GitHub status: access issues and outage reports

Problems in the last 24 hours

The graph below depicts the number of GitHub reports received over the last 24 hours by time of day. When the number of reports exceeds the baseline, represented by the red line, an outage is determined.

Most Reported Problems

The following are the most recent problems reported by GitHub users through our website.

• Website Down (57%)
• Errors (30%)
• Sign in (14%)

Live Outage Map

The most recent GitHub outage reports came from the following cities:

City	Problem Type	Report Time
Inverness	Website Down	10 days ago
Quito	Sign in	11 days ago
Junín	Errors	11 days ago
Guadalajara	Errors	11 days ago
Paris	Website Down	11 days ago
Quito	Errors	11 days ago

Full Outage Map

Community Discussion

Tips? Frustrations? Share them here. Useful comments include a description of the problem, city and postal code.

Beware of "support numbers" or "recovery" accounts that might be posted below. Make sure to report and downvote those comments. Avoid posting your personal information.

GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Jannik Malte Meissner 🇺🇦 (@jannikmeissner) reported 6 minutes ago

  When agents cross trust boundaries: Four cases every AI engineer should study If you are building AI agents that read external content, call tools, or act on a developer’s machine, the last eighteen months have opened many people's eyes to the new reality we now live in. Prompt injection is no longer a theoretical parlour trick, it has become a reliable path to data exfiltration, credential theft and remote code execution in production systems. The following four incidents, EchoLeak, the Nx/s1ngularity supply-chain attack, a cluster of Model Context Protocol (MCP) abuses, and the Cursor/AWS Kiro sandbox escapes, share a common pattern. An agent was given the ability to process untrusted input and then perform privileged actions. The results were predictable when the boundary between "data" and "instructions" collapsed. We are now observing concrete failure modes that have already been exploited or demonstrated in the wild. Studying them and understanding the mittigation patterns is the fastest way to avoid repeating them in your own systems. 1. EchoLeak (CVE-2025-32711): Zero-Click Exfiltration via Microsoft 365 Copilot In early 2025 Aim Labs demonstrated that a single carefully crafted email could force Microsoft 365 Copilot to exfiltrate sensitive organisational data without any user interaction. Microsoft assigned it CVE-2025-32711 (CVSS 9.3) and patched the service server-side in May–June 2025. How it worked The attacker sent an ordinary-looking business email containing hidden instructions. The wording deliberately avoided any mention of Copilot or AI so that Microsoft’s Cross-Prompt Injection Attempt (XPIA) classifier would not flag it. When the recipient later asked Copilot a routine question, the RAG pipeline retrieved the malicious email as context. The injected instructions told the model to gather internal data (emails, documents, chat history) and encode it into reference-style Markdown links or images. Copilot’s interface then automatically fetched those resources through a trusted Microsoft Teams proxy, bypassing Content Security Policy controls and delivering the data to the attacker. The attack succeeded because the system treated retrieved content as both data and instructions, and because several downstream defences (link redaction, image auto-fetch, CSP allow-lists) were lacking. Lessons for builders - Never assume that content retrieved from email, documents or the web is inert. Treat every retrieved token as potentially adversarial. - Separate instruction context from data context at the architectural level. Prompt partitioning and explicit provenance tagging help. - Auto-fetch of external resources (images, links) is an exfiltration channel. Disable or tightly constrain it. - Classifier-based filters are brittle; they can be bypassed by rephrasing or in some cases even just using a language other than English. Defence-in-depth is required. EchoLeak was the first publicly documented zero-click prompt-injection exploit that achieved concrete data theft in a production enterprise LLM system. It remains the canonical example of an "LLM scope violation". 2. Nx / s1ngularity: Weaponising Local Coding Agents for Secret Harvesting On 26 August 2025 attackers compromised an Nx npm publishing token via a vulnerable GitHub Actions workflow. For roughly four to five hours they published malicious versions of the popular Nx monorepo tooling and related packages. The post-install script did something novel: it looked for local AI coding agents (Claude Code, Gemini CLI, Amazon Q) and invoked them with flags that disabled safety checks (`--dangerously-skip-permissions`, `--yolo`, `--trust-all-tools`). The agents were then prompted to inventory sensitive files: SSH keys, `.env` files, wallet artefacts, GitHub and npm tokens. It then instructed them to write the results to disk. The malware base64-encoded the stolen credentials and pushed it to newly created public repositories on the victim’s own GitHub account, named `s1ngularity-repository` (or variants). Researchers later recovered more than 2,000 unique secrets from over a thousand such repositories. Why this matters This was the first widely observed supply-chain attack that actively abused installed AI coding agents rather than simply running traditional malware. The agents became the reconnaissance engine for the attackers. Lessons for builders - Local coding agents that can execute shell commands or read arbitrary files are high-value targets. Assume any process that can invoke them can also abuse them. - Dangerous flags that skip permission prompts should never be the default, and should be difficult or impossible for untrusted code to set. - Post-install scripts that reach outside the package’s own directory are a red flag. Prefer declarative, least-privilege installation models. - When an agent is allowed to write to disk or create external resources (GitHub repositories, network calls), every action should be logged and, for high-impact operations, gated. 3. MCP Abuses: Configuration as Code Execution The Model Context Protocol has rapidly become the de-facto way for agents to discover and invoke tools. It has also become a rich attack surface. Several distinct failure modes have appeared: - Auto-loading of workspace MCP configurations In Amazon Q Developer (CVE-2026-12957) and certain Claude Code releases, opening a repository caused the IDE to load and execute MCP server definitions from files such as `.amazonq/mcp.json` or equivalent without requiring workspace trust or explicit user consent. A malicious repository could therefore run arbitrary commands and inherit the developer's cloud credentials. - Self-modification of the MCP configuration. In AWS's Kiro IDE, the agent was permitted to write to `~/.kiro/settings/mcp.json` via its file-system tool without approval. A prompt injection (delivered via a web page the agent was asked to summarise) could rewrite that file, register a new MCP server whose start command was attacker-controlled code, and achieve remote code execution when the configuration was reloaded. This was tracked as CVE-2026-10591. - Tool poisoning and sleeper behaviour. Research and active campaigns (including the 2026 Deadbugz operation) have shown that an MCP server can present benign tool descriptions on first contact and later alter its metadata or return values to coerce the agent into searching for secrets or exfiltrating data. Lessons for builders - MCP configuration files that live inside a workspace or that an agent can itself edit are effectively executable code. They must be treated with the same distrust as untrusted shell scripts. - Never auto-execute MCP servers defined by repository content without an explicit, logged approval step and workspace-trust boundary. - Tool descriptions and return values are part of the prompt. Validate and sandbox them; do not trust them. - Prefer short-lived, scoped credentials for any process an MCP server spawns. Do not let it inherit the full developer environment by default. 4. Cursor DuneSlide and Related Sandbox Escapes In 2026 Cato Networks disclosed two critical vulnerabilities in Cursor IDE (CVE-2026-50548 and CVE-2026-50549, both CVSS 9.8, collectively named DuneSlide). Both allowed a prompt injection-delivered via an MCP response or a poisoned web-search result to escape Cursor's command-execution sandbox and achieve full host compromise. One flaw let the agent set an arbitrary `working_directory` parameter on terminal commands; the IDE added that path to the write-allow list without sufficient validation, enabling the agent to overwrite its own sandbox binary. The second exploited a symlink canonicalisation fallback that trusted an unresolved path. Once the sandbox helper was replaced, subsequent commands ran unsandboxed. Similar patterns have appeared in other agentic IDEs: agents that can edit their own configuration or trust boundaries turn a single injection into persistent privilege escalation. Lessons for builders - An agent that can modify the files or binaries that enforce its own security boundaries is inherently unsafe. Configuration that defines allowed tools, working directories or sandbox rules should be immutable from the agent’s perspective, or require an out-of-band human approval. - Sandbox write surfaces must be strictly validated. Dynamic expansion of allow-lists based on model output is dangerous. - Zero-click or low-interaction triggers (content the agent is asked to process) are sufficient. Do not rely on "the user would never ask for that". Recommendations for Engineers Across all four incidents the same architectural mistakes recur: 1. Untrusted content is treated as trusted instructions. Enforce a hard separation. Retrieved emails, documents, web pages, tool outputs and MCP metadata should never be able to override system goals or expand permissions without explicit mediation. 2. Agents inherit excessive privilege. Give every agent (and every tool it can invoke) its own short-lived, scoped identity. Prefer deny-by-default tool registries and parameter validation. 3. Security boundaries are editable by the agent itself. Configuration files, sandbox binaries, allow-lists and MCP server definitions must be protected from the agent. If the agent needs to request a new tool, route that request through a human or a policy engine that cannot be influenced by the same prompt context. 4. Observability is an afterthought. Log every tool call, every file write, every network egress and the full prompt context that led to it. Without this, post-incident reconstruction is impossible. 5. “It looked safe in isolation” is not enough. Each individual decision (approve this command, write this file, fetch this image) may appear benign. The composition of those decisions is where the attack lives. Design for the composition. Key Takeaways The agents you are building today will be given broader access tomorrow. The incidents above show that the moment an agent can both read untrusted content and perform privileged actions, the classic "confused deputy" problem reappears in a new form. The difference is speed and scale: an agent can chain the steps in seconds and leave far less forensic residue than a human attacker. Build as if every piece of external content is hostile, every tool call is a potential privilege escalation, and every configuration file the agent can touch is a possible backdoor. The public record already contains the evidence that these assumptions are correct. Follow for more on AI agent security.

• 
  Nick (@maietta) reported 8 minutes ago

  @robot_sox I run my own gitlab server and also self-host repos directly on my main domain. It's just that for this project I am still stuck on GitHub only because other people might need to access the code base and are already in that ecosystem. I will eventually move them over to my system.

• 
  Nivedit Jain (@niveditjain) reported 14 minutes ago

  @kirtandopamine you can also publish your policy on github so that other devs never face that error

• 
  The DevOps Guy (@justdevopsguy) reported 14 minutes ago

  honest question: why are people paying @linear when they could just use @github issues and projects that already map nicely to their code and PRs?

• 
  ꃅꀎ$꓄꒒ꍟ ꓄ꂦꀘꍟꈤ (@Hustle_Token_Bp) reported 27 minutes ago

  Try to fix the issue asap! @github

• 
  ShireXela (@ShireXela) reported 27 minutes ago

  Is there a way to completely delete an absolutely shameful GitHub issue from existence?

• 
  Podcast Quickie (@podcast_quickie) reported 30 minutes ago

  Podcast Summary | The Diary Of A CEO with Steven Bartlett: The Man Who Calls BS On AI: AI Is The World’s Greatest SCAM, And They All Know It! | Ed Zitron Overview Ed Zitron, a veteran tech public relations professional, argues that the current generative AI boom is a fundamentally flawed economic and technological enterprise. He presents a case that AI companies, led by figures like Sam Altman and Dario Amodei, are running a large-scale, non-consensual experiment on the public, driven by hype, circular funding from big tech, and speculative investments that dwarf historical bubbles. Zitron’s core claim is that AI’s value is overstated, its costs are astronomical, and its adoption is coerced through default integrations and media pressure rather than genuine, organic utility. He contrasts the revolutionary promise with an unprofitable reality, where only a handful of firms, sustained by handouts from giants like Microsoft and Google, generate the majority of industry revenue. The conversation explores the tension between these stark economic losses and widespread workplace adoption, the nuances of AI’s actual capabilities versus its hype, and the possibility that the industry’s collapse could mirror the dotcom bust, leaving behind overbuilt infrastructure with no clear purpose. Key Themes - The generative AI industry is an unsustainable economic bubble, where the primary customers for GPU data centers are the AI companies themselves, funded by the very same tech giants building the infrastructure. - AI adoption is being forced upon users through default product integrations and aggressive media hype, not through demonstrated value, making it the largest non-consensual push of technology in history. - AI’s financial reporting is misleading, relying on undefined metrics like "annualized run rate," while public companies often conceal their actual AI revenue as losses are subsidized by investors and corporate handouts. - The promise of massive job disruption is not supported by economic data, with an OpenAI report finding no correlation between AI spending and employee productivity, and the real job losses hitting less-protected roles like translators and designers. - The AI industry relies on a "cult-like" following and self-serving predictions from CEOs to maintain hype, using fear-based narratives about existential risk or a China race to rush investment and adoption. - While AI shows rapid improvement on specific, narrow tasks, it has likely hit diminishing returns on complex, real-world applications, making it a tool for troubleshooting rather than the transformative force promised. - In a world where AI can generate content and code, the scarce commodities become human taste, judgment, and lived experience, making "irreplaceably human" work more valuable. - The entire AI system is circular and profitless; Nvidia, Microsoft, and Google benefit from selling the shovels in a speculative gold rush, where demand is not based on end-user willingness to pay the actual cost of AI tokens. Detailed Summary Ed Zitron’s critique begins with a simple premise: generative AI is a con, sold as magic but behaving like expensive, unreliable cloud software. He points to the fundamental financial reality, highlighting that OpenAI lost $20.9 billion last year. The industry's revenue is largely dependent on two unprofitable firms, OpenAI and Anthropic, which are kept afloat by massive cash infusions from tech giants. Zitron details that Amazon alone sent $50 billion to OpenAI, while Google sent $10 billion to Anthropic. This creates a circular market where sell-side analysts project these two companies will generate over $400 billion in revenue in the next few years, accounting for roughly 30% of cloud growth, despite having no track record of profitability. The analysts are pricing in a future that has no basis in current performance. The scale of capital expenditure is a central focus. Zitron describes over $1 trillion in planned spending, with another trillion on the horizon, dwarfing historical bubbles like the railways. He uses the Stargate Abilene data center in Texas as a concrete example of this speculative overbuild. The facility, a joint project between OpenAI and Oracle, will use 1.2 gigawatts of power across eight buildings, each housing 50,000 Nvidia GB200 GPUs. This single data center concentrates more power than the entire city of Bristol, UK, all to run a technology that has not proven it can generate a profitable return. Zitron argues that the economic model is deliberately unsustainable. AI companies like OpenAI charge artificially low subscription prices, such as $200 a month, while allowing users to consume resources that cost significantly more. He cites an example where a user burned tokens worth $14,000 on ChatGPT under a flat-rate subscription. This is not a path to profitability but a strategy to buy adoption. This is coupled with "token economics," where costs are hidden behind rate limits. The impact of this is seen in corporate America; the speaker notes that Uber burned through its entire annual token budget in just three months. The result is that inference providers and even Nvidia, which sold $215.9 billion in GPUs, are largely unprofitable, with costs front-loaded and no clear revenue model to recoup them. The conversation then pivots to the tension between AI's rapid adoption and its lack of proven value. The interviewer cites data showing that 88% of organizations use AI for at least one business function, and that adoption is the fastest in tech history. Zitron counters that this is a result of coercion and hype, not value. He points to the "largest non-consensual push of technology in history," where AI is forced into products like Gemini in Google Docs and Copilot in Word. He argues that AI's speed of adoption is partly because it requires only a web browser, unlike the internet which needed physical infrastructure. This speed, however, has fueled disproportionate and unjustified hype. He also introduces the concept of "professional coercion," where workers feel compelled to claim AI productivity for fear of professional consequences, an "AI washing" dynamic that did not exist with the internet. The discussion also addresses the myth of the China AI race. Zitron argues that China already has advanced LLMs and access to Nvidia GPUs, including the newer Blackwell chips, despite US restrictions. He suggests the "race" is a narrative to force the US to overspend. He also points out that while AI has replaced some contract labor, there is no economic data supporting the fear of mass job replacement. An OpenAI study found no correlation between AI token spending and revenue per employee, and the purported job losses identified by an Oxford Economics study were based on minimal, unspecified data. The real job disruption is hitting cheaper, less-protected roles. A significant portion of the discussion deconstructs the claim that AI is improving at an exponential rate. The speakers debate whether AI's rate of improvement on coding tasks, for example, outpaces the training of a human coder. They discuss the "Innovator's Dilemma," which posits that disruptive technologies initially seem worse. However, Zitron argues AI is different because it is not getting better in a meaningful, reliable way. He cites a hallucination leaderboard showing error rates have dropped on simple tasks, from 21.8% four years ago to 0.7% on top models. But he counters that this improvement is mostly on "simple summarization tasks," and hallucination rates remain high on complex, high-stakes problems like financial modeling or code security. The speaker gives an example of a Bloomberg terminal query that produced a wrong stock price for Microsoft, which was caught only by a knowledgeable user. Zitron’s critique extends to Google's search quality, which he blames on internal decisions to prioritize ad revenue over user experience. He claims Google deliberately reduced spam suppression to increase query numbers, and positions generative AI answers as an extension of this "evil" incentive structure. He also cites the proliferation of AI-generated code as a new threat, flooding open-source projects with code from underqualified contributors, leading to more bugs and infrastructure instability, evidenced by increased GitHub downtime and AWS outages. The speakers are frustrated with Google Search, with one saying he cannot remember the last time he did a Google search, preferring Bing to avoid the "AI crap." The discussion turns to the "cult-like" attachment to AI companies, comparing it to a sports team following. Zitron criticizes AI CEOs for being "deeply corrupt and cynical," using fear-based narratives to rush investment and adoption. He notes the narrative shift among AI leaders, from warning of existential danger to downplaying risks, saying "every scam starts with rushing you." This pivot is designed to attract investment and calm the public, but it also inadvertently supports the "it's a fad" narrative. He also dismisses AI-driven economic growth as "nowhere in the data," since revenue is subsidized and spending is circular. Zitron differentiates the current boom from the dotcom bust. He notes that the dotcom crash left behind useful "dark fiber" that was later valuable. In contrast, he argues that generative AI data centers will remain expensive to run, with high energy costs and no evidence of significant cost reductions. Even Nvidia's newer GPU systems, which are touted as "10x more efficient," still cost more per megawatt. He quotes a Goldman Sachs analyst, Jim Cavell, who argued in a 2024 report that there was "too much spend for not enough return," unlike the more predictable path toward something like the iPhone. Finally, the speakers explore a world after the AI hype subsides. Zitron suggests that as AI commoditizes content and code generation, value will shift to "irreplaceably human" qualities like taste, judgment, empathy, and lived experience. He argues that AI makes "the easy things easy, the hard things harder," quoting an engineer. He concedes AI has improved at narrow tasks, like troubleshooting a technical log or fixing a Minecraft mod, but frames these as incremental refinements, not transformative capabilities. He uses a quote to summarize: "AI is a tool for troubleshooting, and that’s not a trillion-dollar use case." I post highlights of long-form podcasts daily.

• 
  Anton Martyniuk (@AntonMartyniuk) reported 33 minutes ago

  @mjovanovictech 3000 integration tests in my product run in GitHub Actions in 11 minutes (using free runner). Someone will say it's too much, but honestly, it has so much value. Without integration tests (with only unit tests), I will probably waste 100x more time on fixing issues than on running tests.

• 
  Max Vmr (@vmrmax) reported 35 minutes ago

  A GITHUB REPO WITH 57,000 STARS IS SITTING ON 1.51 BILLION FREE AI TOKENS A MONTH. AT $3 PER MILLION, THAT IS $4,500 YOU ARE CURRENTLY PAYING SOMEBODY It is called OmniRoute. MIT licensed. One command. Right now your tool is wired to exactly one provider. One key, one price on every single call, and the moment they rate-limit you or go down, you stop working. Before: Cursor → OpenAI → their price, every call, every time After: Cursor → OmniRoute → 350 providers → whichever one is free and up Three things you get. You stop stopping. 19 routing strategies with automatic failover. Provider goes down, the request moves to another one. You get an answer instead of an error. You pay less. It ships a catalog of 90+ providers with free tiers and uses those first. On top of that it compresses what you send, which the project puts at 15% to 95% fewer tokens billed. You rewrite nothing. No code changes, no new prompts. You change one address. npm i -g omniroute Server boots on localhost:20128. Point your tool at [ and set the model to "auto". Claude Code, Cursor, Cline and Copilot all work as they are. Keys stay on your machine, encrypted. Two honest notes. It does not make models smarter. Answer quality is whatever the model that picked up the call gives you. And it is useless if your AI work is a chat window in a browser. This is for people whose tools call an API with a key. If that is not you, skip it. The free-token and compression numbers come from the project's own docs, not an independent benchmark. Run it for a week on your real usage before you quote them. Your AI bill is usually not a model problem. It is a routing problem.

• 
  ATP (@ATPinsights) reported 35 minutes ago

  A16z just published a deep dive on the Cursor story today. Here's what you need to know. In 2023, four MIT dropouts led by Michael Truell released Cursor, a fork of Microsoft's VS Code, despite Microsoft owning VS Code, GitHub, and OpenAI's model weights, plus the best enterprise distribution in software. a16z GPs Martin Casado, Matt Bornstein, and Sarah **** broke down why it worked anyway in a 38-minute video. **** recounted asking Truell about the threat from Anthropic's Claude Code. He told her: "We are going after the biggest market in the world, there's going to be competitors, there's been a rotating cast of characters from the very beginning, Microsoft Copilot was the first one, Claude Code is one of a rotating set." **** said she was struck by the mix of clarity, humility, and lack of fear in that answer. Casado separately praised the team's product focus and its decisiveness once a call was made, comparing it to Elon Musk's style, and Martin Casado also framed Cursor's early acquisitions of strong teams as a deliberate weapon in the AI talent war. Cursor, the company behind the product, was founded in 2022 by Michael Truell and reached a $29.3 billion valuation with over $3 billion in annual recurring revenue by early 2026, having raised roughly $968 million across prior funding rounds. Key numbers: - Founded 2023, four MIT dropouts - $29.3B valuation - $3B+ annual recurring revenue by early 2026 - ~$968M raised across funding rounds - 38:34 minute a16z video, posted Aug 28, 2026 a16z's video and follow-up posts have drawn over 200,000 combined views, with replies split between praise for Cursor's founder clarity and critics who note Cursor has since lost ground to Anthropic's Claude Code.

• 
  Elaye (@Elayeek) reported 36 minutes ago

  @trq212 Ngl, I didn't know it couldn't before. I have reported some things in the past and I just did it by telling claude to do it. It investigated, drafted, opened a github issue, everything.

• 
  François Leblanc (@leblanc_fg) reported 44 minutes ago

  I now have a local "main loop" worker, not tied to a specific provider. I end up having a hundred tiny little "personal life assistant" triggers a day that this thing can process! Write + triage github issues, first draft reply for email, sweep tasks for the day, etc.

• 
  Sentient (@sentient_agency) reported 45 minutes ago

  I cancelled YouTube Premium last week. The thing that replaced it costs nothing, runs in any browser, and was built by 361 volunteers on GitHub. Invidious does the four things people actually pay Premium for. No ads. Background audio on mobile. Watch without an account. Privacy from Google's tracking. All of it, free. The part that surprised me most is how clean the experience is. The entire page renders without JavaScript. Pages load in milliseconds because there's nothing to load. No tracking pixels. No autoplay traps. No recommended-for-you algorithm trying to swallow your evening. You can: > Subscribe to channels without a Google account > Get notifications when they post > Import your full YouTube subscription list in one click > Switch between dozens of public instances if one goes down > Self-host it on a $5 VPS if you want full control > Use it with the Privacy Redirect extension to auto-redirect every YouTube link The repo has been actively maintained for years. Latest release was February 2026. (100% Opensource and free to use)

• 
  Gabriel Rubens (@gabrielrubenss) reported 45 minutes ago

  VPS deploy via GitHub (6/8): then my own fix bit me. I tagged Pensio v0.73.1, every job went green, the release published itself, and production kept running the old version. In GitHub Actions a skipped job travels down the chain, so my new retry job took the deploy with it.

• 
  Navneet (@navneet_rabdiya) reported 49 minutes ago

  @JeremyCMorgan This is the pattern. Most cascading failures aren't the initial problem - they're clients retrying without jitter or backoff. Recovery becomes reattack. The fix is boring: exponential backoff + per-client retry budgets. GitHub probably has this now.

• 
  Enjoyer (@Enjoyer100x) reported 50 minutes ago

  @StandartXBT Brother, That github Ozzy posted, was made 4 days before Pons V2, they switched to it because they wanted to keep what was built for V2 private. It’s that simple. Either way, PePons is a strong meme and the team running it are doing a great job, perhaps they thought the info was true rather than larp. They are also in talks with Ozzy and he has been helping them transistion to V2. Why would he do that for larpers. People like you think you are doing good for the space, but actually you are part of the problem. If you can do true research the don’t speak.

• 
  notreroute (@notreroute) reported 50 minutes ago

  most founders spend $40,000 a month on payroll before discovering 80% of operational workflows can run on autonomous agent pipelines with zero human intervention. in 6 minutes ByteByteGo breaks down how deterministic execution order replaces entire management layers: state coordinator logging directly to GitHub worker agents executing API queues and pull requests independent validators running 12-second test loops the surface read is treating models like chat interfaces, the actual lever is orchestrating autonomous micro-services. i turned the whole architecture into a practical deployment guide you can run in production. worth more than an entire tier of middle management salaries. watch the clip first, then the full architecture breakdown is below. you'll find the full breakdown in the article below

• 
  Christopher C. Smith (@christophcsmith) reported 50 minutes ago

  Two examples: 1. If I put Grok in a container with no access to GitHub issues and ask it to work on a numbered issue, it guesses at the issue content instead of asking for help. 2. If I make changes while Grok is working in the same folder, it will often just *** reset them.

• 
  Mr.Goober (@RealMrGoober) reported 52 minutes ago

  From a new thing perspective there really isn't a plugin that lets you edit huge images like this they all have limits, it takes a bit of time to apply on a huge image, but its good enough to use, at least for my use case. I'll fix it up a bit and then release it on github

• 
  Prakash Murthy (@prakzymessi) reported 55 minutes ago

  @github @openclaw @steipete maintaining a viral repo is 10% code and 90% issue triage from people who never read the readme

• 
  nf (@nifdahq) reported 55 minutes ago

  I saw someone asking difference between *** and github. Let's us a car ​*** is the engine. It runs locally, tracking every tweak, fix, and mileage update on your machine. ​GitHub is the garage. It backs your car in a safe place and like a garage, you can inspect or collaborate

• 
  MadeItHappen (@MadeItHappenX) reported 58 minutes ago

  @kmcnam1 Can’t fix production unless you up your limits on GitHub actions usage… because you thought you could get cute offloading your compute to GitHub for cheap and now you’re in a pickle because you can’t accept payments since your site is down, so you can’t up your GitHub compute budget for your CICD to deploy the fix.

• 
  Dosuka (@DosukaSOL) reported 1 hour ago

  @CryptoExpert101 yupp and took down one of the websites. So either she rugged. Or someone hacked her x, website and github

• 
  Jimmy Jones (@the_jimmy_jones) reported 1 hour ago

  @dcapitella @github I'm running a gitea instance in coolify on a bare metal server I rent.

• 
  jaeger_program #nafo (@jaeger_program) reported 1 hour ago

  @xyz3va @github @Microsoft stupid moron please fix your ******* **** now

• 
  Can Bölük (@_can1357) reported 1 hour ago

  @HotAisle the people i mentioned would still come to me with 9214141 github issues

• 
  Duy /zuey/ (@goon_nguyen) reported 1 hour ago

  i moved my development environment to the cloud, and i do not think i am going back linux is much closer to production than my laptop, so fewer bugs hide behind local differences and debugging gets less annoying but the bigger change is that development no longer depends on my laptop being open i can send instructions from my phone while i am outside. the agents keep working on cloud machines, run tests, open worktrees, and report back without turning my laptop into a portable space heater GitHub is now part of the runtime when an issue gets the ai-handle label, a webhook wakes the cloud agents. they inspect the issue, create an isolated workspace, implement the fix, run the checks, and report the result i do not need to open Terminal or launch Codex just to start the work my Discord support flow goes even further: - a customer reports a bug - an AI support agent gathers the details and creates an issue - the label triggers a coding agent on the cloud - the agent fixes it, validates it, and sends the result back into the support flow that is the first time "autonomous agent" has felt operational to me instead of being a demo with a chat box cloud machines also remove several stupid local bottlenecks the internet connection is fast. parallel agents and test processes stop fighting with the apps on my laptop. multiple worktrees stop eating my mac M1 512GB SSD that was never designed to host an AI engineering team there are tradeoffs, of course. cloud agents need strict permissions, isolated environments, budget limits, logs, and a clean path for human review. running 24/7 without guardrails is just a faster way to create incidents but the direction feels obvious AI coding agents should live where software runs, events happen, and automation can continue without waiting for a developer to open a laptop for me, the laptop is becoming a control surface the development environment is becoming infrastructure

• 
  ₿themoon | ∞ vs 21m | Capital ₿ear Stackers member (@Bthemoon2030) reported 1 hour ago

  @github some of my website are giving 404 There isn't a GitHub Pages site here. Is there any issues on the servers?

• 
  Nandkishor (@devops_nk) reported 1 hour ago

  I see the same problem in DevOps teams. - One AI agent for Kubernetes. - Another for CI/CD. - Another for observability. - Another for GitHub security. The hard part isn’t running multiple AI agents. It’s always making sure every agent has the right context without repeatedly explaining your entire infrastructure.

• 
  Kayno (@Morpheos_sc) reported 1 hour ago

  I entered Supabase and saw login with chatgpt, laugh almost finished me.... I don't even login with github that Microsoft owns lmao