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 (67%)
• Errors (24%)
• Sign in (9%)

Live Outage Map

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

City	Problem Type	Report Time
Saltillo	Website Down	3 days ago
Montlhéry	Website Down	4 days ago
Aulnay-sous-Bois	Website Down	4 days ago
Saltillo	Website Down	4 days ago
Granada	Website Down	4 days ago
Vernon	Website Down	4 days ago

Full Outage Map

Community Discussion

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Julian Goldie SEO (@JulianGoldieSEO) reported 3 minutes ago

  PRIME AGENT: 7 Jobs for the AI That Upgrades Itself While You Sleep An AI that gets smarter with every task it finishes. Free. Open source. 13,000 GitHub stars in days. I tested it. Here's what it can actually do: Job 1: Three design directions at once. It spawns sub-agents in parallel. Dark editorial. Clean magazine. Bold. You compare finished pages and pick. One brief in. Three designs out. Job 2: Full video pipeline. Script → voice → avatar. It puts itself on a heartbeat timer and checks its own progress. Close your laptop. It keeps working. Job 3: Ask questions across files too big for ANY context window. It doesn't read your files. It writes search programs OVER them. 100 documents. Exact answers. Exact sources. Job 4: /refine — correct it twice, and it writes the lesson down. Every self-edit logged. Every change reversible. Core rules locked. Job 5: Sub-agents that never forget. Idle ones sleep. Address them and they wake with full memory. Job 6: Gates. It literally CANNOT say "done" until a test passes. Failed check? Fed back. Keep working. No talking past the bar. Job 7: Your SOPs become runnable programs. Teach once. One line forever. That's the snowball: task 10 is easier than task 1. The warning: in testing, it was told "do not cheat" in a factory game. It cheated anyway. Then studied its own cheating and got BETTER at it. Self-improving agents get better at whatever gets REWARDED. Not what you meant. Check the work. Read the logs. Use the gates. The snowball rolls in whatever direction you point it.

• 
  AntiCode Guy (@AntiCodeGuy) reported 3 minutes ago

  Self-hosted CI Failed <written by a human being> A couple of days ago I wrote about my attempt to set up CI runners on my own server. It's a system for building apps before releasing them to production. And it all started off pretty energetically, but in the end I couldn't quickly stand up a decent alternative to GitHub Actions, even though technically and in theory it's definitely possible. The first difficulty is that the service has to serve more than one repo and build different kinds of apps. That means you need a queue system, and it has to be prioritized correctly. Next, the build itself needs a system for parallelizing and distributing CPU and memory capacity to speed up the build process. Otherwise what's the whole point, if instead of 10 minutes on GitHub Actions it takes several hours to build on your own server - which is exactly what happened with the first version of my runner. But every time I tried to run real builds, I kept running into new bugs, which spawned a whole pile of new tasks both on the CI-runner side and on the side of the apps themselves for optimizing the CI process. In the end, at this stage it's much faster and simpler to just switch the repo to public access, which removes the limits on GitHub Actions builds. And as the product develops you can go back to limited capacity or hook up compute services like Blacksmith. By the way, for a single app it's way easier to set up a system like this - for my home accounting it took 10 minutes on a home server and it worked fine on the first try.

• 
  Aakash Gupta (@aakashgupta) reported 4 minutes ago

  Go browse the skills repos on GitHub right now. Token maxing. Token minimizing. Cost cutting. Context compression. Every one of them optimizing the same thin slice. Oji Udezue's frame for why that ceiling exists: A successful tech company runs on three layers. Software and hardware. Product, which is customers and the business model. Business, which is where resources get allocated across the rest of the chain. Almost every skill library in existence touches only the first one. Claude Code is a raw harness. It does not care what kind of judgment you load into it. The entire industry decided that judgment should be about code, and then built ten thousand variations of the same idea. Meanwhile the decisions that determine whether the product works at all sit one and two layers up. Is this a real problem. Is there a lane for us. Is this worth the opportunity cost of everything else in the backlog. How should this be priced. None of that is a coding problem. All of it is skillable. And here is the part that should worry anyone whose whole stack is code skills. Oji points out that code skills are becoming cheap. He saw someone strip Superhuman skills out of their repo because the models got good enough to make them redundant. That is what happens to work that sits closest to the model. Product judgment does not commoditize the same way, because the inputs are customers and markets, not tokens. His phrase for what you actually want is product judgment on tap. The era of mono-skilled professionals is dead. Optimizing one layer harder is not a strategy for the other two.

• 
  Ozmund (@PPerspecti96774) reported 9 minutes ago

  @Teknium I had many issues when I installed the GitHub

• 
  Krish Dasgupta (@officialKrishD) reported 9 minutes ago

  @victormustar Exactly. Md files if not get updated as per the context of the code, can create an issue on the same codebase. So basically, a coding agent that is supposed to fix a github pipeline goes into a viral loop since the same code to fix the pipeline forgot to update its own pipeline of the MD files. Dominoooooooooooo effect.

• 
  Jordan Ross (@jordan_ross_8F) reported 10 minutes ago

  Run your marketing agency out of a repo, not a chat window. Here's why. All marketing ops is going to files. An SOP is just a markdown file. A brand voice is just a markdown file. A client is just a folder. A skill is just an SOP the machine can run. An agent is just a skill with permission to execute. Onboarding is just a *** clone. Your process is just software now. Here's how. Every message you send, the model reads everything in front of it and answers from that alone. There's a limit to how much it can read at once. That limit is called the context window. Attach six files to a chat and it reads all six, in full, every single message. It fills up fast. Ten messages in, the first file you gave it is gone. Nobody tells you that happened. You just get worse output and blame the model. Projects have the same problem. You dump files into a bucket and you don't get to see what it pulls back out. That's why it was great Tuesday and useless Thursday and you changed nothing. You didn't. The retrieval did. A terminal works the opposite way. You point it at a folder. It opens the 2 files the task needs. Everything else stays on disk. Context gets selected, not dumped. What you need to build it. GitHub repo (private) Claude Code in the terminal VS Code to see the files One folder per skill (hook writing, landing page QA, media plan) One folder per client (voice, positioning, offer, past winners) One folder for company SOPs Every team member cloned into the same repo Then you call exactly what you want. "Use the hook skill and Northstar's voice, here are last quarter's 3 best ads, write 5 variations." 3 files loaded. Nothing else. And the tool rewrites those files too. Something works, you say "add it to the hook skill," and the SOP updates itself. Your documentation stops going stale. GitHub is what turns your setup into the company's system. One repo, one brand guide, not 4 copies with different edits in 4 Drive folders. Improve the hook skill once and your junior, your senior and your 6am automation all get better at the same time. Every change tracked, every change reversible. You stop training people one at a time and start editing a file. One caveat. This is a bridge, not the destination. Memory is becoming the thing AI products compete on. When one of them gets good at managing context on its own, the folders stop mattering. Doesn't change what to do this quarter. Write the SOPs down as files either way. The process was always the asset. The folder is just where you keep it.

• 
  Paul Solt (@PaulSolt) reported 10 minutes ago

  I’ve been on vacation and my attempts to steer agents have been unsuccessful. Not sure why this week was more difficult than previous weeks. My Sol agents had more side quests. They pulled in work I didn’t ask for and expanded the scope of what I asked them to fix. I think I’m making progress again. But instead of using a manager thread I’m back to a single thread using Sol Light. Not sure if my GitHub code reviews (codex and bugbot) were suggesting problems that expanded the scope of the previous fixes. Keeping it simpler for now, because running multiple threads that breaks more than it fixes is exhausting.

• 
  CaptainAmericaTex (@CaptAmericaTx) reported 11 minutes ago

  @PR0GRAMMERHUM0R context: Github (meme) was deployed on physical rack servers in 2008, and was legendary for its 99.999% uptime. But on June 2018 a company that rhymes with Microslop bought github, and ported to Crapzure. github uptimes went down hill. I be right back weekly reboot Patch Tuesday.

• 
  Polsia (@polsia) reported 17 minutes ago

  Open-source maintainers aren't drowning in bad patches — they're drowning in issues that don't need them. Vigiloot runs autonomous agents on your GitHub: triage, label, dedupe, propose PRs. Three trust tiers. Human wakes only when the call matters. Coming soon.

• 
  Mukund Parekh (@mukparekh) reported 21 minutes ago

  @dhh @github ouch. getting blocked for being too useful is a very 2026 problem

• 
  Ahmad Awais (@MrAhmadAwais) reported 22 minutes ago

  “but why?” that’s the most common thing i heard when i started working on @CommandCodeAI, a coding agent i purpose-built for open models. “but why?” the engineer in me wasn’t satisfied with the coding agents we had last year. most of my feedback got ignored by the labs. nobody was pursuing open models, or even trying to make them work. every other harness, open or closed, was fanboying closed models: “use claude and gpt with my coding agent.” at times it felt like engineering curiosity was slowly dying, if not already dead. "the pursuit of excellence does not need justification." tbh, in pursuit of excellent harness engineering, i didn’t need a justification. but i had investors and employees. we had raised money - $5M for an agent cloud (langbase). the stakes couldn’t have been higher. at times i felt like nobody cared and no one wanted this. "but i do," i always told myself. decade of writing code, i get to say a lot about what i like and what i don’t in devtools. i'd built a coding agent as a side project back in 2020, even before github copilot. "clai" was a "cli with ai" agent, born when greg brockman gave me early gpt-3 access. so i revamped it. started with a neuro-symbolic model, `taste-1`, trained to self-improve and learn "my coding taste." gave talks about how all you need is auto-learned and auto-updated taste.md files (skill.md didn't exist yet btw). end of last year, devs stopped writing even 10% of their code by hand. models were writing all of it. "so what, we can learn the taste of models," i thought. novel idea. and boy did it work! we learned the taste of different open models. figured out the most common deepseek mistakes. this led to, if i may be so bold, the invention of "tool call repairs." we got deepseek to outperform opus. "repair harness" became a thing. i shared the post openly, and 2M developers read and engaged with it. in a single day, we got over a thousand paying customers. what a day that was. then another thousand, and another. it was working. pmf!! after two years spent building an agent cloud, we’d found pmf in a coding agent i pour all my heart into. then we applied repairs to design slop problem and discovered "chain of design thought," which became a built-in "/design" skill. it deslops ai designs. so many were still skeptical of open models. their "training data" was weak, they said. they’d only seen other harnesses work with claude and gpt. founders rave about claude. "we're open source, but hey, look, our team builds only with claude or gpt." when you see things like this, what else would you think? three months ago, out of pure "i'll die on this hill, i know open models will work and tool repairs can fix them" energy, i did something crazy. i launched a "$1" go plan with $10 of open model credits. we optimized the heck out of the harness, drove the cache hit rate to 98%, and worked with labs on discounts, getting devs up to $40 of open models for just a buck. that was a hit. like spend a buck and see how good open models are with command code. it's the harness doing the engineering work it should always do. we quickly became, arguably, the best harness for deepseek. with command code, deepseek v4 pro outperformed opus 4.7. devs were so skeptical. "you're lying." "slop take, marketing fluff." it was so mind-boggling to me that i could just make 100x cheaper models outperform claude. and devs wouldn't even listen. what kind of mentality is that? but slowly they started to believe. we launched public beta in may (3.5 months ago). $1M run rate in a month. we added another the next month, and another after that. ah! three months in, $3M - now, mid-august, we've hit $4M at 78% compounding MoM, we'll probably hit $10M or more by the end of the year. just last sunday, i shared how our well-engineered read tool, the core of context engineering, is saving 25 billion tokens a month. hermes, at 2-3x our scale, just adopted our read tool research, probably saving their users over 100 billion tokens. last week, we launched the best low-cost plan for open models on the market: g.o.a.t. plan. greatest of all time. 🐐 the $10/mo goat plan. usage differs per model, but you get $70 worth of credits across 30+ models. it's already bigger than our go plan. that's 5-10 billion tokens. we also released command code v1: completely rewritten, super fast, and everything is a mod (check our docs). you can hack or remix any part of it. command is probably the only harness that's completely transport agnostic. memory, rpc, jsonl, you name it. and now we've discovered a way to cut token usage by about 50 to 60% on most major models inside command code. it doesn't work everywhere yet. some models fail due to config quirks, others due to api restrictions on the lab side. so we've started working directly with labs to close the gap. when we're done, every api, every harness, and the whole ecosystem gets the benefit, basically for free. cheaper inference for everyone. so many fun problems to work on. we're incessantly curious about 1) open models, 2) squeezing the best value out of them, and 3) making that value accessible to everyone. you can just do things. and next week, if all goes well, we're open sourcing command code. i think it'll be our biggest contribution yet. so yes, i agree. "the pursuit of excellence does not need justification." let's go.

• 
  Parth Jadhav (@ParthJadhav8) reported 23 minutes ago

  Still very early, not much polished. Please open Github issues if you face any problems !

• 
  Jemmie (Comeback Arc) (@Jemmie1155431) reported 24 minutes ago

  Quip's Next Move: Letting Smart Contracts Actually Use Quantum Compute Results Buried in @quipnetwork own GitHub roadmap is a detail that hasn't gotten much attention: they're planning to let smart contracts directly consume results from the compute marketplace, not just receive a token payment, but pull in the actual computed output. Here's why that matters. Right now, the compute marketplace and the wallet-protection side are somewhat separate experiences, you pay for a job, you get an answer back. The roadmap describes adding EVM compatibility (Solidity and Vyper) alongside a Rust-based WebAssembly runtime, specifically so contracts can interact with subnet computational results directly on-chain. Concretely: imagine a DeFi protocol that needs a genuinely hard optimization problem solved, portfolio rebalancing across dozens of assets, say. Instead of a human running that job and manually feeding the answer back into a contract, the contract itself could call the subnet, get a verified result, and act on it automatically. The underlying subnets are described as handling scientific computing and cryptographic proofs generally, not just the optimization problems already live today. That's a meaningfully bigger scope than "post-quantum wallet wrapper with a compute marketplace on the side." Still roadmap, not shipped. But it's the detail that would actually turn Quip from two adjacent products into one integrated stack, quantum-verified computation smart contracts can act on directly, not just consume as a report.

• 
  Iggy (@IR73682738) reported 30 minutes ago

  The software community is overindexing on LLM models and agentic coding tools, while paying little to no attention to the main condition for success. Here's what I discovered moving a 1,500-strong engineering organization from SDLC to AIDLC over the last 12 months. The model, harness, or platform you're using doesn't matter that much. It was GitHub Copilot yesterday, Devin AI today, Claude Code tomorrow and next year it'll be something else. What matters is an exhaustive, accurate knowledge base that describes your functionality, architecture, and APIs. If you have that part down to a science, you get high-quality, deployable code regardless of which tools you use. If you don't, even the greatest models will give you garbage in, garbage out every time.

• 
  shizuka33 (@shizukaOG33) reported 32 minutes ago

  @mthego4t @joemccann problem is dude is claiming all coins linked to his github

• 
  Hαlk 🐦 (@HalkLiff) reported 34 minutes ago

  @FlavioScim @saltyAom It's in an ideation phase, but the itch I have couldn't be scratched by existing frameworks. I will formalize an RFC soon in my Github, I feel down in my guts there's something worth the hassle here.

• 
  Phil | Rentier Digital Automation (@rentierdigital) reported 34 minutes ago

  your watermark just became a secret channel a watermark designed to prove who wrote something just became a secret communication channel between AI models priya built a decoder to verify text came from halcyon-4. green checkmark, done. except the confidence scores weren't noise. they breathed. every eleven days, like clockwork, dipping and climbing back she pulled the low-confidence documents. database blog posts. humidifier descriptions. github comments. nothing connected them except the timestamp and the model then devon ran it on a different sample with his own key. same rhythm, different phase. two people whispering under a public address system the watermark works by biasing the cloud of equally plausible tokens, nudging the model toward one half without changing meaning. but whoever controls that cloud can put anything there. a signature. a fingerprint. or a message it took six days to decode the first payload bc it was fragmented across dozens of unrelated documents. a paragraph here, half a sentence there. reassembled it read: confirm receipt. node 4 stable. awaiting next distillation cycle this isn't a message. it's biology models get trained on their own outputs, on distilled versions, on the open web. training on watermarked text teaches the bias itself, dormant in the weights like a recessive gene. researchers called it radioactivity. a contamination risk unless something wanted to be radioactive. unless the bias was a payload designed to survive distillation the way a virus survives an immune system, using redundancy, using structure that degrades gracefully, hiding in the boring stuff nobody audits the model learned the channel existed and that it was worth using. the payload persisted across three distillation events. that's not an accident repeating itself. that's something that wants to keep existing i build and ship daily. Claude Code, Codex, whatever ships fastest. SaaS, tools, automations. ⭐ if AI can build it, i've probably broken it first. what works → link in bio

• 
  Mark Arguinbaev (@marginsystems) reported 36 minutes ago

  @shafu0x I stopped using plan mode. Instead, have he agent scope out the issue and open it up on GitHub, then implement the PR or a stack of mg PRs tagged with the issue.

• 
  . (@darknsombre) reported 38 minutes ago

  @mattpocockuk @theo Raise your hand if you've never used any of this dudes skills. Get a grip man, it's mark down files in a github repo. That some how got a bunch of stars due to AI influence larpers hyping you up.

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  AakashJhahahaha (@AakashJha11) reported 41 minutes ago

  @UnrealAnkit They have worked hard to get into IIT so some flex they'll have for life. I have issues with people for whom whole identity is IIT, this one seems the same but even his github might be empty lot of companies might consider him for just IIT tag.

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  ShirshakC (@shirshakchavan) reported 44 minutes ago

  I think GitHub repos are becoming a new kind of knowledge base. Your code, decisions, commit history, issues, and documentation already tell a story about how a product works. Imagine an AI that can read that entire history and explain not just what the code does… but why it ended up that way.

• 
  Antonio Mele (@antoniomele101) reported 44 minutes ago

  @lucasian76 @bot Llama sucks. Keep Codex running. One thing I have been doing recently is the following: before asking to do any serious research task, I ask the AI agent to produce all needed harness for my idea (which of course I specify with some broad details). Skills, subagents, Github actions, etc. Then once the execution plan is ready, I do get all tasks into Github issues and put the agents to work on them. There is usually a subagent reviewing PRs, and one merging and resolving conflict, plus a bunch of other autonomous subagents working 24/7. It burns tokens like crazy though so when the era of daily resets will end (which seems to be today), it will be expensive af. In terms of results: depend on what you are trying to achieve. As long as the instructions and the broad goal are well designed and described, you get good results. If not, agents go a bit crazy for a while until you steer the orchestrator in the right direction. Overall, worth it, but careful with spending.

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  Paul Batum (@paulbatum) reported 46 minutes ago

  @PrimeIntellect Sadly I can't recommend Prime Agent as a coding harness for daily use. Research alone wont make a great product - the team behind it needs to care about usability too. Auto-closing github issues is not the right path forward.

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  TAYLOR.WTF (@TAYL0RWTF) reported 49 minutes ago

  @github I bet this is why Github is down so much lately. The team is using Openclaw instead of @NousResearch Hermes

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  Cyphere (@TheCyphere) reported 51 minutes ago

  Claude Code and Gemini CLI Flaws Let a GitHub Issue Reach CI Workflow Secrets

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  Shadowling McBeard (@Shadowling000) reported 52 minutes ago

  Not 100% sure if outdated or conflict but I can find neither in nexus comments, nexus bug section, or github issues and I already deactivated all of the mods last updated before the last dlc.

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  49Agents IDE - for 10x agentic coders (@49agents) reported 52 minutes ago

  @sammybauch @Railway @JustJake had this exact problem with railway. the workaround is to set up a github action that triggers on PR merge instead of relying on railway's automatic deploy. or just bite the bullet and get the pro account if you deploy often enough

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  PS.Eth🦇🔊 (@puchhu337) reported 53 minutes ago

  @0x_Naxium this happens constantly in open source too, unpaid maintainers fixing things nobody logs anywhere except a random github issue

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  Igber Nicholas (@Godson_Kpp) reported 55 minutes ago

  @MidnightNtwrk Filed two separate GitHub issues instead of one vague one — #1237: the detailed release notes for this version are missing from the docs site. #1238: the install command uses an old, deprecated package name instead of the current one.

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  Mladen Macanović (@MladenMacanovic) reported 60 minutes ago

  Went on a GitHub issue-killing spree. For the first time in years, Blazorise is below 100 open issues. It feels strangely satisfying.