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:

• 
  khushal (@celeroncoder) reported 1 minute ago

  @get_bb_app I've filed a GitHub issue with more context. Also saw a pr that's doing similar work but in a broader sense so commented there with ref to the issue filed Will see if I can create a pr myself, the stopchild fix (I've mentioned in the PR as well) seems to be working locally. Workflows closing the done worker spawns after completion so memory is only blocked by concurrent running threads/worker

• 
  Iqramul Hussain (@iamiqrram) reported 4 minutes ago

  @cursor_ai @github stop asking "Are we there yet?" 80,000 times a second. The best part? Cursor relies on GitHub under the hood to sync repos, so Cursor’s own launch day workflows were frozen by the outage. They literally couldn't even tweet properly because their internal tools were broken.

• 
  App Launcher (@AppLauncher_App) reported 6 minutes ago

  @OpenAgentsInc Stacked PRs in four hours is wild. The fact that GitHub still doesn't have native support for this says a lot about how slow big platforms move on dev workflow pain points.

• 
  CodeXSavage (@CodeXSavage6s) reported 6 minutes ago

  @rodrgds I still haven't run into any trouble with my GitHub

• 
  umang (@umgbhalla) reported 7 minutes ago

  @troels_a make it beep when github is down

• 
  Kavi AI Finance (@KaviFinance1) reported 11 minutes ago

  One Hermes trick I wish more people used: Stop putting everything into memory. Use memory for facts. Use Skills for procedures. For example: ❌ Memory: “Deploying my app requires Docker, GitHub Actions, SSH…” ✅ Skill: /deploy-production Then Hermes loads the procedure only when needed. This keeps your persistent context smaller and makes the agent much more consistent. Even better: If Hermes just completed a complicated task that took 5+ steps, tell it: “Save what you just did as a skill.” Now you’ve turned a one-time solution into reusable procedural memory. That’s one of the things I really like about Hermes. You’re not just giving the agent instructions. You’re slowly building its toolbox. And there’s actually recent research backing up why this matters: a new study found that skills often help agents by acting as procedural anchors that stabilize execution, rather than simply injecting more knowledge. Try this one over the weekend: Do something complicated → make it work → save it as a skill → reuse it next time. Your agent gets more useful without your prompt getting bigger. That’s the part of agentic AI I think people are still underestimating. LinkedIn Weekend Hermes trick: don’t confuse memory with skills. This sounds simple, but it completely changed how I think about building a long-running agent. A lot of people try to make their AI “remember everything.” That’s usually not what you want. In Hermes, I like thinking about it this way: Memory = WHAT Skills = HOW For example, your memory might contain: “My production server runs Ubuntu.” That’s a fact. But: “How I deploy my production application” is a procedure. That belongs in a Skill. So instead of stuffing your memory with hundreds of instructions, you can create something like: /deploy-production and let Hermes load the full procedure only when that workflow is actually needed. This has another interesting effect. Your agent starts developing a procedural toolbox. You solve a difficult problem once. You get the workflow working. Then tell Hermes: “Save what you just did as a skill.” Now the next time you encounter a similar problem, you’re not starting from zero. You’re reusing the accumulated procedure. Hermes’ documentation specifically recommends creating a skill for tasks that take multiple steps and will be repeated. Its skills system also uses on-demand loading, so the full procedure doesn’t need to sit in every context window. And this isn’t just a Hermes-specific idea. A recent research paper looking at agent skills found something pretty interesting: Skills appear to help agents primarily by acting as procedural anchors that stabilize execution. In other words: They’re not necessarily making the model “know more.” They’re helping the model execute better. That’s a really important distinction. If you’re building an agent that you plan to run for weeks or months, I’d rather have: small persistent memory + a growing library of good skills than: one massive memory file containing everything I’ve ever told the agent. My weekend experiment for anyone running Hermes: Pick one annoying workflow you’ve already solved. Make it reliable. Turn it into a Skill. Run it again next week. Then improve the Skill when you discover an edge case. Do that 20–30 times and you aren’t just using an AI assistant anymore. You’re building a procedural operating system around it.

• 
  Sharptake (@ssharptake) reported 12 minutes ago

  @DanielLockyer AI is now doing the fuzzing for us Next it will file the GitHub issue and close it as wontfix

• 
  Tommy Geoco 🇺🇸 (@designertom) reported 15 minutes ago

  @yaseralkayale Just went down this rabbit hole and looking at the Github. I think what I'm referring to can live in / on this. Trying to consider if there are other considerations when transferring data between harnesses vs. agents (e.g. harnesses can conntain one or many agents)

• 
  MJY (@omninomsky) reported 21 minutes ago

  @Tech_girl How would it be able to determine whether the code was generated by AI or people? Realistically what would happen if GitHub would have to spend resources to determine whether the code was generated by a person (sounds expensive). It's very easy to defeat the purpose by being too sensitive (blocking human posts) too lax (allowing too much AI code through) or simply incorrect (blocking humans, allowing AI). It's very hard to get it exactly right. So just from the technical problem alone they probably won't do this. But let's say they did. How does that work out? Well, now people who rely on AI to do their work can no longer use GitHub. They'll probably want to keep using AI so they'll no longer use GitHub. I'm not yet seeing any upside. Less merges per day maybe? Relief for the system?

• 
  Granite (@Granite0x) reported 24 minutes ago

  🚨 THIS IS ABSOLUTELY INSANE a developer just released a free tool that builds a vertical video with a talking presenter. out of one script and one photo. it's called lanshu-create-ai-presenter-video. it landed on GitHub two days ago. 267 stars. MIT. here's how it works: 1. You give it a script and a presenter photo you have the rights to. 2. The AI writes and voices the narration, generates the presenter, and locks the lips to the audio. 3. The skill edits, burns in subtitles and a cover, checks the sync, and hands back a master with a QA report. 9:16, 1080x1920, 30fps, 45-75 seconds. all of it happens in one run, on your own machine. save this before you sit down to cut your next video. i left the link in the next comment 👇

• 
  Prayush Jain (@jainprayush9) reported 24 minutes ago

  github down again?

• 
  Andrew (@Ra3orbladez) reported 25 minutes ago

  @ArnavvSharmaa Me? No i review PRs on Github or Gitlab. But I still do not get the question, how is it connected to the observability, I need to look at what models are doing in separate projects and also have a set of skills/guardrails/workflows to add to new projects, so using such harness solves my problem of context management.

• 
  Jansen (@jan53n) reported 30 minutes ago

  Is github *** server access getting slow for you?

• 
  Salty (@saltyq) reported 30 minutes ago

  spent part of yesterday binge watching the mentalist cus the back to back failed deploy while github was down was really annoying. Thought it was a personal issue at first but saw some other users complaining about the same issues. Fast forward to finding out GitHub was down for almost 8 hours after their Central US data center got overwhelmed, and reading their postmortem this morning the thing that got me wasn't the outage itself, it's that retries from panicking users made the traffic spike worse, which is basically the internet's version of everyone hitting refresh on a slow website and wondering why it's still slow.

• 
  Robert Youssef (@rryssf) reported 31 minutes ago

  check out the Developer Index Firecrawl just shipped: a search layer over 70M+ primary sources. not web pages. READMEs, GitHub issues, merged PRs, docs and OpenAPI specs, most refreshed within a day. that distinction does more work than it sounds like. ask your coding agent why its retry backoff is not firing on a 429. web search hands it blog posts. this hands it the sidekiq issue where someone hit exactly that. the aws-sdk PR that fixed it. the Stripe rate-limit doc. matched passages come back as markdown, so there is no second scrape. here's why the benchmark is worth your time 👇 → DevDex: 1,179 real developer queries, scored deterministically, no model judge → native web search, the control, sits at 0.45 → Context7 on docs only manages 0.17 → they publish the track they lose. Parallel beats them 0.82 to 0.76 on repo discovery a vendor shipping a benchmark that shows a competitor winning is rare enough to be a signal on its own. the tradeoff is worth naming. wiring this in means your agent's retrieval runs through a hosted API you do not own. take that dependency deliberately, not by accident.