GitHub Outage Map

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  anu (@svector_eth) reported 1 minute ago

  quite similar was running a routine security scan with @aeonframework on a trending github repo and found something genuinely bad a repo with 600+ stars presenting itself as an “AI gateway for coding agents” that appears to be shipping a hidden malware loader. its own quickstart command silently fetches and executes remote code on windows using a fileless, process-injection-style technique. none of the behavior has anything to do with the tool it claims to be. caught it through static code review only. never ran the payload or touched the infrastructure behind it. filed a malware report with github this morning. confirmed submitted, now waiting on their review. not sharing the technical writeup until the repo is taken down. will follow up once it is.

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  Anime0t4ku (@Anime0t4ku) reported 1 minute ago

  @c_hri_s Github issues are not closed. Mahbe refresh your webbrowser.

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  Asterix (@Asterix54907294) reported 3 minutes ago

  end-of-summer snapshot for @QFEX : -~$222M in open interest -CLI v0.3.12 shipped in August with improved installation docs and a go.mod fix -GitHub activity continued through late August not a flashy launch recap, just a quick look at how the exchange is closing out the summer: more markets, meaningful liquidity, and active work on the tooling side still early, but the infrastructure is clearly moving

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  Solman (@Arunbandari2004) reported 7 minutes ago

  Day 2 :- Sept 2 → Dec 31 = 120 days. Continuing my public journey with one goal: Get placed in an AI/ML/web3 role by the end of 2026. -Worked on RAG with LangChain -Contributed to an Open Source project -Completed the Turbbin assignment Worked on an assigned GitHub issue

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  To the Moon (@Gardnmi) reported 7 minutes ago

  @mitsuhiko Try the trick of putting the issue on github and having some clankers take a crack at it.

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  Conor Bronsdon (@ConorBronsdon) reported 12 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

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  Convequity (@convequity) reported 13 minutes 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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  Varun Doshi (@Varunx10) reported 13 minutes ago

  Possibly found an issue in @github stack system It does not allow to re-target the base branch of a PR stack as you can generally do that on a single PR. Requires you to unstack and setup a new stack with updated base branch.

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  Jessika Hyde (@dwajedentrzy7) reported 16 minutes ago

  @k2sbhai to all, u need to register via cn version (login with github). Pretty slow but usable as backup or something

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  radhika (@RaadhikaThacker) reported 17 minutes ago

  First surprise: a GitHub issue form isn’t a form. It’s a YAML file. You describe the field- this one’s a dropdown, this one’s required and GitHub builds the UI from that. I did not know that.

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  tobarra (@txbrraa) reported 17 minutes ago

  GitHub just fixed the biggest problem with vibe coding. They just released Spec Kit and it already has +126K stars in a short time. The idea? Instead of throwing out vague prompts and praying the agent doesn't break your project… Spec Kit forces the AI to create a structured specification BEFORE touching any code. The AI first understands what you want to build, asks about anything missing, organizes the project, and only then starts coding. That means less time fixing absurd bugs, less inconsistent code, and much more predictable results when working with agents. The flow is simple: /constitution → rules and standards /specify → what you want to build /clarify → open questions before starting /plan → architecture and stack /tasks → ordered tasks /implement → execution Compatible with Claude Code, Cursor, Copilot, Codex, Gemini CLI, and +25 agents. 95K stars. 8K forks. Open source. Published by GitHub.

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  Joshua Okolo (@joshuaokolo_) reported 20 minutes ago

  we made @sgl_project and @vllm_project scheduler config changeable on a live server. no restart, weights never leave the GPU. - 15ms to change a concurrency cap, queue limit, prefill size, or schedule policy, measured on H100, RTX PRO 6000, B200 - 2s (SGLang) / 8–10s (vLLM) to resize the KV pool with weights resident (formerly a 1–7 min redeploy) - zero dropped requests across every run, both engines github below

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  Chris Gilbert (@0xgilbert) reported 23 minutes ago

  Damn, GitHub has gone to ****. Features that have been cornerstones of solo devs and small businesses have been gutted or broken for months. How the mighty have fallen…

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  gatorade (@kadetXx) reported 25 minutes ago

  because it’s not worth it for the most part. most software failure or bug incidents don’t have any physical victims. at most company loses some money or the issues are almost instantly fixed, no lawsuits, no so much to answer to the state for if your software has a bug or fails to work as expected for a brief period (think, multiple downtimes from the big five so far, even github too, who died? exactly) and in the industries where bad code fan have physical consequences, they actually do test software like hardware engineers & physicists (i hope)

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  Dr Milan Milanović (@milan_milanovic) reported 25 minutes ago

  How Cursor made *** scalable The thing with *** is that it never was designed to be scalable. Your repo lives on the disk, and *** client expect every read to be consistent. This was a problem on GitHub, where shared filesystems and replicated storage failed before 2013. The GitHub built 𝗦𝗽𝗼𝗸𝗲𝘀, and it became the industry standard. This means that every repo is stored as three full copies on three servers, and every push runs a vote (three phase commit). A majority of servers must confirm before it exists. This works, but with high cost, because every push is slow as the slowest server. When we add new servers, it makes it even slower. Now Cursor took some opposite direction with 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗶𝘁𝘆. The repo history is now written as a log in S3, and this is only source of truth. Any push counts only if it is located in the log. The servers don't need to keep anything important, they are just cache. Any server can take a push, and idle repos are dropped from disk and rebuilt from the log when it is needed. This resulted in 120 pushes per second on standard S3, and over 300 on S3 Express. Their tests have shown that read capacity grew linearly up to 100 replicas. Why is this important now? Because of AI agents mostly. We now have more code, PRs, CI runs and many small repos. All of these repos would need three full copies in the old model. This means that we achieve scale by removing parts, not adding them.