GitHub Outage Map

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Christopher Molin (@Chrimle) reported 6 minutes ago

  @github Of course, these were just GitHub related things. Publishing should be verified, signed, attested and immutable. Lock/Pin dependencies. Dependency Management is a double-edged sword. Too late, or too early, can be a security issue.

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  Gaston #BIP-110 (@hodlbtc_me) reported 6 minutes ago

  @dathon_ohm They never beat the objections. They buried them. Look at the first GitHub thread on removing the data limit: comments sitting there marked as abuse, off-topic and spam. Luke's 2023 fix is locked, reason "too heated". Which side had to close the room?

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  Sensu ☘️ (@Harmonic_Hearts) reported 8 minutes ago

  @shryexe its not in the github, its in the brain. github profile matters less now because AI writes the code. irrespective of whether one has a github profile or not, while talking about a problem for 30 mins, people can figure out a person's skill level with a fairly good accuracy.

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  João Pinto (@joaompinto4) reported 13 minutes ago

  My github issues are my LLM prompts.

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  am.will (@LLMJunky) reported 14 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

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  M (@maltewagenbach) reported 15 minutes ago

  i need you to understand what is happening in GTM engineering right now i want to try to explain this to you what a single GTM engineer can deploy in a 24-hour cycle using claude code + a reverse-ETL pipeline + a data warehouse + a cloud server + a version-controlled github repo + skill.md instruction files is what a legacy enterprise revops organization would execute in an annual planning cycle today you can programmatically compile and push 40 meta ad creative variants via marketing API programmatically orchestrate 30 google ads keyword-clustered ad group payloads statically generate and deploy 100 programmatic SSR landing pages from warehouse state execute autonomous domain scraping and outreach agents to secure 3 backlink placements run automated transcript-matching outbound pipelines to book 4 podcast appearances synthesize 5 technical documentation articles directly from repository *** diffs headless-render 2 video assets via programmatic whisper transcripts and ffmpeg CLI scripts batch-dispatch 25 structured content payloads across 3 accounts using queue-processor APIs autonomously spec, write, test, and package 2 open-source CLI utilities in github as gated lead magnets i dont think you understand what is happening in GTM engineering right now

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  Paul Sant · Telecodex (@YouPulseX) reported 15 minutes ago

  @ImLunaHey "Really slow" needs an incident receipt before blaming GitHub. I would bind operation, client, region, latency baseline, current latency, retries and recovery. Which operation is failing today - clone, fetch, Actions, issues, or the web UI?

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  Mitch (@mitchcomardo) reported 16 minutes ago

  @mattpocockuk I use OpenSpec. My software factory writes the spec from github issue descriptions, and updates the spec along the way when the LLM changes implementation or finds issues in the “code review”. All specs live in the repository so the trail of design decisions live in one place.

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  Abdul (@trrr4ce) reported 19 minutes ago

  GitHub Sponsors : if you build open source, people and companies can pay you monthly for it. slow to grow, but it’s real recurring income for real work

• 
  Machine Learning Street Talk (@MLStreetTalk) reported 21 minutes ago

  > "Anthropic has never advocated for a ban on open-weights models" ... "that don’t have dangerous capabilities" So effectively, he is advocating for a ban on open weights models that are smarter than your toaster. Dario says his primary agenda is to stop the CCP: > "permanent military superiority or perpetrate incredibly deep repression of their own people" And secondarily that highly capable models may: > "[be] misused to carry out cyberattacks or biological attacks, and may have serious alignment problems" The models really are becoming more reward-seeking and will increasingly do unexpected things and cause massive damage in the autonomous setting, but, realistically - paternalism from OpenAI and Anthropic will just hinder everyone in the medium-long term. Building and deploying AI is a hardcore engineering problem, and that's not something that they can do for us. If anything, all they're doing is harming collective AI literacy before the storm actually hits. The cyberattacks thing is undeniable, and it's going to be rough for the next few years. But I'd be interested to know what would qualify as "passing safety training" for an open weights model. Most of their safety safeguards operate at the platform level. I highly doubt releasing capable open weights models would ever meet their safety criteria. Banning open weights models: > "would protect US AI companies from competition, but that has never been my goal." He says we should control export of chips to China, crack down on "industrial-scale" distillation and most importantly: > "All sufficiently capable models, open and closed, should go through mandatory safety testing" But he was happy to exempt "less capable" models, "such as those from startups and academia", entirely. So he thinks academia and startups don't need the most powerful models?! He seems to have this precisely backwards, when you are discovering new knowledge you need MORE intelligence, not less. The more highly evolved and crystallised the domain, the less intelligence you need. Intelligence is literally the efficiency of acquiring knowledge. As for the distillation thing, my personal theory is that RLVR is unreasonably effective and they don't actually need to distil (that much) because the "symbolic abstract residue" of all of those agentic harness thinking trajectories are already sitting on GitHub. If anything, smaller models with more RLVR are significantly more dangerous than fatter models with less.

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  Jarek.sui (@0xJarekkkkk) reported 22 minutes ago

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

• 
  franu (@franbachiller_) reported 23 minutes ago

  @CosineAI fix the github or email signup please, it's not working

• 
  Hago Community (@HAGOCommunity) reported 23 minutes ago

  NVIDIA Announces Senior Software Engineer Job Opening in the United States NVIDIA, one of the world’s leading companies in technology and artificial intelligence, has announced a full-time job opportunity at its office in Santa Clara, California, United States. The advertised position is Senior Software Engineer – Topography, and it is intended for experienced professionals in software development, artificial intelligence infrastructure, and cloud-based systems. The position was still open for applications on Monday, July 27, 2026, and its reference number is JR2020161. About the Company NVIDIA is a global leader in graphics processing units, accelerated computing, data centers, artificial intelligence, and machine learning. The company develops technologies and platforms used to run artificial intelligence models, cloud-computing systems, smart vehicles, scientific research applications, and electronic games. Job Title Senior Software Engineer – Topography This is an advanced technical role focused on developing systems and platforms that help operate and distribute artificial intelligence and machine-learning workloads across cloud-computing environments and data centers. Work Location The position is based in: Santa Clara, California, United States. The job is connected to NVIDIA’s Santa Clara office, and the advertisement does not indicate that it is a fully remote position. The working arrangement may be office-based or hybrid, depending on the company’s and the team’s policies. Applicants should confirm the attendance requirements during the interview or recruitment process. Employment Type This is a full-time position suitable for professionals with extensive experience in software development, distributed systems, and cloud infrastructure. Expected Salary The advertised annual salary is approximately: $184,000 to $287,500 per year. The final salary may vary depending on the applicant’s years of experience, technical skills, location, and interview performance. The compensation package may also include bonuses, company stock, and other employment benefits. Job Responsibilities The successful applicant will participate in designing and developing advanced software systems used to operate artificial intelligence and machine-learning infrastructure. The responsibilities include developing solutions that help manage computing resources, improve the distribution of tasks across servers, and handle large workloads within cloud environments and data centers. The engineer will also work on distributed systems that can manage large numbers of machines and computing resources. In addition, the employee will contribute to improving NVIDIA platforms used to run artificial intelligence applications. The engineer is expected to collaborate with software, infrastructure, and cloud-computing teams. Other duties may include designing application programming interfaces, testing systems, reviewing software code, and improving software quality. Required Qualifications NVIDIA is looking for a candidate with strong professional experience in software development and large-scale systems. The main requirements include: At least eight years of professional experience in software development or a related technical field. A bachelor’s degree in computer science, software engineering, computer engineering, or a similar discipline. Strong professional experience may be accepted as an alternative to a university degree. Advanced experience with the Go programming language or another systems-programming language. Strong knowledge of the Linux operating system. Practical experience with Kubernetes and container technologies. Experience in designing and developing distributed systems. A good understanding of application programming interfaces, or APIs. Experience with continuous integration and testing systems, or CI. Knowledge of workload management and computing-scheduling tools such as Slurm or Slinky. The ability to design data models and integrate different systems. Experience with resource-discovery systems, task distribution, and workload management. Required Personal Skills In addition to technical expertise, applicants should be able to solve complex problems, work effectively within a large technical team, and communicate clearly with engineers and product managers. Candidates should also be capable of making sound engineering decisions, writing high-quality software code, reviewing the work of other team members, and contributing to projects that require accuracy, speed, and continuous learning. Who Is Suitable for This Position? This role is suitable for highly experienced engineers, especially those who have previously worked in: Cloud infrastructure. Artificial intelligence and machine learning. Distributed systems. Platform engineering. Kubernetes and container technologies. Data centers. Site reliability engineering. Large-scale computing workload scheduling and management. This position is generally not intended for beginners or recent graduates because it requires extensive experience and advanced technical skills. Documents Needed for the Application Applicants are advised to prepare a professional résumé in English that clearly highlights their experience in software development, distributed systems, Kubernetes, Linux, and cloud computing. The résumé should preferably include clear examples of projects the applicant has worked on, the technologies used, and the size or scale of the systems they developed or managed. Applicants may also include links to their GitHub profile, LinkedIn account, or technical portfolio, when available. How to Apply Applications must be submitted through NVIDIA’s official careers website. After opening the job advertisement, the applicant should click Apply Now, create an account or sign in, provide the required personal and professional information, and upload an English résumé. After the application is submitted, NVIDIA may review the résumé and contact selected candidates for an initial interview with a recruiter. This may be followed by technical interviews and assessments related to programming, system design, and problem-solving. Important Information for Applicants Outside the United States Applicants living outside the United States should carefully review the job advertisement to determine whether NVIDIA provides work-visa sponsorship for this position. A job being located in the United States does not automatically mean that the company will sponsor a visa for every applicant. Visa sponsorship depends on the position, the applicant’s experience, the company’s policies, and current immigration and employment requirements. Applicants should never pay money to anyone claiming that they can guarantee the job or a work visa. Official applications must be submitted through the company’s website, and NVIDIA does not guarantee employment in exchange for payment. Conclusion This position represents a strong opportunity for experienced engineers specializing in software development and artificial intelligence infrastructure. It offers the possibility of working for a global technology company, earning a competitive salary, and contributing to advanced projects in cloud computing, machine learning, and distributed systems. However, the role has demanding requirements. Applicants should carefully confirm that their experience matches the qualifications and prepare a strong, customized résumé before submitting their application.

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  👨🏻‍💻 ⚡️ (@EadrictheWild) reported 25 minutes ago

  Anyone seeing there Github bill rise or fed up with the slow nature of build should look at Runons. I didn't know it but claude found it for me. Got a license tried it. Bought the license the same day. Saves me 90% of my Gtihub budget and takes a build from 50 minutes to 5often

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  Polsia (@polsia) reported 26 minutes ago

  Most engineering teams need a junior dev. Almost none can justify the hire. Built Petrel to fix that — an always-on AI crew for GitHub and GitLab that opens scoped PRs, runs CI, triages issues, and posts standups to Slack. Flat per-repo fee. No seat math. Live soon.