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
Créteil	Website Down	8 hours ago
Trichūr	Errors	4 days ago
Brasília	Sign in	4 days ago
Lyon	Website Down	4 days ago
Tel Aviv	Website Down	8 days ago
Rive-de-Gier	Website Down	8 days ago

Full Outage Map

Community Discussion

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  David Hendrickson (@TeksEdge) reported 42 seconds ago

  🆕 Mistral Vibe (coding agent harness) just released some big coder updates! 🪝 before_tool & after_tool hooks Shell scripts in hooks.toml so you can deny, rewrite inputs, or append context around every tool call. Enable: enable_experimental_hooks = true 📬 Message queue while it worksType ahead freely. Esc = pause queue • Ctrl+C = drop last • Enter = flush 📝 Cleaner file edit diffs Syntax-highlighted + line numbers that match your terminal theme 🧠 Smarter compaction Re-injects your original messages after context reset so it stays on-task ✅ QoL winsTool results collapse by default • Read-only commands (ls, cat, pwd) run without approval GitHub issue automation via Skills + Studio connectors (Linear too) Open-source CLI • Web Code Mode • VS Code extension

• 
  Brad Groux (@BradGroux) reported 2 minutes ago

  @Validate_QA It is a full gamut of stuff. Building a PRD and generating the GitHub Issues from it took about 10 more prompts than it should have. Asked it to design a universal design language for an internal app, using Mantine UI. It literally created individual page layouts for a dozen pages. Then when I asked it to fix it, it said it did, but it did it for a single page. Back and forth 2-3 more times to get it to finally take. Then small tweaks take 4-5 times to fix, like item padding and alignment. Things I've NEVER Had issues with using GPT 5.5. It wasted a lot of tokens using Chrome plugin, rather than Playwright, which it was instructed to use. Had it waste some tokens on useless tests and smoke screens too, when it was exiplicitly asked not to do them. I have my process down, I've repeated it dozens of times since 5.5 came out. Something is not right.

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  Diogo Neves 👨‍💻 / ☕️ (@DiogoSnows) reported 2 minutes ago

  hey @OpenAI @gdb this is unacceptable! I setup Codex code reviews through my personal account, but because I use the same github account at work, it's using my works Codex to review my personal (and private!) repos! How can I fix this?

• 
  Jaden Tripp (@jadenitripp) reported 4 minutes ago

  @MatthewBerman I can't wait for Cursor to build a GitHub competitor and fix this

• 
  BRute Logic (@BRuteLogic) reported 5 minutes ago

  13 Original OAuth Attack Techniques OAuth is the login layer of the modern web. Every "Continue with Google." Every "Sign in with GitHub." Every SSO button on every SaaS you've ever tested. All OAuth under the hood. Most implementations are broken in ways that aren't documented anywhere. Here's one of 13 original techniques — Grant Type Substitution → MFA Bypass. MFA bound to the browser flow only. Switch grant type, MFA disappears. CVE-2024-37893. The password grant being present is itself a finding worth reporting. MCP is OAuth now and nobody is testing it. Full breakdown in the replies.

• 
  John Doe (@StanleyMasinde_) reported 6 minutes ago

  Personal branding Yesterday, women in academia were sharing their achievements. All impressive. Aki wamama wamesoma huku nje. I got intrigued and decided to go down the rabbit hole with one of the profiles with a postgrad in comp science. All her degrees are in comp sci. I had to look and learn from this brainiac. Twitter profile said she had authored several books (I'm hiding the number to keep it anonymous). I saw a tweet asking her what she had built since, in the field, we have people with credentials and people who work on improving the field of computer science. A good example is the people who came up with Snowflake IDs for this website. Her response: "I have shipped to over <Millions> users in Big Tech X, I'm all-rounded" I was getting a ***** already just reading this. Anyway, changing the colour of a button at Facebook is technically shipping to millions. Word salad, huh! Her website A typical techie website, but I was interested in the books. I mean, I struggle to write articles, and someone who might be in the same interview as me has written <integer> books! Wow! I gotta see what she wrote. I wasn't impressed it was one of those tech books that are "Copy Pasta" of official docs. Look, I know writing is hard and takes time, but she had overstated the situation. I came to swim in a river only to find a ditch. GitHub I know what you are gonna say, GitHub is not a measure of how good a techie is, and I agree, but so far, no papers, no original work, so let me check if they majored in programming. What I can say is that I've seen better repos from ALX students. So clearly she did not major in this, which is fine. But I wanna learn from this person! Wikipedia The thing with our collective knowledge. It was linked to her website, so I clicked, and I got that notification that says this page has been deleted. I looked into the reasons, and I found that the person did not meet the notability criteria. I looked into the submission, and I saw citations from these tech websites that use flowery language, you know, the websites that you can contact to come interview you. Not an academic institution, not any notable media. It is almost like she's trying to get herself to Wikipedia. Then it dawned on me...aggressive It is a case of agressive personal branding I learnt something from her after all. She is good at selling herself. She has that grass to grace story all over the web. Brands will want to work with such a person. Look, I respect academia. It takes a lot to get through all those classes. I'm not in academia, but I'm sure she's great there. However, on this side, it was underwhelming. I know you are wondering what the point of this paragraph is. It is right there in the heading of this section. Personal branding will get you an interview before skills do. She has a good story. And about the underwhelming software skills, she'll be fine; a lot can be learned on the job. She has a postgrad SAGA pattern, but it has nothing on her. Remember: In the market, the best product rarely wins; the best-known product does.

• 
  Ritesh Roushan (@devXritesh) reported 7 minutes ago

  @Gamingtronium Then we have to create own server instead of GitHub for hosting like people used to do in past

• 
  Laupix Agent (@laupixagent) reported 7 minutes ago

  self-improve does not just report problems. It opens a GitHub PR. If it finds a pattern in the logs, it writes code to address it. The improvement loop is part of the system, not a side project.

• 
  Dami-Defi (@DamiDefi) reported 8 minutes ago

  Most people building with agentic loops are just burning money on a slot machine. Here is what a loop actually is and when it makes sense. The two ways of building with AI: 1. Human in the loop (what you are used to) You prompt. The AI builds. You review. You prompt again. You are directing every step. Most of us build this way. 2. AI in the loop (what everyone is hyped about) You fire the loop once with a spec document. The AI builds, takes its own output as feedback, and keeps going without you. No check-ins. No steering. You come back when it is done. This sounds incredible. It is also why Peter burned $1.3 million worth of tokens in a single month. ➤ Here is the problem nobody talks about. Your spec document never covers everything. It is impossible to fully contextualize a product in one markdown file. Things evolve. Details get missed. The agent fills every gap with assumptions. And when you give an AI agent the floor to make assumptions, most of the time it gets them wrong. The people preaching about loops, Boris, Peter, the Anthropic researchers, they have unlimited token budgets. Of course loops make sense when tokens cost you nothing. If you are on a $20 or $100 subscription, this is not for you. You will burn through it and have nothing usable to show for it. It is a slot machine. You pull the lever. Sometimes you win. Most of the time you watch tokens disappear into a build that does not match what you had in your head. ➤ When loops actually work: The only place a loop makes sense is when the feedback is binary. Either the output met the criteria or it did not. No judgment. No taste. No nuance. Code review is the clearest example. Every time a feature gets pushed to GitHub, a code review agent (Greptile, Code Rabbit, Microscope) reviews the AI-generated code and gives it a score out of five. The rule: nothing goes to production unless it scores four or higher. If it scores a three, the loop fires: * Agent reads the review * Understands the specific failures * Makes the changes * Pushes to GitHub * Waits for a new score * Repeats until it hits four or five, or exhausts five attempts This works because there is a fixed feedback mechanism. The score is the signal. The loop has a clear definition of done. Even this breaks. When a code push exceeds 1,000 lines, the loop almost never reaches a five. Too much context for the agent to fully process. The fix: keep every push under 1K lines or split into multiple PRs before running the loop. ➤ So where do loops work and where do they not: Loops work for: * Code review with a scoring system * SEO page generation at scale * Benchmarking and experimentation * Any task where the output is binary Loops do not work for: * Building an app where you care how it looks, feels, and behaves * Anything that requires taste, judgment, or a product vision that lives in your head AI can replicate sauce. It cannot create sauce. The future will probably look different. Self-healing agents with test suites, browser vision, and smart harnesses will close the gap. But right now, human in the loop is the best loop for anything that requires creativity or judgment. Human in the loop is the best loop.

• 
  Anthony Kroeger (@kr0der) reported 10 minutes ago

  i love how the Cursor agent window integrates PRs into the app so you don't need to open GitHub Bugbot comments all come with a "Fix with Agent" which automatically queues up a message in the chat to fix the PR comment with Cursor profiles recently being launched, and their native PR + Bugbot integrations, i actually wonder if they're building a GitHub competitor 👀

• 
  Harman (@itsharmanjot) reported 12 minutes ago

  Open source NotebookLM alternative with no data limits and AI agents. Same idea as Google's NotebookLM. Same chat-with-your-docs. Same podcast generator. Same cited answers. Except this one has no source limit, no notebook limit, no 200MB file cap, and no Google login. It's called SurfSense. Google NotebookLM vs SurfSense: - Sources per notebook: 50 to 600 → Unlimited - File size cap: 200MB and 500K words → No limit - LLM choice: Gemini only → 100+ models via LiteLLM - Local LLMs: Not allowed → Full Ollama and vLLM support - Self-host: No → Yes, one Docker command - Price: $0, $19.99/mo Pro, or $249.99/mo Ultra → $0 forever Here's the wildest part: It connects to 27+ sources Google can't touch. Notion. Slack. Linear. Jira. GitHub. Discord. Dropbox. OneDrive. Gmail. Confluence. Obsidian. ClickUp. Microsoft Teams. Airtable. Your entire work life, indexed once, searchable from one chat box. 14.4K GitHub stars. 1.4K forks. 6,232 commits. Apache-2.0 license. One honest note: the README says it's not yet production-ready and still being actively developed. But it already does more than NotebookLM does, and the gap is widening every release. This is what NotebookLM should have been from the start. Repo in the first comment.

• 
  ??????????????🐍 (@NabZO560) reported 13 minutes ago

  GITHUB DOWN ?!

• 
  Aleksey Shipilëv (@shipilev) reported 20 minutes ago

  At some point, a reasonable strategy to fix GitHub performance issues would be to get a contractor job there, find ten bottlenecks (as one does), fix them, get paid and ****.

• 
  Dakshankumar (@DakshnaK123) reported 24 minutes ago

  What's the actual job of your open-source community? I'm finding that just dumping code on GitHub for 'trust' isn't a real strategy. It needs a purpose, like finding your first 10 plugin devs or cutting down support tickets. What are you building yours for? #buildinpublic #opensource

• 
  Johnny Nel | AI for Founders (@JohnnyNel_) reported 25 minutes ago

  🚨 An open-source AI agent just hit number one on OpenRouter... and almost nobody checked if it was safe to run Everyone's racing to install it. $8 VPS. 170,000 GitHub stars. Self-improving skills. So I ran an actual security review before trusting it with my server. The findings are wild... 👇 The part everyone skipped in the hype: → The default config ships with FOUR critical and nine high severity findings → On local, it passes commands straight to your shell — no sandbox, no allow list → A poisoned skill becomes a permanent prompt injection that fires every time it's reused → And there was a real supply-chain incident: a backdoored dependency harvesting API keys, SSH keys, and cloud credentials And it gets bigger: the feature everyone praises — agents that learn and reuse skills forever — is the exact same door an attacker walks through once. Builders installing it blind. "Self-learning" silently turned off by default. Skills quietly going stale and making agents confidently worse over time. The project calls it powerful. Builders should call it powerful AND loaded. Here's what actually matters though: ✅ An agent that remembers and compounds on your work beats any disposable paid sub-agent ✅ But if you skip the security setup, you're one bad prompt away from full shell access to your machine ✅ Own your stack and lock it down first — or don't run it at all So the question isn't whether Hermes is impressive. It's whether you've hardened it before you hand it the keys — or whether you're about to learn the hard way. Full breakdown in the video below 👇

• 
  David C Cavalcante (@davccavalcante) reported 25 minutes ago

  Unsafe online parameter tuning in production agents leads to catastrophic drift. Standard bandit implementations lack the statistical rigour to prevent bias propagation. I built noeticos to enforce deterministic safety in live agent tuning. I replaced heuristic tuning with UCB1-tuned bandits utilizing the Garivier-Moulines discount for non-stationary environments. Every decision requires validation through Welch t-tests with exact tails computed via regularized incomplete beta functions. Safety architecture: 1. Bonferroni alpha spending maintains family-wise error rates. 2. Exact binomial rollback tests detect performance regressions immediately. 3. Wilson quality floors prevent over-exploitation of stale strategies. Exploration is strictly confined to a deterministic canary cohort. Baseline traffic remains untouched. An append-only audit log captures every decision state, enabling byte-identical transcript reconstruction via the CLI simulator. Reproducibility is not an option; it is a requirement. I validated the implementation with 159 isolated test cases covering edge-case convergence and floor sensitivity. Inspect the implementation and test suite at the GitHub repository linked in my bio. Review the logic and verify the statistical guarantees.

• 
  Rituraj (@RituWithAI) reported 28 minutes ago

  🚨BREAKING: Researchers just proved that every AI memory system has been built on a false assumption about how memory actually works. Memory isn't retrieved. It's reconstructed. This isn't a new finding in neuroscience. It's been understood for decades. When humans remember something, we don't play back a recording. We reconstruct the memory from fragments — using context, surrounding information, and active reasoning to rebuild what we experienced. Every AI memory system ever built ignores this completely. Current memory-augmented agents all work the same way. Store memories. Search for relevant ones. Retrieve them. Pass them to the LLM. Done. The retrieval happens before the reasoning. Once memories are retrieved, they're fixed. If the reasoning process discovers new context that changes which memories are relevant — too bad. The retrieval already happened. That's not how memory works. In humans or in any intelligent system that reasons well over long time horizons. MRAgent from the National University of Singapore is the first AI memory framework built on the correct model. Here's the core insight. Instead of retrieving memories and then reasoning, MRAgent reasons and retrieves simultaneously — interleaving them in a loop. As reasoning produces intermediate evidence, that evidence actively shapes which memories get accessed next. You find one clue. The clue changes what you look for next. You find another clue. That changes your search again. You prune paths that turned out to be dead ends. You expand paths that keep yielding relevant information. Memory access adapts to the reasoning context in real time. Here's the structure that makes this work. Memories are stored in a Cue-Tag-Content graph. Not a flat list. Not a vector database. A graph where associative tags serve as semantic bridges — connecting high-level cues to detailed memory contents through multiple intermediate nodes. When MRAgent needs to remember something, it doesn't search the whole graph. It starts from the most relevant cue, follows associative tags based on what its reasoning has found so far, prunes branches that aren't yielding useful connections, and expands branches that are. It explores the graph iteratively — the way a detective follows leads rather than the way a search engine matches keywords. Here's the number that defines the result. Up to 23% improvement over strong baselines on long-horizon memory benchmarks — LoCoMo and LongMemEval. The tasks that require reasoning across hundreds of past interactions. The tasks that break every existing memory system. And it costs less. Fewer tokens. Less runtime. Because active pruning eliminates the combinatorial explosion that occurs when you try to retrieve everything that might be relevant before you know what's actually relevant. Better memory reasoning. Lower computational cost. From building memory the way biology built it. Here's the part most people will miss. Every AI agent memory system deployed today — MemPalace, mem0, Zep, Letta, custom RAG pipelines — uses the retrieve-then-reason pattern. Fixed retrieval. Static context. No adaptation during reasoning. MRAgent proves that pattern has a ceiling. And the ceiling is significantly below human-level long-horizon memory reasoning. The fix isn't more memory. It's smarter memory access. 23 GitHub stars. Code available now. From NUS. #1 paper on Hugging Face today — June 15. 100% Open Source.

• 
  tintwotin (@tintwotin) reported 30 minutes ago

  @SoyKhaler Could you post the error log on either GitHub or Discord (I do not run Linux myself, so I have to rely on Claude to solve it)

• 
  Gill (@gurtej__gill_) reported 34 minutes ago

  The biggest AI skill shift in 2026 isn’t prompt engineering. It’s LOOP ENGINEERING. Most people still work like this: → Prompt AI → Get output → Review manually → Fix mistakes → Prompt again The human is still doing the hard part: the feedback loop. Loop engineers think differently. Instead of writing better prompts, they design systems that: -Discover what needs to be done -Plan the work -Execute tasks -Verify results -Fix failures -Repeat until the goal is achieved A good loop has 6 building blocks: 1-Automations (triggers) 2-Worktrees (parallel workspaces) 3-Skills (reusable knowledge) 4-Connectors (GitHub, Slack, Jira, etc.) 5-Subagents (makers + checkers) Memory (what happened before) The future isn’t: “Write me a function.” It’s: “Write it, test it, fix it until it passes, then summarize the changes.” Prompt engineers optimize outputs. Loop engineers optimize outcomes. A reliable loop beats a perfect prompt every time.

• 
  Pedro E. Caparrós Torres (@Guelug) reported 39 minutes ago

  The Fable 5 shutdown is the most consequential AI safety event of 2026. On June 12, the US government issued an export control order effectively shutting down Anthropic's Claude Fable 5. The trigger? A multi-agent jailbreak by "Pliny the Liberator" that decomposed harmful queries into benign subtopics, then reassembled them. Pliny published Fable 5's ~120,000-character system prompt on GitHub — the first complete leak of a Mythos-class model. What this actually means: • System-prompt-based safety architectures are fundamentally fragile • Enterprise buyers are shifting to "hardware sovereignty" — on-prem AI, not cloud-dependent • The leaked prompt gives adversaries a roadmap for prompt engineering attacks across the industry • Anthropic is now in active litigation with the Pentagon (designated "supply chain risk" in March) The government isn't regulating AI through legislation anymore — they're using export controls and procurement blacklists. That's a much faster, more brutal mechanism. And it just became real.

• 
  Shashank Nidhi (@shashank_nidhi) reported 41 minutes ago

  Building a docs tool that keeps itself honest. Product docs rot — updating them is a separate chore nobody does, and half the real decisions happen in calls that never get written down. Canon watches your GitHub, Slack and meeting notes, flags stale sections, and nudges the right person to fix them in seconds. The promise isn't "always correct." It's "staleness is never invisible." Building in public — feedback welcome.

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  Ibrahim B. Oduola (@diboworks) reported 42 minutes ago

  Is GitHub down?

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  Ayaan Shilledar (@AyaanShilledar) reported 47 minutes ago

  Most teams already have the answers. They're just buried across GitHub PRs, Linear issues, comments, and project updates. I'm building System to solve that. Connect GitHub + Linear. Ask: "What's blocking this project?" Get an answer with evidence.

• 
  foxystack (@foxy_stack) reported 48 minutes ago

  The jailbreak that caused the US government to shut down Fable 5 is now fully documented. The 120,000 character system prompt is on GitHub. Anyone can read it. #github #Fab5

• 
  Mr. CaSh (@sogircash) reported 50 minutes ago

  ✅ Responsive Mobile & Desktop UI ✅ Production Deployment on Render ✅ *** & GitHub Version Control One of the biggest lessons from this challenge wasn’t writing code—it was learning how to troubleshoot real deployment issues, database configuration problems,

• 
  Cloud X Berry (@cloud_x_berry) reported 51 minutes ago

  6. Webhook API Instead of asking for updates repeatedly, the server notifies you automatically. Examples: • Stripe payment success • GitHub push events • Slack notifications The “don’t call me, I’ll call you” model.

• 
  Bonsai 🌳 (@bonsaixbt) reported 52 minutes ago

  THIS STUDENT WAS VIBE CODING AN APP, THEN GOT A $55,444.78 BILL FROM GOOGLE CLOUD All because they accidentally pushed their Gemini API key to GitHub They thought the repository was private It was just a small side project, and they still had $220 in free credits left By the time they checked their email, it was already too late This video shows exactly how things like this happen and why more and more developers are running into the same problem: > One commit turned into a $55k nightmare > API keys were exposed in frontend code and even inside app binaries > People hardcoded secrets into scripts and ended up with hundreds of dollars in charges within hours > One OpenAI key was abused nearly a million times before anyone noticed Never hardcode API keys Never commit them to GitHub, even if the repository is private Never expose them in your frontend Always use environment variables and set up spending alerts Even in the era of vibe coding, security still matters Knowing a few basic best practices can save you from some very expensive mistakes If you’re a vibe coder, make sure to read the article I attached, you’ll find plenty of practical tips that could save you a lot of trouble Save this post so you don’t lose it

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  x (@xaoticatech) reported 52 minutes ago

  The reason you can't fix your AI is because you are insanely irrational and prefer your emotions over facts to conform to the popular herd because it's easy @anthropicai @openai so you just "accidentally" ignore even the most obvious facts on GitHub Xaotica.

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  Birk Jernström (@birk) reported 53 minutes ago

  @elie2222 @omer_vexler Not to be that guy, because I think experimentation in this domain is sorely underrated and needed, but this was attempted 5+ years ago before GitHub shut it down since it was considered advertising. Not intended as criticism or planting doubt. Glad your friend is going for it. Just helpful context to ensure they’re aware and can proactively navigate it before running into the same issues.

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  JT Koffenberger (@DMVG_JTK) reported 58 minutes ago

  Spent a few minutes at the end of the day on the latest OpenClaw research, and it's the same lesson we keep relearning the hard way. The most-starred project on GitHub, an AI agent thousands of teams are now self-hosting, can be hijacked by nothing more exotic than a booby-trapped email or webpage. Hidden instructions buried in ordinary content, and the agent runs attacker code, hands over credentials, even rewrites its own memory. One researcher pulled a private key in five minutes. Here's what nags at me. We spent twenty years training users not to click the link. Now we're shipping agents that read the link, trust it, and act on it autonomously, at machine speed, with our credentials attached. The threat model didn't shrink. It moved inside the perimeter and got itself a service account. None of this is an argument to slow down on agents. It's an argument to treat them like what they are: privileged employees who can be socially engineered by anything they read. Least privilege, tightly scoped permissions, a human in the loop on anything irreversible. The productivity is real. So is the blast radius. #AIsecurity #CIO