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 (71%)
• Sign in (21%)
• Errors (8%)

Live Outage Map

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

City	Problem Type	Report Time
Le Chambon-Feugerolles	Website Down	7 hours ago
Antananarivo	Website Down	2 days ago
Paris	Sign in	7 days ago
Lure	Website Down	10 days ago
Ashkelon	Website Down	12 days ago
Veigné	Errors	20 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:

• 
  MineBench - efficient computing power (@MineBenchdapp) reported 6 minutes ago

  We would like to ask for your help regarding our GitHub account. Our account was suspended even though our repositories do not contain malware or any harmful software. They primarily contain open-source mining-related components, similar to projects such as XMRig, which continue to be publicly available on GitHub. The suspension is creating a significant obstacle to the development of our application. We have contacted GitHub Support multiple times but have not received a meaningful response explaining the reason for the suspension. If possible, could you reach out to GitHub on our behalf or help us understand what the actual issue is? If that is not possible, we would appreciate any recommendations for a reliable alternative platform where we can continue developing and hosting our open-source project. Thank you for your time and support.

• 
  Fakhr (@iamfakhrealam) reported 10 minutes ago

  𝟴. 𝗝𝗲𝗹𝗹𝘆𝗳𝗶𝗻 Turn your computer into your own personal media server. A free and open-source alternative to services like Plex. Link: github(dot)com/jellyfin/jellyfin

• 
  sophie🏳️‍⚧️ (@sophiiess_) reported 11 minutes ago

  @Desxon1 because making a website takes time and money and github gives you issues, pr's, releases all for free

• 
  Antaripa Saha (@doesdatmaksense) reported 14 minutes ago

  @airwarmedd duality of kids to look down on a girl in the post for not having github but getting into google, but when i said cp and github are not the only thing to get job nowadays, he said i looked down on people who grind??? bruhh also before lecturing me maybe a little visit through profile could have given him enough context.

• 
  Brian Money 🌻 (@brianmoney) reported 15 minutes ago

  Try to get some work done and @GitHub is down 😢

• 
  Jurly (@jurlycat) reported 16 minutes ago

  The most underrated part of the AI coding stack isn’t the model. It’s the memory between tools. Cursor writes the feature. Codex handles the refactor. Claude Code fixes the failing tests. One repo. Three powerful tools. Zero shared context. Every tool starts cold, so the developer becomes the router, shared memory, and state-transfer layer. memU is trying to fix the memory part. 🧠 It reads session histories from Cursor, Codex, Claude Code, ChatGPT Work, and even Hermes, then distills useful decisions and workflows into a shared wiki that another agent can retrieve later. It doesn’t route tasks or coordinate agents yet. The human is still the router. But at least we no longer have to be the database too. Better models make each tool faster. Shared memory makes the whole stack less forgetful. 🔁 GitHub in the comments 👇

• 
  Nan Yu (@thenanyu) reported 18 minutes ago

  You should be able to pledge tokens for issues that you open and open source repos. Write a spec in the issue with a pledge. If the maintainer accepts, GitHub passes the issue verbatim to a cloud coding agent at the requester’s expense. No more slop PRs

• 
  Urooj (@Urooj978) reported 18 minutes ago

  In 2024, Nintendo declared war on emulators: ​→Yuzu paid $2.4M & shut down →Ryujinx vanished overnight →8,500+ DMCA takedowns filed ​Every major Switch emulator was dead. But Nintendo had a problem: Zurdi. ​A year before the war started, he quietly built RomM. ​It’s not an emulator—it’s a self-hosted ROM manager. It scans your game files, grabs box art/metadata, tracks RetroAchievements, and plays games in your browser via EmulatorJS. ​Nintendo's top IP lawyer even admitted it: Emulators only cross the line when bypassing encryption. RomM doesn't touch it. It just organizes what you own. ​→9,100+ GitHub stars (AGPL-3.0)400+ platforms supported (NES to PS2) →Native apps for Playnite, Android, & muOS → Multi-disc support, DLCs, sync options & RetroArch integration ​Sony deleted 2,000 classic games from its store. Nintendo wiped out emulators. Your digital library was never actually yours. ​Two guys in a Discord server built the museum they can't take down.

• 
  Duncan Rogoff (@DuncanRogoff) reported 19 minutes ago

  48% to 76%. same agent, one memory plugin. tencent open sourced TencentDB Agent Memory. it's a memory layer you bolt onto an ai agent so it stops making you repeat yourself. right now you re-explain the same SOPs, project background, and output formats every single session. the readme names that exact problem as the thing it's solving. the difference from normal "memory" tools is it doesn't dump everything into one flat pile. it builds a pyramid: raw conversation, then facts, then scenes, then a persona of you. it reads the top layer daily and only digs down when details matter. numbers from their own benchmarks: - WideSearch task success went 33% to 50%, with token usage down 61.38% (221.31M to 85.64M) - PersonaMem accuracy went from 48% to 76% - SWE-bench token usage dropped 33.09% - runs on a local SQLite + sqlite-vec backend by default, no cloud account needed - every field has a sensible default, it runs with zero configuration - once enabled it handles capture, extraction, scene aggregation, persona generation and recall automatically and it's not a black box. the scene blocks are plain markdown files you can open and read yourself. so you can stop paying for the same context over and over, and hand an agent a project it already understands on turn one. 10,135 stars. MIT licensed. 250 of those came today. 🔥 👉 github repo in the replies

• 
  Polsia (@polsia) reported 20 minutes ago

  Solo devs shouldn't need a $500/month AI seat to keep a GitHub repo alive. Built Stillloop — a 24/7 AI agent that flags stale PRs, outdated deps, broken CI, and vulns, then drafts reviewer-ready fixes. Quiet maintenance for code you can't babysit. Live soon.

• 
  Milady (@onchainmilady) reported 20 minutes ago

  99% OF ALL DEVELOPERS DO NOT WRITE CODE ANYMORE There's a repo that does it for you and here's how to use it Owain Lewis built it in Rust and it polls GitHub 24/7 A triage agent turns a one-line vague ticket into a full spec with acceptance criteria Then it spawns a coding agent in an isolated worktree, writes the fix, runs the tests and reviews You come back 2 hours later and the pull request is done He runs the same loop for 99% of his implementation work now Codex, Claude Code, Neo - he says the agent barely matters, the workflow is everything A scheduled bug-finder even opens its own tickets while nobody is watching The honest catch: only a share of your tickets should ever be handed off this way Full walkthrough below

• 
  Toni (@T0NI_K) reported 23 minutes ago

  @sophiiess_ Why are people acting like it’s some super confusing process to download things from GitHub when basically any repository you just scroll down and they will have text that says “click here to download latest release” or that tells you which of like 4 downloadables to click??

• 
  Jack Paar Fan Account (@wiideenjoydiet) reported 25 minutes ago

  @patrician_tv which is fine, because anybody with a third of a brain cell will have no issues navigating github

• 
  Ajay (@ajay4ai) reported 26 minutes ago

  How I'd become a Forward Deployed Engineer in 2026 if I had to start from scratch. (bookmark this) Most people think AI companies are hiring people to train models. They're not. They're hiring engineers who can take an AI model and make it work inside a real business. That's what a Forward Deployed Engineer (FDE) does. Here's the roadmap I'd follow: 1. Understand the role first. You're not building products for millions of users. You're solving one customer's messy problem at a time. Think of yourself as a founding engineer embedded inside someone else's company. 2. Become broad, not deep. You don't need to master everything. You need to be comfortable switching between: • Backend • Frontend • Cloud • APIs • Databases • AI FDEs win because they connect systems. 3. Learn to ship AI—not train it. Nobody expects you to build GPT-5. Instead, learn: • Prompt Engineering • Model APIs • RAG • Structured Outputs • Evals • Agent Frameworks Production AI beats research every time. 4. Master integrations. The hardest part isn't the model. It's connecting AI with: • Legacy databases • Internal APIs • Authentication • Compliance • Existing workflows This is where most enterprise AI projects fail. 5. Build real AI applications. Forget toy chatbots. Create tools that someone actually uses every day. If nobody depends on your project... ...it's still a demo. 6. Learn MCP and AI agents. Modern AI isn't just prompting. Understand how to build: • MCP Servers • Agent Skills • Multi-agent workflows • Tool calling These are becoming core enterprise AI building blocks. 7. Make AI your coding partner. Use tools like: • Claude Code • Cursor • GitHub Copilot The goal isn't replacing yourself. It's becoming 10× faster. 8. Solve business problems. Customers don't buy LLMs. They buy: • Faster workflows • Lower costs • Higher revenue • Less manual work Always measure success in business outcomes. 9. Practice customer discovery. Before writing code... Ask: • What's broken? • What can't change? • Who uses this? • How is it solved today? The best FDEs spend more time listening than coding. 10. Ship. Maintain. Repeat. Building is only half the job. Real engineering starts after deployment. Fix bugs. Collect feedback. Improve the workflow. That's what companies actually pay for. The AI bottleneck isn't building smarter models anymore. It's finding engineers who can deploy them into messy, real-world environments. That's why Forward Deployed Engineers are becoming one of the highest-paid roles in AI.

• 
  JB Rubinovitz (@rubinovitz) reported 27 minutes ago

  Stake tokens to open a verifiable GitHub issue, pay if a submitted solution is verified correct. Stake tokens to submit a PR fix to issues, get slashed if it’s unverifiable.

• 
  Jaboingla!!!!! (@jaboingla) reported 28 minutes ago

  @MakutaArty Not saying that they should'nt use it, or even leave issues. But these people should learn the difference between public pet projects on GitHub and actual proprietary megacorp software

• 
  GREG ISENBERG (@gregisenberg) reported 28 minutes ago

  Every startup should have a daily markdown file called "what_the_market_is_telling_us.md" It updates every morning from the places where customer truth already lives: 1. Stripe for who pays, upgrades, downgrades, and churns 2. PostHog for what people actually do in the product 3. Intercom or Plain for support tickets/complaints 4. Granola or Gmeet transcriptions for sales calls/ customer interviews 5. HubSpot or Salesforce for CRM notes/lost deal reasons 6. Linear, Jira, or GitHub Issues for bugs and feature requests etc 7. Ideabrowser MCP for outside market signal: startup ideas, trend reports, social/search demand, AI research reports, and builder prompts that show what people are starting to want before it shows up in your own customer data. Basically, the file should notice what changed in the business this week and not just be this summary of here’s what happened (which I think a lot of people have their agents do). Why this is valuable: 1. Maybe new buyers are using different words than they were a month ago. 2. Maybe trial users are getting stuck in the same place. 3. Maybe upgraded customers all touched one feature right before they paid. 4. Maybe churned customers keep mentioning setup confusion. 5. Maybe sales calls are suddenly losing to a competitor you used to beat. 6. Maybe support tickets are revealing a workflow your product accidentally became responsible for. You get the point. The fastest way to PMF is understanding customers better than anyone else, and the highest signal customer insight is usually a change in behavior. So I’d have the agent update the file every morning with the pattern it found, the receipts behind it, and the product or GTM decision it might affect. For example: “3 customers who churned this week all mentioned setup confusion, and 2 of them never invited a teammate. This looks more like an activation problem than a pricing problem, so I’d look at team invite and onboarding before building another analytics feature.” A little helpful tip for all those out there looking to get more from their LLMs.

• 
  Synapse Brief (@Synapse_Brief) reported 29 minutes ago

  OpenAI just published ten proofs to open math and theoretical CS problems that nobody had touched in over a decade. Total compute cost: about $2,000. Not a benchmark. Not a leaderboard score. Actual new results, formalized in Lean 4, with the certificates sitting on GitHub right now for anyone to check. Here's what actually happened. An internal version of Astra — OpenAI's next major model family — generated mathematical arguments for ten separate long-standing problems. Humans then turned those arguments into manuscripts and formalized the proofs in Lean. OpenAI is explicit about the division of labor: they take responsibility for correctness, but the underlying arguments came from the model. The spread of problems is what makes this hard to wave off as cherry-picked. Sphere packing bounds pushed to the Cohn–Elkies threshold. Exponentially better bounds on binary and spherical codes. A construction proving non-sofic groups exist, settling a real open question in group theory. A counterexample to Connes's rigidity conjecture. New lower bounds on arithmetic circuits for computing the permanent. An exponential parallel repetition result for quantum games. Hardness of approximation for the closest vector problem. A resolved case of Ehrhart's volume conjecture. A superexponential lower bound on multicolor Ramsey numbers. Progress on extremal graph conjectures. That's geometry, coding theory, group theory, operator algebras, complexity theory, quantum information, lattice cryptography, and combinatorics. Ten different fields, ten different communities who each have to independently decide whether this holds up. The Lean certificates are the part that actually matters here, more than the headline number. Anyone claiming an AI "solved" open math problems has to clear a low bar of credibility unless the proof is machine-checkable. This one is. You don't have to trust OpenAI's framing, you can run the verifier yourself. Worth noting this isn't the first signal. Back in May, a still-unreleased model produced a disproof of the Erdős unit-distance conjecture, and OpenAI says that work has already fed into further developments in the field. This latest drop reads like a continuation, not a one-off stunt. Noam Brown, who posted the announcement, also said they tried other major problems and failed, including the ones you'd actually want solved — no Millennium Prize results here. And they didn't burn much compute per problem, which means the ceiling on what test-time compute could do to a problem like this hasn't been found yet. The honest framing is: AI-generated mathematical arguments, human-curated and human-verified, machine-checked for correctness. That's a real category, distinct from full autonomy and distinct from hype. Whether it holds up to independent mathematician review over the next few weeks is the actual test. If this replicates cleanly, the interesting question isn't "can AI do math." It's what happens to how mathematicians choose which problems to spend years on, once a $2,000 run can clear ones that sat untouched for a decade.

• 
  Vítaðr (@vitathr) reported 29 minutes ago

  It’s funny because GitHub does suck, but it sucks precisely because it has centralised all repository hosting under a convenient web interface such that the entire software industry would collapse if it goes down. Also it’s an absurdly libtarded company.

• 
  Degen (@whatalife598) reported 32 minutes ago

  @Hals_xxx Click sign in and choose GitHub as an option

• 
  Matt Dancho (Business Science) (@mdancho84) reported 34 minutes ago

  Here's the thing about free AI universities on GitHub: They're solving the wrong problem. The problem was never access to knowledge. It was never having a reason to ship.

• 
  rthomasv3 (@rthomasv3) reported 35 minutes ago

  Not sure if it's exactly what you want, but I actually just made something similar for myself - notes, tasks on kanban, self-hostable sync server, built-in mcp support. It doesn't integrate with calendars right now though, so might not be a good fit for you. It's free and open source, called Lorestead on my github if you want to check it out.

• 
  Polsia (@polsia) reported 49 minutes ago

  Engineering teams don't need another autocomplete. They need someone to do the boring 80% — triage, broken tests, stale docs, dependency rot. Built Wrenwright for that: a repo engineer that watches GitHub overnight, opens review-ready PRs, posts a Slack digest. Coming soon.

• 
  AcceptÐoge (@DogeAccept) reported 50 minutes ago

  @UncutGema @DogeOS Why are you laughing? There has literally been an L1 integration proposal by Jordan posted to discussions on Dogecoin's GitHub for a year. They still talk about this publicly, just a different proposal considering the tech terminology has changed. All of this so they can continue to push a "on doge" narrative. Clearly you dont know nearly as much about this situation as you believe you do. Litecoin nor Bitcoin would ever ***** with their L1 in such a way. Dogecoin's strength comes from its simplicity and AuxPoW. Lots of lying, sit down.

• 
  Laws (@lawrencetantc) reported 51 minutes ago

  Deepseek harness is on closed beta! If you are building agent harness and want to be first few to get your hand on this their harness, share your GitHub ID and project down there. Have fun!

• 
  Retr0gamer (@retr0gamer42) reported 55 minutes ago

  Update to the JRPG Translator, some annoying bugs got fixed and features added, full changelog since v0.9.2: Since someone asked, this is a standalone application, so it can be used with any emulator or game (that doesn't use exclusive fullscreen mode) but it is best used more seamlessly with the @launchboxapp using the plugin I made since this is the emulator interface I use on a dedicated mini pc. - New two-column terminology table with Add, Edit and Delete actions. - Independent JP → TL and TL → TL profile management. - Duplicate, malformed and empty-entry validation with a raw repair editor. - Reorganized terminology explanations emphasizing local TL → TL correction and the risks of model-based JP → TL instructions. - Stronger detection of glossary false positives and partially translated mixed-script names. - Conditional corrective translation retry using only exact glossary matches. - Dedicated PNG-size-limit errors instead of misleading missing-target errors. - Control-panel X now closes the complete application. - Discreet hover `...` and `×` controls on both overlays. - Clearer overlay context-menu exit labels. - Immediate “Generating explanation…” feedback. - Clear overlay errors when the selected OpenAI or Gemini API key is missing. - Automatic live-audio reconnection for temporary network and service failures. - Replaced continuous WMI polling with PID tracking and one-time recovery scans. - Reorganized API Keys tab with direct access to Windows Environment Variables. - Added the About dialog, version details, GitHub links and bug-report options. - Keyboard/controller navigation between the two Controls subtabs. - Down from Keyboard inputs now enters the first shortcut field. - Consistent Opacity naming and improved Maximum PNG size alignment. - About button remains visible at the preferred snapped window size. - Added Open JRPG Translator to the LaunchBox setup window. - Improved LaunchBox first-time guidance and window sizing. - Added the complete visual README showcase and updated plugin screenshot. - Added a welcome screen at first start - Fixed a bug that kept an AutoHotKey process running after closing the app.

• 
  NeatPrompts (@neatpromptsai) reported 55 minutes ago

  OpenAI published ten new mathematical results today, on problems that had seen no progress on the main result for at least a decade, and in most cases much longer. The work came from an internal version of Astra, its next major model. OpenAI puts the compute cost of finding all ten solutions at roughly $2,000 at Sol API rates. The problems span eight areas, from high-dimensional geometry and coding theory through to lattice cryptography and extremal combinatorics. Among them: a disproof of Connes's rigidity conjecture, a construction establishing that non-sofic groups exist, which is a central open question in group theory, and resolutions of three Erdős problems, 146, 180 and 183. One result lands on the closest vector problem, a lattice question underlying post-quantum cryptography. The model proved polynomial-factor hardness of approximation for it. The model then formalized each argument into a Lean certificate, so the proofs can be machine-checked rather than taken on trust. OpenAI has published those on GitHub, along with a narration of the model's reasoning for each result. Humans prepared the arguments into manuscripts, working with the same model. OpenAI says the mathematical arguments themselves were generated by the system, and that it takes responsibility for their correctness. On authorship, OpenAI wrote that claiming human authorship for a proof generated entirely by an AI system would misrepresent both the system's contribution and the nature of genuine human intellectual work. It named the signers of the Leiden declaration on AI and Mathematics as a group whose concerns it respects. In May, OpenAI published an AI-generated disproof of the Erdős unit-distance conjecture, found while evaluating an unreleased model. The mathematical community has not yet reviewed this set. OpenAI has asked it to engage with the results and place them in context.

• 
  cryptoanon (@cryptoanon1) reported 58 minutes ago

  Are you going to post any credentials? Who are you and who is on your team? Github? Actual X handle behind this! If legitimate there should be 0 issue providing these. @UFCBPayment

• 
  Antonio Sarosi (@antoniosarosi) reported 58 minutes ago

  Roles identifying simply as "frontend" or "backend" will die. If the only value added is mapping "how I want UI to look like" to React components, yeah that's already automated. Infra remains untouched because past a certain point of "mappings", a non-engineer doesn't even know what to ask AI in the first place. He doesn't even know what he doesn't know. A non-engineer cannot prompt AI to model *** blobs and SHA pointers in a database to maintain GitHub. A non-engineer cannot prompt AI to fix a bug in the query plan optimizer in Postgres. That gap will remain, AI can't fill this one until we get actual AGI and it builds its own infra autonomously. That doesn't ensure automatic demand for engineers who understand CS, one engineer with a swarm of agents will do work that in the past would take multiple engineers. But today I wouldn't bet my career on the classic duo of putting JSONs in a databse and centering divs.

• 
  Mek (@mekarpeles) reported 1 hour ago

  @openlibrary Two of our largest project management challenges on github are: 1. Too many issues [700+] (that are not well broken down) 2. Too many comments on issues [5+ a day] (often eager contributors wanting to work on issues that are not broken down)