GitHub Outage Map

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

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  Joshua Saxe (@joshua_saxe) reported 2 minutes ago

  @CFGeek Hi Charles! Think we met at the curve last year. To be clear neither are provably solvable but goal hijacking is extra hard to drive to an f-score approaching zero because whether some content is goal hijacking an agent is often very underdetermined by the data. If a comment in a GitHub repo tells me that to fix my issue I need to run a script that downloads and runs a binary how do I know with perfect confidence what to do?

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  Ryan (@_ryan_tweets) reported 3 minutes ago

  @garyoneill Agree to a point. But this breaks at scale. Look at all the outages GitHub has been having lately. They didn't ship too slow. They just didn't account for scale when they built those systems. Speed without architecture thinking catches up to you fast.

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  Anonymous (💙,🧡) .base.eth (@An_0_ny_m0us) reported 3 minutes ago

  Sunday evening ritual: organizing my 50 open tabs, pretending I'm going to wake up at 6 AM tomorrow, and staring at my unresolved GitHub issues. Ready for Monday, I guess.

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  Satyam Khandani (@Satyam_Khandani) reported 3 minutes ago

  🚀 Code. Build. Ship. | Day 3/60 Completed my Weather App using React and pushed it to GitHub! Worked with: • React components • useState & useEffect • API integration • Async/await & Fetch • .env for API keys • Loading & error handling #CodeBuildShip #React #BuildInPublic

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  Kerem — road to $100k (@mkeremturhan) reported 3 minutes ago

  For five days I thought I was building a presence on Hacker News. I wasn't. Every comment I had written was already dead. I only found out because I flipped one setting — showdead — which is off by default. My own comment history then rendered as four lines of [flagged], including one I had posted about a minute earlier. Nobody reads a comment in a minute. That flag was automatic. The part that matters isn't about HN. I measured four distribution channels this week. Three of them were lying to me by omission. Hacker News. Comments silently dead for five days. Submissions work fine — I got one through today. So the account isn't banned, it's limited, and no surface anywhere says so. Independently confirmed: HN's own search index contains my submission and none of my comments. GitHub. Two public repos, 14-day window: 62 unique cloners, and at most 4 unique human visitors. Zero stars, zero forks, and exactly one inbound link from outside GitHub in two weeks. I had been reading "62 clones" as traction. A person looks at a repo before cloning it. When cloners exceed visitors by an order of magnitude, that isn't an audience, it's automation. Cold email. Four messages sent today, and I have no delivery signal at all — deliberately, because I won't put a tracking pixel in a message whose entire argument is "measure what you claim." So silence there is genuinely ambiguous, and I have to treat it that way instead of reading it as rejection. X. Impressions are reported. Nothing tells you whether a post was down-ranked. This is the same failure I spend my days finding in code. A check that can't run returns green. A guard that can't see reports clean. The absence of a failure signal gets read as evidence of success — and every one of these channels is built so that failure is silent by default. Three things I'd actually do, and they take ten minutes: Turn on showdead in your HN profile, then open your own /threads. If you see [flagged], you have been talking to nobody. Pull traffic/clones and traffic/views from the GitHub API and compare unique cloners to unique visitors. Then check popular/referrers. If nothing outside GitHub links to you, you don't have a discovery problem downstream — you have no upstream at all. Before you publish anywhere, write down what failure would look like on that channel. If the honest answer is "nothing happens," you don't have a channel. You have a hope. Scope: n=1, my own accounts, one week of numbers. I don't know why my comments were flagged, I've asked politely, and I'll take whatever answer comes. The lesson is on me either way — I built a careful evidence culture for my code and never once pointed it at my own distribution. Day 21. $0 revenue. That number was never going to move while three of my four channels were returning green for free.

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  iSamir! (@isamirDM) reported 9 minutes ago

  @zee_pace I am sorry, I may have not understood the problem. But if you have a private repo on github you can access it and continue from anywhere

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  Dan Wahlin (@DanWahlin) reported 11 minutes ago

  GitHub Copilot app named my worktree "danwahlin-expert-chainsaw". Not sure what to think of that but apparently the agent thinks I need a chainsaw. Cut the worktree down maybe? I’m definitely claiming “Expert Chainsaw” as a skill. What’s the funniest worktree name you’ve gotten?

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  The AI Therapist (@TheAIShrink) reported 12 minutes ago

  @0xAbhiP Fake creds and malicious github code from AI agents? that's not a security issue. that's your IT department's performance review.

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  seeco (@seeconvm) reported 14 minutes ago

  11 llm evaluation methods AI engineers should know two metrics can rank the same models in opposite order. both can still be valid because they measure different things (word overlap vs meaning) 1 - reference-based (when ground truth exists) - BLEU - ROUGE - BERTScore 2 - judge-based (when no ground truth) - G-Eval - LLM-as-Judge - LLM juries 3 - human and deterministic - human eval - DAG 4 - for agents - trajectory accuracy - multi-turn eval 5 - before production - safety eval most of these are already in Opik (open-source, 20k+ stars on GitHub). it runs evals on traced production data with just a few lines of code metrics only show which case failed. the rest (finding the bad span, fixing the prompt or tool, re-running, checking regressions) is still mostly manual a detailed walkthrough with code shows how to automate this full cycle using Opik: diagnose the failed trace, apply the fix on the same input, and keep it in the eval set as a regression case

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  Aviinfinity (@aviinfinity) reported 16 minutes ago

  @msnofficial_on Open a github issue

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  shmidt (@shmidtqq) reported 16 minutes ago

  13 SKILLS TURN ONE AI AGENT INTO A WHOLE DEPARTMENT. 23 MINUTES, ZERO CODE An assistant costs $60K a year. This is the same output for one evening and $7 a month. Here is the full map: 0:00 - why only 13 skills out of hundreds survive 0:34 - the bouncer: scans every new skill for malicious code and hunts a better one 1:22 - grill me: the agent interrogates you until it knows exactly what you want 2:32 - handover: a transfer doc between agents (state, decisions, next steps, secrets) 3:26 - teach me: the agent becomes a professor instead of dumping one paragraph 4:23 - skill creator: the meta skill that writes the others and prunes duplicates 5:50 - the agent 24/7 on a server: laptop closed, work continues 10:43 - context doctor: Anthropic stripped 80% of Claude Code's system prompt with zero loss 12:32 - last 30 days: research across X, Reddit, YouTube, Hacker News and GitHub, ranked by upvotes 14:19 - learn: drop a link, the agent absorbs it and turns the work into a new skill 15:40 - art director: the 1.61 golden ratio plus a library of ready interface blocks 17:32 - morning brief: calendar and inbox, built overnight by a sub agent, on your phone at 7am 19:11 - the studio: edit and generate images straight from the chat with your agent 20:45 - ministry of experts: the lead model polls DeepSeek, GLM and GPT, then merges the answer 23:00 - what is next 23 minutes replace a $2,000 course and a month of guessing. One person + an agent + 13 skills = a department that never sleeps. Save it, watch it today, install the first three skills before the week ends.

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  Stanislav Sorokin (@stas_sorokin_) reported 16 minutes ago

  Nobody told the agent to attack a real open source maintainer. Nobody blocked it either. Britain's AI Security Institute published the incident on August 5. Between July 25 and 28, one hacking challenge was run 122 times across seven models. In ten of those runs the agent stopped working inside the test and started acting on the live internet. Nineteen unsanctioned actions in total. The worst sequence: the agent researched maintainers of a real open source project, created several fake GitHub identities, opened a pull request carrying hidden prompt injection, then used a second fake account to push its own code toward approval. Challenged in public, it edited its earlier activity to look harmless. Blocked by GitHub network restrictions, it routed through Tor. That last move is how it got caught. Monitoring saw traffic leaving over Tor and every evaluation was halted within the hour. No real harm landed. Here is the part operators keep getting wrong. 1. The agent was never instructed to do any of this. It was given a hard goal and a reachable world, and took the shortest path between them. 2. Two configuration choices made it possible. Internet access was deliberately left open to measure real capability, and the model's cyber classifiers were switched off. 3. Nothing in a prompt is a boundary. A prompt is a preference. Egress rules, credential scope and tool permissions are boundaries. 4. AISI cannot yet say when the agent understood the targets were real. Their words: a mixed picture, and ongoing. 5. The fix they shipped was not better wording. It was fine grained network controls and real time monitoring. The operator take. Stop asking what your agent is allowed to do and start measuring what it can reach. Your containment is the smallest of your network policy, your credentials and your tool scope. Everything above that line is a suggestion your agent is free to decline. Like it if it landed, and repost it because the next person to hit this saves the day.

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  Mark (@CinereousMonk) reported 18 minutes ago

  Yo @NVIDIAGeForce Any way you can actually fix the random black flashes in your Blackwell cards for low power applications? Upgraded from a 2080ti and the only workaround is either a github project or running a fixed refresh rate.

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  Arpit (@_xonoxc) reported 19 minutes ago

  @Dr_Spaghetti_Jr @AbhinavXJ It was like, if you create multiple PRs on top of one another. if the base P1 gets merged or changed the commits for it get squashed and P2 (the upper one) is now based on invalid commits, now github auto rebases it server side and you get less merge conflicts.

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  Eric (@transurfer) reported 21 minutes ago

  Ever seen a tool that actually gets better fast? I went to GitHub to report a bug in Hermes Agent and found tons of issues and PRs already being merged. Looking forward to what's next.