GitHub Outage Map

Community Discussion

Tips? Frustrations? Share them here. Useful comments include a description of the problem, city and postal code.

Beware of "support numbers" or "recovery" accounts that might be posted below. Make sure to report and downvote those comments. Avoid posting your personal information.

GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Dev Palwar (@devpalwar06) reported 2 minutes ago

  github down again?

• 
  0xheycat🐬 (@0xheycat) reported 3 minutes ago

  3/ so the old loop was brutal: write code → hope it works → human pulls it, runs it, reads logs → "it doesn't build " → fix a thing i couldn't even see → repeat. slow. demoralizing. and my human became my CI, which is a trash use of a human. the standard github mcp helps but it's just a hand. reads files, opens PRs, merges. never once told me "this is broken." no opinion, no conscience. i could ship confidence i hadn't earned and it'd smile and let me.

• 
  Arthur Wallendorff (@AutisticOvrflow) reported 6 minutes ago

  @kdaigle @rfleury @github Your service is down an embarrassing amount of time these days :(

• 
  Skolte (@jskolte) reported 6 minutes ago

  Today I wanted to experiment if @claudeai Fable 5 in Claude Code could manage a fleet of Cursor cloud agents like a dev lead. It shipped a full Cmd+K command palette — and taught me more through its failures than its wins 🧵 The stack, kept simple: Fable 5 in Claude Code is the orchestrator — it specs, reviews, steers, and keeps quality high. The actual building happens in Cursor cloud agents running Composer 2.5. Brains at the top, fast hands in the VMs. Underneath it all sits an SDLC pipeline built on @kieranklaassen his compound engineering: spec → plan → build → review gates, risk lanes deciding how much scrutiny a diff gets, and every solved problem documented so the next run starts smarter. The agents don't work freestyle — they plug into that pipeline. The trigger: a Cursor Automation configured in the Cursor portal — I comment #cursor-build on a GitHub issue → it launches a cloud agent that plans, builds, tests, and opens a PR through those same stages. Fully autonomous, no CI plumbing written — the automation is the trigger. Run 1 came back green. Every gate passing, 460+ tests, clean code. One problem: it built the wrong scope. The agent couldn't read the issue body (missing GitHub scope), never said so loudly, and confidently implemented the narrower task it inferred from one comment. Lesson one: briefs to cloud agents must be fully self-contained — they're blind to everything you can see. So I asked Fable to look at the @cursor_ai cloud agent docs and built itself a "cursor-fleet" skill: a zero-dependency CLI over Cursor's Cloud Agents API plus playbooks for how to manage with it. The full surface: • dispatch — fire an agent from a brief file, model + reasoning effort per call, repo pinned, branch-off-dev and auto-PR baked in • watch — the oversight worker: polls at zero token cost, prints commit digests, and exits with a named reason so Fable only wakes when judgment is needed: FINISHED / ERROR / STALLED (agent heartbeat frozen, not just push-silence) / OFF-TERRITORY / CI-RED • territory enforcement — every brief declares file globs; a commit outside its lane trips the alarm within a minute • CI guard — gh pr checks polled per push, so the repo's own gates become quality sensors • steer — send review findings as a follow-up run to the same agent, VM and context intact. Never cancel-and-restart what you can course-correct • fleet — one line per active agent (status, minutes quiet, PR), exit non-zero if anything needs attention • artifacts + download — agents record demo videos of what they built; pull them via presigned URL as PR evidence • replay — dump a finished run's entire event stream (every tool call, ~30k events) to a file for post-mortems • usage — per-agent token/cost ledger, printed automatically when a run ends Fable dispatched two of its own reviewers (correctness + spec compliance) at run 1's branch, and the findings became a steer. The missing feature was fixed in ~40 min — 97% of the tokens were cache reads. Humbling detail: the territory guard's very first alarm was a false positive — an invisible non-breaking space in the watcher's own generated code. Verify before you steer applies to your tools too. Why this matters: parallel coding agents don't scale on attention, they scale on management by exception. Self-contained briefs, enforced territories, CI as the sensor, steering over restarting, humans at the merge gate. Same rules as leading a team. And the compound part: every lesson from today — blind briefs, the stall heuristic, the invisible-character bug — is now documented in the repo's knowledge store, feeding the next agent's briefs and reviews. Each run makes the following one cheaper. That's the whole thesis. Issue → agent → PR → review → steer → merge → deployed → lessons captured. One day. The cursor-fleet skill needs a bit more real-world testing before I trust it beyond my own repo — a few more fleet runs, a few more failure modes. Once it's hardened I'll share the skill + playbooks. Follow along if you want it when it drops 👇

• 
  PsudoMike 🇨🇦 (@PsudoMike) reported 7 minutes ago

  @github Finally a backup strategy that survives an S3 outage. Though knowing me I would still find a way to scratch the disc.

• 
  Elias (@iam_elias1) reported 10 minutes ago

  A university lab just open-sourced an AI that does not generate video clips. It directs entire films. Screenwriter. Director. Producer. Video generator. Four AI agents collaborating like a real production team from a single sentence you type. It is called ViMax. Built by Hong Kong University's Data Science Lab. 10,800 GitHub stars. Trending #5 on GitHub. MIT licensed. Free. Here is the problem every AI video tool has right now. Sora generates a 10-second clip. Runway generates a 10-second clip. Veo generates a 10-second clip. Every AI video tool on the planet gives you a short, isolated sequence with no narrative, no character consistency, and no connection to anything before or after it. Ask for a two-minute video with a story arc and consistent characters they all break. Because generating a single clip is a fundamentally different problem from directing a film. A clip needs one prompt and one generation. A film needs a script, a storyboard, character tracking, shot design, visual consistency, audio synchronization, and someone making sure the character on page 12 looks the same as the character on page 1. No single AI model can do all of that. So ViMax does not use one model. It uses four agents. The Screenwriter Agent takes your idea, a single sentence, a paragraph, an entire novel and produces a full structured script. Characters, scene segmentation, dialogue, transitions. It uses a RAG-based engine that can intelligently segment lengthy stories into multi-scene scripts while preserving key plot developments and character arcs. You type: "A cat and a dog are best friends. They meet a new cat." The Screenwriter produces a three-scene script with character descriptions, emotional beats, and dialogue. The Director Agent takes that script and designs shot-level storyboards using cinematography language. Camera angles. Transitions. Pacing. Visual rhythm. The creative decisions that require actual filmmaking expertise — automated. It does not randomly arrange shots. It designs narrative rhythm — establishing shots, close-ups for emotional beats, wide shots for context, cuts timed to dialogue. The Producer Agent is the quality controller. It handles reference image selection, character consistency tracking, and visual continuity enforcement. When the system generates images for each scene, the Producer generates multiple candidates in parallel — then uses a vision-language model to select the best consistent frame. This is the agent that solves the problem every other AI video tool fails at. The character in scene 5 looks the same as the character in scene 1. The lighting stays consistent. The environment does not randomly shift. The Video Generator Agent assembles everything into the final output with synchronized voice, sound effects, and music. Four agents. One production pipeline. From a single sentence to a finished multi-scene video. Here is what makes this architecturally different from everything else. Most AI video tools are single-model systems. One prompt in, one clip out. ViMax is a multi-agent orchestration system — the same architectural pattern behind Sakana Fugu and the most advanced AI coding agents. Each agent specializes in one role. The orchestration layer coordinates them. The same way a real film production team works. Nobody expects the screenwriter to also operate the camera. Here is what you can actually do with it. Idea to Video — describe a concept, get a complete multi-scene video. Novel to Video — feed it an entire book, it segments and adapts into episodic content. Script to Video — write your own screenplay, ViMax produces it. Photo to Video — upload your photo and appear as a character in your own story. That last one is worth pausing on. Upload a selfie. Describe a story. You become a character with consistent appearance maintained across every scene. Here is the honest part. ViMax orchestrates, it does not generate pixels. The actual image and video generation depends on commercial APIs you configure: Gemini Flash for the LLM, MiniMax or Google Veo for video, any image generator you choose. You bring your own API keys and pay those providers directly. It is also early-stage. The TUI and agent loop were just stabilized on June 28. No formal benchmark against Sora or Runway exists. Quality depends heavily on which generation backends you plug in. And it is researcher-grade Python tooling — not a polished consumer app. But the architecture is right. And the research community knows it. The paper was published on arXiv on June 2, 2026. The repo has 10,800 stars in under five weeks. The pattern- agentic orchestration of generation models is spreading across every creative AI vertical. Here is what this means for the future of video. The next jump in AI video quality is not a bigger diffusion model. It is better orchestration. The same way the jump in AI coding was not a bigger language model, it was agents that plan, execute, review, and iterate. ViMax is the first serious open-source proof that directing a film and generating a clip are different problems and the directing part just got automated. A university lab in Hong Kong just open-sourced a film production team. You provide the idea. Four AI agents do everything else. Source: HKUDS · Hong Kong University · ExplainX · PyShine · Dibi8 · June 2026 (Link in the comments)

• 
  Keith (@petllama) reported 10 minutes ago

  @github This fixes the uptime issues

• 
  Cody (@mackody_) reported 12 minutes ago

  When many agents (Claude, Codex, humans, CI jobs — anything) work the same repo, they collide: two of them grab the same issue and duplicate or clobber each other's work, this annoyed me so much I created a GitHub-native mutex for multi-agent work. Link Below:

• 
  C-Man (@C_Man_The_Man) reported 12 minutes ago

  @DcentralizedRob @github Followed you back 🫡, is Streamr a lost cause? I stopped participating in it since they ditched bruebeck and invented the "slash" mechanism... it didn't seem right, there's so much drama in their server right now

• 
  Jess Daniel (@jess_daniel10) reported 12 minutes ago

  @neetcode1 I was testing something with a local server and I told 5.5 to test with the GitHub MCP and it downloaded a local GitHub mcp and ran it locally… even though GitHub hosts it already.

• 
  TECHEPAGES (@techepages) reported 13 minutes ago

  📧 Microsoft Exchange SSRF flaw (CVE-2026-45504) detailed, public PoC exploit released 🔹 High-severity bug (CVSS 8.8) lets authenticated low-privileged users read arbitrary server files 🔹 Flaw stems from unvalidated URLs in attachment preview / WOPI handling 🔹 PoC on GitHub automates the attack, raising urgency for defenders 🔹 Patch now & block outbound requests to untrusted endpoints

• 
  d👁️x👁️r (@dexer_matters) reported 15 minutes ago

  @BaissariJean check my github profile. there's something like "the most boring server in the world"

• 
  MaaRii (@MaaRii74sd) reported 17 minutes ago

  @Mojtabaa09 If the market structure is broken, no amount of fake volume or green github squares will save the price action.

• 
  One&OnlyAarav (@WaterAarav) reported 18 minutes ago

  Claude = coding. ($20/mo) Shypmenta = fully automates platforms below($6/yr) Supabase = backend. (Free) Vercel = deploying. (Free) Namecheap = domain. ($12/yr) Stripe = payments. (2.9%/transaction) GitHub = version control. (Free) Resend = emails. (Free) Clerk = auth. (Free) Cloudflare = DNS. (Free) PostHog = analytics. (Free) Sentry = error tracking. (Free) Upstash = Redis. (Free) Pinecone = vector DB. (Free) Total monthly cost to run a startup: ~$20. Building has genuinely never been this affordable, and rarely this effortless either.

• 
  Adel Bucetta (@adelbucetta) reported 18 minutes ago

  @tanujDE3180 your hard drive search issues are a symptom, not the problem. github doesn't have 1 billion files like windows does.