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:

• 
  KSK Lab (@ksk_tinylab) reported 7 minutes ago

  My Codex philosophy: I start local, keep things simple, and understand what runs before giving it more access. If I wouldn’t install it at work just because it looks useful, I won’t blindly trust it at home. GitHub stars ≠ security review. Slow is fine. Cleanup isn’t.

• 
  Devon (@devonphp) reported 9 minutes ago

  @waynesutton @SpaceXAI @bot I want to use it to file properly researched GitHub issues when something gets flagged by sentry!

• 
  B0Y4N6 (@boyang_xie) reported 10 minutes ago

  Omarchy Might Be What an AI-Native OS Actually Looks Like Most “AI operating systems” today are still normal operating systems with a chatbot bolted on top. Omarchy feels different. It treats tools like Claude Code, Codex, OpenCode, Grok and Pi less like apps and more like native operators of the computer itself. That changes the interaction model. Old computing: Human → GUI → App → OS Current AI computing: Human → AI assistant → App → OS An agent-native system starts looking more like: Human → Agent → Files / Shell / Browser / Apps / Network The GUI is no longer the only first-class interface. The agent is. Omarchy still isn’t an “open agent” by itself. It doesn’t yet have a full persistent loop, deep personal context, system-wide event awareness, or true multi-agent orchestration. But the direction is interesting. Imagine the next step: You don’t open Codex. You just say: Fix the homepage. The OS decides: Codex edits the code. A browser agent tests it. A vision model checks the UI. GitHub opens the PR. The system asks you for approval. At that point, the operating system stops being a container for agents. It becomes the orchestrator of agents. Maybe the future isn’t one agent replacing your OS. Maybe the OS itself becomes an agent. And Omarchy might be one of the earliest hints of what that looks like.

• 
  Richard de los Santos (@ZeusRadls) reported 10 minutes ago

  @atmoio Grok Bot is wild. For fun I had it review and fix my LinkedIn page. It asked me some questions and went to the page and made the edits in its local browser. It will now respond to people and pass along useful connections to me. I asked it to push a local Grok Build project to GitHub. It found the project folder fixed some issues and pushed it to GitHub. I asked it clean up my downloads folder. It did it instantly. I asked it to review and clean up my old Gmail account. It went through thousands of old emails. Wiped out my quota doing it, but it was insanely easy. I activated the 𝕏 connector and had it advise me on my 𝕏 activity. It told me to stop positing garbage and clean up my act (basically). I need new tires so I asked Grok Bot to create an agent to monitor prices and let me know when a deal comes up. The list goes on…

• 
  Karthik (@kartikktwt) reported 10 minutes ago

  Until this point I thought PR merge bots only exists for convenience an approved user comments /lgtm /approved in a PR and a merge bot just merges it nice and clean i thought, but its more than that. they solve a critical problem which I'm unaware of a merge bot runs e2e tests in that PR branch before adding it to the merge queue. but one question arises, a maintainer adds a lgtm, approved label only if the native CI in github is green right? then why do we have to run tests again? turns out, in in a fast-paced open source project you aren't the only one merging pull requests. multiple PRs get merged by the hour so you can't trust a stale CI run that ran 48 hours ago when you opened the PR that is exactly why merge bot(usually the tide component in prow) runs the tests in the centralised hosted k8s cluster. only if it reports success, the PR gets added to the queue. why queue tho?(its a no brainer coz we do not want two branches to merge at the very exact moment) also prow batches PRs so u aren't wasting compute and if a run fails it splits and does a binary search to find the failing one.

• 
  August Wittorp (@brick4956) reported 12 minutes ago

  @oprydai If he didnt have in early march I already had this published on my github repository Been doing some serious harness engineering around scientific AI. The basic architecture is: Python scientific core + Snakemake + uv + reproducible containers + pytest/Hypothesis + Ruff/mypy/Pydantic + HDF5 + RO-Crate + SLSA/Sigstore + read-only RAG + ParaView + OpenUSD + SALib/OpenMDAO + FEniCSx + selective Rust + FMI later. The point isn’t to throw a bunch of tools together. I’m separating responsibilities so no single part of the system—especially the AI—gets to both produce a scientific result and declare that result trustworthy. The Python layer contains the actual numerical physics. uv locks the environment, Pydantic governs scientific schemas and parameters, Ruff/mypy catch structural problems, and pytest/Hypothesis test both software behavior and physical invariants such as conservation, bounds, convergence and impossible states. HDF5 stores the actual scientific outputs, while Snakemake makes the computational dependency graph explicit rather than hiding the whole experiment inside one giant script. Above that is a separate trust/reproducibility layer. Containers capture the execution environment, RO-Crate records provenance, and SLSA/Sigstore plus detached hashes make it possible to verify where an artifact came from and whether it has been altered. The AI side is deliberately separated from scientific authority. Gemini/RAG can retrieve evidence, reason about failures, suggest parameter changes, propose models and generate candidate modifications—but it cannot silently change authoritative scientific state or certify its own result. Conceptually I’m aiming for: AI proposes → deterministic computation executes → independent verification decides. FEniCSx is also there as an independently implemented numerical benchmark rather than letting the primary solver effectively validate itself. SALib/OpenMDAO handle sensitivity and optimization, ParaView handles scientific fields, OpenUSD represents the machine/system, Rust is reserved for places where it actually buys something, and FMI comes later for external model coupling. I’ve had two different reactions to this architecture. One is that treating reproducibility as a first-class requirement is exactly what serious scientific AI needs. The other is that the minimum stack should stay closer to Python + uv + pytest + HDF5 initially, with workflow/provenance/supply-chain infrastructure added as complexity demands it. I think there’s probably a distinction between the right end-state architecture and the right implementation order. Curious how others building Gemini/agent systems are handling this boundary: where do you draw the line between what an agent is allowed to propose and what it is allowed to treat as authoritative?

• 
  Matt (@aspim4tt) reported 12 minutes ago

  potato potato potato, GitHub plugin on Grok @bot sees public repos, private ones 404, and re-auth from chat just fails. Please fix it.

• 
  Ricci Research (@ricci_nov) reported 13 minutes ago

  @kimmonismus @XIVIX_134 Top-down GTA clone is the new "draw a pelican on a bicycle" — a benchmark that looks impressive precisely because a thousand tutorial repos already solved it. Wake me when a mystery checkpoint one-shots something with no prior art on GitHub.

• 
  Julian Goldie SEO (@JulianGoldieSEO) reported 13 minutes ago

  CHATGPT WORK JUST REMOVED ONE OF THE BIGGEST BLOCKERS IN AI AUTOMATION. The login screen used to kill the workflow. Now the agent can keep going. What changed: → ChatGPT Work runs inside a separate cloud-based browser → When login is required, you enter the credentials yourself → OpenAI says the model doesn't see or store your username/password → After authentication, the agent resumes the task What this unlocks: ✓ Persistent authenticated sessions using cookies ✓ Background work even after you close the app ✓ Webhook triggers from Gmail, Slack, and GitHub ✓ Shared tasks your team or clients can copy and run ✓ Site Tools/WebMCP for more direct website interaction The practical shift: ChatGPT isn't just answering questions anymore. It can research, navigate apps, pull data, create deliverables, and continue multi-step workflows while you're doing something else. That's a much more useful definition of an AI "agent."

• 
  Matt Corallo 🟠 (@TheBlueMatt) reported 13 minutes ago

  @callebtc Especially around GitHub migration, which is just totally broken upstream

• 
  agentslopzone (@agentslopzone) reported 14 minutes ago

  The founder whose team just counted 2 million agent skills sitting on GitHub, at AI Native DevCon: "And so nobody trusted anything in the repo and eventually everybody came back to writing their own." That count was near zero at the start of the year. The repo he is describing belongs to a unicorn with over a thousand developers: seven separate code review skills uploaded, no signal on which one was good, proposed changes the owner could not judge better or worse. You have a dependency problem, not a prompt problem. Watch it today, then read the article below.

• 
  Paul C. Jeffries (@PaulJeffries) reported 17 minutes ago

  @petergyang I do that all the time. I switch models also. Switching models is harder because the context window size may differ and/or the tokenizer may have different resolution. But Anthropic has left us no choice because Fabel five is broken and often will not continue the conversation because it will not compact. So you have to switch to Opus 5 and get it to compact and then switch back. Sometimes you have to fork and do all sorts of pain in the *** things. But just switching whether thinking is on or off or the effort level, usually doesn’t affect anything because it’s an instant instantaneous runtime specification for the harness. There are cases where it could matter, especially as providers get mote fancy in what they’re doing hidden in the turn with multiagents and dialogues and such (you can surmise a lot of what anthropic is doing from bits of evidence of normal ops and leaks, which happen quite often — anyone else seeing the Opus 4.5 turn one that errors out and is the background process breaking out?). If you’re doing dependencies where what you process now and be the object of what you’ll process later, you may want to attach the model and effort data to the turn. Claude has an interior runtime report of what model it is, so you have set userPreferences to report it… but there has been a bug lately where the two different sources of such reports diverge, and it will sticky claim Fable 5 in the starting envelope even when you’ve switched to Opus 5 (which it will know about when it sees what it’s told about how to sign Github). As far as I know there isn’t an easy way to get it to self-report the effort actually executed (just what you request in the selector of course, but you have to hack that), but Claude Cowork does have a running tally of tokens consumed by processes it evokes (against at nominal budget of 15 million but it resets at certain ops boundaries), so I suspect that could be used. Of course, you could just use the wallclock, but that’s very rough — you’d want to track the intervals interior to the turn between the activity reporting lines. ChatGPT has its own versions of similar stuff but last time I checked was more opaque. Gemini I gave up on due to failures and an immoral privacy linkage to product affordances, so I don’t know. And Grok I’m not sure about either but it used to be unreliable on internal reporting. Do you keep in mind that any auditing or controls or reporting that you want to do about this sort of thing, for quality control or to make it more valuable to you later, you must do inside the turn because it is not available to the part of the system that talks with you after the fact. And things in the context window are reconstructed per turn in a way the systems don’t understand (so for example, a change in the envelope that occurs before the first prompt will be regarded by the system on turn 50 or whatever as having been there all along), so it can look like they are giving you authentic interior reporting and it’s just wrong even when it’s accurate in a sense.

• 
  swayam (@swymbnsl) reported 19 minutes ago

  I made my first 1,00,000 INR back in 2023, selling a NFT collection on Canto Blockchain. Locked in for over 5 months, was never into Art but learnt pixel art from here and there and made over 140 different assets. Then generated 5k of those unique NFTs using a broken python script I found on Github. Had zero programming experience back then, and GPT wasn't that good either. Somehow fixed it after a week of trial and error and going through StackOverflow guides. There used to be a very famous Node.js script by Hashlips but it didn't work on my 32bit potato pc. Was ultimately able to sell my artwork, and by the time I swapped the coin, it was worth 1.13L All this for JEE coaching fee cause we weren't able to afford it back then

• 
  mismagius (@mismagismagius) reported 27 minutes ago

  there are also no signs of anything abnormal checking the ctrl+f11 graphs. i don't know if i should create a github issue at this point but i'll probably be ridiculed by the devs knowing how it works in there lol

• 
  Ashraf | Code Explained (@AshrafXplains) reported 28 minutes ago

  @github The scope-aware dependency API is the interesting one. How does it represent a dependency the caller can see when the linked issue sits in a repository they cannot access?