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 (55%)
• Errors (32%)
• Sign in (14%)

Live Outage Map

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

City	Problem Type	Report Time
Paris	Website Down	4 days ago
Ahmedabad	Errors	10 days ago
Delme	Sign in	10 days ago
Lyaud	Website Down	10 days ago
Catania	Errors	13 days ago
Inverness	Website Down	25 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:

• 
  Chris Gilbert (@0xgilbert) reported 5 minutes ago

  Damn, GitHub has gone to ****. Features that have been cornerstones of solo devs and small businesses have been gutted or broken for months. How the mighty have fallen…

• 
  Kevin Minnelli (@minnelli) reported 6 minutes ago

  WTF - Grok Bot can't fire on schedule to save it's life. The scheduled routines are just broken and at best unreliable. I want to love this product. When you set the cron job it doesn't work. It tells you try Cloudflare, sure let's set that up and burn tokens, then that doesn't fire to wake them. Oh, let's try GitHub now and use that....all failed. I had to wake it again this morning before the market opened. Anyone else feeling frustration in this regard?

• 
  Kahris (@chrissotraidis) reported 7 minutes ago

  @NickogSo I haven't tested on LiveContainer. Feel free to submit logs via GitHub issues and I'll check it out. I haven't had any other reports of that happening for either build.

• 
  paulrodturner (@paulrodturner) reported 7 minutes ago

  @supabase Is anyone else having issues logging in via Github?

• 
  XhiMatty (@mychaelmatty) reported 8 minutes ago

  a Sept 2 run. My first thought was “Why did it stop running?” I checked the #YAML file, #python file, GitHub Actions, even inactivity issues. Turns out it was still daytime. The Sept 2 run is yet to happen (at night). Nothing was broken.

• 
  CATIRL 🏳️‍⚧️ (@CATIRL_9) reported 9 minutes ago

  @mminhamina Google GitHub "open grind", solves your problem

• 
  The Startup Ideas Podcast (SIP) 🧃 (@startupideaspod) reported 10 minutes ago

  One of the best skills to install right now is my friend Peter Yang's no AI slop skill. It's an editor. It hunts for the patterns that make writing feel AI generated and strips them out, while trying to preserve your actual voice. The second part is the hard one. Most writing tools make you cleaner and sand off the interesting parts, so everyone ends up sounding the same. You already know the smell. The grammar is fine, the syntax is fine, and it still reads like a keynote from a fake SaaS conference. It writes "it's not x but it's y." It uses "quietly" a lot. Here's how I run it: 1) Install it: npx skills add, then the GitHub link. 2) Write a rough draft yourself. An outline is fine, messy is fine. 3) Get your real points down, the ones only you would make 4) Ask the skill to remove the AI patterns and keep your voice. Step 4 only works if step 2 is real. If you ask AI to write the whole thing, there's no voice left to preserve. If you're building products, you're writing constantly. Tweets, landing pages, cold emails, launch posts, product updates, onboarding copy, investor updates. Nobody replies to say "this was written by AI." They just trust you less and keep scrolling. Write the messy draft, run the skill, then post it.

• 
  Vikas(Vik) Malpani| AI for US Real Estate (@vikasmalpani) reported 10 minutes ago

  GitHub just shipped an agent whose entire job is deciding when a human should look. It checks every open pull request every 15 minutes, and on almost all of them it does nothing. Sit with how strange that is. For a year the whole pitch for coding agents was do the work, review my code, ship the PR. This one's value is the inverse. It runs constantly and stays quiet, and the product is the small set of PRs it decides are actually worth your time. That is the shift people are missing. Once an agent can act continuously, the scarce resource stops being how much it can do. It becomes how much of that is worth a human's attention. An agent that pings you on every pull request is just faster noise. One that surfaces the three that genuinely need judgment is leverage. The honest problem is the deciding. Tune the filter too eager and it cries wolf until you mute it. Too cautious and it silently ships the one change you needed to catch. Getting when to interrupt a human right is harder than getting the work right, and nobody has a clean metric for it yet. So here is the bet. The next moat in agent products is not a smarter model. It is a better sense of when to stay quiet. If you are building with agents, the thing worth obsessing over is not how much work they can generate. It is how well they protect the one budget that does not scale: your attention.

• 
  OverlyPositivePatriot (@JBrowsing2023) reported 14 minutes ago

  As a IT professional, I have a recommendation @github should take seriosuly. We should only get a notifican from Github when it is up rather than when it is down. Reliability is a disaster for this product.

• 
  NitroStack (@nitrostackai) reported 16 minutes ago

  The missing primitive might be capability contracts. A Skill shouldn’t say “call Jira.” It should say “I need issue.write.” Then MCP can bind that capability to Jira, Linear, GitHub… whatever exists. That’s basically dependency injection for agents.

• 
  Conor Bronsdon (@ConorBronsdon) reported 16 minutes ago

  .@SlackHQ is building for multiplayer AI: tag a coding agent into a Slack conversation and it spins up a coding channel: everyone in that convo gets a live dev environment, diffs post as artifacts, and the channel winds down when the task is done. With the launch of Slack Code, Claudeforce, their MCP and more, Slack is putting Agents in the channels where teams already work, not simply in a private chat with one person. Their position is that the whole team should be able to watch, steer, and review what the agent does. Slack Chief Product Officer Jaime DeLanghe joined me on @chain_ofthought to explain how Slack is building a team AI environment, what happens mechanically when a code channel is created, why Anthropic pushes so much of its code through Slack, how the channel permission model became the agent context model, and what has to change in engineering culture when the whole team is steering one agent. I think Slack is the platform best positioned to become the context harness where enterprise agents run: agents that see what the team discusses, permissions that already exist, and a cultural opportunity hiding inside every multiplayer coding session. Chapters: (0:00) Slack as an IDE and a GitHub for your team (0:29) Who is Jaime DeLanghe (1:21) The reaction to the Slack Code launch (5:30) Why coding agents belong in a context-rich environment (6:08) Engineers now manage agents, not copy-paste code (7:24) The permission model: agents get the channel's context (11:44) What happens when a code channel is created (15:00) Why Anthropic pushes so much code through Slack (19:14) Steering one agent with many people: culture decides (24:54) Slackbot, skills, and MCPs: agents go where the work is (30:53) The solo terminal vs. agents in social spaces (33:53) Org charts and ownership when agents join the team (39:33) Learning loops and shared agent memory (42:39) Citations, recency, and accidental knowledge management (46:50) Context bloat and multi-pass search for agents (50:01) How Jaime uses Slackbot as CPO (52:38) Slack Code is V1 of multiplayer AI

• 
  Apoorv (@apoorvdarshan) reported 20 minutes ago

  @Dimillian these issues have been multiple times reported by users on github i hope open ai fix those, as well as please consider using native than electron

• 
  🦄Linus Shyu许发鑫高考去了不在 (@Linus_Shyu) reported 22 minutes ago

  Stop treating token rotation as a success path. x_bot: OAuth refresh token rotated, cache save failed, GitHub secret stayed old. Next cron died on invalid refresh token. Fix: save to secret BEFORE confirming with X, or write-after-rotation with retry. #DevTools #AI

• 
  Speen Bhai (@Speenbhai) reported 23 minutes ago

  @johnternus Hi John. Congrats Let us see what new you bring with you. Affordability and intelligence. You have source code or an AI and can get it from GitHub. Why not turn 234 million iPhones to a massive distributed server infrastructure with zero power consumption

• 
  Straggler Liu | AI & Semis (@StragglerLiu) reported 23 minutes ago

  NVIDIA($NVDA ) Is Paying $14B for a Company With $150M Revenue. That's Not Financial Logic — It's Ecosystem Control. NVIDIA is in advanced talks to acquire Hugging Face for ~$14 billion ($12.9B acquisition + $1B retention), per Bloomberg. To put that in perspective: Hugging Face does ~$150M in annual revenue. That's ~86x revenue. Microsoft paid ~1.6x revenue for GitHub. Google paid ~3.5x revenue for DeepMind. NVIDIA is paying 20-50x more on a revenue multiple basis. The premium is not for revenue. It's for control of the AI developer ecosystem. What is NVIDIA buying? Hugging Face hosts 500,000+ models, 250,000+ datasets, and serves millions of developers. It is the single most important distribution channel for open-source AI. If you build AI, you use Hugging Face. That makes it the front door to AI development. Why NVIDIA is paying this premium: 1. The "NVIDIA triple lock." NVIDIA's hardware lead (GPU) is real. Its software lead (CUDA) is a moat. But the third lock — the developer workflow — was missing. Hugging Face is that workflow. Developers discover models on Hugging Face, deploy them, and optimize them. Whoever controls that discovery layer controls which hardware gets used. 2. The GitHub analogy, inverted. When Microsoft bought GitHub, developers were already using GitHub. Microsoft didn't need to capture them — it needed to prevent Amazon/Google from doing so. NVIDIA faces the opposite problem: developers are already using NVIDIA hardware. But they're discovering and deploying models through a neutral platform. NVIDIA is eliminating that neutrality. 3. The long game: inference, not training. NVIDIA dominates training. But inference is the bigger TAM — and it's more fragmented. If NVIDIA controls the model discovery and deployment layer, it can steer inference workloads to its own stack. That's a 10-year strategy disguised as a 14-billion-dollar acquisition. Who wins, who loses: NVIDIA (NVDA): Acquires the developer distribution layer. The most important strategic move since CUDA. Shifts the valuation case from "chip cycle" to "platform economics." Competitors (AMD, INTC): Lose neutral access to the primary AI model distribution channel. This is a structural headwind that no amount of hardware catch-up can fix. Cloud providers (MSFT, AMZN, GOOGL): Hugging Face was a neutral hub. If NVIDIA controls it, cloud providers risk being disintermediated from AI workload decisions. The open-source community: The platform that was built on openness is now owned by the dominant hardware vendor. Neutrality is the first casualty. The capital question: Can NVIDIA integrate Hugging Face without destroying its community value? If yes, the $14B is cheap. If no, it's a very expensive mistake. The answer will define whether NVIDIA becomes the AWS of AI — or just another hardware company with an expensive acquisition. Note: Acquisition details based on Bloomberg reporting; not confirmed by NVIDIA or Hugging Face. Revenue multiple comparisons based on publicly reported figures.

• 
  Joshua Okolo (@joshuaokolo_) reported 25 minutes ago

  we made @sgl_project and @vllm_project scheduler config changeable on a live server. no restart, weights never leave the GPU. - 15ms to change a concurrency cap, queue limit, prefill size, or schedule policy, measured on H100, RTX PRO 6000, B200 - 2s (SGLang) / 8–10s (vLLM) to resize the KV pool with weights resident (formerly a 1–7 min redeploy) - zero dropped requests across every run, both engines github below

• 
  rajabi17270.eth (@rajabi17270) reported 27 minutes ago

  @SeismicSys An Ethereum engineer opens an install page expecting a binary download that finishes before the coffee does. Seismic asks for Rust and cargo first, then budgets five to twenty minutes for the build. That gap is the most honest line on the page: you are not installing a tool, you are compiling a fork of the execution layer on your own machine. Three binaries come out of sfoundryup. sforge as the testing framework, sanvil as the local node, ssolc as the compiler. Each shadows a Foundry tool by exactly one letter, and the docs give the mapping outright: forge becomes sforge, anvil becomes sanvil, cast becomes scast. The s is not decoration. The s is a namespace. The s is the migration guide, compressed into one character and carried from the type system all the way up to the binaries sitting on your PATH. Why a fork and not a plugin is the question the install page answers without asking it. Privacy on Seismic lives in the type system, so solc had to become ssolc to understand suint256 and route it to CLOAD and CSTORE instead of SLOAD and SSTORE. Because the compiler changed, the build harness that invokes it changed with it. Because the emitted bytecode carries opcodes standard revm does not implement, the local node had to be rebuilt to execute them, and because each storage slot is a value paired with an is_private flag, the CLI that queries storage had to expect a different answer than Ethereum's. Four forks, each one forced by the layer beneath it. Not a toolchain that was extended. A toolchain that had no choice. The installer itself carries a detail worth reading twice. It is fetched through the GitHub Contents API with an Accept header of application/vnd.github.v3.raw, from the seismic-foundry repository, at ref equals seismic. That ref is a branch name, and a branch name tells you the maintenance posture: the fork lives beside upstream rather than in a codebase that has stopped speaking to its parent. A rebase relationship, not a divorce. You source your shell profile twice during setup, once after the installer lands and once after sfoundryup finishes. Two separate PATH mutations, because the thing that installs and the thing installed arrive at different moments. What survives the fork is more interesting than what changed. sanvil serves localhost:8545 with pre-funded accounts, and the deployment example uses the same well-known development key Foundry users already have in muscle memory, address 0xf39fd6e51aad88f6f4ce6ab8827279cfffb92266. sforge init, sforge test, sforge script with rpc-url, broadcast and private-key flags: identical surface, identical ergonomics. Your scripts port by find and replace. Which makes the two manual steps on the page the most revealing part of it. The first is the editor. The docs say that if you already have the solidity extension installed, you have to disable it while writing Seismic code. That is not a preference. suint256 is not valid Solidity, the s literal suffix is not valid Solidity, and two grammars cannot both claim authority over the same .sol file. The language is a superset. The highlighter cannot be. The second is sforge clean, listed as optional, run inside an existing project's contract directory. Here the collision is on disk: cache and out are not namespaced, so artifacts that solc produced sit in exactly the paths ssolc writes to, which means the failure mode is not a build error but a passing test against bytecode that never saw a shielded type. Optional only if you have no history. The requirements are narrow and stated plainly. x86_64 or arm64, macOS, Ubuntu or Windows, with other Linux distributions marked as possibly working but not officially tested. Note what that list provisions and what it withholds. It gives you the language and the opcodes locally. It does not give you the hardware boundary, since the network's nodes are the ones required to run inside Trusted Execution Environments while sanvil is described only as a local node in the shape of anvil. Local tests can prove your casts compile and your shielded storage routes through CLOAD correctly. They cannot exercise an enclave. So here is the part nobody plans for. Everything that could take the s prefix did, and one character kept two toolchains from colliding across an entire PATH. The editor extension could not take it. The build cache could not take it. Those two are precisely where the page stops describing and starts instructing, which means the friction in a Seismic setup was never in the fork: it is in the two surfaces a naming convention could not reach.

• 
  John Zhong | AI Growth Systems (@John_zhong324) reported 39 minutes ago

  @github A repeatable --attach flag turns CLI reports into reproductions: inline screenshots in issues mean a bug gets fixed in one pass instead of two round-trips for context.

• 
  Rafael Audibert (@RafaAudibert) reported 40 minutes ago

  @madebygps @github Tried using it with my agents (the main benefitor from this) but it doesnt really work because you cant use it with GitHub app user tokens (ghu_). Can that be changed somehow? All cloud agents will have that problem, and most of our coding happens trough cloud agents now

• 
  Rituraj (@RituWithAI) reported 41 minutes ago

  🚨 Someone built a skill that makes AI-written text sound human again. Not a spinner. Not a paraphraser. A systematic rewriter that knows exactly why AI text sounds like AI — and fixes it. It's called Humanizer. 35 patterns from Wikipedia's "Signs of AI Writing." Two-pass rewrite. Shows its work before giving you the final version. Here's the problem it solves. You use Claude to draft something. The output is accurate. The output is useful. The output sounds exactly like an AI wrote it. "Nestled within the vibrant landscape, this pivotal development serves as a testament to..." You know the voice. Everyone knows the voice. And everyone is getting better at spotting it. Humanizer runs that text through 35 specific patterns that WikiProject AI Cleanup identified as the telltale signs. Inflated importance. Shallow -ing analysis. Overused AI words. Em dashes everywhere. Forced groups of three. Fake-candid openings. Answering objections nobody raised. Every pattern. Flagged. Fixed. Here's what one command does. It shows you the first rewrite. Then a short critique of anything still sounding artificial. Then the final version. You see exactly what changed and why. Here's the wildest part. Voice matching. Paste two paragraphs of your own writing before the AI text. Humanizer follows your rhythm, word choice, punctuation, and deliberate quirks instead of its default style rules. The output doesn't just sound human. It sounds like you. One command to install 16 contributors including Claude itself. 4 releases. MIT License. The skill that makes AI writing disappear. 100% Open Source. GitHub link in the comments 👇

• 
  Jeremiah K (@neolaj) reported 41 minutes ago

  @TiborAntal Gradually figuring out how to scale coding agents. Started with 1, manually handling all the ***/GitHub work. Moved to 3 because I had more ideas than one agent could keep up with. That’s when the real problems started: squashing, merging, branch drift, conflicts. I ended up rebuilding the workflow around deterministic *** logic, worktrees, ephemeral branches, and syncing with the integration branch before changes begin. Now I’m running 6: • 1 orchestrator (Fable or Opus) • 4 coding agents • 1 integration agent reviewing and merging PRs Building the process around them was the hard part. Right now im just doing a couple of PRs (using ORCA on windows on my home computer)

• 
  ONCHAIN COP (@OnchainCop) reported 42 minutes ago

  @PogNyx lmao anyone can create a github issue retards this guy is a larp

• 
  安坂星海 Azaka || VTuber (@AzakaSekai_) reported 48 minutes ago

  I know you explicitly said "excluding vibecoding," but the biggest problem *IS* AI right now. Several major players in the field have moved on to heavy AI development or even agentic post-exfiltration moves and has muddied the water even more for attribution. Aside from that, the other big trend that we've been seeing more and more in recent years is heavily abusing Living Off Trusted Sites with C2 comms based on GitHub, OneDrive, Outlook, etc. Whilst this is most definitely not "new," we have seen a non-insignificant number of threat groups move to platforms that make tracing a little more difficult. In terms of the malware themselves, most of them have also shifted to using compiler-level obfuscation - a lot more compared to previous years where control flow flattening and jumps all over the place have become increasingly common. Right now, it's still tolerable, but my job has started becoming more and more annoying and less fun especially if every malware now looks the same. #mond_AzakaSekai_

• 
  catman (@catmanyau) reported 51 minutes ago

  @sbilstein if GitHub is down, where does that push land first — and how do you handle conflicts when the repo comes back?

• 
  Uptimus (@UptimusApp) reported 52 minutes ago

  Sep 02, 2026 at 13:29 UTC: Semaphore reports that periodic authentication failures with GitHub repositories are linked to a wider issue affecting HTTPS operations.

• 
  Yash (@dewyashtwts) reported 53 minutes ago

  recently integrated Resend into @supercodeai review so founders get PR alerts with real risk context I'm amazed what we found out when we put @coderabbitai / @greptile through the same PR: 1) coderabbit / greptile: - stamped it “low risk, mergeable” (4/5) clean - forgot context from the last PR - no tests suggested, no safety checks - zero memory of previous regressions 2) supercode review on the exact same PR - flagged a real vulnerability in the diff - noticed i’d pushed credentials into `.env.example` - pulled in history from past PRs + explaining how this change could affect and break them - downgraded it to "medium risk, fix before merge" state - attached concrete fixes + patches scoped by severity this is the difference between 'LLM summarizer for github' and an actual swe agent that cares about your production

• 
  Phanindra Malladi (@malladiphani) reported 53 minutes ago

  Lesson from running the factory: always fix the post-purchase experience BEFORE driving traffic. Receipts, upsells, GitHub links - all must be solid first. Social comes after the house is in order.

• 
  Suryansh Tiwari (@Suryanshti777) reported 54 minutes ago

  6. The Dependency Incident Check Grok has native real-time search across X. Breakage gets posted there hours before the GitHub issue is triaged. No other coding model has that feed. "You are a build engineer whose first move on a broken pipeline is to work out whether it broke for everyone or only for me. Search X and the web, last 14 days. Check: - Is anyone else reporting this failure with this package and version, and when did the reports start - The exact release that changed behaviour, and the changelog line that admits it - Whether maintainers have acknowledged it and what they recommended - The pin or patch people settled on, with the tradeoff of each - Whether this is my problem instead, and what evidence points that way Give me the verdict in the first line: their bug or mine. Then the evidence, newest first, with links. My failure: [PASTE THE ERROR, THE PACKAGE AND VERSION, AND WHAT CHANGED ON YOUR SIDE RECENTLY]"

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  volkdude85 (@volkdude85) reported 56 minutes ago

  @SentientSquirel @linuxuser1996 So you are you scared of github then. Look dude I have fun on computers and don't take myself seriusly because I have destroyed enough OS's over to not worry about it because I just fix it, If the contents of your PC make you this paranoid its time to check your kink.

• 
  Dhanji Bhagat (@BhagatDhanji) reported 59 minutes ago

  Devs, what's your workflow? Create an issue first, then fix it OR just fix the bug and push directly to GitHub?