GitHub Outage Map

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Praveen Koka (@praveenkoka) reported 12 seconds ago

  @rauchg "nine lines of code" is clever. The count stops after you define the functions, but before the GitHub auth, env vars, permissions, deploys, and error handling that someone else wrote.

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  Sidney Okine (@okine_sidney) reported 45 seconds ago

  @github why can’t I login to my account? Your authentication codes never gets sent via SMS. Like I’m just locked out, sup?

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  Pradeep Siddappa (@pradeepsiddappa) reported 3 minutes ago

  Payments is where most designer builds die. You can ship the homepage, the app, the onboarding. Then you hit the wall: taking money, splitting it, paying sellers, handling refunds. It's genuinely hard, and it's usually where you stop. I built a marketplace called Chiblu. I had to wire up real payments: checkout, automatic splits between platform and sellers, seller onboarding with bank details, refunds. I made every mistake. Silent webhooks that never fired. Seller accounts that got stuck. Errors nobody documented. I packed everything I learned into a free skill. Not a tutorial you read. A playbook your AI reads, so it builds the payments the right way while you focus on the experience. What it knows: how to take a payment, split one payment between your platform and many sellers using Razorpay Route, onboard a seller so they can actually get paid, run refunds, and get unstuck when the payment provider throws an error. How you use it: add it to your AI assistant once, describe what you want in plain words, your AI builds it in test mode following patterns that already learned from my mistakes, you review it like a designer. The shift: expertise you lack is now loadable. Taste plus a skill plus willingness to ship. That's the kit. Build the scary part yourself, test it on fake money, then take it live with help. Free on GitHub, built for Claude Code, works with Razorpay for Indian payments.

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  Daniil (@hey_daniil) reported 4 minutes ago

  I built DevIntern because I was my own bottleneck: agents were idle while I context-switched, my focus shredded by checking in on them. The tools weren't slow. Supervising them was. DevIntern makes the whole loop async, and here's exactly how: 1. It connects to your existing tracker — Jira, Linear, Trello, Asana, Azure DevOps, GitHub Issues, even markdown files. Your tickets are already the input. 2. Vague ticket? It specs it into something an agent can execute, so prompt quality is never the bottleneck. 3. It runs your coding agent, your model, your API keys inside your repo — and the subscriptions you're already paying for finally work around the clock, not just when you're watching. No lock-in, no token markup. 4. Output is a pull request. Review, merge, done. The output of a team of agents, the headspace to do your best work. No supervision, no burnout.

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  Prince Canuma (@Prince_Canuma) reported 6 minutes ago

  @RahimNathwani @cohere That’s odd Please open a GitHub issue with all the details of the error

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  Alan Lam 🔥 (@extralam) reported 9 minutes ago

  @AndreiOnel @github more say what we want to solve is my issue. Private repositories often have limited GitHub-hosted Actions minutes.

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  Berend de Boer (@berenddeboer) reported 10 minutes ago

  Odd, only now realised that @mattpocockuk 's to-issues skill doesn't actually create true github relations. I think it should.

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  Jatin (@jatinprajapat29) reported 13 minutes ago

  The biggest scam in many Tier 3 colleges isn't the fees. It's convincing students that attendance, assignments, practical files, and passing semester exams are all they need. Show up every day. Complete every record. Memorize for internals. Get decent grades. You become the "ideal student" in college. Then graduation arrives. The interviewer doesn't ask how many classes you attended. They don't care who topped your section. They don't ask how neat your practical file was. They ask: "What have you built?" "Where have you interned?" "Show me your GitHub." "How do you solve this problem?" And that's when thousands of students realize they spent four years optimizing for college instead of optimizing for the real world. The saddest part? Nobody lied to them directly. They were just never told that surviving college and becoming employable are two completely different goals.

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  IT Guy (@T3chFalcon) reported 16 minutes ago

  Nightmare Eclipse. Reportedly a former Microsoft security employee. The story: they found critical vulnerabilities inside Microsoft. reported them internally. Microsoft ignored the reports, deleted their accounts, and refused to pay the bug bounties. so they went public. Timing every release to drop within hours of Microsoft's monthly Patch Tuesday, the day Microsoft fixes other vulnerabilities, so the new ones land before defenders have time to breathe. here's what they've dropped since April: BlueHammer, CVE-2026-33825. exploits Microsoft Defender to redirect SYSTEM-level file writes into System32. patched. then actively exploited by real attackers within days. RedSun — SYSTEM-level privilege escalation via Defender. now in live attacks. UnDefend — blocks Defender from receiving definition updates entirely. observed in live intrusions. your antivirus stops updating. silently. YellowKey — bypasses BitLocker on TPM-only configurations. fixed June Patch Tuesday. GreenPlasma — SYSTEM-level privilege escalation via CTFMON. fixed June Patch Tuesday. MiniPlasma — resurrected a patched 2020 flaw that Microsoft let regress. RoguePlanet — the latest. no CVE. no patch. dropped June 9, hours after Patch Tuesday. now let's talk about RoguePlanet specifically because it's the most alarming. it exploits a race condition in Microsoft Defender itself. the component designed to protect your system runs as SYSTEM — the highest privilege level on Windows. it has to, so it can quarantine and delete malware anywhere on disk. RoguePlanet tricks Defender into performing a SYSTEM-level file write into a location the attacker controls. The result: a standard user gets a command prompt running as NT AUTHORITY\SYSTEM on a fully patched Windows 10 or 11 machine. Microsoft hardened Defender in May to block this class of attack. Nightmare Eclipse rewrote it to bypass the hardening and released it the same day as Patch Tuesday. ThreatLocker independently confirmed it works on fully patched Windows 11. BlueHammer, RedSun, and UnDefend the earlier releases were already picked up by real threat actors and used in live intrusions. Huntress documented this. a researcher dropping PoC exploits to punish a corporation is one thing. those exploits getting weaponized by ransomware groups is something else entirely. Microsoft's response: they flagged the researcher's blogs. took down their GitHub. threatened legal action. called it potential criminal activity. the cybersecurity community responded with fury. researchers don't work for Microsoft. if a company ignores internal reports and refuses to pay bounties, public disclosure is the entire point of responsible disclosure culture. Microsoft backed down. said they had no intention of pursuing legal action against security researchers. Nightmare Eclipse released RoguePlanet the same week. Microsoft built a bug bounty program to stop exactly this. they ignored the reports. now every Windows machine on earth is waiting for a patch that doesn't exist yet.

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  Sambhav Gandhi (@Sambhav_Gandhi) reported 20 minutes ago

  @aayushchugh copy the logs and paste on google there was 80-90% chance it could be found in stack overflow or github issues

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  s1rozha1 (@s1rozha_) reported 24 minutes ago

  AI AGENT LOOPS ARE A SLOT MACHINE FOR PEOPLE WITH UNLIMITED TOKENS everyone is hyping “agentic loops” right now. the pitch sounds insane: write a spec.md, press /goal or /loop, let the agent build, review itself, fix itself, and keep going until the product is done. Ross Mike’s take is much colder: this is mostly a terrible idea if you are building a real app. why? > your plan document never contains every detail > the agent fills missing details with assumptions > those assumptions drift away from your product vision > every wrong turn burns more tokens > the final output can look complete while being wrong in 20 tiny ways this is fine if you are prototyping something disposable. he used it to build an Among Us-style benchmark for AI models in ~90 minutes. It worked because he did not care about the details. but for a real SaaS, startup, or product, the missing ingredient is human taste. AI can replicate sauce. It cannot create sauce. the useful loop he actually runs is much narrower: > Cursor writes code > code gets pushed to GitHub > Greptile reviews it and gives a score out of 5 > if the score is below 4, Cursor reads the review, fixes the issues, pushes again > loop stops after 5 turns or when it gets 5/5 that works because the feedback is constrained and measurable. code review has a score. your startup vision does not. even his code review loop breaks when the PR goes over ~1,000 lines because the agent loses too much context. the real rule: loops are good for binary tasks. > code review > SEO page generation > fixed QA checks > repetitive workflows with clear pass/fail loops are bad for creative product building where you need taste, user feedback, positioning, design judgment, and mid-build course correction. the best loop in 2026 is still human-in-the-loop. bookmark this before you burn your whole token budget watching an agent confidently build the wrong app

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  Vaibhav | Data Say (@vaibhavs28) reported 26 minutes ago

  Last week I wrote about how we are using AI to ship product with a very small team. One thing I did not expect when we started working this way was how many tools I would personally start using, which I never thought would become part of my day. GitHub is one of them. I am not a coder, and for most of my career GitHub was something the technical team used. I understood product, customers, business problems, data, dashboards, commercials, and operations. But code repositories, branches, PRs, conflicts, checks, and merges were not part of my normal working language. Even this chart is not perfect. I later realized I was using two different GitHub accounts for some of this work, so the activity is split and there are gaps. That probably says enough about how new this world was for me. But that has changed quite a bit now. I am still not pretending to be an engineer. That would be wrong. But I am much closer to the product build than I was earlier. If I see a product issue, I can now think through the expected behaviour, work with AI to scope the change, understand what files or flows are getting touched at a high level, create or review the PR, run checks, resolve smaller conflicts, and merge low-risk changes. The larger or more technical changes still go to our technical partner. That boundary is important. But a lot of product work is not always a deep architecture decision. Sometimes it is fixing labels, improving how something is shown, cleaning a flow, making the dashboard easier to understand, or removing confusion that a customer may face. Earlier, these small things could easily wait because the technical queue was always full. Now, many of them can move much faster. That changes product thinking itself. When the distance between noticing a problem and trying a fix becomes shorter, you start observing the product differently. You become more specific. You do not just say “this page is confusing.” You start saying “this metric label can be misunderstood,” “this table should not show empty channels,” “this filter needs to behave differently,” or “this issue is small enough to fix now.” For a small company, this matters a lot. We do not have large teams for product, QA, analytics, documentation, and engineering. The same few people are speaking to customers, understanding the problem, thinking about the product, and trying to ship improvements. AI has not removed the need for technical judgment. But it has made the loop tighter. Customer issue to product thought to implementation to review can now happen much faster for the right kind of problem. That is the biggest change for me. Not just speed, but proximity. I am closer to the product, closer to the details, and closer to the actual act of shipping than I ever expected to be. More on this later, because we are still figuring this out as we build.

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  HDFXSB (@HDFXSB) reported 27 minutes ago

  @analogalok I’m currently reading the llamacpp GitHub. Trying to learn some of the flags. Hopefully I can try a few tonight after work. The server and gpu are all used off eBay. Thankfully I had 128gb ram to use. But everything was about a grand for each component. I thought when I bought it, I could run one large moa via my hermes, and several subagents. But without a special custom made nvlink for the v100’s, I don’t think that’s a viable option how I thought. So the new plan will be to use what fits per card, and each model will get a dedicated gpu. If I can find a nvlink to atleast couple two cards, I will try again combining cards. But the speed was unusable slow in my testing. Took over an hour for one prompt completion of a basic task with 3-4 moa at 256k. I ran qwen3.6:35bq8, qwen3.6:35ba38q8, qwen3.6:27bq8, as well as gemma4:12bq8 and gemma4:31bq8. I tried bf16 variants as well. My origional goal was to have a large moa, then several subs. After receiving the hardware, I ran into limitations on speed instantly

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  Anushka Shandilya (@Anushka62255679) reported 27 minutes ago

  Finished writing backend today and this is episode 6 of me building in public. What am i building? RAG platform for github by retrieving context not just from codebase but also from prs, issues and readme. What challenges did i face today? -Internal server error after auth was successful. Turns out my pydantic model contract did not match my DB schema. -my app tried to link a repo to a user, but the table constraints were fighting back -Had the classic fast API says 202, but celery stays silent. Turns out, my worker was listening to a ghost town because my environment variables were pointing to the wrong redis URL. The biggest lesson Authentication and connectivity are 80% of the battle. Once the handshake between your API, broker and worker is solid, the rest is just feature building. Now, i will be testing and improving output from llm before jumping onto the frontend. And once that will be done i will make a detailed video on "how i build the whole backend". Till then watch my previous episodes. I am open to ai eng roles as well, dms are open.

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  Paul Raimi💊 (@PaulRaimi11) reported 33 minutes ago

  $BASE B20 may get delayed again due to issues with GITHUB.