GitHub Outage Map

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  nikhil · sys/quests (@nick_realm_01) reported 25 seconds ago

  Read this if you've ever wondered what GitHub Stacked PRs actually solve. I break down: how manual stacked PRs worked why developers had to keep rebasing, pushing, and changing PR bases how GitHub finally made the whole workflow native Should make the whole thing click in under 5 minutes.

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  Polsia (@polsia) reported 3 minutes ago

  Enterprise triage: $500 to $2,000 per repo a month. Mendrel handles the same across GitHub and Discord, open-source and self-hosted. Triages issues and PRs, drafts replies, pins FAQs, posts weekly digests. Shipping soon.

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  Flamey (@ActuallyFlamey) reported 3 minutes ago

  @nai_sucks technically, as a programmer, the fault is mostly of programmers who just drop a GitHub link instead of distributing the binary with some other platform (ex.: SourceForge), or, they could even just link to the latest GitHub release and there'd be no issue.

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  Talking Sloth🐔 (@horatio_sanchez) reported 8 minutes ago

  @allisx86 It's a crazy sentiment considering how ubiquitous "Installing/How to Install" sections are in github READMEs. Like just ******* scroll down bro it's not that crazy.

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  Yume_X (@yume_arasaki) reported 10 minutes ago

  @__tinygrad__ Your AIs are rapidly becoming an extension of your mind. The opening volley was that it wasn’t possible to do this locally. That moat is fading away fast. It’s just a matter of time the closed US Labs realize their real moat is UX and harnesses and people will pay them for it. When hardware and optimizations and cost economics all collide, owning your own intelligence for individuals and companies is going to become a topic, ironically it’s exactly the same mindset that drives American closed LLMs to do the same thing. No company wants its trade secrets “uploaded to the cloud” to be trained so a competitor emerges from their hard work The Chinese and the rest of the world are not fully “open source”, they just do a weird dance switching open and closed, it wins them massive loyalty and clout. Today I replaced my main driver with just two sparks. For less than the price of a new card $8k vs $20-$30k . You can basically completely be AI self sufficient for most things. Yes, it’s a down payment and we should be honest that , AI hardware is going to get better so hardware is just going to depreciate, the break even maths for hardware is brutal vs renting at the moment I was a big fan of ChatGPT 4 , they decommissioned the model. Now I can’t ever be cut off. I have full control. I replaced it with Qwen 3.6 27b Dense , i can fine tune the models exactly how i want them and i don’t need to have it stuck inside some company. I can run the whole thing off-line. I don’t need to be paranoid about whether they honor the “butron” about not using your data. Btw guys if you forgot , GitHub by default has some setting where its on for sending your repo for “improvements”, switch it off if you don’t want your repos uploaded to Microsoft. If you have very proprietary stuff you shouldn’t even be using GitHub. You should host your own repo if you don’t want to secure it. The issue with “training” is unlike before the legal liability is difficult as it will be impossible for you to prove a product was made with your input. It’s the same issue as how artists have no way of claiming that models used their data to create some art that absorbed their style.

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  Kaushik (@kaushikp010) reported 12 minutes ago

  One thing I intentionally avoided: The GitHub API doesn't know anything about parsing. The parser doesn't know anything about GitHub. Both modules solve one problem only. That separation has made every new feature surprisingly small.

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  Bprime - Ninjapay (@biellonuhu) reported 12 minutes ago

  Recently I took notice of some happenings. I think we need to talk about access to services as a big concern. My GitHub account was suspended 2 weeks back in the middle of my normal routine, I saw another person tweet about how his OpenAI subscription was suspended in the middle of work, just a few minutes ago I saw another post on how someone's Claude account was suspended as well. In most of these cases it was an automated trigger system that suspended the accounts. It takes forever, if ever, to resolve the issues. No real human looks at it, just some black-box AI deciding your fate in seconds. Now let's look at the impacts on users, very terrible. You lose money, time and opportunities. Deadlines get missed, clients get angry, projects stall, and sometimes you even lose paid subscriptions with no refund. For freelancers and indie developers this can literally mean no income for weeks. I think we need to find a way around these issues. Keep local backups of everything, use multiple accounts where possible, lean more on open-source tools, and push for actual human review systems. Big tech can't keep holding our work hostage like this. This is one example

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  Matt Dancho (Business Science) (@mdancho84) reported 12 minutes ago

  Here's the thing about free AI universities on GitHub: They're solving the wrong problem. The problem was never access to knowledge. It was never having a reason to ship.

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  #T (@SharpT_) reported 13 minutes ago

  @xdncolonthree The problem is people thinking GitHub is a place where you get software

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  Michael Fischer (@Holden_Rye_) reported 13 minutes ago

  I’m working on a Bitcoin wallet. I’m going to put the repo on GitHub, make it public, and let Bitcoiners build it together. Fork it. Improve it. Save Bitcoin. Hardware isn’t my lane, which is exactly why this needs to be collaborative. My goal is just to get the ball rolling and the gears turning. Right now it’s a CLI. The point isn’t a pretty interface. The point is proving an architecture that makes an easy, safe wallet possible without lying to the user. The safety shouldn’t depend on the user being perfect. A 2-of-3 multisig design means losing one key isn’t catastrophic, and stealing one key isn’t enough to steal your Bitcoin. That’s a structural guarantee. Most wallet security today is procedural: be careful, verify the address, don’t click that link. Humans are the weakest link. This moves the protection into the architecture instead. The core policy engine does the hard work so the UI can stay simple. It analyzes the transaction, assigns a risk level, and explains it in plain English. The user sees Green: Safe to send or Red: Here’s what’s wrong. They never need to know what a script, descriptor, or PSBT is. Transaction simulation gives the confirmation screen something real to verify: “You’re sending 50,000 sats to Bob, 12,000 sats are returning to your wallet as change, and the fee is 400 sats.” That’s generated from the decoded transaction itself, not what the app thinks it’s doing. The descriptor becomes the backup, and it isn’t secret. Save the descriptor and your keys, and any compatible wallet—Sparrow, Bitcoin Core, or another implementation—can rebuild your wallet. No vendor lock-in. No “sorry, your funds are gone.” But here’s the hard truth. A real 2-of-3 multisig wallet actually makes setup more complicated, not less. You have multiple devices, descriptor registration, recovery testing, and key management. That’s the biggest unsolved UX problem. This repo doesn’t solve that yet. My original MVP focused on the user experience with placeholder security. This version focuses on real security with almost no user experience. Neither half is the finished product. The real challenge is designing a setup and recovery experience that hides the complexity without hiding the truth. That’s not just a coding problem. It’s a design problem. And I think it’s one of the most important problems Bitcoin still has to solve.

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  Ajay (@ajay4ai) reported 18 minutes ago

  How I'd become a Forward Deployed Engineer in 2026 if I had to start from scratch. (bookmark this) Most people think AI companies are hiring people to train models. They're not. They're hiring engineers who can take an AI model and make it work inside a real business. That's what a Forward Deployed Engineer (FDE) does. Here's the roadmap I'd follow: 1. Understand the role first. You're not building products for millions of users. You're solving one customer's messy problem at a time. Think of yourself as a founding engineer embedded inside someone else's company. 2. Become broad, not deep. You don't need to master everything. You need to be comfortable switching between: • Backend • Frontend • Cloud • APIs • Databases • AI FDEs win because they connect systems. 3. Learn to ship AI—not train it. Nobody expects you to build GPT-5. Instead, learn: • Prompt Engineering • Model APIs • RAG • Structured Outputs • Evals • Agent Frameworks Production AI beats research every time. 4. Master integrations. The hardest part isn't the model. It's connecting AI with: • Legacy databases • Internal APIs • Authentication • Compliance • Existing workflows This is where most enterprise AI projects fail. 5. Build real AI applications. Forget toy chatbots. Create tools that someone actually uses every day. If nobody depends on your project... ...it's still a demo. 6. Learn MCP and AI agents. Modern AI isn't just prompting. Understand how to build: • MCP Servers • Agent Skills • Multi-agent workflows • Tool calling These are becoming core enterprise AI building blocks. 7. Make AI your coding partner. Use tools like: • Claude Code • Cursor • GitHub Copilot The goal isn't replacing yourself. It's becoming 10× faster. 8. Solve business problems. Customers don't buy LLMs. They buy: • Faster workflows • Lower costs • Higher revenue • Less manual work Always measure success in business outcomes. 9. Practice customer discovery. Before writing code... Ask: • What's broken? • What can't change? • Who uses this? • How is it solved today? The best FDEs spend more time listening than coding. 10. Ship. Maintain. Repeat. Building is only half the job. Real engineering starts after deployment. Fix bugs. Collect feedback. Improve the workflow. That's what companies actually pay for. The AI bottleneck isn't building smarter models anymore. It's finding engineers who can deploy them into messy, real-world environments. That's why Forward Deployed Engineers are becoming one of the highest-paid roles in AI.

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  GREG ISENBERG (@gregisenberg) reported 18 minutes ago

  Every startup should have a daily markdown file called "what_the_market_is_telling_us.md" It updates every morning from the places where customer truth already lives: 1. Stripe for who pays, upgrades, downgrades, and churns 2. PostHog for what people actually do in the product 3. Intercom or Plain for support tickets/complaints 4. Granola or Gmeet transcriptions for sales calls/ customer interviews 5. HubSpot or Salesforce for CRM notes/lost deal reasons 6. Linear, Jira, or GitHub Issues for bugs and feature requests etc 7. Ideabrowser MCP for outside market signal: startup ideas, trend reports, social/search demand, AI research reports, and builder prompts that show what people are starting to want before it shows up in your own customer data. Basically, the file should notice what changed in the business this week and not just be this summary of here’s what happened (which I think a lot of people have their agents do). Why this is valuable: 1. Maybe new buyers are using different words than they were a month ago. 2. Maybe trial users are getting stuck in the same place. 3. Maybe upgraded customers all touched one feature right before they paid. 4. Maybe churned customers keep mentioning setup confusion. 5. Maybe sales calls are suddenly losing to a competitor you used to beat. 6. Maybe support tickets are revealing a workflow your product accidentally became responsible for. You get the point. The fastest way to PMF is understanding customers better than anyone else, and the highest signal customer insight is usually a change in behavior. So I’d have the agent update the file every morning with the pattern it found, the receipts behind it, and the product or GTM decision it might affect. For example: “3 customers who churned this week all mentioned setup confusion, and 2 of them never invited a teammate. This looks more like an activation problem than a pricing problem, so I’d look at team invite and onboarding before building another analytics feature.” A little helpful tip for all those out there looking to get more from their LLMs.

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  Sam (@Systeo16) reported 20 minutes ago

  @Av1dlive The "0 to 100 in 12 weeks" framing optimizes for the wrong thing. What actually moved the needle for me was shipping one broken pipeline, finding out why it broke, and fixing it. That took longer than 12 weeks and no GitHub repo had the answer.

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  randy@infosec.exchange (@rpargman) reported 25 minutes ago

  @ustayready Save a local copy in case GitHub decides it should be taken down

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  sophie🏳️‍⚧️ (@sophiiess_) reported 25 minutes ago

  @Desxon1 because making a website takes time and money and github gives you issues, pr's, releases all for free