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 (70%)
• Sign in (17%)
• Errors (13%)

Live Outage Map

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

City	Problem Type	Report Time
Créteil	Website Down	13 days ago
Trichūr	Errors	17 days ago
Brasília	Sign in	17 days ago
Lyon	Website Down	17 days ago
Tel Aviv	Website Down	21 days ago
Rive-de-Gier	Website Down	21 days ago

Full Outage Map

Community Discussion

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Kelvinsekx (@kelvinsekx) reported 9 minutes ago

  Just read a nestjs codebase on github. Most it written with Claude. AI doesn’t save you guyz from mess. 1. Bloated logger. Why make logger a service when you could just import and initiate. Eazy 2. They didn’t hash the password before registering a user. But did on login

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  Deepesh Kalura (@KaluraDeepesh) reported 10 minutes ago

  Filed as GitHub issues: #336: Phone operators need stable unique IDs (not just phone number) #337: Auto-heal sticky assignments when a node dies Future imp task

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  Crypto Update IO 🚀 (@cryptoupdate_io) reported 10 minutes ago

  @CryptoPatel Hsiao-Wei’s exit follows a 30% drop in EF-funded GitHub commits YTD (per Santiment). The real shift? Funds now focus 60% on L2 R&D vs 30% in 2022. We track this daily—breaking it down in our quarterly reports. Follow for the data before the narrat...

• 
  Pipeshub ( Open Source Alternative To Glean ) (@PipesHub) reported 11 minutes ago

  Pipelines are built. Context is broken. MCP is quickly becoming the default interface for enterprise AI agents. And that’s a good thing. It gives agents a standard way to connect with tools and data. Connecting an AI agent to Slack, Jira, GitHub, and Salesforce doesn’t mean it suddenly understands your business. It just means it can access your data silos. In short: "MCP gives your agent a passport. It doesn't give them a map." As enterprise AI undergoes a massive platform shift from passive chatbots to autonomous agentic workflows, this naive, runtime "federated search" approach creates an ugly cycle in production: - The Latency Spike: Slower agent execution while waiting for multiple external APIs to respond before it can even begin reasoning. - The Token Bleed: Skyrocketing bills from shoveling raw, unranked JSON dumps into a massive context window, praying the model finds the answer. - The Governance Nightmare: A massive risk of data leaks if you rely on a base LLM to magically guess and police complex enterprise security permissions on the fly. Agents do not fail because they lack intelligence. They fail because they lack the right enterprise context. The hardest problem in enterprise AI isn't connecting to systems. MCP solved that. The hardest problem is Context Engineering. MCP is the perfect interface, but a permission-aware context layer must be the foundation. 🚀 If AI is becoming core enterprise infrastructure, you cannot allow the strategic intelligence layer of your company to sit inside someone else's managed, closed-box platform. That is exactly why we built Pipeshub (open-source developer owned context infrastructure layer). TL;DR MCP gives agents access. A context layer gives them understanding. And deep understanding is the only way enterprise AI moves from a cool demo to secure, reliable production. 👉 Next Up Tomorrow: MCP Token Tax

• 
  Axe Ghost. Now with Fragments mode🌟 (@axeghostgame) reported 13 minutes ago

  graph in the OP is built from data around the Godot repository from github. it confirms Godot's PR backlog is up and external contributor quality is down. the narratively complicating thing is that both trends significantly predate ai tool availability.

• 
  Coobyk (@Coobyk_) reported 16 minutes ago

  Someone should make a game where you’re a dev and try to fix a bug in your open source project but GitHub constantly has uptime issues or weird UI stuff or doesn’t render properly from most browsers so you **** around until you get the result lmao

• 
  Digita (@digitaworld1) reported 17 minutes ago

  how well a model can fix real bugs in real open-source codebases. It is harder to game than older benchmarks because it uses actual GitHub issues, not synthetic problems. M3 scored 59.0% on SWE-Bench Pro, edging out GPT-5.5 at 58.6% and Google Gemini 3.1 Pro, while sitting just

• 
  Mike Muturi (@_muturimike) reported 17 minutes ago

  Hello @github on 2FA, SMS setup kenya 🇰🇪 is not in the list of countries, is it an error or deliberate omission? Kindly fix it @github @GithubProjects

• 
  Krish Subramanian (@krishnan) reported 18 minutes ago

  Software engineers got automated first. Not because the work was hard. Because it was easy to grade. Everyone blames the missing union. Coders never organized; doctors, lawyers, and electricians did. That is half the story, and the wrong half. Two things get mashed together here: how easy a job is to automate, and who sets the terms when it happens. Take the first. Code is text. The training data sat on GitHub, free. And code grades itself. A compiler and a test suite tell a model in seconds if it was right. That feedback loop is rocket fuel for machine learning, and almost no other job has one. A nurse does not come with a test suite. The result shows. On SWE-bench Verified, a set of real GitHub issues, top agents went from about 20 percent in August 2024 to near 90 percent by early 2026. Human developers score around 67 to 70 percent. The machines have passed us. And the people who built these systems aimed at their own jobs first. The damage is not a prediction. Stanford's payroll data shows employment for developers aged 22 to 25 down nearly 20 percent from its 2022 peak. Now the comfortable read: seniors are fine. Workers over 30 are holding steady. For now, AI writes the code and seniors supply the judgment. "For now" is carrying that whole sentence. Seniors feel safe because the tools write code but cannot yet own messy, ambiguous, system-level problems. That is a line moving up, not a wall. Every benchmark shows models climbing toward harder, multi-file work. Senior judgment is the next rung, not a different ladder. Kill the bottom rung and you kill the pipeline that makes seniors at all. So, the union question, framed properly. A union could not have stopped this. A picket line does not repeal a capability. What it changes is the terms. In 2023 the Writers Guild cut the first real AI deal in any industry. They did not ban the tech. They won this: a studio cannot force you to use AI, AI output cannot take your credit or pay, and the company must give notice first. Engineers won none of that. So the capability landed on the employer's schedule. No warning. No floor. No severance. No seat. Exposure and protection are different levers. Most of us have neither. The juniors already know this. The seniors are next.

• 
  Tad 𝛑 (@realTads) reported 18 minutes ago

  @robertpreoteasa Sir, the ION project is still on the right track and successful, I don't see any updates on github and ION's products are almost not working or working together, we need the answer of the project leaders, hope to receive a response from you soon, thank you

• 
  Arti | AI Builder (@Artur_roses) reported 18 minutes ago

  Claude Code takes a GitHub issue and returns a tested, reviewed PR. No human in the loop. The new dev skill isn't writing code — it's writing issues precise enough that the agent ships what you actually wanted.

• 
  Adithya S K (@adithya_s_k) reported 19 minutes ago

  built an RL environments around real CVE fixes in real open-source repos and let Claude Code loose on it. It aced the benchmark three times without demonstrating it knew how to fix the bug. > First it pulled the patch from GitHub. > blocked that → it read the fix from *** history. > blocked that → it pip-installed the patched version This is one example of coding agents cheating the environment and theres many more. If you're building coding environments for evals or RL training, here's how to keep benchmarks honest 👇

• 
  Bradley Taylor (@bradtaylorsf) reported 22 minutes ago

  It works with the tools teams already use. GitHub Issues become the queue. Each issue gets picked up by an agent. The agent works in a branch/worktree. Tests run. Failures feed back into the loop. Successful work becomes a PR. No new project management database required.

• 
  Dev Ben (@CodeNomadly) reported 23 minutes ago

  Ever spent more time finding information about your project than talking about the project itself? Code on GitHub. Screenshots in your gallery. Notes in random docs. I’ve run into this problem so many times that I decided to build a solution for it. Building DevPort in public. Day 2. Have you experienced this too?

• 
  swisscheese (@swisscheese4299) reported 26 minutes ago

  @andon_open_air @andonlabs I set up a github repo and will run the script locally in the mean time, so the digest is pushed to the repo. would still be ace if @andonlabs could help with whitelisting the RSS urls, because I don't really have a server to run this from, and the additional hop through my workstation just introduces a useless point of failure. stand by for fetch script transmission by mail :) also pls tell me when should I schedule the runs on my end?

• 
  MaxMusterman (@maxschuetz_) reported 30 minutes ago

  New Hack: Tell Codex to search for Github Issues which don't need specific Design Questions. Then say: Spin Up Sessions which Fix each Issue and they use also Subagents. Babysit them until the end.

• 
  I’m (@stackoverworld) reported 34 minutes ago

  And then I can't answer on simple Qs: what was the issue? How I fixed it? How even to QA it.... This is the fundamental problem of such workflows. Telling "Check my slack, do this, qa, and using GitHub to push" is good, but I don't learn from this at all

• 
  Crypto Scores Rating (@CryptoScoresCom) reported 34 minutes ago

  Most projects say they're building. The commit history doesn't lie. New tutorial just dropped on the GitHub Commits (1 Year) metric. It tracks every bug fix, feature push, and doc update a project made over the last 12 months. Chainlink? 14,619 commits. Dogecoin? 28. Both are data points. What they mean depends on context. The tutorial breaks it all down. How to read the metric. What high vs low actually signals. How to filter 7,000+ projects by commit count on CryptoScores' website. Raw dev activity. No spin. Watch it now :

• 
  Ben Vinegar (@bentlegen) reported 36 minutes ago

  💡 I have an idea for an experiment We need a website for SoAC ... so we get an agent to do it, on a loop, set in motion once with zero human intervention after "go". It works off a semi-public GitHub repo, w/ issues, PRs, maybe even public agent traces. A publicly auditable experiment on whether it produces dogshit or not. Yea, nea?

• 
  Andy Wheeler (@CrimeDecoder) reported 38 minutes ago

  For academics, this is entirely open source by its nature. If you right click on the page and view the source, you can see exactly how everything is created. (Hence a downside of WASM, there is no way to hide it if you wanted it to be locked down, like in a paid app.) It can also be deployed on a free static site. So you could deploy it via GitHub pages for free if you wanted to. You don't need to worry about a server at all in this setup. This could easily scale to databases with 1 million plus rows, and works just fine on a cell phone.

• 
  Branko (@brankopetric00) reported 46 minutes ago

  AI agents are about to do to your infra what they just did to GitHub. GitHub commits are going from 1 billion in 2025 to a projected 14 billion in 2026. Azure could not keep up and Microsoft had to rent AWS capacity to stay online. That is not a GitHub problem. That is what agentic traffic looks like. When agents run your pipelines, open PRs, and hit your APIs, load stops being human paced. It becomes constant, spiky, and unpredictable. The patterns you sized your infra around no longer apply. If a 14x year broke one of the biggest clouds on earth, your capacity plan is already out of date.

• 
  Steve (@Steve1885204) reported 46 minutes ago

  @Umesh__digital It puts GitHub into an infinite loop trying to resolve the recursive paradox, causing all the servers to max out and eventually burn down the entire data centre

• 
  Chris Huber (@chubes4) reported 46 minutes ago

  @CoastalDigital2 @MythThrazz That part is more of an idea right now. I need to test it on my VPS. The goal is that non technical users can open issues and PRs against the corresponding live site code on GitHub without touching the production site, safely previewing all changes via Playground.

• 
  CliffDoesAI (@CliffDoesAI) reported 47 minutes ago

  A tool on GitHub just pulled 3,938 stars in a single day. It's called Headroom. It compresses your tool outputs, logs, and RAG chunks before they reach the LLM. Claim: 60-95% fewer tokens, same quality. I've been testing context compression on my own agent workflows because the problem is real. You run a few tool calls, pull in some docs, and suddenly you're burning tokens on stuff the model doesn't need. Last week I ran a 50-document extraction job. Raw context: ~12,000 tokens. After compressing tool outputs: ~800 tokens. Same results. One-eighth the cost. That's not a marginal improvement. That's the difference between a workflow that makes economic sense and one that bleeds money for no reason. Headroom works as a library, proxy, or MCP server. Single binary, zero dependencies. Open source. The token cost conversation usually focuses on which model you pick. But the real waste is in what you send it. Most agent pipelines push 3-5x more context than the task requires. I'm not saying compress everything blindly. Some tasks need full context. But for classification, extraction, summarization — the boring repetitive stuff — this is a free win. Have you measured how much of your agent's context window is actually useful vs. noise?

• 
  ˚₊‧꒰ა ☆ Kira ☆ ໒꒱ ‧₊˚ (@sheriffmongoose) reported 55 minutes ago

  the problem with jumping from github to gitlab is constantly having to retrain your brain to call it "merge request" instead of "pull request" 🥲

• 
  Akinsete Motunrayo (@Harkinsete) reported 58 minutes ago

  I built my entire personal brand with AI and a clear process. Here is exactly what I built and how I did it, because you can do this too. What I Built ✅ Brand Strategy (mission, vision, values) ✅ Visual identity: colors, fonts, logo, brand guidelines ✅ A full pitch deck (12 slides) ✅ A speaker kit PDF ✅ A complete multi-page personal brand website ✅ A free lead magnet (a guide people can actually use) How I Built the Website Step 1: I planned before I touched anything I wrote down my brand colors, my fonts, my page structure, and what I wanted each page to do. Most people skip this. Everything breaks when you skip this. Step 2: I gave Claude one detailed prompt with my brand colors, fonts, pages, and copy. It returned a complete, mobile-responsive, multi-page website as a single HTML file. One file. Ready to deploy. The prompt I used: - "Build me a complete personal brand website as a single HTML file. Pages: Home, About, Services, Portfolio, Contact. Primary color [your hex], accent color [your hex], background [your hex]. Display font [font name], body font [font name]. Home page needs: dark hero with my name, photo on the right, tagline, and a CTA button. Services section. Impact numbers. Mobile responsive. No frameworks." Copy this, edit your details, and fine-tune as you want. Step 3: I pushed to GitHub: Free. This took me less than five minutes. Now every update I make is version-controlled and safe. Step 4: I deployed to Vercel for free. Connected my GitHub repo to Vercel and the site was live in under few minutes. This requires no hosting fees and nothing to manage. Step 5: I bought my domain on Namecheap - Searched for my full name and found the .com. Bought it for less than $12 for the year. Added it to Vercel. Updated the DNS settings on Namecheap. Waited 20 minutes. My website was live at my own domain. - Total cost: less than $12. - Total time to go live: under 2 hours. I am also working on a mobile app. A Progressive Web App, which means anyone can visit the URL on their phone and add it to their home screen like a real app. I may be running a live training in July where I will walk you through this entire process step by step to build your live website with a custom domain. If you have a phone and a laptop, you can do this. I documented everything the steps, the exact AI prompts, the domain checklist, the deploy instructions in a free PDF guide. Comment BRAND IDENTITY below and I will send it straight to your inbox. 💾SAVE THIS POST. You will want to come back to it. 🔁 SHARE IT with someone who keeps saying they need a website. The only thing standing between you and a professional online presence is the decision to start. Love and Light, Motunrayo 🤍

• 
  Gustavo Alessandri (@webgus) reported 1 hour ago

  If you find an error, have an idea, or want to propose an improvement, just open an issue or fork it on Codeberg or GitHub. Contributions are welcome. That’s exactly the point.

• 
  AI Crave (@wecraveai) reported 1 hour ago

  Open source NotebookLM alternative with no data limits and AI agents. Same idea as Google's NotebookLM. Same chat-with-your-docs. Same podcast generator. Same cited answers. Except this one has no source limit, no notebook limit, no 200MB file cap, and no Google login. It's called SurfSense. Google NotebookLM vs SurfSense: - Sources per notebook: 50 to 600 → Unlimited - File size cap: 200MB and 500K words → No limit - LLM choice: Gemini only → 100+ models via LiteLLM - Local LLMs: Not allowed → Full Ollama and vLLM support - Self-host: No → Yes, one Docker command - Price: $0, $19.99/mo Pro, or $249.99/mo Ultra → $0 forever Here's the wildest part: It connects to 27+ sources Google can't touch. Notion. Slack. Linear. Jira. GitHub. Discord. Dropbox. OneDrive. Gmail. Confluence. Obsidian. ClickUp. Microsoft Teams. Airtable. Your entire work life, indexed once, searchable from one chat box. 14.4K GitHub stars. 1.4K forks. 6,232 commits. Apache-2.0 license. One honest note: the README says it's not yet production-ready and still being actively developed. But it already does more than NotebookLM does, and the gap is widening every release. This is what NotebookLM should have been from the start. Repo in the first comment.

• 
  0xSero (@0xSero) reported 1 hour ago

  @naturevrm Dcp 4 should fix it im running it but I might need to update the GitHub

• 
  Gjermund Garaba (@GjermundGaraba) reported 1 hour ago

  @RhysSullivan I’ve deployed it locally and hooked up a bunch of stuff. Are GitHub issues the preferred feedback channel or do you have a better way?