GitHub Outage Map

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  DefiantAsUsual (@DefiantAsUsual) reported 3 minutes ago

  @furgotti @mega_strimp Ah yes, the companies with good data security. Like the massive corporations that get hacked and have leaks quite frequently in this day and age? Even Microsoft had major hacker issues a few days ago that affected GitHub and involved malware distribution.

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  Upwind Security MDR (@UpwindMDR) reported 4 minutes ago

  🚨Critical - Apache CXF JNDI Injection in JMSConfigFactory (CVE-2026-50632) This is yet another incomplete-fix follow-up in the Apache CXF JMS RCE saga (after CVE-2025-48913 and CVE-2026-44417). If an application lets untrusted users configure JMS settings for CXF, an attacker can supply a malicious JNDI lookup URL through JMSConfigFactory and trigger remote code execution. The risk only applies where JMS configuration is exposed to untrusted input, but where it is, the impact is full code execution on the server. Note GitHub rates this CVSS 9.8 while Apache's own advisory rates it moderate. 👉Upgrade to Apache CXF 4.2.2 or 4.1.7.

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  aichina.news (@AiChinaNews) reported 5 minutes ago

  The story of this cycle is practical engineering over parameter bloat. While Western attention defaults to Hugging Face, Alibaba's ModelScope platform continues to ship highly capable open-weight foundations. The standout release is Qwen3.6-35B-A3B, a multimodal Mixture-of-Experts model aimed directly at the autonomous agent space. It houses 35 billion parameters but activates just 3 billion during inference, keeping compute costs in check while retaining heavy-duty reasoning. More importantly, it integrates native "Thinking Preservation"—forcing the model to deliberate internally before committing to an output. This isn't for generating isolated snippets; it is explicitly engineered for repository-level software development. Meanwhile, the Chinese open-source community is aggressively filling the workflow gaps left by Western AI giants. A flurry of updates hit GitHub this week for the localised Claude Desktop client, pushing it to version 1.6.26. What began as a simple language patch has evolved into a full-scale project console. The community has bundled a Windows runtime to drastically lower the setup barrier for Anthropic's "Computer Use" capabilities in China. They didn't stop at API access—the client now features Kanban boards, local *** integration, IDE-style multi-tab workspaces, and multi-agent task orchestration. This is what happens when developers tire of waiting for official enterprise tools and build the scaffolding themselves. Hardware reality continues to dictate software deployment in the domestic market. Eco-Tech released highly optimised, production-ready versions of Zhipu AI's GLM-5.1 specifically tailored for Huawei Ascend NPUs. Available in W4A8 and W8A8 quantization, this is actual engineering substance. Rather than chasing theoretical benchmark supremacy, these releases are built for high-throughput inference, solving the memory overhead bottlenecks required to run heavy models on domestic data centre and edge hardware. The rest of the cycle's open-source radar is clogged with automated filler. Projects like SpecFusion, ZLabs-RoundPix-12px, and a dizzying number of game localisation patches pushed updates where the public summaries literally contain unrendered placeholder variables like '{release_date}' and '{explanation}'. If a team cannot be bothered to fill out their own PR templates, no working professional should be bothered to review their code. Elsewhere, YiMu-Subtitle-Translator pushed a minor update for AI video localisation that boils down to standard API configuration tweaks dressed up as a launch. The industry continues to bifurcate: teams building production-grade infrastructure for real constraints, and teams automating their own noise.

• 
  bonduelle (@bonduelleioat) reported 9 minutes ago

  How are developers building autonomous AI loops that cut API costs by 5–10x and eliminate manual prompt writing forever? Most users still interact with AI like amateurs: they write a prompt, wait for a result, manually review the code or text, fix mistakes themselves, and then write another prompt. Congratulations, you’re still “inside the loop” (human in the loop), acting as a free operator while burning thousands of dollars on tokens from the most expensive models. Meanwhile, Boris Cherny, Head of Claude Code at Anthropic, officially stated: “I no longer write prompts for Claude. My job is to build autonomous loops that manage Claude themselves.” This is called Loop Engineering - the key skill for reducing costs and achieving true automation. Instead of giving an AI a one-time instruction, you design a closed system once. You set a global objective, and the architecture handles the rest: researching context, planning steps, running a working model to complete the task, sending the output to a separate low-cost reviewer agent for strict validation, and automatically correcting mistakes in a loop until the result is ideal. The secret behind the massive savings is implementing Closed Loops with strict constraints, where you maintain full control over spending. A typical coding loop can easily consume up to 200K tokens during self-correction cycles. If you run that entire process on a premium model, your balance can disappear within days. But if you split responsibilities (for example, coding with Sonnet and reviewing with Haiku) and store knowledge in memory files such as VISION.md or ARCHITECTURE.md, the system can perform the same work for a fraction of the cost while operating completely autonomously. To build this kind of pipeline, you need six core components: - trigger automation - isolated worktrees for agents - reusable skills - plugins for GitHub and Slack integration - separate Maker and Checker sub-agents - memory logs so the AI does not start every cycle from scratch Stop babysitting chatbots - start building systems that work on their own.

• 
  DNems (@Devons_nemesis) reported 14 minutes ago

  @16vchq @sridharfyi A bunch. 😳 About to spill my guts. 🙃 First and foremost, my execution was poor. I am not a good leader. Also, I'm more of an engineer than an entrepreneur. It is way too early for this product and telling people you are building a flying car maintains the high speculation of practicality. Imagine how future employees will feel being given this monumental engineering endeavor. Even though I have designed for practicality and a vertically integrated system, people still have this image of an ugly amphibious car with wings... not at all what this is. I have continued to iterate and develop the design. Stuck in an engineering loop of relentlessly improving every system. Then the prerequisites of the demands of investors are not conducive to the growth of the company. Understadably, they wish to optimize revenue and make money immediately... I get it. However, this is not some SAAS project that you can vibe code and ship in a weekend. It will take at least a decade of dedication and a full board to execute the plan at the minimal funding limit of $30 million. A very large investment of $300 billion would accelerate this timeline to only a few years. Yet the regulatory system will need to catch up. Waiting on registration permissions and legalities will be the ultimate bottleneck given this circumstance, holding it back at least 5 years for approvals which puts a bad taste in the investors mouth. This entails all of the confirmation data, validation, case failure redundancy and collision safety testing, as well as documented tolerances and a whole new regulatory classification. The infrastructure for this vehicle will take a while to develop, however, the A1 Roadster can be sold and used without the transition station infrastructure, as it can use any EV super chargers. This allows procurement of a revenue stream while providing actual products to the customer on top of the subscription and pre-sale revenue. Not just promises. Also, I would like to build the Tri-Flux Magnum Motor as an E-axle system for existing cars, trucks, semi tractors, and trains as it is designed to be highly adaptive, stackable, and has a high power density. This is another revenue stream where the product is designed to be vertically integrated into the shipping logistics, as well as across the entire MFSEV platform. (Excluding A5). Yet, this wont be if I cant get people to see the vision. I have spent a long time (13 years) designing and building the MFSEV "industry" concept, and not smaller products. As well as bootstrapping. This significantly hurts my credibility and fundability. Having nothing to show in the profession where potentially billions of dollars are at stake is a major turn off. Let alone the multiple failures. "Dude hasnt even shipped an app. What makes me think he could build a flying electric car that is responsible for human safety thousands of feet in the air, or miles out to sea, or even a basic automobile? Definitely suspect." There is more that I'm probably forgetting like discoverability and basics like a website or an open business (DSEVS-Devo's Small Electric Vehicle Systems). I have closed it all down and now i just yap and iterate. This is entirely my fault. I let it die. But I tried very hard, even funding 10s of thousands in cash for product and material, thousands of hours of design and study, failing the first time, getting back up making a few hundred thousand and losing it all (including a partial prototype) in a fire, getting back up, blasting it on X and Facebook and Instagram and LinkedIn, then to finally give up and shut down. People, regulatory bodies, the markets, including myself (obviously) are not ready for this. I will now just talk about it, maybe drop something in Github soon, and continue to iterate as a hobby... ...Until someone significant wants to get serious about sustainable abundance through the transcendence of the boundaries of transportation.

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  Dr. Martin | AI x Business (@Dr_Martiin) reported 15 minutes ago

  Codex will also determine which browser to use based on the task. Its priority is: use a dedicated plugin if available (such as Jira or GitHub integrations), use Chrome if a login state is required, and use the built-in browser in all other cases.

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  Superintendent (@wawamachine23) reported 16 minutes ago

  @github can you guys unlock my ******* account, your ****** support doesnt respond, your ****** addons dont work, so now i cant update any of my websites that pull from github, and all of my public repos are shut down. no wonder no one uses u guys anymore. terrible service.

• 
  KIRILL (@Copenhagen0x) reported 16 minutes ago

  @GuiBibeau every real hack gets distilled into a rule. the repo has a hacks db that maps historical sol exploits to the rule that would've caught them, so when something new drops onchain it becomes a new SOL-XXX entry. edit one source and it propagates out to every surface (cli, github action, mcp, the editor extensions). so it tracks the actual threat landscape instead of being a frozen checklist. js not ts: fully on purpose lol. the scanner is zero-dep with no build step. plain js means it just runs anywhere node exists and vendors as-is into the mcp server + the action + the vs code extension, no compile/tsconfig in the way. types are nicer dx but the second you add a build you lose "clone and run." kept it boring so it can live everywhere.

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  Zhirayr Gumruyan (@gumruyanzh) reported 23 minutes ago

  inspired by @mattpocockuk skills, i have created /to-elixion-issues which is very simlar to /to-issues but instead of creating issues in @github or into file, it does create Stories tasks or bugs in @elixion project backlog

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  Ian Smith (@IanSmith_HSA) reported 27 minutes ago

  Hoping we can gamify GPU based search for Quantum Error Correction codes. I found a GitHub that does this, but it was extremely limited in scope.

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  Matthew Fornear | WINDFISH (@fixitorgotojail) reported 28 minutes ago

  @DavidSHolz agents need function-level leases. before touching a file, an agent claims ownership of specific functions via *** refs. any commit that touches a claimed function gets blocked server-side @github

• 
  DFIR Lab (@DFIR_Lab) reported 28 minutes ago

  🦅 Tool Tuesday: Hayabusa — Fast Windows Event Log Analysis for Threat Hunters When you're knee-deep in a Windows compromise and staring at gigabytes of EVTX files, speed matters. Hayabusa is a Rust-based event log analyzer that rips through Windows event logs at scale, applying Sigma-compatible detection rules to surface threats fast. Built by Yamato Security, it ships with 4000+ built-in detection rules covering everything from credential dumping to lateral movement. It scans EVTX files offline, generates a consolidated timeline of security-relevant events, and outputs to CSV, JSON, or HTML — whatever fits your workflow. Real-world use case: You've pulled EVTX logs from 50 endpoints during an active IR engagement. Instead of manually parsing Security.evtx looking for 4624/4625 patterns, you point Hayabusa at the entire dataset. Within minutes, you have a sorted timeline flagging Mimikatz execution, suspicious PowerShell, and abnormal logon patterns — all color-coded by severity. Why it matters: Traditional EVTX analysis is slow. Hayabusa's Rust core makes it blazing fast, and Sigma rule compatibility means your existing detection content works out of the box. It's offline-capable, so you can analyze logs on an isolated IR laptop without network dependencies. Alternatives: DeepBlueCLI (PowerShell-based, lighter but slower), EvtxECmd (Eric Zimmerman's tool, great for parsing but less detection-focused), and Chainsaw (another Rust option with Sigma support). Get it: hXXps://github[.]com/Yamato-Security/hayabusa #DFIRTools #IncidentResponse

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  Kuruś (@donqrakko) reported 30 minutes ago

  @HermesAgentTips @Teknium 1/2 @Teknium please fix hermes desktop for windows. I don't have time to create issue on github 1. I get error - *** not installed, but i have latest version of *** 2. I noticed hermes and hermes desktop is almost 3gb od size. So i got angry and tried to uninstall it

• 
  Nick Puru (@NicholasPuru) reported 33 minutes ago

  You spent 10 years learning your job. Openai just turned it into a free download. 110 skills across 6 careers, written down by their own experts, published on github for anyone to install. If a markdown file can do your work, it was never expertise. it was a checklist nobody had written down yet. study this 👇

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  Jerralyn Franzic (@JeromFranzic) reported 35 minutes ago

  @aya_tokyo_sl Oh... was about to download this for Win11. I also run Linux and can't download the prebuilt one for that. Can you fix the link for that on GitHub?