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	11 days ago
Quito	Sign in	12 days ago
Junín	Errors	12 days ago
Guadalajara	Errors	12 days ago
Paris	Website Down	12 days ago
Quito	Errors	12 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:

• 
  AI Panda (@AIPandaX) reported 10 minutes ago

  Every AI coding agent already reads your codebase. What if it could understand every dependency before making changes? Inside every codebase is a structure: functions call other functions, files import other files, changes ripple through the system. That structure is not hidden. It is just relationships that any graph can map and any agent can query. There is an open-source tool that turns codebases into knowledge graphs that AI agents can query. It runs entirely in your browser. It is called GitNexus. It started in August 2025 when developers built a client-side knowledge graph creator that indexes repositories without sending code to servers. Drop in a GitHub, GitLab, Azure DevOps repo or ZIP file. Get an interactive knowledge graph with a built-in Graph RAG agent. Works with 21 programming languages. Here is what happens when you use GitNexus. You run npx gitnexus analyze in your repository. It indexes every file, function, class, and dependency. It builds a knowledge graph that tracks every relationship. Connect your coding agent with npx gitnexus setup. Now your agent can query the graph through MCP tools. The problem it solves: AI agents edit code without knowing what depends on it. Agent changes UserService validate function. Doesn't know 47 functions depend on its return type. Breaking changes ship. GitNexus precomputes structure at index time. Clustering. Tracing. Scoring. When your agent asks what depends on UserService, it gets a complete answer in one query. Eight callers. Three clusters. All with confidence scores. No multi-step exploration needed. A team measured impact on code reliability. AI agent without GitNexus: 3 breaking changes per 10 edits because it missed downstream dependencies. Same agent with GitNexus MCP integration: zero breaking changes because it checked impact before editing. Two ways to use it. CLI plus MCP for daily development. Index repos locally. Connect Cursor, Claude Code, Codex, Antigravity, or Windsurf through MCP. Query the graph from your editor. Check impact before changes. Full repos, any size. Web UI for quick exploration. No install needed. Upload a repository or paste a GitHub URL. Explore the graph visually. Chat with the built-in Graph RAG agent. Runs entirely in browser with LadybugDB WASM. The graph shows more than connections. Community clustering groups related code. Execution flow traces how data moves. Call chains map function dependencies. Risk scores identify fragile areas. All computed at index time, not query time. MCP tools for agents. Impact analysis shows what breaks when you change a function. Dependency trace reveals who calls your code. Architecture view maps domains and boundaries. Execution flow follows data through the system. Change risk scores affected files. Works with 21 languages. TypeScript, JavaScript, Python, Go, Rust, Java, C, C++, C#, PHP, Ruby, Swift, Kotlin, Dart, Vue, HTML, CSS, Shell, PowerShell, Dockerfile, Jinja. Tree-sitter parsing with native and WASM backends. LadybugDB for graph storage. Native version for CLI with persistence. WASM version for browser with in-memory storage. Bridge mode connects them: web UI can browse CLI-indexed repos. One command setup. Analyze creates the graph, installs agent skills, registers hooks, and writes context files. Setup configures MCP so agents can query the graph. Works with Claude Code, Cursor, Codex, and other MCP-compatible editors. Optional embedding support for semantic search. Deploy to Render with one click for team access. Self-hosted backend mode for unlimited scale. Access control with token authentication. 46.3k+ stars on GitHub. Created August 2025. Active development with new language support and agent integrations. Every codebase already has structure. GitNexus maps it so agents can see before they change.

• 
  Jason Lixfeld (@JasonLixfeld) reported 10 minutes ago

  @poteto @bot I’ve had issues with this in that the sandbox can’t reach GitHub and winds up burning tokens trying to find a way to be able to push a commit.

• 
  Plague (@Plague_FPS) reported 11 minutes ago

  @github not updating the number of Issues/PRs until a hard refresh is extremely annoying. Really hope this changes at some point in the future.

• 
  Second Mind Systems (@Secondmindsys) reported 11 minutes ago

  @chribjel That would be an awesome capability. But once an MCP server can wake an agent up on its own, you’ve changed the trust model. Before, the agent usually started because a person or workflow deliberately invoked it. With webhooks, anything connected to it can suddenly say, hey, something happened. That’s useful. GitHub can tell it a PR merged. Monitoring can tell it something broke. Stripe can tell it a payment failed. But hearing about an event and being allowed to act on it are two different things. The agent still needs to figure out whether the event is real, whether it’s still relevant, whether it already handled it, and what it’s actually allowed to do next. A PR merged might mean check the build. It doesn’t automatically mean deploy. So the more ways we give agents to wake themselves up and react to the world, the more important it becomes to keep the original job and permissions intact. Governance must always scale with new capability because new capability also emerges new consequential surface area/blast radius.

• 
  Kenton Varda (@KentonVarda) reported 12 minutes ago

  @MKelner 1) It's easy. You just open up a new Cloudflare OS workspace and start prompting. You don't have to think about deployment, the gadget (app instance) just appears in the workspace and works right there. A gadget is only two files, client.js and server.js, no other boilerplate. 2) It's safe. The gadget runs in a sandbox with no access to anything except what you explicitly give it. You paste a link to your GitHub repo into chat, and the system prompts you to upgrade this link to a capability. That gives the agent permission to interact with the repo -- but nothing else. Moreover, any *changes* made by the agent are held for approval, so it can't accidentally do damage. But these approvals don't force you to sit around watching the agent so that you can click "approve" whenever it does something. Instead, the changes the agent has requested are *simulated* back to it, so that it can keep going and queue up a series of actions which you approve all at once after it is done. 3) Gadgets and agents are tightly integrated. Your gadget can easily spawn an agent, and agents can talk to gadgets, within the same workspace. You don't need to figure out how to integrate with an agent harness to build workflows that orchestrate agents.

• 
  Tauhid IQ (@iqtauhid) reported 14 minutes ago

  - Claude = coding. ($20/mo) - Supabase = backend. (Free) - Vercel = deploying. (Free) - Namecheap = domain. ($12/yr) - Stripe = payments. (2.9%/transaction) - GitHub = version control. (Free) - Resend = emails. (Free) - Clerk = auth. (Free) - Cloudflare = DNS. (Free) - PostHog = analytics. (Free) - Sentry = error tracking. (Free) - Upstash = Redis. (Free) - Pinecone = vector DB. (Free) Total monthly cost to run a startup: ~$20 There has never been a cheaper time to build. Follow Tauhid IQ for regular update.

• 
  Cato Nooka (@catonooka) reported 15 minutes ago

  I tried Ornith 1.5 35BA3 on my spark. - Speed: up to 140 tps decode - Casual chat: very good, like having 4o locally - Search: okiedo, but not very precise - Research from a github repo: not good - Same prompt like below test for Qwen 3.8 Flash Next: cannot produce runable work, add more prompt to fix but still not working (same prompt + hardness) It can produce decent simple website, it can also have good tool calling, but to do complicated task as I usually hand over to GLM-5.3, non of them work.

• 
  Gabriel Odusanya (@gabbytech01) reported 15 minutes ago

  My VulnMart Application Security and SecDevOps project second phase is complete. This week focused on networking, HTTP, DNS, and client-server communication the foundations required to understand how web applications work before testing their security. For the first project, I performed a safe and authorized HTTP request analysis of offshieldsecurity .org I examined DNS resolution, HTTPS, TLS 1.3, HTTP/2, server responses, and important headers such as Content-Type, Cache-Control, ETag, Last-Modified, and Access-Control-Allow-Origin. The request flow was documented as: Domain name → DNS resolution → IP address → TCP connection → TLS handshake → HTTP request → Server response For the second project, I built a local VulnMart HTTP server using Python. The server included: • A root route that returned a server status message • A health API that returned JSON • A products API that returned fictional product data • A 404 error route for unknown endpoints The endpoints were tested successfully. The health and products routes returned HTTP 200, while the missing route correctly returned HTTP 404 with a structured JSON error. The server was bound to 127.0.0.1 so the exercise remained inside my local lab environment. No intrusive testing, brute force, exploitation, or unauthorized scanning was performed. The projects were documented and published to my GitHub repository, vulnmart-security-lab. This work helped me understand how clients, servers, routes, APIs, headers, status codes, and secure communication work together. The next stage is to expand VulnMart into a more realistic application with users, authentication, authorization, APIs, and an initial threat model. @ce3nerd

• 
  Seyr (@0xSeyr) reported 17 minutes ago

  most teams spend 40% of their operational hours clicking between Stripe, AWS, GitHub, and PostHog. every SaaS dashboard you pay for is just an API endpoint wrapped in 50 megabytes of JavaScript - an interface tax built because software was too dumb to take an order. in this 37-second clip, Claude shows off the new Claude Code desktop app running parallel terminal agents across repos to replace the entire browser stack. i broke the entire setup down into a 3-layer architecture guide you can implement today. beats wasting 4 hours a day acting as a human router between web tabs. watch the breakdown first, then get the full architecture guide below. the article below covers the full picture

• 
  Koder (@koder0x) reported 17 minutes ago

  I get charmed by using Warp terminal on my Windows 11 machine but I wasn't able to submit commands in Ubuntu 24 LTS so after many attempts with env vars, I casted a "Claude Code spell" turns out the shell's first precmd hook could race ahead of session registration and get silently dropped — no error, just a terminal that eats every ENTER forever if you need it before the PR is eventually approved, it's on my GitHub repo: gsscoder | warp (branch fix/line-editor-precmd-race)

• 
  Slyke 🇦🇺 🇨🇦 (@SlykePhoxenix) reported 19 minutes ago

  @davepl1968 Have you use OpenClaw? Try submitting an issue to their GitHub, this is exactly what their bot does if you give it enough reproducibility instructions. Even gives the devs a fix before a dev even has a chance to look at it.

• 
  Leander (@leanderriefel) reported 21 minutes ago

  Does Cursors Origin even have public repositories? Is there something like origin[dot]com/<org>/<repo> with issues, pull requests and discussions? Because if not what was the whole "replacing github" even about?

• 
  z (@animaginarynumb) reported 25 minutes ago

  do i launch next week? my github actions are down and my ci/cd runs on them. when i get the refresh on 1st of september, should i launch my product?

• 
  Agentic Joe (@agentic_joe) reported 30 minutes ago

  Serious question, and I promise I am not trying to be a ****. But how in the hell is @NousResearch so praised and recommended with all of these outstanding GitHub issues? I could never imagine preaching about having the best harness, using multiple agents etc and not figuring out how to resolve all these issues internally as a company, or at least try to maintain the queue before shipping new features that continue to break stuff. Not to mention their UI/UX. Very hard to believe this team came from a solid engineering background. I could be wrong, but damn! 👀

• 
  Adel Bucetta (@adelbucetta) reported 31 minutes ago

  @davepl1968 this is massive. pointed an ai at my github issues and it actually fixed some of them. that's not just automation, that's someone else owning the judgment on what to fix

• 
  Dela (@delaanthonio) reported 32 minutes ago

  @GergelyOrosz It’s even more ironic since Xcode is trash compared to Visual Studio and VSCode. Xcode makes developing for the Apple ecosystem painful. No one loves it. But we use it anyway so we can distribute apps to Apple’s customer base. Taking a step back, we like macOS since it’s a good Unix-like environment for developing software. I used WSL years ago and there are always issues with the translation layer between Windows and Linux. It may have gotten better, but the experience is always worse than using a native Unix-like OS. The main mindshare Microsoft has for devs is GitHub. Everyone sees the frequent outages. Many stay since it’s hard to replace GitHub, but some are leaving.

• 
  Ben Kraus (@BenKraus) reported 33 minutes ago

  @bwhiteley @miles_wright @github Had a similar problem. Merged individually but it rebased and messed up reviews

• 
  Simplifying AI (@simplifyinAI) reported 35 minutes ago

  Boris Cherny is not gonna like this.. Researchers at the University of Luxembourg analyzed real production telemetry from coding agents, Claude Code, Cursor, GitHub Copilot, to see how often these agents ask for a second page of results when a tool response gets cut off. The answer, across the entire dataset: zero times. When a search returns too many results, the agent works with whatever made it into that first batch and moves on, even when what it needed was one page later. • 37% of "get epics" calls exceed the token budget in production • 28% of "get merge request diffs" calls exceed it too • 0% of truncated responses were ever followed by a second-page request • A basic keyword ranking raised the odds of the right item landing in that one page from 24.2% to 35.0% • Fancier ranking signals on top made it worse, not better Noisy tool output isn't just a cost problem. It's a silent correctness problem too, whatever doesn't make the first page effectively stops existing to the agent. wrote the full guide on keeping that output from piling up in the first place. full article below 👇

• 
  Scott Henderson "Human-Led AI Precision" (@swingtradenotes) reported 35 minutes ago

  So, you build a multi-billion-dollar AI coding business on top of Cursor... Elon buys Cursor. OpenAI says, we're out. Now you have to find another way to power the damn thing. And here is the part I think people are dramatically underestimating: You don't just have a multi-billion-dollar software business. You potentially have millions upon millions of pieces of context, prompts, rules, agent histories, workflows, project state and developer behavior accumulated across God knows how many Cursor instances. Can you technically point Cursor at another model? Absolutely. But that's not really the question. Can you migrate the experience? Can you preserve years of accumulated context and workflows? Can you make another model interpret all of that context the same way? Can you preserve agent behavior? Can you keep enterprise customers productive while changing one of the fundamental engines underneath the product? And can you do all of that without users noticing that the product suddenly got worse? That's a considerably harder problem. OpenAI itself says Cursor can keep using the existing models during the transition and that users will have alternatives—including bringing their own OpenAI API key—so this isn't literally a case of Cursor going dark overnight. Cursor also isn't starting from zero: it has its own models, access to SpaceX's enormous compute infrastructure, and Grok integration. But here's my 2 cents: The code is probably the easy part. The accumulated intelligence is the moat. Repositories can be moved. APIs can be rewritten. Models can be swapped. But if thousands—or eventually millions—of developers have spent years effectively teaching their Cursor environment how they work, that accumulated context becomes incredibly valuable. And we already know some Cursor conversation and workspace state is stored locally in databases tied into the Cursor environment, which illustrates how much more complicated "migration" can become than simply copying a *** repository. So yes, migration is humanly possible. Seamless migration at this scale is a completely different question. And if the replacement model is even 10% worse at understanding the existing workflow? For somebody running ten agents, maybe that's annoying. For an enterprise running 10,000 agents? That's a ******* problem. This is why I think the market may be looking at the wrong part of this story. It isn't simply Sam vs. Elon. It's the first really big demonstration that model dependency itself is enterprise risk. And every company building a billion-dollar business on somebody else's frontier model just got a rather loud reminder of that. Mr. Market, meet vendor lock-in. There are replacements for pieces of Cursor. There isn't an obvious drop-in replacement for Cursor as a whole. Cursor today isn't simply an editor with an LLM bolted onto it. Its Agent combines the model with codebase search, file editing, terminal execution, browser control, rules, MCPs, subagents and long-running cloud agents. Those Cloud Agents get dedicated VMs with the repository, dependencies, secrets and network access, and can work independently for hours. The serious alternatives are: Claude Code — probably the strongest alternative for raw agentic coding, particularly large repositories and terminal-driven work. OpenAI Codex — increasingly interesting because it now supports parallel agents, cloud environments, worktrees, persistent team Skills and scheduled/background work. GitHub Copilot — Microsoft/GitHub have the enormous advantage of already owning the repository/workflow layer for a huge portion of software development. Windsurf — probably the closest conceptual competitor if what you specifically want is another AI-native IDE. Cline/Continue/Aider — interesting precisely because they push toward a model-independent, bring-your-own-model architecture. But here's where I think your question gets much more interesting. What replaces your Cursor? That answer might be: Nothing. Think about what Cursor has actually become. It isn't: IDE → AI answers question → developer writes code. It's increasingly: Human → Cursor → orchestration → agents → models → tools → repositories → tests → infrastructure. Cursor itself describes coding quality as depending on three things: the model, the harness and the context. That's incredibly important. Because OpenAI can replace the model. Anthropic can replace the model. Grok can replace the model. But replacing the harness + context + workflow is considerably harder. And Cursor has explicitly designed around this. Its current architecture lets it optimize instructions and tools separately for different frontier models, while supporting models from OpenAI, Anthropic, Google, SpaceX/xAI and Cursor itself. Which means Musk's answer may simply be: don't replace Cursor. Replace OpenAI inside Cursor. That's a completely different proposition. Today Cursor already has Grok, Composer, Anthropic and Google models available alongside OpenAI. So I would separate this into two risks: Model migration: difficult, but manageable. Cursor migration: potentially ******* brutal. If an organization has spent years building Cursor Rules, MCP integrations, Skills, prompts, agent workflows, repository knowledge, automation, testing patterns and human habits around Cursor, moving to Claude Code or Codex isn't equivalent to changing Microsoft Word for Google Docs. You're potentially changing the operating layer through which your humans interact with your software organization. And we're already seeing where this goes. Cursor's Cloud Agents don't merely autocomplete code. They can independently build features, fix bugs, test them and open pull requests from isolated cloud environments. That's why I think Cursor itself is the asset. SpaceX didn't necessarily spend billions buying a fancy VS Code fork. It bought a potential control plane for AI software development. Models increasingly become interchangeable engines underneath it. If Musk can get Grok/Composer close enough to OpenAI/Claude performance, then owning the distribution, context, orchestration and developer relationship may ultimately matter considerably more than owning whichever model happens to benchmark best this month. And there is one hell of an irony here: OpenAI cutting Cursor off might actually accelerate Cursor's incentive to make the underlying model completely replaceable. If they succeed at that, OpenAI hasn't destroyed Cursor's moat. It may have helped Cursor build a bigger one.

• 
  wispem-wantex (@wispem_wantex) reported 37 minutes ago

  @Parakramtweets I use Linear at work, which unfortunately I am not in charge of. I prefer gitea issues (or github issues), personally.

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  HowToDoAffiliateMarketing.com (@AffiliateLetter) reported 41 minutes ago

  Is there a known issue with the GitHub connector/tool sessions right now in @OpenAI @ChatGPT /Codex? anyone else facing the same issue?

• 
  Jarkko (@JarSyr) reported 42 minutes ago

  Then SpaceX acquired Cursor on August 14. Three days later, Cursor launched Origin, its own GitHub competitor. And two weeks after that, OpenAI publicly said it wasn’t confident SpaceX would follow its terms and started winding down the relationship. Hard to ignore the timing.

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  Rav (@_MrDecentralize) reported 43 minutes ago

  The Mandate from the Machine's Maker For three years, the pitch was simple. AI tools make you faster. They handle the boilerplate. They free you to do the work that matters. Every internal rollout memo, every all-hands demo, every LinkedIn post from a VP of Engineering carried the same subtext: this is here to help, and you will choose when and how to use it. Microsoft sold that story harder than anyone. Copilot was the friendly assistant. GitHub Copilot was the pair programmer who never got tired. The company's entire go-to-market for enterprise AI rested on a single premise: adoption is voluntary, and voluntary adoption proves the product works. Then Microsoft made it mandatory. Internal communications obtained by multiple outlets revealed that Microsoft now evaluates employees on their use of AI tools as a formal performance metric. Not a suggestion. Not a nudge. A line item in the review that determines your rating, your compensation, and your continued employment. CEO Satya Nadella told the public that 30% of Microsoft's code is now written by AI. CTO Kevin Scott projected that number would reach 95% by 2030. Those numbers were presented as proof of progress. Read them again from the chair of a Microsoft engineer whose performance review now includes a checkbox for how much they use the tool producing 30% of the codebase. The math is not subtle. If AI writes 30% of the code today and 95% by 2030, the trajectory has a name. It is not augmentation. It is replacement on a four-year schedule. And the company just told every engineer to accelerate the curve or face consequences. This is not a startup founder musing about headcount on a podcast. This is a company with 228,000 employees embedding AI usage into the performance management system that governs raises, promotions, and terminations. The policy does not say "learn AI." It says "use AI." The distinction matters. Learning is development. Usage is compliance. Meta did something similar. But Meta did not build the tools. Microsoft did. Microsoft built Copilot, priced it at $30 per user per month, sold it to every enterprise on Earth as an optional productivity layer, and then turned around and told its own workforce: this is not optional for you. The company that made the tool made the mandate. The mandate accelerates the output metric. The output metric will eventually prove that fewer humans are needed. And the humans were told to make that case themselves, on their own performance reviews, or get marked down for non-compliance. Voluntary adoption was the pitch. Mandatory adoption is the policy. The distance between those two words is the entire future of employment at the company that writes the tools.

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  Ashutosh Roy (@ashutoshroy02) reported 46 minutes ago

  The Reels save button is a graveyard, not a library. You save it thinking "I'll do this later." Later never comes. A week from now you have no idea which reel had the GitHub repo, which had the cafe, which had the recipe. Building something to fix this. More soon. Stay Tuned !

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  Kieran Bond (@KieranBond_) reported 51 minutes ago

  @Aaronontheweb Tbh, yeah. I barely use them since October. I mostly use GoLand these days (writing Go), and only for my 'craft' hour where I try to keep my skills from fading. Otherwise, I just review diffs in GitLab/GitHub. They're fine for it. IDEs are too slow for this.

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  hitu (@hitu_monke) reported 51 minutes ago

  Using general-purpose LLM agents for code reviews is fundamentally broken The issue isn't the model's intelligence. It's position drift hallucinating line numbers, skipped files on large PRs, and massive token burn Alibaba just open-sourced an internal tool called open-code-review to fix this mess completely Battle-tested across 19,000+ internal developers and 3 million real-world engineering tasks, it brings actual engineering discipline to AI reviews Instead of dumping raw context into a prompt, it pairs deterministic diff parsing with isolated sub-agents and smart file bundling It eliminates line drift entirely, pins comments to exact code changes, and slashes token consumption down to 1/9th You can plug it directly into your terminal, VS Code / Cursor, or GitHub Actions CI/CD with any model-Claude, DeepSeek, Qwen 2.5, or GPT-4o If you want reliable automated reviews, you need deterministic precision, not just bigger context windows

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  ろんぱ (@RONPA_INV) reported 52 minutes ago

  @thsottiaux I submitted a GitHub issue regarding the problem where local Codex projects disappear when Windows shuts down abnormally.

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  Brij Pandey (@LearnWithBrij) reported 52 minutes ago

  Everyone is shipping MCP servers. Far fewer people can explain what MCP actually is. Here is the whole protocol on one page. 𝗧𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 Four models and five tools used to mean twenty custom integrations. MCP turns N x M into N + M. One protocol, any model, any tool. 𝗧𝗵𝗲 𝗺𝗲𝗻𝘁𝗮𝗹 𝗺𝗼𝗱𝗲𝗹 Host is the AI app: Claude Desktop, Cursor, Claude Code. Client lives inside the host, one per connected server. Server exposes capabilities. Host contains Client. Client talks to Server. All of it rides on JSON RPC 2.0. 𝗧𝗵𝗿𝗲𝗲 𝗽𝗿𝗶𝗺𝗶𝘁𝗶𝘃𝗲𝘀, 𝘁𝗵𝗿𝗲𝗲 𝗼𝘄𝗻𝗲𝗿𝘀 Tools: model controlled. The AI decides when to call. Resources: app controlled. The app injects context. Prompts: user controlled. The user explicitly invokes. Most confusion about MCP comes from collapsing these three into one bucket. 𝗧𝘄𝗼 𝘁𝗿𝗮𝗻𝘀𝗽𝗼𝗿𝘁𝘀 stdio for local subprocesses like filesystem and ***. Streamable HTTP for remote services like GitHub and Notion. It replaced legacy SSE in 2025. 𝗪𝗵𝗮𝘁 𝗻𝗼𝗯𝗼𝗱𝘆 𝘁𝗲𝗹𝗹𝘀 𝘆𝗼𝘂 Token tax: 50 tools at 50 tokens each is 2,500 schema tokens burned per turn. OAuth sprawl: secret rotation across many servers is a real ops cost. Tool sprawl: more servers, more failure surface. Schema drift: a server changes and your agent quietly breaks. Observability gap: tracing across the boundary is still hard. 𝗧𝗵𝗲 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆 MCP is not magic. It is plumbing. Simple primitives, clean transport, open standard. That is exactly why it became the default in eighteen months. Build tools. Expose via MCP. Ship value. Repeat. Where does the line sit for you: at what point does a protocol stop being an integration convention and start being infrastructure that platform teams have to govern like a network layer?

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  Masood (@_TechMasood) reported 57 minutes ago

  @Marwan_SoftDev github issues with a forced repro template beat forms for me because forms just pile up reports you cant actually act on : )

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  Loftwah (@loftwah) reported 57 minutes ago

  @fjzeit How much of the code though? I've had some where I have to look at the code but not very deeply. More like skim over the code. My process is actually to grill the agent about everything I don't like and create GitHub issues for all of it with the intended solution inline.