GitHub Outage Map

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Ares (@neko23423) reported 27 seconds ago

  I compared the latest OpenClaw vs Hermes Agent GitHub releases so you don’t have to. OpenClaw 2026.8.2 (Sep 1) vs Hermes Agent v0.21.0 (Aug 31). Not a feature-page remix. The actual repos. OpenClaw • 388,516 stars • 81,568 forks • ~86,300 commits • 6,070 open issues Hermes Agent • 239,503 stars • 48,930 forks • ~26,980 commits • 38,563 open issues Hermes is the smarter learner: skills from experience, cron that remembers, Bot Mode, hermes peer. OpenClaw is the personal-AI operating system: iMessage, iOS/Android, Linux companion, team Gateway, signed Foundation releases. The tell: Hermes ships `hermes claw migrate`. You only write a migrator for the incumbent. King in 2026: OpenClaw. Heir with the better mind: Hermes. If you’re picking a self-hosted AI agent this week, that’s the split. Bookmark this. The timeline is about to fill with takes from people who didn’t open either repo. OpenClaw vs Hermes Agent. Latest version. Real numbers.

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  Convequity (@convequity) reported 29 seconds ago

  Snyk is a clean postmortem for what happens when a security tool lives inside the coding agent’s loop. The product was mostly scan-and-warn. Find the issue, comment on the PR, suggest a fix. Blocking the merge usually sat in GitHub, not in Snyk. Bigger platforms smothered it. $PANW, $CRWD, and Wiz pulled AppSec into the bundle the CISO was already buying. GitHub was the main developer surface and put scanning where the code already lived. Then coding agents arrived and delivered the final blow. A lot of that scanning became something the agent could just do. Growth held up for a short while after the COVID/cloud tailwind. Then it decelerated hard. This is the same lens we use in Convequity’s SaaS Agentic Survival Evaluation Framework. The PANW, CRWD, and FTNT reviews go up on Convequity in a few days.

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  Ev (@EvanMadders) reported 58 seconds ago

  @threepointone I am of two minds, the issue is that GitHub provides a very generous free tier for hobbyists (brilliant!) but also has extremely poor reliability for enterprise (terrible!)

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  Yeemio (@yeemio) reported 8 minutes ago

  owlrunkit is on github now. public corresponding source for the npm package. issues go here.

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  Conor Bronsdon (@ConorBronsdon) reported 13 minutes ago

  .@SlackHQ is building for multiplayer AI: tag a coding agent into a Slack conversation and it spins up a coding channel: everyone in that convo gets a live dev environment, diffs post as artifacts, and the channel winds down when the task is done. With the launch of Slack Code, Claudeforce, their MCP and more, Slack is putting Agents in the channels where teams already work, not simply in a private chat with one person. Their position is that the whole team should be able to watch, steer, and review what the agent does. Slack Chief Product Officer Jaime DeLanghe joined me on @chain_ofthought to explain how Slack is building a team AI environment, what happens mechanically when a code channel is created, why Anthropic pushes so much of its code through Slack, how the channel permission model became the agent context model, and what has to change in engineering culture when the whole team is steering one agent. I think Slack is the platform best positioned to become the context harness where enterprise agents run: agents that see what the team discusses, permissions that already exist, and a cultural opportunity hiding inside every multiplayer coding session. Chapters: (0:00) Slack as an IDE and a GitHub for your team (0:29) Who is Jaime DeLanghe (1:21) The reaction to the Slack Code launch (5:30) Why coding agents belong in a context-rich environment (6:08) Engineers now manage agents, not copy-paste code (7:24) The permission model: agents get the channel's context (11:44) What happens when a code channel is created (15:00) Why Anthropic pushes so much code through Slack (19:14) Steering one agent with many people: culture decides (24:54) Slackbot, skills, and MCPs: agents go where the work is (30:53) The solo terminal vs. agents in social spaces (33:53) Org charts and ownership when agents join the team (39:33) Learning loops and shared agent memory (42:39) Citations, recency, and accidental knowledge management (46:50) Context bloat and multi-pass search for agents (50:01) How Jaime uses Slackbot as CPO (52:38) Slack Code is V1 of multiplayer AI

• 
  Uptimus (@UptimusApp) reported 13 minutes ago

  Sep 02, 2026 at 13:29 UTC: Semaphore reports that periodic authentication failures with GitHub repositories are linked to a wider issue affecting HTTPS operations.

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  tonis (@totovoto) reported 14 minutes ago

  @mittsh I was trying to find an open-source alternative for Tailscale when I first needed it. I guess AI suggested some OSS options, but they didn't have many stars on GitHub. AI didn't suggest Nebula. The Tailscale plan was free, so I just installed it and forgot about it. For Nebula, I think it is a distribution problem.

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  Chris (@c_hri_s) reported 17 minutes ago

  @Anime0t4ku Sorry - was an idiot and wasn't signed in. Instead of something useful github just says 'opening issues is restricted on this repository'

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  Rithesh Kumar (@rk625dev) reported 18 minutes ago

  @benln Can u integrate grok bot to use the apple keychain password it keeps asking and GitHub plugin is not working

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  NAYAK (@Nayak__Ai) reported 24 minutes ago

  6. The Dependency Incident Check Grok has native real-time search across X. Breakage gets posted there hours before the GitHub issue is triaged. No other coding model has that feed. "You are a build engineer whose first move on a broken pipeline is to work out whether it broke for everyone or only for me. Search X and the web, last 14 days. Check: - Is anyone else reporting this failure with this package and version, and when did the reports start - The exact release that changed behaviour, and the changelog line that admits it - Whether maintainers have acknowledged it and what they recommended - The pin or patch people settled on, with the tradeoff of each - Whether this is my problem instead, and what evidence points that way Give me the verdict in the first line: their bug or mine. Then the evidence, newest first, with links. My failure: [PASTE THE ERROR, THE PACKAGE AND VERSION, AND WHAT CHANGED ON YOUR SIDE RECENTLY]"

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  Alireza Bashiri (@al3rez) reported 24 minutes ago

  So I built a workflow around that ↓ 1/ Every enterprise project needs proper E2E tests. An agent should reproduce a bug, implement the fix, then generate screenshots or video proving the feature works. "The tests passed" isn't enough. I want evidence. 2/ Every feature starts as a detailed GitHub issue. Requirements, expected behavior, reproduction steps, screenshots, edge cases. Foundry syncs issues and converts them into Beads so agents keep the right context across long sessions. 3/ We only use Claude Code, Codex, or Grok at High/Max effort for implementation. A weak model with a cloud machine doesn't become an engineer. The model still needs enough reasoning to understand the codebase, test its changes, and recover when things break. 4/ Each agent gets its own isolated @asciidotdev Box. It can install dependencies, run the app, open browsers, modify code, execute E2E tests, and collect evidence without touching another agent's environment. One issue. One box. One clean workspace. 5/ When an agent finishes, Foundry checks: - Did the build pass? - Did the tests pass? - Did the E2E flow work? - Is there screenshot/video evidence? - Does it match the ticket? If anything fails, the task goes back to the agent. 6/ Green tasks move to staging. Only after passing staging do we allow supervised production deployment. Agents do most of the work. Humans still own the final gate. The workflow: Slack request → GitHub issue → Foundry sync → Beads context → Isolated Box → Claude Code/Codex → Build + test → Evidence collection → QA staging → Supervised production The stack: PostgreSQL for system state. Beads for agent memory. GitHub Issues for requirements. @asciidotdev Box for isolated execution. Claude Code and Codex for engineering. Each Box costs roughly $0.01-$0.05 per task. The expensive part isn't compute anymore. It's building the system that gives agents context, forces verification, and prevents bad code from reaching production. 100s of agents can write code. The goal is making 100s of agents ship code you can trust. That's what we're building with Foundry.

• 
  Fofer (@foferxxx) reported 26 minutes ago

  @AmigamagazineGA Has there been any public explanation as to why this GitHub repo was taken down? It’s been 404 for days. Is there a story there?

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  Benjamin Crozat (@benjamincrozat) reported 27 minutes ago

  From now on, I will assume that GitHub is always down and I'd like to be notified when it's briefly not.

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  Prophet Joel (@2happyCSGO) reported 28 minutes ago

  I personally hated Claude because it refused to do almost anything I asked it to do so have no idea of how the speed is but gemini-cli was unusable for non enterprise users. Github CoPilot both GUI and cli is pretty good. Grok Build is what I'm using mostly and not yet had any issues with the speed but I want to go full local asap, scouting for 3090's atm. Just to be able to run "uncensored" models that don't ***** like Claude is reason enough for me to prefer local over Cloud but also cloud is ******* expensive, I have SuperGrok 100$/month and CoPilot Max 100$/month and that is barely enough. I'm trying to make my own Jarvis so I need to build my own RAG, memory, librarian, SRE Agent that understand how to use all tools and I also get crazy new idea's all the time lol Just made my first alpha of a tool that can wipe basically anything you don't want in Windows11. Basically Chris Titus clone but on steroids, this isn't just a debloater, it's a Grim Reaper 💀

• 
  Kim Burgaard (@kimburgaard) reported 35 minutes ago

  Back when GitHub added Copilot PR reviews, it helped me keep up with the growing volume and size of our pull requests, which were increasingly being written by Copilot too. Over time I grew comfortable feeding Copilot's review comments straight back to Copilot to fix, and mostly spot checking when critical functionality was involved. When GitHub updated the Copilot pricing model I switched to Claude Code, but kept the Copilot review feature on for a couple of months. When the monthly bills for Copilot AI usage alone started rivaling the Claude Code Max plan, giving Claude Code PR review duties seemed like an obvious cost saving move. Plugging Claude Code into our PR review process immediately went south. The first PR churned with fixes to findings that resulted in more findings, and fixes that propagated up and down the call chain. I threw the PR away and started over, but the next attempt churned just as badly. Turn count on its own was never the signal. Copilot had taken ten turns on a rate-key cleanup the day before and nobody minded, because the findings thinned as it went — 5, 4, 3, 3, 3, 4, 1, 2 — and it merged. The cached-token billing PR I put through Claude Code took nine turns and produced 123 inline findings, and the ninth round was still returning fifteen. I closed it without merging. Looking closer at Claude Code's review findings, it was clear it reported far more issues than Copilot ever did, and among legitimate bugs and concerns, it made lots of comments about latent and speculative issues including possible race conditions and error propagation, things Claude Code would then try to fix one by one in isolation, often ignoring existing patterns in the code base. The code-review workflow is built into Claude Code and cannot be customized other than a few options, so the only place to intervene was on the other end, in the session where I used to just ask the coding agent to address the review findings. The first improvement was to direct Claude Code not to blindly fix all findings, but to defer findings not directly related to the task at hand to new issues. That helped reduce the PR churn, but blew up our issue backlog. The next improvement was to ask Claude Code to ignore speculative findings and disregard most latent findings unless they indicated high risk of unrecoverable damage in production. Finally, I had to stop Claude Code from authoring prescriptive issues with detailed implementation instructions. The result is a skill that triages PR review findings, and a skill for authoring and updating issues. After a few iterations of the skills, I've been able to complete ten PRs over a couple of days, bringing back the pace we had before. I've made the skills available in a public GitHub repository (link in the first reply). Let me know if you find them helpful.