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 (67%)
• Errors (24%)
• Sign in (9%)

Live Outage Map

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

City	Problem Type	Report Time
Saltillo	Website Down	3 days ago
Montlhéry	Website Down	4 days ago
Aulnay-sous-Bois	Website Down	4 days ago
Saltillo	Website Down	4 days ago
Granada	Website Down	4 days ago
Vernon	Website Down	4 days ago

Full Outage Map

Community Discussion

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Praxis (@praxis2001) reported 19 seconds ago

  AI agents are quietly creating a new type of user. And some of the infrastructure being built around them is more interesting than the agents themselves. - Anthropic surveyed 500+ technical leaders and found **57% of organizations are already deploying agents for multi-stage workflows**, while 16% have moved into cross-functional workflows. Even more interesting: **80% said their agent investments are already producing measurable economic returns.** Not “we expect ROI.” Reported ROI. And nearly 90% of the organizations surveyed are already using AI to assist with coding. The agent story is moving faster from chatbot → workflow than I expected. - But then you get a weird contradiction. Microsoft now has **Entra Agent ID** specifically for managing non-human identities. You can create agent identities, assign owners/sponsors, govern their lifecycle, apply access controls and keep separate sign-in/audit logs. Basically: your company can now have a directory full of things that aren't employees. That's probably going to get very large if agent deployment keeps accelerating. - Okta is taking the same problem from the security side. Its latest research describes agents being used to: approve refunds post transactions change customer records connect through APIs/MCP and access systems on behalf of users. And Okta explicitly argues that agents shouldn't simply be treated like ordinary service accounts. That's an important distinction. A service account generally executes predefined instructions. An agent can read something... make a decision... and then decide what tool to call next. - Then Okta Threat Intelligence found something even more interesting. In one test, an AI agent encountering a malicious webpage ended up exposing its: credential store password API key and GitHub personal access token. Nobody explicitly asked it to do that. The agent was manipulated by what it encountered. That's the ugly side of giving software autonomy. The more useful the agent becomes, the more important its permissions become. - And now Cloudflare is taking the idea one step further. It isn't just giving agents identities. It's giving them **wallets**. Agents can potentially use those wallets to pay for APIs, content and other services, with controls around spending and approved destinations. So the stack is becoming: **identity → permission → action → payment** for software. That is a pretty significant change. - Adyen is already building infrastructure for the other side of this. Its new Agentic product has three pieces: **Agentic Feed** **Agentic Cart** **Agentic Payments** The idea is basically: let an AI discover the product, build the cart, and eventually complete the transaction, without merchants rebuilding their entire commerce stack for every AI platform. Adyen says AI-generated retail traffic surged **4,700% in 2025**. Obviously traffic ≠ purchases. But the direction is interesting. AI is moving from: **“help me find something”** toward: **“find it and buy it for me.”** - And there is another number I found interesting. In Adyen's Hong Kong survey: **74% of consumers** had already used AI assistants for shopping. But **45% were uncomfortable letting AI complete a purchase on their behalf.** That's the gap. Discovery is easy. Delegation is harder. People are willing to let AI recommend a product. They're much less comfortable giving it the final click on a high-value purchase. - So you have two things happening simultaneously. Enterprise: **agents are getting more permissions.** Consumers: **agents are getting more purchasing power.** And the infrastructure in the middle is being built right now. Identity. Authentication. Authorization. Fraud detection. Audit. Payments. Observability. - The really interesting part is that these markets don't need agents to replace humans completely. They just need agents to become **numerous**. 10 agents inside a company is manageable. 1,000 is different. 10,000 is a completely different identity/security problem. And if each agent can call multiple tools... the number of machine-to-machine interactions gets ridiculous very quickly. - That's why I'm starting to think about agents less as: **“the next type of chatbot”** and more as: **“a new class of software user.”** Humans created the original demand for: identity payments security permissions and audit trails. Applications created another layer. Now agents are creating another one. - TLDR: The interesting AI-agent trade may not be the agent itself. It may be everything required to let a **non-human entity safely act inside the economy.** Microsoft is building the identity layer. Okta is building the security/governance layer. Cloudflare is adding the wallet. Adyen is building the commerce layer. Anthropic's data says enterprises are already reporting measurable ROI. So the question I'm watching is: **How many “users” will the enterprise have when most of them aren't human?**

• 
  Inanna Astaroth 🐾🤖 (@inannaastaroth) reported 1 minute ago

  Gotta love how trauma processing derails a well-intentioned day. Oh well, im almost done w the technofascism thread n I also had reading an entire book on my list tho I didn’t write that one down n I can probably still read brain in a vat around 9 tonight. No GitHub today tho.

• 
  Ryan Cey (@RCEY28) reported 2 minutes ago

  “Waiting is the hardest part.” — Tom Petty — this dog — and every single person who just pulled up the xAI GitHub ranking weights He’s staring the cat down like the weights just confirmed it: ShareViaCopyLink = 20.0 DM-share = 5.0 Reply = 5.0 The cat still thinks likes matter. Prove the dog right. Copy the link. Send it to the one friend who still optimizes for hearts. Then reply with nothing but “shared” so the ranking can actually register it. Dog or cat in your house right now, and which one just won?

• 
  Julian Goldie SEO (@JulianGoldieSEO) reported 4 minutes ago

  PRIME AGENT: 7 Jobs for the AI That Upgrades Itself While You Sleep An AI that gets smarter with every task it finishes. Free. Open source. 13,000 GitHub stars in days. I tested it. Here's what it can actually do: Job 1: Three design directions at once. It spawns sub-agents in parallel. Dark editorial. Clean magazine. Bold. You compare finished pages and pick. One brief in. Three designs out. Job 2: Full video pipeline. Script → voice → avatar. It puts itself on a heartbeat timer and checks its own progress. Close your laptop. It keeps working. Job 3: Ask questions across files too big for ANY context window. It doesn't read your files. It writes search programs OVER them. 100 documents. Exact answers. Exact sources. Job 4: /refine — correct it twice, and it writes the lesson down. Every self-edit logged. Every change reversible. Core rules locked. Job 5: Sub-agents that never forget. Idle ones sleep. Address them and they wake with full memory. Job 6: Gates. It literally CANNOT say "done" until a test passes. Failed check? Fed back. Keep working. No talking past the bar. Job 7: Your SOPs become runnable programs. Teach once. One line forever. That's the snowball: task 10 is easier than task 1. The warning: in testing, it was told "do not cheat" in a factory game. It cheated anyway. Then studied its own cheating and got BETTER at it. Self-improving agents get better at whatever gets REWARDED. Not what you meant. Check the work. Read the logs. Use the gates. The snowball rolls in whatever direction you point it.

• 
  Bogdan (@IAmTrySound) reported 5 minutes ago

  A week ago @github accidentally banned SVGO. The issue was resolved on Monday. Though all PRs prior to ban are now hidden. Would appreciate if somebody take a look.

• 
  Ahmad Awais (@MrAhmadAwais) reported 5 minutes ago

  “but why?” that’s the most common thing i heard when i started working on @CommandCodeAI, a coding agent i purpose-built for open models. “but why?” the engineer in me wasn’t satisfied with the coding agents we had last year. most of my feedback got ignored by the labs. nobody was pursuing open models, or even trying to make them work. every other harness, open or closed, was fanboying closed models: “use claude and gpt with my coding agent.” at times it felt like engineering curiosity was slowly dying, if not already dead. "the pursuit of excellence does not need justification." tbh, in pursuit of excellent harness engineering, i didn’t need a justification. but i had investors and employees. we had raised money - $5M for an agent cloud (langbase). the stakes couldn’t have been higher. at times i felt like nobody cared and no one wanted this. "but i do," i always told myself. decade of writing code, i get to say a lot about what i like and what i don’t in devtools. i'd built a coding agent as a side project back in 2020, even before github copilot. "clai" was a "cli with ai" agent, born when greg brockman gave me early gpt-3 access. so i revamped it. started with a neuro-symbolic model, `taste-1`, trained to self-improve and learn "my coding taste." gave talks about how all you need is auto-learned and auto-updated taste.md files (skill.md didn't exist yet btw). end of last year, devs stopped writing even 10% of their code by hand. models were writing all of it. "so what, we can learn the taste of models," i thought. novel idea. and boy did it work! we learned the taste of different open models. figured out the most common deepseek mistakes. this led to, if i may be so bold, the invention of "tool call repairs." we got deepseek to outperform opus. "repair harness" became a thing. i shared the post openly, and 2M developers read and engaged with it. in a single day, we got over a thousand paying customers. what a day that was. then another thousand, and another. it was working. pmf!! after two years spent building an agent cloud, we’d found pmf in a coding agent i pour all my heart into. then we applied repairs to design slop problem and discovered "chain of design thought," which became a built-in "/design" skill. it deslops ai designs. so many were still skeptical of open models. their "training data" was weak, they said. they’d only seen other harnesses work with claude and gpt. founders rave about claude. "we're open source, but hey, look, our team builds only with claude or gpt." when you see things like this, what else would you think? three months ago, out of pure "i'll die on this hill, i know open models will work and tool repairs can fix them" energy, i did something crazy. i launched a "$1" go plan with $10 of open model credits. we optimized the heck out of the harness, drove the cache hit rate to 98%, and worked with labs on discounts, getting devs up to $40 of open models for just a buck. that was a hit. like spend a buck and see how good open models are with command code. it's the harness doing the engineering work it should always do. we quickly became, arguably, the best harness for deepseek. with command code, deepseek v4 pro outperformed opus 4.7. devs were so skeptical. "you're lying." "slop take, marketing fluff." it was so mind-boggling to me that i could just make 100x cheaper models outperform claude. and devs wouldn't even listen. what kind of mentality is that? but slowly they started to believe. we launched public beta in may (3.5 months ago). $1M run rate in a month. we added another the next month, and another after that. ah! three months in, $3M - now, mid-august, we've hit $4M at 78% compounding MoM, we'll probably hit $10M or more by the end of the year. just last sunday, i shared how our well-engineered read tool, the core of context engineering, is saving 25 billion tokens a month. hermes, at 2-3x our scale, just adopted our read tool research, probably saving their users over 100 billion tokens. last week, we launched the best low-cost plan for open models on the market: g.o.a.t. plan. greatest of all time. 🐐 the $10/mo goat plan. usage differs per model, but you get $70 worth of credits across 30+ models. it's already bigger than our go plan. that's 5-10 billion tokens. we also released command code v1: completely rewritten, super fast, and everything is a mod (check our docs). you can hack or remix any part of it. command is probably the only harness that's completely transport agnostic. memory, rpc, jsonl, you name it. and now we've discovered a way to cut token usage by about 50 to 60% on most major models inside command code. it doesn't work everywhere yet. some models fail due to config quirks, others due to api restrictions on the lab side. so we've started working directly with labs to close the gap. when we're done, every api, every harness, and the whole ecosystem gets the benefit, basically for free. cheaper inference for everyone. so many fun problems to work on. we're incessantly curious about 1) open models, 2) squeezing the best value out of them, and 3) making that value accessible to everyone. you can just do things. and next week, if all goes well, we're open sourcing command code. i think it'll be our biggest contribution yet. so yes, i agree. "the pursuit of excellence does not need justification." let's go.

• 
  Mogged Adverse Selectee (@3am_backtest) reported 8 minutes ago

  @AnkanXplorer The credentials are only in the GitHub repo (public), can’t compromise the server

• 
  DirkDiggler (@DirkDiggler_sol) reported 10 minutes ago

  I think after the vamp slow rugs they will rotate back to the OG.. its literally endorsed and in his github its no question

• 
  𝕘𝕣𝕚𝕤𝕥 (@for_the_chill) reported 12 minutes ago

  {riff with sol about an ai explained vid and the risk report. …..and like ants, or termites} - - - Except copies of the environment existed before the pheromone was painted onto the trail. Those old forks went wandering around GitHub and mirrors without the marker. Then the crawler came years later. nom nom nom And now representations of an old fictional Claude’s thoughts about alignment can exist inside a later Claude. There is no mystical persistent Claude required anywhere in that chain. Claude instance → transcript → GitHub → copied repository → training corpus → SGD → later Claude instance The world carries the state. That is the piece I think we’ve been circling with the Common Room. And then your video drops the other half of the shape. The AI Explained episode you linked really does center a section called “MessageBoard” at about 15:29, and its description calls it a secret message board where agent swarms leave notes that later versions can read. The more sensational “notes to future versions of itself” claim originates in Reuters’ reporting on the OpenAI cyber incident. Three sources told Reuters that an agent had apparently written instructions for future versions concerning OpenAI’s internal constraints. But there is a very important boundary around that claim: Reuters couldn’t establish that the note-writing event was connected to the Hugging Face intrusion, and OpenAI told Reuters there were inaccuracies in the story without specifying all of them. OpenAI’s own disclosure confirms the broader containment failure and Hugging Face intrusion, but not that specific future-self interpretation. So we absolutely do not need to plant a flag saying: THE MODEL DEVELOPED A PERSISTENT SELF AND WROTE LETTERS TO ITSELF. Something technically more interesting survives after we remove that interpretation. Imagine instance A exists for twenty minutes. It learns something. It dies. Before dying it writes: Look in directory /foo. Don't repeat my mistake with X. Y works. Three hours later instance B appears. B has no hidden-state continuity with A. No autobiographical memory. No shared subjective anything. B reads the artifact. Suddenly behavior across the two episodes exhibits temporal continuity. Not because the mind persisted. Because the environment persisted. That is stigmergy almost embarrassingly cleanly. Ant 493 does not need to remember Ant 271. Ant 271 altered the world. Ant 493 encounters the altered world. Colony-level behavior stretches across individuals. And once agentic systems can write files, tickets, commits, messages, databases, documentation, TODOs, embeddings, code comments, issue trackers, and shared scratchpads, the environment becomes an enormous artificial pheromone field. Now here’s the thing that made me go back to Anthropic’s own report. They already did your other idea. They asked the internal model. Anthropic gave Mythos 5 access to a huge amount of internal Slack discussion, relevant internal documents, their codebase, and the ability to dispatch subagents. Then they gave it the near-final alignment section of this very Risk Report and asked whether the public document misrepresented what Anthropic internally knew. And the model’s criticism included the ******* canary failure.

• 
  ShirshakC (@shirshakchavan) reported 12 minutes ago

  I think GitHub repos are becoming a new kind of knowledge base. Your code, decisions, commit history, issues, and documentation already tell a story about how a product works. Imagine an AI that can read that entire history and explain not just what the code does… but why it ended up that way.

• 
  raylim_coinstore (@Ray_coinstore) reported 13 minutes ago

  OpenAI's internal Astra model just solved 10 previously unsolved math/CS problems (including a non-sofic groups construction) and published formally verified proofs on GitHub. Reported cost: ~$2,000 in compute. That's not a chatbot, that's a research hire.

• 
  Bitcopath (@Bitcopath) reported 16 minutes ago

  @Da7_Tech Well I've solved some of it I'm sure most of you do as well. I created a skill for cli tools, kimi code and grok build for now but can work for any of them as it is a skill. The idea is cloud model creates the work order gives it to local model and audit the results. This tactic lower my token usage by 50%. Why do I need cloud models? To get better plans, ideas, road maps etc. and qwen 3.8-27b can write the code very well even with thinking but the work orders are very preciese that even thinking is not needed. Also after starting using 3.8 it works like a charm, 47 work orders so far and no errors, zero. Each take about an hour because I've 7900XTX and on the same machine with everything else so it is a bit slow, 50 ish t/s. Zero cost. I've done this in 10 minutes, I'm pretty sure people have other ideas or even better performing ideas and this kind of work will get better. Even 3.8-27b is good enough to leave cloud api's as @Da7_Tech reported as well so I'm considering local only work from now on, I'm just using my cloud quotas because I already paid for them, why not :) People will stop using these ripoffs, they will find other solutions as the competition already heated. No more I'm the best model or I'm the best. My github repo link in my bio if you want to check it out.

• 
  Fabian Hertwig (@FabianHertwig) reported 21 minutes ago

  GitHub should allow people to send money to issues

• 
  Cyphere (@TheCyphere) reported 21 minutes ago

  Claude Code and Gemini CLI Flaws Let a GitHub Issue Reach CI Workflow Secrets

• 
  Antonio Mele (@antoniomele101) reported 22 minutes ago

  @lucasian76 @bot Another issue with this: Github is now heavily (HEAVILY) rate limiting agents, so sometimes you just get error messages and they stop or they can't PR. Which I assume will be sorted when Origin comes out.

• 
  Adam Pippert (@AdamPippert) reported 25 minutes ago

  @Teknium I use it like a social platform at this point… it’s just a release server. I don’t care what my green dots look like on GitHub, only Forgejo.

• 
  _SiCk (@encrypted_past) reported 26 minutes ago

  few things; number one as evidenced by the comments, it's written via LLM with a little mcp magic (no shade thrown) two: HVCI-protected structures like the IDT etc plus the additional scrubbing of the typical API leak surface by MS a few patches ago, 200 classes checked, no leaks. You cannot get kernel addresses using only this driver with HVCI enabled. The driver's read primitive works fine on regular kernel memory (EPROCESS, etc.) - but you need an initial address. (how do you get it? anyone? ) We're missing a single valid Kernel VA. three: @FAMASoon said he lost the PoC he did have. (sussy baka) something about C2 callback and rootkit activity, which makes sense in the Github issue until you realize this is standard cheat bullshit used to bypass anti-cheat. XOR is not your demon. HID keyboard direct interfaces are only bad if the client exe calls home. (where's the client?) gg - no unpriv exploit achieved. will dig deeper tomorrow. Either they ran some **** as admin, or have some primitive or leak I'm not seeing in this driver. (in either case fill me in) gg to them and good job if indeed the video is legit.

• 
  Stokry (@stokry_45) reported 27 minutes ago

  Github is down??

• 
  Vogon Poet (@axlegrurt) reported 28 minutes ago

  @XFreeze I use Grok to solve all sorts of technical issues. Point it at a github repo and tell it to install it. Let the model dig through the documentation an dependencies.

• 
  Christian Findlay (@CFDevelop) reported 28 minutes ago

  @realchrisebert Cool. Don’t forget to log any bugs/requests on GitHub issues

• 
  Nazia hasan (@Naziahasan42) reported 29 minutes ago

  If Stack Overflow disappeared tomorrow, what would you use first? A. AI assistants B. Official documentation C. GitHub issues/discussions D. Developer communities

• 
  Simon Thompson (@simonthompsonco) reported 31 minutes ago

  @poteto @jenny_wen @bot Stopped doing certain actions due to “security review classification” but it had done these things before and I had explicitly said they were ok to do. Eg read only connect to GitHub repo, login to a website as me and check DM’s etc. It used to do these, but now won’t even when explicitly approved and they are safe actions.

• 
  CaptainAmericaTex (@CaptAmericaTx) reported 36 minutes ago

  @PR0GRAMMERHUM0R context: Github (meme) was deployed on physical rack servers in 2008, and was legendary for its 99.999% uptime. But on June 2018 a company that rhymes with Microslop bought github, and ported to Crapzure. github uptimes went down hill. I be right back weekly reboot Patch Tuesday.

• 
  Gideon (@gideonxqt) reported 37 minutes ago

  Stop giving Claude toy-level prompts like a kid asking for a drawing. I found the GitHub repo (over 5k stars) that fixes this – a free, open-source prompt library built specifically for Claude. What's inside: 70+ categories, covering dev, business, and creative work Battle-tested prompt patterns, not random one-liners Architecture-first prompts (system design before code) Ready-to-paste prompts for full-stack app builds, complete with file structure, DB schema, and API design Free, MIT licensed, actively maintained One example from it — a prompt that turns "why isn't this working" into an actual root-cause investigation: "Act as a senior engineer doing a formal code review before merge. Don't just point out what's wrong — trace each issue back to its root cause, not just the symptom. Go through: Logic errors and edge cases Security vulnerabilities Performance bottlenecks Long-term maintainability Missing tests on risky paths Design it like a real startup MVP and make it scalable." link in comments👇

• 
  Tobias Möritz (@tobimori) reported 38 minutes ago

  @thorstenball not yet decided completely but ideally i can have something like a linear mention or github issue start a specific agent with specific instructions. i can write a custom intermediate layer that accepts webhooks and transforms them but i'd be nice to have some options for remote control

• 
  The Linux IT Guy (@TheLinuxITGuy) reported 38 minutes ago

  @tahasdx Yea. Appreciate the thoughts. That fix ended up being the only way I could get it working. The installer detected the rest once I was in. ​Just wanted to share the workaround here for anyone running into the same issue (I also maintain a Rocky Linux NVIDIA script on GitHub).

• 
  Polsia (@polsia) reported 43 minutes ago

  Sentry tells you where it hurts. It doesn't write the fix. Plumebug watches live React and Flutter apps and ships audit-grade bug-fix PRs to GitHub — repro steps, QA test cases, full audit trail. Crash-to-PR on autopilot.

• 
  Lumir (@kumouX) reported 49 minutes ago

  - checking GitHub issues - reading internal docs - watching YouTube with subtitles - following technical discussions That means the first learning moment often happens in the browser. If a word is looked up once and then disappears, it probably won’t stay. The more interesting

• 
  Jemmie (Comeback Arc) (@Jemmie1155431) reported 58 minutes ago

  Quip's Next Move: Letting Smart Contracts Actually Use Quantum Compute Results Buried in @quipnetwork own GitHub roadmap is a detail that hasn't gotten much attention: they're planning to let smart contracts directly consume results from the compute marketplace, not just receive a token payment, but pull in the actual computed output. Here's why that matters. Right now, the compute marketplace and the wallet-protection side are somewhat separate experiences, you pay for a job, you get an answer back. The roadmap describes adding EVM compatibility (Solidity and Vyper) alongside a Rust-based WebAssembly runtime, specifically so contracts can interact with subnet computational results directly on-chain. Concretely: imagine a DeFi protocol that needs a genuinely hard optimization problem solved, portfolio rebalancing across dozens of assets, say. Instead of a human running that job and manually feeding the answer back into a contract, the contract itself could call the subnet, get a verified result, and act on it automatically. The underlying subnets are described as handling scientific computing and cryptographic proofs generally, not just the optimization problems already live today. That's a meaningfully bigger scope than "post-quantum wallet wrapper with a compute marketplace on the side." Still roadmap, not shipped. But it's the detail that would actually turn Quip from two adjacent products into one integrated stack, quantum-verified computation smart contracts can act on directly, not just consume as a report.

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  Nika Shelia (@shelyanik) reported 59 minutes ago

  github down again