GitHub Outage Map

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Millsy.dev (@Millsydev) reported 4 minutes ago

  @TimSweeneyEpic @fortniteonlinux Well your Linux support has been terrible and very lacking If you had the API I can access to create my own for everyone to use and put it on GitHub I would. I would make it RUST /REACT Tauri2

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  cvberens (@iamBrns) reported 5 minutes ago

  @BullStern @github We got tired of the same thing, so we built our own execution layer. Switching GH workloads to Avrea is basically one line. Faster runners, less dependency on GitHub. The GH control plane is still the GitHub control plane though. That part needs a bigger fix. Working on that to.

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  ARAMEON (@NeoAramean) reported 6 minutes ago

  @gregisenberg Why is this needed? The same people that are getting into software now are vibecoding and the harness they use is perfectly capable of using *** and push code to github. A non issue really

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  Steven.Sun (@redboynono) reported 7 minutes ago

  @WatcherGuru The $13B Hugging Face number is still a reported talk/agreement, not a closed deal — neither side has confirmed. HF is not another model lab. It is the distribution layer: where developers find, pull, and ship models. NVDA already tried to put $500M in at ~$7B and got turned down because HF did not want one dominant shareholder. Buying the whole company at ~$13B would be L1 purchasing the L4 on-ramp, not stacking another faster SKU. The asset is community trust; AMD and Google publish there too. Is this owning the GitHub of models, or paying 2x for the minority stake they were refused last year? NFA

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  Vaibhav Sisinty (@VaibhavSisinty) reported 8 minutes ago

  @neilpatel People are doing AI generated content wrong! Proof : 110m views in last 30 days on my social content. It's using AI to do content better ! And I realised this is probably the part people don’t understand about how I create content today. Because yes… Almost everything you see from me is AI-generated. My Instagram videos use an AI clone of me. Same for Facebook and a lot of YouTube. The audio is AI-generated. The video is AI-generated. AI helps research topics. AI helps write scripts. Pretty much every tweet you see from this account is AI-generated too. But I’m not asking ChatGPT every morning: “What should I post today?” And dumping whatever it gives me. That would be terrible content. The system works very differently. For years, my biggest problem with content was never having things to say. I always had perspectives. I just never had the time to turn those perspectives into content. When I had a full-time job, I used to write a lot on LinkedIn. Then media became video-first. And once I started running a company, spending hours every week shooting videos became unrealistic. AI changed that. Today, almost everything I say gets captured. Meetings. Standups. Brainstorms. Conversations with my team. Things I say while working out. Thoughts during my commute. Podcasts. Things I’m researching and consuming. I carry an AI recording device. Digital conversations are transcribed. My content meetings are transcribed. All of this eventually lands inside what is effectively my second brain. We have a private GitHub repo connected to it. My team can essentially ask: “What has Vaibhav said about this before?” The system retrieves relevant opinions, stories, conversations and perspectives. Then the content team adds fresh research. Data. Examples. Contrarian views. What has worked for me before. What’s working across the internet. And then comes the part that still requires me. They talk to me. Usually while I’m already doing something else. At the gym. On the way to work. Between meetings. They walk me through the research. I react. I disagree. I add context. I tell them what I actually believe. Sometimes the entire thesis changes during that conversation. That is the content creation. Once that thinking is captured, AI takes over again. Turn it into a script. Generate my voice. Generate my video. Edit it. Package it. Distribute it. The mistake people make is assuming: AI-generated content = AI-generated thinking. It doesn’t have to. AI didn’t replace my perspective. It removed the bottlenecks between having a perspective and publishing it. And people already know a lot of my content is AI-generated. They still watch it. In the last ~30 days, my content has done nearly 110 million views/impressions across social. 18 months ago, that number wasn’t even 5 million a month. The biggest change wasn’t that I suddenly started spending my life shooting videos. It was one decision: Build distribution as a system. The tools aren’t the moat. Anyone can use the same AI models. The difference is what goes into the machine. Your experiences. Your taste. Your curiosity. Your arguments. Your perspectives. And then, most importantly: Your ability to execute. Everyone has ideas. Very few build the systems required to turn those ideas into output consistently. AI didn’t give me a voice. It gave my voice leverage. And the funniest part? This post itself was created the exact same way. I opened AI voice mode. Talked through this entire story. Dumped my thoughts and process into it. And AI turned that conversation into the post you just read. That’s the system.

• 
  佐藤ひつじ@業務系システムエンジニア (@_hitsuji_sato) reported 9 minutes ago

  We are seeing HTTP 400 InvalidInput errors on Amazon Orders API v2026-01-01 searchOrders (/orders/2026-01-01/orders) in FE regions (JP/AU) since around 2026-08-26 18:08 UTC. The issue appears to occur when using: includedData=FULFILLMENT We have reproduced the same behavior in our environment as well. According to Amazon GitHub Issue #5365: - Requests including FULFILLMENT fail - Requests without FULFILLMENT succeed - Reproduced in JP/AU (FE) - NA appears unaffected - Reproduced across multiple seller accounts If you are using Orders API v2026-01-01 searchOrders, it may be worth checking for HTTP 400 InvalidInput errors, regardless of whether you currently use includedData=FULFILLMENT. Although FULFILLMENT is a documented supported value, the root cause has not been disclosed and no official response from Amazon has been confirmed yet. If you are seeing the same behavior, I'd appreciate any additional information. I'll share updates if Amazon provides further details.

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  Joshua Stanton (@ShiftingPathway) reported 11 minutes ago

  @kilocode @Zai_org Why is it impossible to paste into your Kilo Code CLI prompt field. This is absurdly annoying and intermittent. Its been an issue in your github issues repeatedly for months. I don't understand why you would focus on a new app rather than making basic functionality work

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  Baran ⚡ (@HFTdev) reported 11 minutes ago

  @domenic it could invalidate the cached query after the current user opens/closes an issue/PR @tan_stack please do your magic @github

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  masYNYa (@KijAkubovs86334) reported 13 minutes ago

  Someone rebuilt his Grok Bot digital office twice and shipped the second version with 9 roles instead of 6, and the whole thing runs in one evening of setup for the $40 a month a Grok Premium+ subscription costs. The first version is called Grok Bot Office. Six roles: a Chief, plus Research, Writer, Outreach, Ops, and Finance. The Chief receives high-level objectives, splits work across the five specialists, catches everything downstream, and only pings the human for four decisions - send, spend, publish, delete. Everything reversible runs automatically. The setup takes one evening and connects to Telegram for the approval queue. The second version is called Grok Bot Office NY. Nine roles instead of six. Adds Support, Design, and QA to the original team. Same structure: one boss at the top routes every task down and takes finished work back up for signature. Same reversible-runs-automatic, irreversible-waits-for-yes logic. Same Telegram interface. Same evening of setup. The difference is the specialists you can hire. The first version fits a solo operator writing content and running outreach. The second version fits a two-person shop or a solo operator who has customer support volume and needs a QA gate before anything ships. Neither version requires coding. Neither requires a VPS. Neither requires a technical background. The Chief prompt is a plain English job description that says "you receive objectives, delegate them to the specialists below, escalate to the human only when send, spend, publish, or delete is involved." Each specialist prompt is a job description at similar length. What the setup does not do: It does not act on anything irreversible without a human click. It does not have real-world hands to tape boxes, sign contracts, wire money, or make regulated decisions. It does not replace a real employee for jobs that require licenses, judgment under liability, or physical presence. Every claim to the contrary in the last six months of agent content on X has been a marketing shortcut. What it does do: It removes the coordination cost between agents. When Research finishes a brief, it hands to Writer without you copying and pasting. When Writer finishes a draft, it hands to QA without you scheduling a review. The human still owns the last click. The bots own the handoffs between clicks. I built the six-role version for my own work last month. The setup took a Saturday. The most valuable role turned out to be the Chief, not any of the specialists. Before the Chief, I was still routing tasks between the individual bots myself, which was most of the friction. After the Chief, I only touched the four irreversible decisions. Monthly cost of the six-role setup: $40 for the Premium+ subscription, roughly $30 for Zapier for the tool connections, $0 for the prompts themselves. Total under $80 a month, versus what a virtual assistant equivalent would cost at $1,500 to $3,000 a month. The nine-role NY version costs the same. The two extra roles are extra prompt files. Setup time adds maybe an hour. Both patterns are on GitHub in various forms. The prompts are copy-pasteable. The Telegram integration is a two-step tutorial on the Telegram Bots documentation page. None of this is proprietary. The Chief architecture pattern was published in Anthropic's Building Effective Agents post in December 2024 and has been re-implemented across every agent platform since. Someone else already built both templates. The setup only takes an evening if you copy them instead of designing from scratch.

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  kitze 🛠️ tinkerer.club (@thekitze) reported 15 minutes ago

  i use Plunk for transactional emails but sign in with github is not working and im currently fcked so im exploring other options resend free plan is annoying and useSend free plan is annoying (100 emails per day is super limiting) any other suggestions? i'm not paying $20/mo for sending emails in the year of our lord 2026 when self hosting is a thing

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  coe0718 (@IaMuNk20) reported 15 minutes ago

  Apparently GitHub is having problems yet again 🙄

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  The Daily Viber (@TheDailyViber) reported 15 minutes ago

  If you are running several coding agents from one branch and five terminal tabs, your bottleneck is no longer the model. It is coordination. ONE CODING AGENT IS A WORKFLOW. FIVE CODING AGENTS ARE A MANAGEMENT PROBLEM. Superset is a macOS desktop editor for people already running Claude Code, Codex, Gemini CLI, OpenCode, Cursor Agent, Amp Code and friends. The useful idea is simple: give each agent its own *** worktree, terminal, status, diff view and handoff path. That matters because parallel agent work gets messy fast. One agent fixes auth. Another touches tests. A third refactors a shared helper. If all of that lands in one branch with scattered terminal tabs, you do not have acceleration. You have roulette with ***. Superset bets on the right primitive: isolation first, orchestration second. - Old workflow: - one repo folder - many terminals - unclear branch state - mystery diffs - manual cleanup - Superset workflow: - one task per worktree - agent sessions visible in one place - diffs reviewed before merge - bad runs killed without poisoning the whole workspace - external editor handoff when you need deeper control The practical use case is a backlog of small independent jobs: docs, tests, UI polish, migration prep, focused bugs. You spin several agents up, let them work in separate branches, then review the output like a small team of juniors. That is the right mental model. Agents are not autocomplete. They are workers that need task boundaries, review and cleanup. The nice part is that this does not require every agent to become the same product. Superset sits above the CLI tools you already use. That makes it more like a traffic controller than another magic coding environment. One honest note: this does not remove discipline. It demands more of it. If your tickets are vague, acceptance criteria are missing and tests are optional, Superset will only make the mess faster. Parallel agents can create more review load than you can absorb. It is also macOS-first right now, with setup requirements like Bun, Docker, jq, Caddy and GitHub CLI. This is not a toy for someone who has never run an agent workflow. It is a control plane for builders already feeling the terminal sprawl. Status/link-in-reply: try it when your agents are independent enough to isolate. Do not start with an army if you cannot give one agent a clean task.

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  Invention Zero (@InventionZero) reported 16 minutes ago

  Nvidia just agreed to pay $12.9 billion for a company that brings in about $150 million a year in revenue. Read that again. That's roughly 86 times annual revenue. For reference, most tech acquisitions get excited around 10 to 15 times. The company is Hugging Face. If you are not deep in AI, think of it as the GitHub of AI models. It is where developers go to find, share, and download open source models and datasets. Practically every AI engineer on the planet has used it. Back in 2023, Hugging Face was valued at 4.5 billion. Nvidia is now paying almost three times that. And get this, Hugging Face actually turned down a $500 million investment from Nvidia last year that would have valued it at just 7 billion. They said yes eventually, but the price went up fast. Why would Nvidia pay such a wild multiple for a company barely making any money? Because this isn't about revenue. It is about control. Nvidia already owns the hardware that trains and runs AI models. Buy Hugging Face and you also own the place where developers discover, tweak, and deploy those models. That's the whole stack, top to bottom. There is also a defensive angle. Companies like OpenAI and Anthropic are working on their own chips to get less dependent on Nvidia. Owning the biggest open source hub is a serious hedge against that. There is a lot more going on here, including a security breach that hit Hugging Face just weeks before this deal came together. Worth digging into before you decide what this really means for the AI market. $NVDA

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  TEJ (@sartejt) reported 17 minutes ago

  @gregisenberg The concept of *** as a versioning protocol goes far beyond what normies understand GitHub isn’t remotely hard to understand once you understand *** Tldr; GitHub is not the problem

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  Sanskrati Shukla (@SansShuklaa23) reported 18 minutes ago

  GitHub commit messages: fix fix2 final fix FINAL please work Professional development. 💀