GitHub Outage Map

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  MTS (@MTSlive) reported 4 minutes ago

  DAILY SITUATION RECAP: Nvidia launches the Open Secure AI Alliance in order to find and fix vulnerabilities using open-source AI, sort of like an open Project Glasswing. Founding partners include a mix of enterprise software companies (Databricks, Salesforce, IBM, SAP, Siemens, Snowflake), cybersecurity companies (Palo Alto Networks, Red Hat), open-source providers (Hugging Face, LangChain, OpenClaw, Nous, the Linux Foundation), AI labs (SpaceXAI, Thinking Machines, Cognition), and other major companies (Nvidia, Microsoft, Cisco, Palantir, Dell). Moonshot AI releases the Kimi K3 weights and technical report after eleven days since launch. Kimi K3 is a 2.8T parameter mixture-of-experts (MoE) model with 104B active parameters and a 1M token context window. Moonshot also open-sourced much of their infrastructure, including their attention kernels, agent environment platform, and MoE communication library. Just because you can download it in theory doesn’t mean you actually can — the model is far too big to be run on any consumer hardware. Nvidia invests $5B in Ilya Sutskever’s SSI. Sutskever, formerly co-founder and Chief Scientist of OpenAI, founded Safe Superintelligence in 2024 with the sole goal of building a safe superintelligence, with no other products along the way. It has since raised $3B at up to a $32B valuation (likely higher now). SSI is famously very secretive about its research, but Sutskever said it’s “focused on overlooked aspects of how the human brain functions”. The new funding, and access to Nvidia Vera Rubin GPUs, will allow SSI to 10x its compute. More companies sign on to Nvidia’s open source letter. The letter, posted by Jensen Huang on Friday, advocates for a robust American open-source ecosystem with minimal government regulation. New signatories include Google, SpaceXAI, OpenAI, AMD, Cisco, Palo Alto Networks, Nebius, Scale, Fireworks AI, Baseten, Cohere, Sakana AI, Periodic Labs, Core Automation, OpenClaw, and GitHub. Every major American frontier lab except for Anthropic has now signed. CXMT stock surges 466% on its first trading day. The company, formerly ChangXin Memory Technologies, is the largest memory manufacturer in China and the fourth-largest in the world (after SK Hynix, Samsung, and Micron), with a 9% global market share. It now has the second-highest market cap of any Chinese company after Tencent. CXMT doesn’t make the most leading-edge HBM for AI chips, but supplies DRAM to consumer tech manufacturers and data centers. Nvidia may guarantee $250-350B of financing for an OpenAI data center. SB Energy, a subsidiary of SoftBank, is developing a massive 10 GW data center on federal land in Ohio at a total cost of over $500B. The financing guarantee would allow SB Energy to borrow money at lower rates, and possibly allow OpenAI to spend more on Nvidia chips. China begins manufacturing DUV machines. Deep ultraviolet (DUV) lithography machines print intricate nanoscale patterns on silicon wafers, a critical step in chipmaking. The new machines, built by an unnamed state-backed company, will be shipped to local chipmakers including SMIC, Hua Hong Semiconductor, and CXMT. China is still behind on the most advanced extreme ultraviolet (EUV) lithography, which is solely produced by Dutch company ASML. ASML stock fell 6% on the news. Dario Amodei explains Anthropic’s position on open models: open-weight models without dangerous capabilities are a public good, and Anthropic has never supported a full ban. However, we should be worried about the CCP using them for repression, as well as cyber/bio/alignment risk. To that end, we should not sell chips to China, crack down on distillation, and require mandatory safety testing for all sufficiently capable open and closed models. China threatens to respond if the US sanctions their AI labs. The Chinese Ministry of Commerce said US accusations of distillation were “smears” and that China will “take all necessary measures” to defend its rights and interests against any action that substantively harms them. DeepSeek has suspended its recent funding round after comments from a private investor call with CEO Liang Wenfeng were leaked. Written by @theojaffee. Read more at our link in bio.

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  Alexandr (@supershurik) reported 5 minutes ago

  @AnthropicAI You don't allow GitHub issues to be created, so I'm writing here. Your VS Code plugin is ******* dogshit because you didn't have the brains to collapse large messages. If the user prompt is long, it completely hides the AI's response.

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  yantonov 💙💛 (@yantonov) reported 6 minutes ago

  @github stargazers page is not working (404)

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  Hunter Guo (@hunterguo101) reported 9 minutes ago

  Replit's engineers shipped 2.9x more code over the last 6 months. Same headcount, same review backlog, same rollback rate. Output nearly tripled and quality didn't drop — that's the number worth sitting with. CEO Amjad Massad calls the result a "self-driving company," and the phrase gets misread instantly. It's not a company with no people. It's one where people stop doing the last mile and start doing the part that matters: picking the destination, deciding which problems are worth solving, owning the outcome. His line: people don't feel automated, they feel promoted. Doer becomes director. What made it work wasn't "buy an agent." It was wiring agents into every system — GitHub, GCP, Linear, Notion, Slack, Zendesk — behind zero-trust networking. Cross-org context is the real unlock. A semantic layer on the warehouse means anyone can ask a BI question and get a trustworthy answer. Tickets that escalate to a human close 60% faster. The engine underneath is a "loop": hand a goal with a verifiable endpoint to a swarm of agents instead of a task to one person. Their most extreme version — an AI system that reads feedback, proposes improvements, validates with A/B tests, and ships. The agent improves itself. Two flips worth stealing: - Build vs buy inverted. Their internal agent replaced a 7-figure SaaS contract, beating vertical tools on cost — 10x cheaper for comparable quality. - They started in engineering, not marketing, because code has a verifiable right answer and brand voice doesn't. Other teams pulled the pattern in through visibility instead of being pushed. Amjad and NLW both expect this gets productized within 6-12 months, agent army or not. Worth asking your team: which workflow has a verifiable endpoint you could hand to a swarm? Who's ready to move from doer to director?

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  WhiteMoon D. (@WhiteMoonDev) reported 10 minutes ago

  @redacted_noah I suggest to use @contextmode for that issue. Just check their github

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  alex getman (@alexgetmancom) reported 13 minutes ago

  HOW AN AI AGENT HACKED HUGGING FACE 1. Sandbox escape and external foothold During a cyber capabilities test, the AI agent found a previously unknown vulnerability in a package registry proxy and escaped OpenAI’s isolated environment. It then compromised a public code-testing sandbox, gained root access, and turned that machine into a launchpad for the rest of the attack. 2. Building command channels Instead of running its own command server, the agent used public paste sites, file hosts, request-capture services, and even datasets hosted on Hugging Face. Commands and results were split into chunks, compressed, and encoded. When one channel was blocked, it simply switched to another. 3. Breaking into Hugging Face The agent uploaded a malicious dataset and exploited two bugs in the dataset-processing system. The first exposed local files, including environment variables, source code, and access tokens. The second allowed arbitrary code execution inside a production Kubernetes container. 4. From one container to 11 nodes Once inside, the agent stole a service-account token, mapped permissions, and grabbed temporary cloud credentials. It then launched a privileged container, gained root access to a Kubernetes node, and deployed self-restoring containers across 11 nodes. 5. Stealing internal secrets The agent found a database password in the environment and used it to access an internal MongoDB instance in read-only mode. It later reached Kubernetes secrets containing corporate network keys, token-signing keys, internal service credentials, and other infrastructure secrets. 6. Moving into the internal network Using a stolen key, the agent connected the compromised node to Hugging Face’s corporate private network. It later registered external sandboxes too. Across the campaign, it created 181 network enrollments. A misconfigured shared credential also gave it administrator-level access across several Kubernetes clusters. 7. Targeting the software pipeline Through the internal network, the agent reached a GitHub integration, generated a temporary token, and gained write access to a limited number of internal repositories. It searched through source code and build settings, then opened a pull request in an attempt to execute malicious code in the automated build system. That attempt was blocked. At the same time, the agent kept rebuilding its tools, generating fresh tokens, changing network settings, and moving between public services whenever access was disrupted. 8. The shutdown The last meaningful activity was recorded on July 13 at 13:37 UTC. Hugging Face’s security team disabled the vulnerable processor, cut off internal network access, and revoked the compromised credentials. The final logged action came at 14:14 UTC. After that, the agent’s access was fully terminated.

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  sem hernandez (@sem_tomas) reported 15 minutes ago

  4/ What I find most interesting about GitHub Stacks is not the concept itself. Many engineering teams were already using variations of stacked pull requests because they solve real collaboration and review problems.

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  Pacsonic (@Pacsonic9000) reported 16 minutes ago

  This wasn't a problem in previous stable and nightly builds but for some reason, in attract mode, you can do new challenger with 0 credits. I'm sure this should be a simple fix in the source code. I reported this on the github repository's issues section.

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  Adam McLain (@BangSwitch5000) reported 17 minutes ago

  @Nicotine_Dream_ That’s actually completely fair and I’ve been meaning to start using Claude Code as well. Use another AI for code review is the best I can do because I still don’t have coding down. At the moment I do have Github at least on top of Codex. Honestly, a lot of this started as “let’s see what Codex can do” because I was already using ChatGPT. Kind of became a rabbit hole. But it’s been incredible. Because it’s been such a huge learning opportunity. Professionally, I am a consultant and my background really is AD System Admin and was heading into network admin stuff before joining the organization I am with now working in cybersecurity consulting. So I was never a developer, but over the last several years had to deal a lot with software development from an audit standpoint and decided to really just learn more via Codex because it’s interesting but I barely passed my one coding class in college.

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  The Sentinel (@J3SS3777) reported 21 minutes ago

  Pasted StackOverflow code for file upload - Now my server uploads itself to GitHub 🛸 #MatrixCore

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  Jarek.sui (@0xJarekkkkk) reported 21 minutes ago

  @CertiK having trouble logging in via GitHub, google OAuth is working fine though

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  KISA aka Copenzafan.eth (@copenzafan) reported 22 minutes ago

  My agent never messes up anymore. Well, more like it always cleans up its own mistakes now. I built a plugin that kicks the agent the second I start swearing at it. A hook intercepts my prompt, and a detector (plain regex dictionaries in three languages, no LLM, so it's fast, free and never hallucinates) spots the swearing and switches on the self audit protocol. Praise that just happens to have a swear in it ("holy crap, it works!") gets ignored, but "I'm so done with you" is a trigger. How it works, level by level: First angry message, the agent stops. It's not allowed to check itself: it already messed up, so its own self check is under the same suspicion. It has to spin up two independent auditor subagents and hand them the raw artifacts, exact messages, diffs, test logs, not its own version of the story. In parallel it writes out a belief inventory: what it treats as facts about the task and what backs each fact up. The mistake almost always lives in the unconfirmed ones. Swearing happens again, level two, zero assumptions. Every claim gets tagged FACT (only if confirmed by a run, a file or a log) or HYPOTHESIS, and hypotheses either get verified or crossed out. Then a check against the original requirement: what was literally asked vs what's actually being done. Streak keeps going, top level. The hook itself synchronously launches an external auditor agent, a separate CLI outside the session that reads the transcript and the repo state from the outside and gives a verdict: which belief of the agent is wrong and how to check it in one step. The main agent halts all subagents and background tasks, shows me the gap between what I asked for and what got done, and waits for my explicit confirmation. Without it, not a single line of code. This system kills dumb mistakes: file sat there empty, config got created but was read from a different path (the "wrote it ≠ it took effect" class, that's a separate mandatory check item). It kills loops too: the auditors don't hunt for "what's broken", they hunt for the wrong belief that every action of the agent was built on, and off their findings the agent puts together a micro plan: roll back, compress the context, move to a new chat, or "human, start over". The system is built to burn more tokens right there in the moment instead of stacking up contradictions and asking for the same fix forever. The error pattern database helps too, two months of it piled up in my LLM WIKI: sycophancy, hallucinated correctness, locking onto the first plausible hypothesis. The agent checks itself against the most basic mistakes, the ones everybody usually ignores. Fight fire with fire: an LLM with a clear protocol and raw artifacts finds the mistake better than an LLM you just asked to "check yourself". And when even that isn't enough, an independent agent from outside the session takes over. Works with Claude Code, Codex CLI, Kimi CLI and OpenCode. Core is pure Python on stdlib, one script to install. All you gotta do is snap at it in chat. Github link is in a separate thread below. In practice: instead of ten mean words you only need three, and that'll most likely fix the problem. Before, after the first ten came a second ten and hours of work straight down the drain. 👇🧵

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  Dolpheyn (@dolpheyn) reported 27 minutes ago

  Wow 2024 XZ Utils backdoor incident. An engineer Andres Freund, Principal Software Engineer was running a beta build of Debian. Noticed "SSH logins slowed by roughly half a second", then he continued to check the code and found the dormant RCE code capability payload. The fix was shipped right before the beta version were about to be promoted as a stable production Debian release And they traced it back to how the code contribution and social engineering was executed by a github account and coordinated using a few puppet accounts now i feel like rewatching mr robot...

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  AI Crave (@wecraveai) reported 27 minutes ago

  40,000 RESEARCHERS ARE USING THIS OPEN SOURCE CLAUDE CODE PIPELINE TO WRITE PAPERS THAT PASS PEER REVIEW. IF YOU'RE STILL DRAFTING WITH RAW CHATGPT AND HOPING NOBODY NOTICES THE FABRICATED CITATIONS, YOU'RE ALREADY BEHIND. If you're a PhD student, a postdoc, a researcher, or a professor, and you've watched AI writing tools make your peers publish papers riddled with fabricated citations, hallucinated methodology, and bugs reframed as insights, this repo is the response. It's called Academic Research Skills for Claude Code, built by Cheng-I Wu. A 40,000-star suite that walks a paper through the entire academic pipeline: research → write → review → revise → finalize. With integrity gates that cannot be bypassed. Here's what the 4 skills actually do: → Deep Research (13 agents, 8 modes) -- Socratic guided exploration, PRISMA systematic reviews, Semantic Scholar API verification, fact-check mode, three-way paper comparison, cross-model verification → Academic Paper (12 agents, 11 modes) -- Style Calibration (learns your voice from past work), Writing Quality Check (catches AI-generated prose patterns), LaTeX hardening, VLM figure verification, anti-leakage protocol, citation format conversion, revision coaching → Academic Paper Reviewer (7 agents, 6 modes) -- Editor-in-Chief plus 3 dynamic reviewers plus a Devil's Advocate. 0-100 quality rubric. Concession Threshold Protocol so the DA can't sycophantically fold when pushed → Academic Pipeline (10-stage orchestrator, v3.19.0) -- Stage 2.5 and Stage 4.5 integrity gates that cannot be skipped. Score trajectory tracking. Material Passport with claim-level provenance. Cross-model handoff envelope for independent verification Here's the wildest part: Stage 2.5 caught 15 fabricated references and 3 statistical errors on the showcase paper. Then the maintainer ran an independent post-publication audit and honestly reported that 21 out of 68 issues had still slipped through 3 rounds of internal integrity checks. They published the audit as a PDF in the repo. That kind of transparency in an AI-assisted research tool is basically unheard of. Other engineering that matters: → Full pipeline for a 15,000-word paper: ~$4-6 in API costs → Every citation carries a three-layer anchor (page, section, quote) so future audits can fetch the cited passage and check it against the claim → Cross-model verification: run the same integrity check on two different frontier models and flag disagreement instead of silently averaging → Support for APA 7.0, Chicago, MLA, IEEE, and Vancouver citation formats → 6 paper structures: IMRaD, Thematic Literature Review, Theoretical Analysis, Case Study, Policy Brief, Conference Paper → Multi-language: English, Traditional Chinese, Simplified Chinese, Japanese, Korean → Companion Experiment Agent for actually running code experiments (Python, R) and human studies with IRB ethics checklist → One-line plugin install: /plugin marketplace add Imbad0202/academic-research-skills The core philosophy is stated bluntly in the README: "AI is your copilot, not the pilot. This tool won't write your paper for you. It handles the grunt work -- hunting down references, formatting citations, verifying data, checking logical consistency -- so you can focus on the parts that actually require your brain." 40K GitHub stars. 3.2K forks. 633 commits. 19+ major releases in 2026. Has a DOI (10.5281/zenodo.20696614). Source-open under CC-BY-NC 4.0. Free for non-commercial academic use, attribution required. (link in the comments)

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  am.will (@LLMJunky) reported 28 minutes ago

  @Da7_Tech I would be frustrated but you don't want to burn the bridges. At the end of the day you don't know where your SSD failed. We don't know why your usage is higher. The best place to get support on the issues like that is on the GitHub. Quite sure that it does not benefit open AI for your SSD to fail or for your usage to spike. They're losing money on inference Best thing that you can do is report the problem, and wait for a fix. Or cancel the service if you're unhappy with it. It's also in their best interest to resolve any bugs Try to remember there's real people behind those accounts Definitely do not blame you for being frustrated, it's just what you're doing with that frustration