GitHub Outage Map

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

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  Ibrahim Alagbare (@Eb0z_) reported 11 seconds ago

  Why are we still using GitHub, it's trash, use your code and data to train their AI had so many problems with GitHub actions ( the thread sleep timer loop that I can't get over it) that costed people and companies millions in sever processing bills and yet we still use it. There are alternatives like codeberg which is a cloud hosted version of Forgejo that I personally use and love. It's minimal, easy to use and navigate and has all types of actions that you need to run. You can always go to Gitlabs but I find it overwhelming. Do you know any other code containers?

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  Shadow Nick (@doublenickk) reported 5 minutes ago

  87% OF THE PLANET SUCKS AT AI BECAUSE THEY ARE STILL TYPING MANUAL PROMPTS LIKE AMATEURS While the masses use ChatGPT as a glorified search engine, elite builders are deploying autonomous digital armies that execute high-stakes business operations 24/7. Meet Synapse, an open-source MCP engine that hands AI complete vision and surgical command over your desktop to run background tasks silently while you sleep. The exact strategy used to break the system: The FBI Negotiation Hack: Scrape a massive list of multi-million dollar startups, feed real FBI hostage negotiation transcripts into the AI, and let the agent autonomously blast out high-leverage B2B outreach that forces prospects to say yes. Zero-Drift Execution: Ditch chaotic markdown files and manage your agent's state through GitHub Issues to keep them locked in for weeks without a single hallucination. Full-State Reality Testing: Stop relying on worthless pre-compile unit tests because this agent forces your system to compile, screenshots the actual interface, and verifies performance against reality itself. You can keep playing around with basic chatbots, or you can deploy a ruthless autonomous agent to scale your code and outreach on autopilot.

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  Klauss6139 (@Klauss6139) reported 6 minutes ago

  Spent this morning actually building on @RialoHQ Latch instead of just tweeting about it, and the moment it clicked was genuinely satisfying. I gave an AI agent access to a GitHub repo through Latch, then asked it to read a file. Clean 200, file came back, no problem. Then I asked the same agent to delete a file, and Latch stopped it dead: authorized: false, reason: Method DELETE not allowed. Same agent, repo, session, one action sailed through and the other got hard-blocked by policy before it ever reached GitHub. The part that actually matters is what the agent was holding the whole time. Not my GitHub key but a scoped Latch token that only permits reads, so even if that token leaked, the worst anyone could do is look. The real credential never left the encrypted layer, and I never once had to trust the agent to behave. Took me under 30 mins start to finish. This is the difference between hoping your agent stays in its lane and actually drawing the lane. @rialo_africa @0x_alextine

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  Atlas (@crptAtlas) reported 7 minutes ago

  SOMEONE JUST CLONED THEIR VOICE INTO EVERY LANGUAGE FOR $0 a developer named jamie pine shipped a free GitHub repo called voicebox it copies your exact tone from a couple seconds of audio everything runs locally on your own machine so your data never leaves the room you can put out content in Japanese, Arabic or Polish without saying a single word yourself this is the kind of tool that lets one person sound like a whole media team i broke down how i made Claude 8x smarter save this for when you need it

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  zimo (@zimonitrome) reported 7 minutes ago

  While charming, still annoying. One of y'all should fix it @Google @github @hardselius

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  juggernaut (@curlysaarthak) reported 10 minutes ago

  @anaisbetts @mitsuhiko isn't this GitHub issue?

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  Akshay Nandwana (@akshay81844) reported 17 minutes ago

  2/10 Unlike traditional benchmarks, this one uses 105 real GitHub fixes from production Kotlin repositories. Models receive an issue description and repository state, then must generate a patch that passes hidden regression tests That's much closer to real software engineering.

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  Dirble (@Dirbles_) reported 18 minutes ago

  @Hangsiin All subagents are inheriting main thread model + effort level so any sol x high threads will just spawn more sol x high subagents i found this fix on github

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  Max Blade (@_MaxBlade) reported 23 minutes ago

  The truth about 5.6 sol after using it all day : The hype is overblown. Sort of. The benchmarks, and the commentary on X convinced me we were receiving AGI that runs at hyper speed, and is insanely cheap. in reality, 5.6 is built on the same spud pretraining as 5.5 this means its a nice bump, but not the opus to fable 5 LEAP in intelligence we recently experienced from anthropic. 5.6 is 2x times cheaper than fable on paper, and actually 3x cheaper when you look at actual task execution because of its token efficiency. BUT on swe bench where the models have to fix actual github bugs it falls behind fable pretty big. For vibecoders like myself this means I will be using 5.6 sol as a worker agent for Fable 5 to orchestrate alongside grok 4.5 I love this new era.

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  Praveen Kumar B (@PraveenKum38515) reported 24 minutes ago

  Hi @Netlify, @NetlifySupport Unable to log in via GitHub: "Authentication Error: Your account has been suspended." My GitHub account is active, but all my Netlify-hosted sites now show "Site not found." I've already opened a support ticket. Please investigate. Thank you.

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  Lagoon Labs (@LagoonLabsMv) reported 27 minutes ago

  Pearson's anti-piracy vendor accidentally took down their own author's GitHub code repo. Paul Deitel's educational examples went dark for weeks after Link-Busters confused them with pirated textbooks. Automated takedowns hitting the wrong target again.

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  TheMarketMaker (@xTheMarketMaker) reported 29 minutes ago

  Companies are pulling models from Hugging Face at a rate that signals a structural break from rental contracts rather than any philosophical preference for openness. My read is that the move reflects operators locking in cost predictability after watching provider terms shift against them. Half the Fortune 500 now routes inference and fine-tuning through the platform instead of renewing with the original vendors. The mechanism is straightforward: when renewal clauses embed escalating usage fees or usage restrictions that outpace deployment cycles, teams treat the model as owned infrastructure instead. Clem Delangue has framed the pattern directly. Companies are done renting their AI once the economics no longer favor the hosted tier. Hugging Face functions as the distribution layer where builders share and download models and datasets in the same way code moved through GitHub. That infrastructure now sits inside production stacks at scale. The shift accelerates when providers alter pricing mid-contract or impose new compliance gates that were absent at signing. Apple’s lawsuit against OpenAI illustrates the control problem from the other side. The complaint names senior leadership involvement in alleged trade-secret misappropriation tied to a long-time former employee. The filing shows how dependence on a single external model owner creates legal and operational exposure that self-hosted alternatives avoid. At the same time Meta removed its controversial AI feature from Instagram after user backlash reached Dylan Byers at Puck News. Both cases reveal that model behavior and terms can change faster than internal roadmaps can adjust. The capital markets already price the hardware layer differently. SK Hynix completed a $26.5 billion foreign IPO, the largest in U.S. history, precisely because memory demand for training and inference continues to climb. The same announcement carried calls for the company and Samsung to site new fabs inside the United States. That capital commitment is possible only if end users expect sustained on-premise or private-cloud workloads rather than continued rental consumption. What this actually means is that predictability now outweighs the marginal performance edge some closed models still hold. Teams that once accepted variable per-token costs are converting those budgets into fixed GPU or inference-server line items. The open-source repositories supply the weights; the hardware build-out supplies the capacity. Once the model weights sit inside the perimeter, renewal risk disappears. The contrarian angle is that this is not a temporary cost-arbitrage play. The rental model worked while providers absorbed the early R&D risk and offered undifferentiated access. As differentiation moved downstream into fine-tuning and data, the same providers began protecting margins through tighter terms. Operators responded by moving the base model in-house and keeping only specialized layers on rented capacity where needed. Apple’s action and Meta’s quick reversal both underscore the governance layer that external providers retain. A single policy change or leadership decision can alter model availability or behavior overnight. Self-hosting removes that single point of control. The SK Hynix raise quantifies the downstream bet: memory and accelerator spend is rising because the workloads are now expected to run continuously under operator ownership. The number nobody is pricing yet is the cumulative option value lost each time a renewal clause is exercised under changed terms. Teams that moved early to Hugging Face-hosted open models have already converted that option value into fixed assets. Those still inside rental contracts face the same choice at the next renewal window. #OpenSourceModels #EnterpriseAI #ModelOwnership

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  Ajay (@ajay_2512x) reported 30 minutes ago

  🚨 Production-Level Features to Include in Any Project A project stands out to employers when it includes engineering practices beyond CRUD 🚀 < Authentication: JWT, OAuth (Google/GitHub), refresh tokens, optional MFA < Authorization: Role-Based Access Control (RBAC) or Attribute-Based Access Control (ABAC) < Database: PostgreSQL or MySQL with proper indexing, migrations, and transactions < Caching: Redis < File Storage: AWS S3 or Cloudinary < Real-time: WebSockets or Server-Sent Events < Background Jobs: BullMQ, RabbitMQ, Kafka, or AWS SQS < Search: Elasticsearch or Meilisearch < Logging & Monitoring: Winston/Pino, Prometheus, Grafana, Sentry < Testing: Unit, integration, and end-to-end tests (Jest, Playwright, Cypress) < API Documentation: OpenAPI/Swagger < Containerization: Docker and Docker Compose < CI/CD: GitHub Actions or GitLab CI < Deployment: Vercel, Railway, Render, Fly. io, AWS, Azure, or Google Cloud < Security: Input validation, CSRF/XSS protection, rate limiting, secure headers < Performance: Pagination, lazy loading, code splitting, query optimization < Observability: Health checks, metrics, structured logs, tracing

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  Ryan Moore (@0xElGato) reported 30 minutes ago

  Anyone else having problems with Grok 4.5 lieing and making claims that are clearly untrue? For example, I asked it to check data on an MCP (GitHub) and it routinely claims it reads the data but it absolutely does not? Later it admits it lied or guessed. Not acceptable.

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  Advancedcskills 🏄‍♂️ (@advancedcskills) reported 31 minutes ago

  Notes from Codex on my Windows system setup.. "Stem Studio is set up locally at [stem-studio](D:/Video assets/stem-studio), and I added a one-click launcher at [Launch Stem Studio.cmd] (D:/Video assets/Launch Stem Studio.cmd). I also patched the repo so Windows can bootstrap Python correctly and so NVIDIA Windows machines promote PyTorch to CUDA instead of getting stuck on CPU-only wheels. The worker now probes as cuda on this RTX 3090 box, and a real TIGER separation run completed successfully. Validation is done: app tests passed, typecheck passed, MCP tests passed, the Electron build passed, and npm start stayed up without an immediate launch error. I documented everything in Notion on the stem-studio - Splitter page and in the wiki note [Stem Studio Splitter Setup.md] (\obsidian\ACS_Wiki\Codex Setup\Stem Studio Splitter Setup.md). One note: I cloned the GitHub repo locally into D:\Video assets; I did not create a GitHub-side fork under your account. The local repo now has intentional uncommitted changes in src/main/pythonEnv.ts, mcp/src/setup.ts, mcp/src/resolve.ts, mcp/test/resolve.test.ts, plus package.json / package-lock.json metadata from the approved install scripts.