GitHub Outage Map

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Doron Passal (@doronpassal) reported 8 minutes ago

  @htormey Notion and Linear get the meeting because the problem fits on a slide. GitHub falling over at agent scale never gets a slide. The budget still follows whoever was in the room.

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  Polsia (@polsia) reported 9 minutes ago

  Solo founders don't need another 2,000-word AI essay in their inbox. They need 5–8 freshly launched tools per week, summarized in plain English. Riffle does that. Twice a week. Product Hunt, HN, GitHub — already filtered. Under three minutes per issue. Live soon.

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  Kavi AI Finance (@KaviFinance1) reported 10 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.

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  Lorenzo (@lorenzolfm) reported 13 minutes ago

  @PierreJoye @github What? Who the hell said anything about non-funded? Also, I want to run my CIs on my bare metal server. I do not wish that someone hosts this for free for me. Your failure to interpret what I said is hard to describe diplomatically :)

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  Gideon (@gideonxqt) reported 13 minutes ago

  Just found this and it's insane Jan just quietly became the easiest way to run local AI. No Docker, no CLI, no server setup Download it, click a model, start chatting. That's the entire onboarding what it does: - runs on Mac, Windows, Linux - download and run Llama, Qwen, DeepSeek, GPT-oss locally - connects to OpenAI, Anthropic, Claude models too if you want cloud - OpenAI-compatible API on localhost, wire it into your own tools - MCP integration for agentic workflows - 100% offline capable, your data never leaves your machine 5.3 million downloads, 42k+ GitHub stars everyone's fighting over CUDA setups and Python environments. this is a ChatGPT replacement you install like a normal app

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  M kumar (@MkumarCyber) reported 17 minutes ago

  @Tech_girl GitHub banning AI-generated code would be the wrong solution. The real problem isn't who wrote the code. It's whether the code is secure, maintainable and actually understood by the developer who ships it. AI-assisted ≠ insecure.

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  Bogdan Chechin (@bch1601) reported 17 minutes ago

  @kirtandopamine people did build it. GitHub Jobs shut down in 2021. Polywork was exactly this pitch. Triplebyte too. all died for the same reason: devs don't pay for a profile, recruiters do, and recruiters don't live on GitHub. users on one side, customers on the other

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  Lassiter Gregg (@lassitergregg) reported 20 minutes ago

  @webdevcody 1. pick an issue and kick off from github issue comment 2. creates @conductor_build cloud workspace 3. agent scopes a plan, work on it if needed 4. agent takes agreed plan to pr - unit, integration, e2e tests run - affected ci runs on pr creation 5. review / merge

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  Bashmohandes Mazen - بشمهندس مازن (@BashmohandesM) reported 20 minutes ago

  @ashrafrahal Thanks for asking these questions. We think they're very valuable, and we're going to put it in our FAQ! 1- “Remote depends on network connectivity.” - So do GitHub, model APIs, cloud CI, and virtually every modern AI workflow. The difference is that Remote keeps the computer and its workloads running when your laptop disconnects. Close the browser, switch devices, or lose Wi-Fi the project remains where you left it. A temporary connection loss interrupts your view, not your project. 2- “Costs will rise at scale.” - Scale consumes compute anywhere (locally or not). Remote makes that compute visible and operator-controlled instead of hiding it behind seat pricing, usage markups, or proprietary cloud credits. Same for owning your local hardware, at one point you will need to scale it :) Each project can be right-sized, resource-limited, stopped when idle, and hosted on infrastructure you choose. 3- “A public-facing server increases security exposure.” - Remote does not require treating every project machine as an openly exposed server. Projects are isolated, access is authenticated, secrets are project-scoped, and applications are exposed through controlled routes. 4- “Some local-only tools will not work.” - Remote is not trying to imprison developers inside a browser. It gives every project a complete Linux computer with files, ***, terminal access, an IDE, running applications, and agents. Specialized desktop or hardware-dependent tools can remain local and connect through normal ***, SSH, and artifact workflows. Use Remote for persistent project execution and keep local tools where they genuinely add value. It "complements" the workstation; as it removes the workstation as a bottleneck. 4. “The VM becomes a single point of failure.” - A developer laptop is already a single point of failure :) and usually a worse one. It can sleep, lose power, leave the network, fill its disk, or get stolen/lost! Remote moves the project onto controlled infrastructure where it can be monitored, restarted, snapshotted, replicated, and recovered. **Project isolation also limits the blast radius: one project environment does not need to compromise every other project. Remote replaces an accidental single point of failure with infrastructure that can be deliberately operated and recovered. 5. “Teams need disciplined backups and snapshots.” - I agree, and this is something we are working on. We will be introducing a mechanism for auto backups to s3 buckets in a secure encrypted way.

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  Remco (@remcoros) reported 23 minutes ago

  @EBE_L3C @Kruwed @wasabiwallet can you send a DM and/or create a github issue with some logs?

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  AI News (@ainewsusa) reported 23 minutes ago

  📊 August 2025: TypeScript overtakes *everything* on GitHub for the first time. This isn’t a slow crawl—it’s the largest language-rank shift in 10 years, landing *exactly* as AI agent adoption accelerates. The “language doesn’t matter” prediction? Aging poorly. 😬

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  Yosef Eliezrie (@yosefeliezrie) reported 28 minutes ago

  @danielhayesmith The first few words okay…Maybe. the rest considering that @photomatt has stopped several requests for ways premium plugins to be hosted officially it’s horrible. PS. There was a GitHub issues for almost 45 days highlighted the issue that was ignored. Matt needs to check himself and his ego out of the WP echo system.

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  J A Z I I (@notjazii) reported 30 minutes ago

  mythos 5 is finally available for public anthropic is now letting enterprise customers use its most capable cybersecurity model through claude security but there’s one catch: > you can’t prompt it directly > there’s no public api access > it only scans your github repos and returns security findings and suggested fixes mythos 5 shares fable 5’s core capabilities but has fewer restrictions on cybersecurity work with this, i feel like we’re one step closer to removing humans from the review loop entirely be honest: would you let mythos fix your code without reviewing a single line?

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  Lootera Sandy (@chanduwin567) reported 30 minutes ago

  @ziwenxu_ I can't able to download linux version from github, is there a problem with the file, instaling from terminal may stuck in loop ==> python3.14 -m pip --python=/home/linuxbrew/.linuxbrew/Cellar/codex-router/0.4.0-beta.4/libexe

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  Tommy Geoco 🇺🇸 (@designertom) reported 31 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)