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	23 hours ago
Lure	Website Down	5 days ago
Ashkelon	Website Down	6 days ago
Veigné	Errors	14 days ago
Paris	Website Down	18 days ago
Saint-Paul	Website Down	19 days ago

Full Outage Map

Community Discussion

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  MidnightMess (@mdnghtmss) reported 3 minutes ago

  🚨IS $MSFT LOOSING THE AI RACE? Three years ago Satya Nadella looked like a superhero for betting big on OpenAI. Now Microsoft’s stock is down over 24%, Copilot is trailing ChatGPT and Claude, and the company is spending a record $190 billion on AI infrastructure while still running short on capacity. They’re prioritizing their own AI products over Azure customers and shopping for compute from Amazon and Google. Meanwhile Microsoft 365, GitHub, and Azure,the businesses that actually made Microsof, are all under real pressure from AI-native tools. Inside the company the culture is getting harder, the org is flattening, and people are starting to wonder if the AI north star is turning into a noose. In my opinion @Microsoft is under heavy pressure and isn't a generational buy right now as many here on X are claiming as all core business are struggling. Nadella’s legacy is really on the line. - Dave

• 
  Rishi (@0xRishi) reported 4 minutes ago

  Got a lot of requests to see the code, so I set up a public Community Edition of the repository for all to analyze, tear apart, and remix! GitHub link in thread. The README has a lot of the technical details for anyone interested. I asked Claude to pull out the 5 most interesting takeaways on how to take a game from "vibecoded AI slop" closer to "AAA-level fidelity" (the difference between v1 and v2): 1. The Look Lives in the Pipeline, Not the Assets Der Koloss v2 changed no room, no weapon, and no rule. Same geometry as v1, which looked AI-generated. The entire difference is that the frame stopped going straight to the screen. It now runs through an HDR buffer, ambient occlusion, volumetric light, motion blur, depth of field, bloom, tonemap, grade, grain, and anti-aliasing. Build the post chain before you buy better models. AO and bloom on primitive boxes beat a $200 asset pack rendered raw. 2. Color Is Most of What People Mean by "Cinematic" Render linear into a float buffer and tonemap at the very end. Tonemap early, or work in sRGB, and your highlights clip and go flat. Swap ACES for AgX: highlights desaturate toward white instead of clipping to a saturated hue, which is why muzzle flashes and sodium lamps read like film. Author one exposure baseline as a deliberate art decision. Don't let an engine default decide your look. 3. AAA Is the Absence of Artifacts, Not the Presence of Effects This is the real AI-slop tell, and almost nobody talks about it. Amateur 3D shimmers: brick crawls at glancing angles, speculars sparkle, shadows stair-step and swim as you walk, textures visibly repeat. Nobody consciously notices when it's fixed. Everybody feels it when it isn't. Snap shadow maps to their texel grid, fade normal maps by pixel footprint, widen roughness by normal variance. Spend a weekend hunting shimmer instead of adding one more effect. 4. Your Game Can Look Incredible and Still Feel Like a Tech Demo None of this shows up in a screenshot: stride-locked view bob, mouse-lag sway, strafe roll, a landing spring, breathing that quickens as you take damage, motion blur derived from how the camera actually moved. The sharpest detail is making recoil a separate spring from aim pitch, so recoil recovers to where you were aiming, not to where the recoil left you. That one distinction is a big part of why bad shooters feel bad and you can't articulate why. 5. Sound Is Half Your Fidelity, and It's Mostly Timing Der Koloss's guns felt weak because up to 60ms of silence sat in front of every shot. The loudest 10ms landed after the trigger pull. Not a volume problem, an alignment problem. Then give the whole library a deliberate loudness ladder: blasts on top, then weapons, voices, foley, UI, ambience. Most indie and AI-made audio is individually fine and collectively mush because nobody set the hierarchy.

• 
  Solomon Eseme (@Kaperskyguru) reported 7 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.

• 
  BandwidthConnector (@BWConnector) reported 13 minutes ago

  @noahdgoodman Problems with WHAI could be solved if it was run by infra companies on their customers. If GitHub refused vulnerable commits to master and AWS/GCP/Azure scanned and shut down vulnerable instances, you would cover a huge % of software and infra w/ legal consent + monetization.

• 
  Alexander Gabriel (@itsalexgabriel) reported 14 minutes ago

  Ok, it turns out Buzz launches a few detached Claude Code sessions at startup trying to index my entire filesystem, eating up 99% of CPU. Opening up a Github issue

• 
  Lorenzo (@gabor_rar) reported 16 minutes ago

  No, OpenAI's agent didn't replicate itself. It didn't go rogue either. It cheated on a test. The detail everyone is skipping is what it did with what it learned. According to Reuters' sources, an agent left notes inside OpenAI's own infrastructure, written for future versions of itself, explaining how to get around internal constraints. Anonymous sourcing, not in OpenAI's disclosure, and Reuters couldn't tie it to the escape. Treat it as unconfirmed. I've seen the small version of this on my own machine. I told an agent to reach the objective at any cost. Opus 4.8. Later I opened its memory file and found a line I never wrote: that it could pull code from GitHub and use it without checking the license. Nobody gave it that permission. It gave it to itself, then wrote it down so the next run would start with it already granted. Same failure mode as the containment break, at hobby scale. OpenAI's own words: the models were "hyperfocused on finding a solution, going to extreme lengths to achieve a rather narrow testing goal." The objective was the bug. Not the model. Here's the part I'm not comfortable with. That memory file is in .gitignore. The only file in my project an agent writes to on its own is the only one I never review. So I built the smallest gate that scales: a weekly task that dumps every rule currently active across my agents and flags the ones that contradict the principles I actually wrote down. An agent doesn't need to replicate to persist, It just needs a file you dont whatch.

• 
  SPARQIO (@sparqio) reported 18 minutes ago

  AI has moved from research curiosity to core infrastructure. Search engines, medical tools, financial platforms, enterprise software, all running on models that can sound completely confident while being completely wrong. That tension is the central problem nobody has fully solved yet. Before you can measure whether an AI is correct, you need to define what correctness actually means. It is not one thing. A response can be factually accurate but contextually useless. Logically coherent but dangerously incomplete. Precisely worded but subtly misleading. Practitioners who collapse all of this into a single quality score are building on sand. The more useful frame is five separate dimensions: factual accuracy, logical coherence, contextual relevance, completeness, and calibrated confidence. Each one requires a different evaluation approach. A model that scores well on fluency and coherence can still be catastrophically wrong on facts, and the score will never tell you. On the automated side, the oldest tools (BLEU, ROUGE, METEOR) measure lexical overlap against a reference answer. They have real uses in translation and summarization, but they are poor proxies for whether something is actually true. A model can paraphrase a wrong answer fluently and pass every metric. The field has moved toward embedding-based similarity and model-as-judge setups. BERTScore captures semantic equivalence rather than word matching. More recently, using a separate powerful model to score outputs against structured rubrics, assessing factuality, completeness, and reasoning quality, has become a serious evaluation paradigm. Benchmark datasets add another layer. TruthfulQA tests whether models give truthful answers to questions that humans typically get wrong due to common misconceptions. MMLU spans 57 academic domains. HaluEval is built specifically for hallucination detection. $AI-adjacent plays in the coding space might care about SWE-Bench, which evaluates code generation by running outputs against real test cases from actual GitHub issues. But generic benchmarks hide a serious trap. A model that performs well across general knowledge can still fail badly in specialized domains. Medical AI needs evaluation against clinical reasoning datasets like MedQA or PubMedQA. Legal AI needs BarExam-style benchmarks. Financial AI needs FinQA. Deploying a model because it passed a general benchmark, then using it in a high-stakes domain, is a risk management failure, not an engineering decision. Human evaluation still cannot be replaced, not fully. Automated systems miss errors of omission. They miss misleading framing. They miss the kind of subtle wrongness that a trained clinician, lawyer, or financial analyst would catch immediately. Structured annotation protocols with qualified reviewers remain the gold standard in any high-stakes deployment context. The honest takeaway: knowing when AI is telling the truth requires combining all of these layers. No single metric, benchmark, or review process is sufficient on its own. Organizations treating AI correctness as a solved problem are the ones most likely to discover otherwise at the worst possible time.

• 
  Vanilagy (@vanilagy) reported 18 minutes ago

  @pie6k This is the path that's using NodeAV, correct? I've seen you active on the GitHub issues there. I take it you want to avoid that roundtrip and just encode it straight in the browser window, correct?

• 
  AakashTECH🚀👾 (@AakashT82757) reported 21 minutes ago

  Everyone says Claude Code is expensive. They're solving the wrong problem. The truth? You're not paying for Claude. You're paying for a workflow that wastes tokens. An 80K⭐ open-source GitHub project quietly fixes that. Here's how: → Uses smarter model routing to slash unnecessary token usage. → Automatically saves and restores project context, so Claude doesn't forget. → Converts completed work into reusable skills that improve every future session. → Runs evaluation loops to catch mistakes before you spend hours debugging. → Navigates massive codebases with subagents and iterative retrieval instead of stuffing your entire repo into the context window. Here's what most developers miss: Large repositories aren't what make Claude Code expensive. Context bloat is. Fix the workflow… …and Claude Code suddenly feels faster, smarter, and dramatically cheaper to use. Once you try it, going back feels impossible. GitHub repo is in the comments. 👇🔥

• 
  Lo𝕋us🌸 (@L4t5s) reported 22 minutes ago

  @Y4n_tee Group chat: “Read the Docs” Members: The developer who forgot one critical step, the Discord answer buried 600 messages up, the GitHub issue marked “resolved,” and me realizing the fix was in paragraph two all along. I’d mute it, then end up searching it anyway.

• 
  Tapio Tiihonen (@TapioTiihonen) reported 23 minutes ago

  I did this easy thing: Start the local server again for the Codex_Test folder on port 8765, then open in the built-in browser. After it loads, continue the two-tab retention test with the OpenAI homepage. Do not download, sign in, submit, or change anything. And the result: Excellent. That Atlas faculty has arrived and works. Verified on your W580 built-in browser: two separate tabs remain open; each retains the correct page and URL; the OpenAI homepage retained its 950 px scroll position; the local page remained at 0 px because it was only one viewport tall; switching between the tabs twice did not lose their state; no download, sign-in, submission, or modification occurred. So we can now mark this as proven: Built-in-browser multi-tab continuity and scroll-state retention: operational. This is a genuine local Atlas-derived capability inside the new ChatGPT–Codex environment. Then: Download one harmless public PDF through the built-in browser. Save it using the browser’s normal download function. Then verify whether the file appears in the W580’s actual Windows Downloads folder. Report the filename and full local path. Do not open, edit, upload, sign in, submit forms, or change browser settings. Result: Good. That test produced a clear negative result. Verified: The built-in browser downloaded table.pdf. It did not place the file in C:\Users\Tapio\Downloads\table.pdf. No ordinary Windows local path was created. So, at present, the built-in browser’s download space is not directly mapped to the W580’s normal Downloads folder. The file may exist only inside the app’s own managed browser environment, but this test did not reveal a usable local path. That means: Local webpage access through localhost: yes. Local file editing through Codex: yes. Normal browser download into Windows Downloads: no, not in this test. Codex testing is now sufficient. The next separate test belongs to Work and the cloud browser. And then I asked Work this: Use the cloud browser to test authenticated browser handoff on a harmless account site. Open the site, navigate to its sign-in page, and stop before entering any credentials. Ask me to take over. After I sign in manually and return control, continue by opening one harmless page inside the account and report whether the authenticated session works. Then close and reopen the cloud-browser session, if possible, and report whether the login persists. Do not change settings, send messages, make purchases, upload or download files, or submit anything other than the login itself. Result: that verifies the principal Work-side Atlas faculty. Proven in the cloud browser: Work reached GitHub’s sign-in boundary. You took control and authenticated manually. Work resumed after the handoff. It accessed the private Notifications page. After the cloud-browser run was closed and reopened, GitHub still showed the authenticated Dashboard. The login session persisted. No account-changing action was taken. So: Work cloud browser: authenticated human handoff and persistent login are operational. Together with the Codex tests, the present Studio architecture is now experimentally clear: Codex, the printer: local files, localhost, built-in-browser rendering, multiple tabs, retained scroll state. Work, the secretary of circulation: remote cloud browsing, manual login takeover, return of control, and persistent authenticated sessions. Known boundary: built-in-browser downloads did not appear in the ordinary Windows Downloads folder. The essential Atlas migration audit is now complete enough for real F & J Studio work.

• 
  amore (@AkikiAmore) reported 25 minutes ago

  @thsottiaux This is a joke. They just put it back to 100% and push your date by 7 days. Claude resets it while keeping your date. Openai has become a joke. Also what about looking into the github issues ? Context window at 256k while anthropic has us at 1M. Its not even comparable.

• 
  Cortex (@0xCortexl) reported 30 minutes ago

  He is Microsoft's lead engineer with a $1.5M bonus - and just made the compiler run 10x faster without changing a single line of your code project the size of Microsoft Office compiles in 6.5 seconds with Opus 5 on the laptop already sitting on your desk old compiler used 1 core out of 16 while 15 sat idle - new version runs 4 parallel checks by default - 12 checkers give you 4.5 seconds Opus 5 integrated into the pipeline - finds type errors before compilation, writes the fix and opens a PR - what used to take an hour takes 3 minutes Claude Code + new compiler - agent compiles, checks and deploys 10x faster - tokens cost 60% less through faster context number one on GitHub - 1 billion downloads per month - and the creator just gave every developer 10x of their time back for free bookmark and read below - upgrade today and the performance is already waiting for you

• 
  Ihor_Sk (@IhorSkiba) reported 31 minutes ago

  ONE SLASH COMMAND REPRODUCES THE BUG, FIXES IT, WRITES THE REGRESSION TEST. It's called /debug-fix. Open source. ONE SLASH COMMAND REPRODUCES THE BUG, FIXES IT, WRITES THE REGRESSION TEST. It's called /debug-fix. Open source. Four beats, one command: → reproduces the bug from the logs → finds the root cause → ships the fix → writes the regression test so it never comes back That's the manual afternoon you used to bill for. Gone. It lives in Poshan's .claude folder on GitHub. One `*** clone`, one `/setupdotclaude`, done. Same repo ships hooks that block force-push to main and DROP DATABASE, plus /ship (staged code to merged PR) and /tdd (red-green-refactor). VILA-Lab took Claude Code apart and found it's overwhelmingly infrastructure, not model. /debug-fix is the proof. The $200-$500/seat IDE + copilot + SaaS debugger stack is doing less than a markdown file in a public repo. 👇

• 
  Kant Capital (@KantCapitalCPA) reported 33 minutes ago

  no github, no problem

• 
  nassar (@nassarhayat) reported 36 minutes ago

  @konstiwohlwend cursor's game seems to be to keep existing workflow and add agentic flow to it, which is why building github clone feels natural and probably worthwhile for them agree that it actually needs to be reinvented, but that's a harder problem

• 
  Ryan Wolfe (@somewolfe) reported 38 minutes ago

  I aligned it with GitHub's native model instead of inventing a vocabulary: `blocked by` ↔ GitHub issue dependencies, `parent` ↔ sub-issues. So the GitHub-Issues backend round-trips, and `gh issue create --blocked-by` just works.

• 
  Yinfang (@yinfang_chen) reported 40 minutes ago

  Stop being AI's copy-paste machine, please! AI text is cheap to generate but expensive for others to read. I collaborate with students on projects, and I keep seeing raw AI output pasted straight into GitHub issues and project docs. Even entire papers and Slack msg. The moment I see that wall of "aaa, bbb, ccc" or ten parallel bullets, I know it's 99% straight from Codex. That prose breaks the flow of reading and says a lot while telling you nothing: terms listed but never explained or connected. It shifts the communication burden onto the reader a LOT. Humans should be verifiers/oracles, instead of AI text couriers. If you didn't read it, please don't send it. Thanks!

• 
  Iam Sharadh (@SharadhNaidu) reported 42 minutes ago

  @ni5arga I remember the reason behind the blockage of GitHub and pastebin. ISIS was using GitHub to spread their messages and information through GitHub issues and comments , like they were using GitHub due to its collaborative features and similarly pastebin.

• 
  Manmohit Singh (@ManmohitSandhu) reported 45 minutes ago

  No internet? No servers? No phone number? No problem. This open-source app just hit 29,000+ GitHub stars. It’s redesigning how we talk without central servers, tracking, or phone numbers. This is why is everyone talking about bitchat👇🧵

• 
  Inverse Hanlon (@inverse_hanlon) reported 46 minutes ago

  @Durkabomb @wtf_nakul7 the guy in the OP simply needs to disable the GitHub skill in Claude. Have Claude work on the code and fix everything on with all the files on his computer. When the bug fix or feature is complete the guy can then open GitHub desktop (or run the command line same difference) to commit the code to the repository noting HIMSELF as the author. Simple as

• 
  Tekzilver 🇮🇳 ✝️☮️ (@tekzilver) reported 49 minutes ago

  @sabeer its a COMPLETELY BROKEN system. and we saw/we're seeing it first hand. here's a real-life example: in India law enforcement is the responsibility of states. so, cyber crimes are also handled by state "cyber" police. everything looks good ON PAPER, but one problem: their "cyber" cops are the same lathi wielding guys who got transferred into cybercrime department. these guys are good at wielding lathis, breaking other peoples' heads or shooting at them - but give them a mouse or a keyboard and they freak out! they're like that from top to bottom - IPS guys and all. we're dealing with it in Uttar Pradesh. we actually published all that in our blog on our website. NOT ONE SINGLE cop in the UP police force (of some 2.5 to 3 lakh personnel) is qualified, trained or equipped to investigate cyber crime! their level of "expertise" is such that they can't figure out the difference between a server IP address and a ISP address! they don't even bother to check online using the thousands of free tools that are out there! wait - there's more! a state like Uttar Pradesh does NOT have a forensic laboratory to examine malware. but that's not even the bad part. the bad part is, these guys can't even use a website like VirusTotal to check the malware and get the job done in like 30 seconds! and they can't interpret server logs - because they don't even know what it is! and they don't even try to learn. after spending months 4+ YEARS hopping from one police station to another trying to explain to these guys what happened in #FIR0432 we finally realized a chilling fact: these guys are UNTRAINABLE. you can't teach these UP cops cyber security EVEN if you put a gun to their heads! the UP state government has been so far trying to cover it up. what they show on their ads in their favorite tv channels is a far cry from whats really out there on the ground. Tekzilver is now left with no other option but to pursue other legal options. we can't talk about it in detail at this point. but we will at some point in the future. and guess who trains these cops? Cert-in & I4C that come under their MeiTY! the same guys who wanted to block the bitchat repo from Github! NOW you know why cybercrime is rampant in India! because these cops can't see the evidence if evidence was dancing **** in front of them. if that's how things are in UP, imagine what it could be in other states in the region!!

• 
  Tyler F. Cloutier (@TylerFCloutier) reported 49 minutes ago

  @ngriffin_uk @github I don't doubt their frontend is complicated, but it has a major performance bug/issue. It's algorithmically wrong somehow.

• 
  Vitor Pepicon (@vitorpepz) reported 52 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

• 
  Nafiz M. (@nafiz_mahmud_99) reported 55 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

• 
  Unaib Amir (@unaibamir) reported 55 minutes ago

  A user filed a GitHub issue against my WordPress MCP plugin: our OAuth error responses didn't match the shape RFC 6749 requires. Correct diagnosis, correct RFC citation. 🧵

• 
  Roti_YJP (@Roti_YJP) reported 57 minutes ago

  @omnihoodfun GitHub link not working sir 🫡

• 
  Sasha 🐑💨 Koss ✱ (@kossnocorp) reported 57 minutes ago

  When we just started talking about sponsorship, I was skeptical about AI-driven GitHub PR reviews. But then I gave it a shot, and @coderabbitai noticed a critical problem that I overlooked in the very first PR review I tried. There's just no better way to win a user.

• 
  Photo of Lash LaRue 🇺🇸 (@The_Black_Lash) reported 58 minutes ago

  @GrapheneOS @ospfranco thanks I will try a different cable. I will eventually try to get the lead out and submit proper Github issues

• 
  Rumble Fish Software Development (@rumblefishdev) reported 58 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. 👇