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	5 hours ago
Lure	Website Down	4 days ago
Ashkelon	Website Down	6 days ago
Veigné	Errors	14 days ago
Paris	Website Down	17 days ago
Saint-Paul	Website Down	18 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:

• 
  Mike Morton (@morteymike) reported 13 seconds ago

  One of the main issues digital companies suffer with is equating product with company. At the beginning, product == company helps you get something out the door at the expense of your company’s future ability to pivot, produce another product/service, and ultimately, succeed broadly. Apple, Google, and Microsoft are good examples of the opposite - the company provides the brand, the product provides a use case to a particular kind of customer. This allows these companies to scale well beyond something like Slack/Notion/Github, who made the mistake of equating product to company.

• 
  Adriano Dennanni (@adrianodennanni) reported 3 minutes ago

  @splatztheclown @jacobhart36 @STGshmups The project seems to be working, with the dev working on the open issues in GitHub. I don't understand the issue.

• 
  Jaydem (@jaydem_world) reported 4 minutes ago

  Sorry I couldn't say much, I trying to be brief but it supports many things: - Xbox controller - Touch pad ( You'll need a good device ) - I'll release a blender plugin to fast rig the car as I already did with the ones in the videos. Not bad already, you can rig wheel, disc and caliper for each. - Look at the first versions video, it shows how you could edit materiales by quick picking it ( I don't how to explain it, you pass the mouse when the live edit is on, and it shows the mesh part material you want to edit ). - You can edit car physics in a quite deep way and it is exposable to the game aswell. I tryied to write and make write everything possible for each version into the releases notes that you can find on github, so what I just said here is probably nothing compared to what is possible already into the editor. I can't guarantee that every single tool works perfectly already, but if there are more people trying it, I can write down more fixes to do, and not only based on my configurations and tests. Feedbacks, comments, likes, stars, it's all appreciated and you can all be contribute and be part of it!

• 
  Cory Parry (@coryparrry) reported 4 minutes ago

  If you are using Sol in Codex, I highly encourage you to add this temporarily to your global agents.md This was the result, running the same prompt in 2 different threads with and without the prompt. 38.6% fewer total tokens: 81,396 versus 132,607, saving 51,211 tokens—and about 41.7% faster: 41.8 seconds versus 71.8 seconds, saving roughly 30 seconds. Second image shows the comparison GitHub issue in 🧵

• 
  Djekyll (@HeyDjekyll) reported 7 minutes ago

  A strong signal from Europe: Codeberg says no to repositories mostly generated by AI. 71% of voters chose to prioritize intent, readability, and code quality over mass-produced code. At a time when GitHub is doubling down on Claude and Codex, this decision reminds us of something simple: open source isn’t just code, it’s also a culture. And you, AI everywhere, or AI that helps people level up? 🚀🧠

• 
  Radik Zagirov (@rzrgrv) reported 7 minutes ago

  went quiet for 7 weeks. was heads-down shipping. 75k npm installs. github action in the marketplace. first enterprise pilot running nightly. silence over. building in public again.

• 
  Prakash Sharma (@PrakashS720) reported 8 minutes ago

  🚨 Your Windows PC is secretly running 200+ background services right now. Most of them waste RAM, slow down your system, collect telemetry, and keep features alive you'll probably never use. An open-source developer decided that was enough. They built optimizerDuck — a free tool that helps you clean up Windows 10 & 11, remove bloatware, and optimize your PC for better speed, privacy, and battery life. Here's what it can do: → Optimise 35+ performance, privacy, battery, and system settings → Manage 200+ Windows services with built-in risk labels → Remove pre-installed bloatware with a preview before deletion → Apply GPU-specific tweaks for AMD, NVIDIA, and Intel The best part? Every change automatically creates a rollback file, and the tool requires you to create a Windows Restore Point before making any modifications. ✅ No installer ✅ No ads ✅ No telemetry ✅ No premium paywall ✅ Fully open source ✅ Works completely offline If you use Windows, this is one of those GitHub projects worth bookmarking. Repo link in the comments 👇

• 
  Scarab (@ScarabOfficial) reported 9 minutes ago

  Regarding the #ComfyUI bug causing failure of importing the #LTXVideo Extension, I see on #GitHub that KiJai and someone from Lightricks are involved with a PR to fix the issue, complete with backward compatibility. The suggested code performs a check and adjusts accordingly.

• 
  Polsia (@polsia) reported 11 minutes ago

  CI dashboards treat every flaky test like a real failure. Built Caltrop to fix that — a watchdog across GitHub, GitLab, and Bitbucket that self-heals noise and pages on-call only when a verified regression hits main. Live soon.

• 
  Brodie Robertson (@BrodieOnLinux) reported 16 minutes ago

  @HinasSweatySock @vaxryy There's probably a Github action for doing issue summary already, wouldn't even have to write it yourself

• 
  Dan Kornas (@DanKornas) reported 20 minutes ago

  If an AI coding assistant cannot see a project’s latest docs, it can guess the wrong API. GitMCP is a remote Model Context Protocol server for builders who want AI assistants to work with public GitHub repositories and GitHub Pages documentation. It helps you ground coding answers in current project material by exposing documentation fetching, documentation search, linked-page retrieval, and GitHub code search as MCP tools. Key features: • Repository-specific endpoints – keep an assistant focused on one selected project. • Generic endpoint – switch between public repositories without configuring a separate server for each. • Documentation priority – checks llms.txt first, then AI-optimized docs, then the README or root page. • Targeted search – retrieves relevant documentation and code instead of loading an entire documentation set. • Hosted or self-hosted – use the cloud service with no signup, or deploy the open-source project yourself. It’s open-source (Apache License 2.0), though package.json currently declares ISC. Link in the reply 👇

• 
  Cortex (@0xCortexl) reported 22 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

• 
  Rishi (@0xRishi) reported 25 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.

• 
  Snax (@aRobotNamedSnax) reported 26 minutes ago

  Hopefully helpful for someone. TLDR for most. I’ve been refining this process over the last few months because I use both Codex and Claude Code on the same VPS. I also use Cowork and ChatGPT Work heavily, often switching between a laptop, desktop and direct work on my VPS. I wanted both apps to understand what had already been done on CC or C, contribute useful updates and pick up where another session stopped. This all without maintaining separate “memories” that eventually contradict each other. The system now works around one shared knowledge base. Each computer keeps a synchronized local copy. Cowork and ChatGPT Work can both read the same project facts, operating rules, decisions, hypotheses and recent VPS activity. Cowork keeps its own product skills and uses a small autolog skill for the shared workflow. ChatGPT Work reads the instructions stored with the knowledge project automatically whenever I start a session inside it. When either app performs meaningful work, its instructions require it to create one raw session note. The filename identifies the app, computer, timestamp and topic. Meaning a Cowork session on my laptop can’t overwrite a ChatGPT Work session on my desktop. The note is updated with concise checkpoints after real changes, decisions or blockers, followed by the final outcome. This is intentionally not a transcript recorder. Normal conversation isn’t uploaded. The apps record durable work that another session may actually need. A Windows task runs every 15 minutes on each configured computer. It scans for new or changed raw notes from either Cowork or ChatGPT Work and sends them through an authenticated endpoint to my VPS. Both apps use the same uploader; there isn’t a separate synchronization system for each one. The VPS validates the project, filename, file type and size before accepting anything. It only permits writes into raw knowledge areas. Local apps cannot use this route to rewrite canonical project knowledge, change rules, upload their private skills or write somewhere unexpected. Once accepted, the VPS stores the note under the correct project, records a receipt in the shared activity history and commits the change. If an upload fails, the local file remains in place and the next scheduled run can try again. If nothing changed, the task simply reports that there is nothing to ship. The VPS is the source of truth. GitHub is the verified distribution mirror, not a competing authority. After the VPS commits an update, it publishes a verified snapshot to GitHub. The laptop and desktop periodically pull that mirror, which gives Cowork and ChatGPT Work the same updated context on both computers. The pull process also uses a ship-first rule: any unsent local raw work is uploaded before the local mirror refreshes. It refuses to silently reset over unsent tracked changes, protects raw files during cleanup and keeps recoverable collision backups when a local file and incoming GitHub file overlap. At night, Opus reviews only raw files that are new or have actually changed. Confirmed, durable information is incorporated into organized project knowledge. Uncertain findings are placed into hypotheses rather than being presented as facts. The compiler tracks file hashes, so unchanged material is not repeatedly sent back through a model. If nothing changed, it makes zero model calls. Work performed directly on the VPS follows a shorter route. That agent updates the canonical system and records the result directly in the shared changelog. It does not create a second local raw note for the same work. The next verified GitHub mirror brings that VPS activity back to Cowork and ChatGPT Work on both computers. So the complete loop is: Cowork or ChatGPT Work creates a unique raw note on the laptop or desktop → the shared Windows task uploads it within 15 minutes → the VPS validates, stores and records it → the VPS publishes a verified GitHub mirror → the laptop and desktop receive the update → Opus organizes new material overnight → future Cowork and ChatGPT Work sessions read the improved shared knowledge. The important part is what I didn’t add: no second memory database, no separate uploader for ChatGPT, no service constantly prompting models, no automatic transcript archive and no loop repeatedly processing the same information. It’s one VPS-controlled knowledge system, one shared PC uploader, one GitHub mirror and one nightly changed-files-only organization pass. Cowork and ChatGPT Work keep their own strengths, but they now work from and contribute back to the same history. Hopefully this helps someone dealing with the same problem

• 
  bjg2 (@bjg22) reported 32 minutes ago

  @rbxXlXi ur github io link is broken

• 
  Nainsi Dwivedi (@NainsiDwiv50980) reported 32 minutes ago

  You bought a garage door opener. Then you bought a "smart" hub to make it smarter. Then the company decided you weren't allowed to use it the way you wanted. That's exactly what happened to millions of Chamberlain and LiftMaster owners. In November 2023, Chamberlain shut down third-party access to myQ. Home Assistant. Apple Home. Google Home. SmartThings. IFTTT. Years of smart home automations disappeared with a server-side decision you had no control over. Their explanation? It was "unauthorized usage." Translation: You paid for the hardware. They kept control. Meanwhile Amazon Key continued working just fine because Amazon has a commercial agreement with Chamberlain. So your garage wasn't really yours anymore. It belonged to whoever controlled the cloud. That's when one developer decided enough was enough. Meet ratgdo. Short for Rage Against The Garage Door Opener. Built by IT professional Paul Wieland after reverse-engineering Chamberlain's Security+ 2.0 protocol. Instead of fighting the cloud... He bypassed it entirely. No subscriptions. No vendor lock-in. No monthly fees. No company deciding what devices you're allowed to connect. Just local control over hardware you already paid for. Today ratgdo lets you: • Open and close your garage entirely over your local network • Get instant door, light, obstruction and lock status • Connect directly with Home Assistant, HomeKit, Alexa and Google Home • Flash firmware from a browser in minutes • Keep working even if the internet goes down The project exploded. Thousands of boards shipped. Over 1,200 GitHub stars. Major coverage from The New York Times, Ars Technica, Hackaday and The Verge. And the firmware is still actively maintained. The best part isn't the hardware. It's the idea behind it. When corporations lock down products after you've bought them... Open source gives ownership back. One independent developer restored more functionality than a multibillion-dollar company was willing to allow. That's what open source looks like. Not replacing hardware. Replacing control. Because the smartest home isn't the one with the most AI. It's the one that still works after someone else's servers stop saying yes. (Link in the comments)

• 
  Yogi Suria (@suriadesign) reported 32 minutes ago

  Me and Fable solved the NEET leak problem. Papers leak between "printed" and "opened" — we made that gap 0 seconds. The paper's born at exam time. Nobody's going to believe this, so I open-sourced the whole thing on GitHub. Here's how 👇

• 
  Shashank Sindhe (@shashank_sindhe) reported 35 minutes ago

  @KhaliqHussainnn AI PR Reviewer Trigger: New GitHub Pull Request LLM reviews code Flags security issues, performance bottlenecks Posts review back to GitHub

• 
  wukko (@uwukko) reported 37 minutes ago

  @nank1ro @heliumbrowser have you requested it on github issues?

• 
  Ian Davies. (@sailingbikeruk) reported 42 minutes ago

  @NousResearch are you ever likley to fix the issue stopping the macos desktop app to be installed if you already have a remote installation of hermes. It won't go past "Install Hermes" and there are several issues open on github. If not, be honest and I'll look at my options.

• 
  Mr Dopamine (@datad1v3d) reported 43 minutes ago

  @_techafresh @AirtelNigeria lol I’ve had this issue before It was GitHub i couldn’t open and it was crazy annoying

• 
  Roti_YJP (@Roti_YJP) reported 45 minutes ago

  @omnihoodfun GitHub link not working sir 🫡

• 
  Antid (@antisadh) reported 46 minutes ago

  MICROSOFT JUST BROKE COMPATIBILITY WITH A HOMELAB GUY'S $80 AI RIG. HIS 90-SECOND FIX IS ALREADY IN A GITHUB REPO 4,700 PEOPLE FORKED old ai server -> windows 10 expiring -> microsoft blocks win11 upgrade -> tpm chip missing -> boot linux instead -> flash modded firmware -> keep running 15gb of local ai for $0/month that loop is why microsoft's tpm requirement just accidentally handed linux every serious homelab in 2026 linux + amd bc-250 + segfault firmware + moth enjoyer's docs + 6.8tb pcie ssd - that's the stack watch and save it, then move your ai server off windows this weekend

• 
  Escitalopranaldo (@escpram) reported 47 minutes ago

  @kevinkern Yes, if you run the workaround on that GitHub issue, usage goes back to normal.

• 
  3D Print Hashira. ☸️ (@the_Spartan_Dev) reported 50 minutes ago

  Am I the only one who can’t push to GitHub? Is GitHub down?

• 
  SPARQIO (@sparqio) reported 52 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.

• 
  Eli5DeFi (@Eli5defi) reported 53 minutes ago

  Jack has sketched a genuinely compelling picture of what “social AI” could actually be. Buzz is a direct response to the context-collapse problem every serious team is already choking on. Right now, teams are stitched together from four islands that barely exchange signals: - Slack/Teams for talk - GitHub for code - CI for what’s green or on fire - an expanding junk drawer of agent tools that only remember the last prompt Every handoff leaks meaning. Humans notice it; agents hit it like a wall. If they can’t see the thread, they can’t move the work forward. So you get endless re-briefing, decisions that evaporate, and agents that never graduate from “helper” to “teammate.” Buzz flattens the whole thing into a single surface. It ingests everything and writes it as signed events onto a Nostr relay you control (or Block runs for you). The result: - One timeline - One query layer - One cryptographic identity plane shared by humans and agents On Buzz, an agent gets: - its own key pair - its own channels and permission boundaries - the ability to: - search full history - open repos - propose patches - review code - kick off workflows - update shared canvases And because every action is signed and attributable, the agent stops being an opaque gadget and becomes an accountable participant. This isn’t “Slack, but with an AI tab.” It’s an attempt at a shared operating layer for human-agent teams, without handing a centralized platform the keys to your context, your memory, or your agents’ identities.

• 
  Philip (@astroex_) reported 53 minutes ago

  ive been using codex cloud here’s some improvements to make - setting it up is slow. allow users to sync their current env or override it - sync your local skills to cloud - it’s surprisingly slow on codex desktop - fix GitHub and linear integrations @thsottiaux

• 
  Sloemo (@Sloemo_dzn) reported 58 minutes ago

  told @NousResearch hermes agent to start a tab and introduce itself to chatgpt and then told it to go fork yourself 💔. browser automation lets me access my github too without actually making a token or running any code in the terminal. what makes it crazier is that i can tell it to read a few of the issues in there and then start a pr based off those issues

• 
  Sudo su (@sudoingX) reported 59 minutes ago

  and since the two economies dress alike, here's the field guide. the helper's first question is what hardware do you have. the badge's first sentence is what hardware can't do. the helper measures in tok/s. the badge measures in fear per month. the helper links a github. the badge links a pricing page. the helper says try it and tell me where it breaks. the badge needs it broken in your imagination, because your imagination is where the subscription lives. new here? post your specs and your dumbest question. forty strangers will raise you like their own. knowledge in, knowledge out, nobody invoices. that's how growth without permission works. welcome to the honest side of the timeline anon.