but does not fully eliminate the diagnostic logging writes for everyone. If you apply the trigger, quit Codex completely first; it can be dropped later if a definitive upstream fix lands.”

• 
  Cody Schneider (@codyschneider) reported 51 minutes ago

  i need you to understand what is happening in marketing engineering right now i want to try to explain this to you what someone can do in a day with claude code + data pipeline + data warehouse + server + github repo + skill md files is what a fortune 500 would do in a year today you can make 40 facebook ads launch 30 google ads ad groups 100 landing pages write 3 guest blog posts for backlinks booked yourself on 4 podcasts write 5 help desk articles edited two vlog videos scheduled 25 tweets across 3 accounts wrote 2 pieces of scripting software to give away as linkedin lead magnets i dont think you understand what is happening in marketing engineering right now

• 
  Habibi Code (@habibicode) reported 53 minutes ago

  Another big round of updates on iamsingle today! Here's the full list: 📜 Certified SFWA badge We load every app and check if its code really sits in one file. Only 10 of the 21 entries pass this test. 🔧 Fixes are suggested The page lists exactly how to fix the app to make it a sfwa, and opens a pre-filled issue on github ✅ You fixed it and made it a sfwa? One click re-measures and credits you as co-author, verified from your commit

• 
  Kevin Kaminski (@kkaminsk) reported 53 minutes ago

  @jc_za Something happened with Github and OpenClaw. Let's see if this is painless to fix.

• 
  Alex Cinovoj (@AlexCinovoj) reported 53 minutes ago

  The useful agent news is not the agent. GitHub Agentic Workflows posted a weekly update today, and the interesting part was not another shiny autonomous demo. It was the boring production layer around the agent: container CVE scanning before deployment, YAML and license checks in compile, shell and *** injection fixes, SHA validation after updates, and workflows that file issues instead of spraying PRs everywhere. That is the part most AI teams still underbuild. A real agent system needs a control surface before it needs more autonomy. What can it touch, what can it change, what gets verified, and what receipt does the owner get when it is done?

• 
  WorktreeWise (@worktreewise) reported 54 minutes ago

  Your *** workflow is probably slowing you down. The cost of context switching isn't just mental—it is technical. Rebuilding dependencies, re-running servers, and stash conflicts add up to hours lost every week. Worktrees fix this. #*** #GitHub #DevTools

• 
  Gustavo Alessandri (@webgus) reported 56 minutes ago

  If you find an error, have an idea, or want to propose an improvement, just open an issue or fork it on Codeberg or GitHub. Contributions are welcome. That’s exactly the point.

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  Mo (@mosyaseen) reported 58 minutes ago

  Best way to run agents in cloud? I wanna run multiple cli agents on the same codebase, each in its own sandbox, while still having access to and interacting with their codes. Considered GitHub Codespaces, but they're slow to start, laggy, and don't seem scalable. any ideas?

• 
  Nafiz M. (@nafiz_mahmud_99) reported 58 minutes ago

  I have 99 problems but a merge conflict ain't one... Until you forget to pull main before starting your feature branch. #*** #GitHub #Developer

• 
  Amadeus Protocol (@ama_protocol) reported 60 minutes ago

  Most institutional crypto ops still run on a to-do list: someone monitors, someone executes, someone reconciles after the fact. That's an operating model problem, not a tech one. We're seeing treasuries shift from managing tasks to setting policy. You define the guardrails, and an agent trades within them, flags what needs sign-off, and keeps a full audit trail. It's less to-do, and more oversight at a higher layer. It only works if execution can be private and security is foundational, not bolted on. Amadeus runs on infrastructure that's already ISO 27001, SOC2 II, GDPR, AI EU Act, and HIPAA compliant. And it takes less than people expect: a GitHub repo and an executive summary gets you a customized demo agent, fast. If your treasury is still running on a to-do list, it's worth seeing what a policy looks like instead. #AgentEconomy #Amadeus #Web3