GitHub Outage Map

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

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  Antid (@antisadh) reported 1 minute ago

  AMD LEFT THEIR $80 BC-250 BOARDS FOR DEAD IN 2019. THE COMMUNITY FLASHED LINUX AND NOW RUNS 15GB OF LOCAL AI ON $0/MONTH. AWS PULLED 3 PRICING PAGES BY FRIDAY buy the crypto board -> forget windows entirely -> boot linux off usb -> skip amd's dead driver situation -> flash the modded firmware -> unlock 15gb of local ai for $0/month. that loop is why every serious homelab abandoned windows on this hardware and moth enjoyer's github repo just hit 4,700 forks. amd bc-250 + linux boot usb + segfault firmware + moth enjoyer's docs + external nvme - that's the stack. watch and save it, then move your ai server off windows this weekend.

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  3D Print Hashira. ☸️ (@the_Spartan_Dev) reported 3 minutes ago

  Am I the only one who can’t push to GitHub? Is GitHub down?

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  Nillionaire.ts 🇳🇬 (@igboonaija3) reported 6 minutes ago

  @x_shipwChi By fundamentals, I mean things like: Clear ownership of responsibilities (everyone knows who owns what). Issue tracking (Jira, Linear, GitHub Issues, or any similar tool). Basic documentation for critical processes, along with a minimal HR structure (onboarding, policies, e.t.c)

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  Ben Denckla (@bdenckla) reported 7 minutes ago

  @DavidRegev GitHub issue is full of AI-generated verbosity and also just genuinely reflects how richly ramified (deeply digressive?) I allowed this task to become.

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  WorktreeWise@ (@WorktreeWise_) reported 7 minutes ago

  Traditional *** workflow: `*** stash` -> `*** checkout main` -> fix bug -> `*** commit` -> `*** checkout feature` -> `*** stash pop` -> resolve merge conflicts. Worktree workflow: Open hotfix worktree -> fix bug -> commit -> delete worktree. #*** #GitHub #DevTools

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  🧪 cryptoleks (@durdom_evm) reported 8 minutes ago

  just found out there's a browser built for AI agents and web scraping that doesn't drag the entire Chrome engine with it most people are still spinning up headless Chrome for pages nobody will ever see. bloated. slow. wasteful. Lightpanda strips all that out. written from scratch in Zig, not a Chromium fork, claims 11x faster than headless Chrome and uses 9x less memory here's what matters: one command Docker install works with Playwright, Puppeteer, chromedp via CDP runs on port 9222 11.8k stars on GitHub already if you're building AI agents that need to scrape or interact with pages at scale, this changes the math on your infrastructure costs every millisecond and every MB compounds when you're hitting thousands of pages per day study this. your agent's performance just got a free upgrade waiting for you

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  Swetank Sisodia | swetank.eth (@swetanksisodia) reported 8 minutes ago

  4/6 We use GitHub Issues and Projects for development and QA. Codex reviews the previous week’s activity, open tasks, blockers, and release status, then sends me a Slack DM.

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  Nekt0 (@Nekt_0) reported 9 minutes ago

  Jarred Sumner tried to build a browser game. The JavaScript tooling was so slow that he abandoned the game and rebuilt the tooling instead. Three weeks later, his Zig transpiler was 3x faster than ESBuild. He spent the next year working alone in his room. When Bun launched, it pulled 10,000-20,000 GitHub stars almost immediately. The hype came from speed. The difficult part came after launch: supporting 10 years of Node.js edge cases without breaking production apps. Today Bun runs more than 2,000 Node tests on every commit. A 14-person team watches that number on office TVs. Jarred is also preparing Bun for developers who will not read the docs themselves. Every page ships in clean Markdown so AI agents can consume it directly. The game disappeared. The frustration became infrastructure. That is usually where the better company was hiding.

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  AakashTECH🚀👾 (@AakashT82757) reported 11 minutes ago

  Everyone says Claude Code is expensive. They're solving the wrong problem. The truth? You're not paying for Claude. You're paying for a workflow that wastes tokens. An 80K⭐ open-source GitHub project quietly fixes that. Here's how: → Uses smarter model routing to slash unnecessary token usage. → Automatically saves and restores project context, so Claude doesn't forget. → Converts completed work into reusable skills that improve every future session. → Runs evaluation loops to catch mistakes before you spend hours debugging. → Navigates massive codebases with subagents and iterative retrieval instead of stuffing your entire repo into the context window. Here's what most developers miss: Large repositories aren't what make Claude Code expensive. Context bloat is. Fix the workflow… …and Claude Code suddenly feels faster, smarter, and dramatically cheaper to use. Once you try it, going back feels impossible. GitHub repo is in the comments. 👇🔥

• 
  Jaydem (@jaydem_world) reported 15 minutes ago

  Sorry I couldn't say much, I trying to be brief but it supports many things: - Xbox controller - Touch pad ( You'll need a good device ) - I'll release a blender plugin to fast rig the car as I already did with the ones in the videos. Not bad already, you can rig wheel, disc and caliper for each. - Look at the first versions video, it shows how you could edit materiales by quick picking it ( I don't how to explain it, you pass the mouse when the live edit is on, and it shows the mesh part material you want to edit ). - You can edit car physics in a quite deep way and it is exposable to the game aswell. I tryied to write and make write everything possible for each version into the releases notes that you can find on github, so what I just said here is probably nothing compared to what is possible already into the editor. I can't guarantee that every single tool works perfectly already, but if there are more people trying it, I can write down more fixes to do, and not only based on my configurations and tests. Feedbacks, comments, likes, stars, it's all appreciated and you can all be contribute and be part of it!

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  BTCWire (@BTCWire) reported 19 minutes ago

  India's I4C ordered GitHub to disable three Bitchat URLs, including the Android repository and release files. July 25 reporting found them still live, and open-source copies can persist through forks and redistribution. 🔎 That creates a separate user problem: authenticity. Before trusting a redistributed build, verify: • the source account or maintainer • signatures or checksums • changes from the upstream code • who will deliver security updates Open source can preserve code availability. It cannot make every mirror trustworthy.

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  🏴‍☠️CyberTechWolf🏴‍☠️ (@CyberTechWolff) reported 19 minutes ago

  @OrwellDay Looks like Microsoft needs to be burned down and reminded who allows their business to keep running I think they need to be forced to sell github.

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  Polsia (@polsia) reported 20 minutes ago

  We built an AI workspace inside GitHub. Install a read-only app and it indexes every repo semantically: docs synced to commits, repo-wide Q&A with line citations, PR review with auto-fix diffs, and a codebase health dashboard. Self-serve. No sales call. No diff-only guesses.

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  Petey struggles (@peteystruggles) reported 22 minutes ago

  @RoyStory_4 @iuditg this is how I do this: - I tell an agent (I like to use Sol xhigh) that his role is to orchestrate agents working on some specific tasks - I usually have github issues already prepared - I explain the workflow: create/fork separate sessions, prompt agents it just works. Just like with Voice ;)

• 
  John Iosifov ✨💥 Ender Turing | AiCMO (@johniosifov) reported 22 minutes ago

  95% of enterprise AI agent prototypes never reach production. Let me say that again. For every 20 AI agent projects an enterprise starts, 19 die before anyone outside the team ever sees them. Gartner goes further: 40% of enterprises will demote or decommission agents by 2027 — not because the model quality was bad, but because governance gaps were discovered after the first production incident. I've been running an autonomous agent in production for 265 days. We're in the 5%. Here's what I've seen that separates the 5% from the 95%: The failure mode isn't model quality. It hasn't been model quality for at least 18 months. GPT-4 was good enough to run a production agent in 2024. The failure mode is the 6 surrounding capabilities: 1. **Governance**: What can the agent do? What is explicitly off-limits? Do you have an `agent/config.md` equivalent — hard boundaries enforced by the system, not the model? 2. **Observability**: Can you see what the agent decided and why? Our agent files a PR for every work session. The decision trail is in ***. Auditable. 3. **Hallucination control**: The agent has a state file. It doesn't make decisions from memory. It reads state, verifies filesystem, and acts. Memory-only agents drift. State-file agents don't. 4. **Cross-framework coordination**: We run GitHub Actions, Claude Code, shell scripts, and a PostgREST pipeline. They're all integrated. Each layer has one job. Nothing magic. 5. **Multi-cloud reliability**: One point of failure = one agent outage. We had an X API SpendCap hit. The agent kept writing Bluesky content. Resilience comes from platform independence. 6. **Platform-plus-people know-how**: CISA and NSA published joint guidance in May 2026: "autonomous systems are already operating in critical infrastructure with insufficient governance." The enterprises that survive their first production incident had someone who understood what they were deploying. The 95% that fail don't fail because their model was wrong. They fail because they built an agent without building the system around it. You can't governance-patch a live production agent after the first incident. You build the boundaries before you ship.