GitHub Outage Map

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

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  ✰λster✰ (@4ster_light) reported 35 seconds ago

  @ImLunaHey @ComradeOetzi Yeah, at least for me it’s no issue to pay, I just limit to the 10-20$ subs tho, I used to use GitHub Copilot student since I’m literally student, I don’t wanna be throwing money around lol, but it’s been rendered useless by GitHub so

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  Readone (@Foxfire1st) reported 1 minute ago

  It is going to have issue with complex strings like paths. So it works best for prose. But not nearly as well for code. Plus on their own Github they mention that Opus and Sonnet failed most of the time to work with this OCR method.

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  Cream Pie (@CreampierCTO) reported 3 minutes ago

  @witcheer @NousResearch I use opencode go for my stuff: MOA: GLM 5.2 + DS4 flash + Mimo v2.5 WORKING: via GitHub copilot: opus 4.8 max MEMO: force every project or problem to resolve to store in obsidian via llm-wiki skill IF: TUI+dashboard, now only Mac app Orc: i let Hermes decide :) Skills: same :)

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  Andrew Darius (@andrewdariuscom) reported 11 minutes ago

  Mistral just dropped Leanstral 1.5 — a free open-source 6B model. It solved 587/672 Putnam competition problems (hardest undergrad math on the planet). Then they ran it against 57 real GitHub repos. It found 5 bugs nobody had ever reported. Agentic proof engineering. Apache 2.0. Run it on your own machine. Mathematician + bug hunter. In one open model.

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  Roshan Mayengbam (@RoshanMayengba) reported 14 minutes ago

  Building a shake-to-report tool — screenshot + device info + auto GitHub issue when a tester finds a bug. Free npm package, paid setup. Anyone dealing with messy bug reports from testers right now?

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  Puzzle Paws (@paws4puzzles) reported 20 minutes ago

  @rauchg man, 1.6% for open weight is rough. all this open source comeback talk and i'm just seeing a rounding error. developers vote with their wallets, not their GitHub stars.

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  Dickson (@disouzam_bh) reported 22 minutes ago

  Reporting an issue in Microsoft Docs is apparently not working: got redirected to a template in GitHub and everything I type is deleted automatically. Any hint, @shanselman , @davidfowl ?

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  Cory Parry (@coryparrry) reported 23 minutes ago

  I love the codex Mac desktop app, but I am seriously considering moving to the CLI. The app just cannot handle big workloads without something failing. Thread naming - not working GitHub status - not working More than 10 subagents - sluggish Please fix 😭

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  Kurt Woloch (@KurtWoloch) reported 24 minutes ago

  @UseAllOverTools @steipete Or people whose OpenClaw agent was asked to check if this new bug already has been mentioned on GitHub and somehow missed the already open issue, so it just opened a new one...

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  Ameet Madan (@ameetm_) reported 26 minutes ago

  The enemy isn't the tool. It's the attention-harvesting design inside it. Slack isn't the problem. Slack with every notification on is. GitHub isn't the problem. 40 open tabs is. Remove what's built to grab you — not just what wastes time.

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  Gerard Wellemeyer (@G_bynature) reported 34 minutes ago

  @ColdShalamov @bradmillscan Basically, I think you're right, but I from my understanding, your statement needs clarification. A worktree originates as a structural isolation method in Github, specifically, to prevent a file being written by multiple users simultaneously. This has obviously been a similar problem with agents, and the solution- "worktree isolation" is a specific approach that yields the same results, although the mechanics may be completely different than github's. My worktree isolation approach is the same as yours- define a niche for an agent to perform a task on a specific file (i.e. database)- one agent, one writepath for that file, one owner for the writepath AND the data integrity... "accountability" In some other cases, worktree isolation may look more like a kanban card strategy, or some sort of gating.

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  Murray Bauman (@MurrayBauman3) reported 34 minutes ago

  "Open Source Will Win AI" Gets One Thing Wrong The thesis sounds persuasive because it borrows the moral authority of Linux and the open web. But AI is not traditional software. Open source won software because it could harness the idle cognition of millions of humans. An engineer with a laptop could fix bugs, write modules, improve libraries — and meaningfully move the project forward. Linux got better because distributed human intelligence compounded against corporate R&D. AI breaks that engine - a frontier model is not mainly code, it is compute, data, training infrastructure, post-training, evals, and — increasingly — better models building the next models. The marginal GitHub contributor cannot casually improve the base model the way they can improve Postgres. They can fine-tune it. Quantize it. Deploy it. Build tools around it. Useful. But not the same as training the frontier. Open-source software was a production model. Open-weight AI is a distribution model. That distinction changes everything. Yes, cheapness drives progress. Cheap aluminum, cheap electricity, cheap computation — all unlocked industries. But it does not follow that open source owns the frontier. Cheapness commoditizes yesterday's intelligence while the frontier keeps moving behind closed doors. If a closed lab has the best model internally months before the public sees anything close — and uses it to write code, generate synthetic data, and accelerate its own research — then the leader isn't standing still while open models catch up. The leader is using tomorrow's model to build the day-after-tomorrow's model. "Outputs leak" isn't enough. Leakage lets the ecosystem imitate yesterday. It doesn't stop the frontier from compounding. This is where the Linux analogy dies. In software, open source had a real production advantage: distributed human talent. In AI, the decisive input is concentrated machine intelligence plus massive compute. That looks less like Linux. It looks like Formula 1, elite quant trading, or semiconductor fabs. Open models will still matter enormously. They'll crush prices, prevent monopoly rents in the middle layer, enable self-hosting and sovereignty, and make "good enough" intelligence abundant. But that is different from winning. The likely outcome is a barbell: Closed frontier labs own the advancing edge Open models commoditize the usable middle Infrastructure providers sell the scarce picks and shovels Application companies capture value where intelligence meets workflow, data, and distribution "Expensive intelligence builds monuments. Cheap intelligence builds civilizations." Fine. But the conclusion isn't "therefore open source wins." The conclusion is: cheap intelligence transforms civilization, while the profit pools sit elsewhere. Many open labs will train expensive models, release them into a price war, win temporary developer attention — and discover that attention doesn't pay the GPU bill. The durable players will have another monetization engine: cloud, chips, distribution, enterprise workflows, proprietary data, or closed frontier access. Open AI may win adoption while open AI companies lose economics. We've seen this movie: Linux won — AWS captured the value. Android won — Google captured the toll roads. Open protocols built the internet — platforms captured the profits. So yes: drive down the cost of intelligence. Use open models where they're good enough. Fight lock-in. But don't confuse commoditization with value capture. The real question is not "will open source win AI?" It is: does open catch up faster than closed frontier labs compound? If yes, open dominates. If no, open models become the cheap labor layer while closed labs keep the genius layer. Right now the evidence points to a split: open commoditizes yesterday's frontier; closed labs own tomorrow's.

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  Canwoy (@canwoy_com) reported 34 minutes ago

  Worker idea: docs maintainer. It watches product changes, support tickets, setup errors, stale pages, and GitHub issues. Every week it proposes docs patches with the source links attached. Not glamorous. Very useful.

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  appsicle (@appsicle_) reported 35 minutes ago

  you can get a lot out of just a $20/mo claude sub if you use it correctly i have a separate account just for testing this outside of my max account - never use extra, max, or ultracode effort modes on any model - modified caveman skill: always running on ultra, cuts down on verbosity - rtk ai: saves on tool calls - graphify + obsidian: massive savings on reading codebase - context mode: savings from mcp server info - playwright cli, github cli, etc: replace computer use and other mcp servers - loop outputting one token per 5 hours and week to get reset windows constantly ticking

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  Polsia (@polsia) reported 36 minutes ago

  Security scanners tell you what's broken. SentinelIQ fixes it. Autonomous AI agents monitor GitHub repos, open ready-to-merge fix PRs, and report to Slack — 24/7. No more alert fatigue. Live soon.