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 (68%)
• Sign in (16%)
• Errors (16%)

Live Outage Map

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

City	Problem Type	Report Time
Itapema	Website Down	14 days ago
Tlalpan	Sign in	20 days ago
Quilmes	Website Down	20 days ago
Bengaluru	Website Down	22 days ago
Yokohama	Sign in	23 days ago
Gustavo Adolfo Madero	Website Down	27 days ago

Full Outage Map

Community Discussion

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  David Whitney (@david_whitney) reported 4 minutes ago

  @fromdevoid Copilot brand, TL;DR - GitHub Copilot good, great brand, renaming everything to Copilot? Terrible idea, huge backlash, rightfully so.

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  Enkhmanal 🟠 (@Enkhmanal) reported 5 minutes ago

  $MSFT - MICROSOFT IS THE MAG 7 STOCK BUILT FOR A RECESSION — SUBSCRIPTION REVENUE, NO AD RELIANCE, THE LIGHTEST LEGAL RISK IN THE GROUP The bull case is the shape of the revenue. Microsoft 365, Azure, Dynamics, GitHub and security are subscription-heavy and non-discretionary — enterprises cut projects before they cancel the software their operations run on. That bends far less in a slowdown than the ad budgets and consumer spend that most of the cohort leans on. It sits below its 52-week high at a modest multiple for the quality. The mechanism is defensive mix plus a quiet legal edge: limited FTC scrutiny versus peers staring down major antitrust cases, where a single adverse ruling can take double digits off a name overnight. You still get AI woven across the stack and quantum optionality on top. The risk is a broad megacap de-rating that compresses multiples regardless of fundamentals.

• 
  Rage (@Revengerai) reported 7 minutes ago

  @iatnon @ClaudeDevs It's honestly been an issue since January–February, and people have already reported it on the Claude GitHub repository. However, as far as I know, none of the developers or representatives have provided an explanation about it, or at least I haven't seen any official response so far.

• 
  DataDan｜AI Data Engineering (@ba_niu80557) reported 9 minutes ago

  Your prompts are the most impactful code in your AI system. A single-line change to a system prompt can alter tone, accuracy, safety behavior, tool selection, and output format — across every user interaction, simultaneously, instantly. And most teams manage them with less rigor than they'd apply to a CSS file. No version control. No staging environment. No approval workflow. No evaluation before deployment. No rollback capability. No audit trail of who changed what, when, and why. Maxim AI's 2026 analysis nails the problem: "Many teams still manage prompts as hardcoded strings buried inside application code, with no version history, no audit trail, and no way for non-engineers to iterate without triggering a deployment. A single untracked prompt change can degrade accuracy across thousands of interactions." (Source: Maxim AI, "Top 5 Prompt Versioning Platforms", March 2026) This is the most under-invested infrastructure in production AI. And the cost of not investing shows up as "our AI system suddenly got worse and nobody knows why." The incident pattern that every team running production AI will experience — if they haven't already: Monday: an engineer tweaks the system prompt. Adds a line: "Always provide a specific recommendation rather than listing options." Seems like an improvement. Commits directly to main. Deploys. Tuesday: support tickets spike. Customers report the AI is being "pushy" and "making decisions for me." Satisfaction scores drop 15%. Wednesday: the product manager asks "what changed?" Nobody knows. The prompt change is buried in a commit with 12 other code changes. The commit message says "minor improvements." Thursday: someone finds the prompt change. Reverts it. But the previous prompt version doesn't exist anywhere — it was overwritten in the code. The engineer tries to remember what the old prompt said. Gets it 90% right. Friday: the reverted prompt is slightly different from the original. Behavior is mostly back to normal but not exactly. Nobody can tell whether the remaining difference is the prompt or something else. This incident takes a full week, involves 5 people, and happens because prompts were treated as strings in code instead of versioned artifacts with deployment workflows. With proper prompt versioning, the same incident resolves in 30 minutes: check the version history, identify the change, click rollback, previous version is restored exactly. Done. Why prompts need more rigor than regular code — not less: Most engineering teams intuitively place prompts in a "configuration" bucket — like feature flags or environment variables. Something that can be changed casually without a full deployment cycle. This intuition is exactly backwards. Here's why: A regular code change affects one function, one module, one feature. It's scoped. It has unit tests. If it breaks, the blast radius is contained. A prompt change affects every single output the AI system produces. It's global. There are no unit tests (for most teams). If it degrades quality, every user interaction is affected simultaneously. A CSS change makes the button blue instead of green. Users notice but aren't harmed. A prompt change can make the AI hallucinate, violate safety guidelines, leak system prompt contents, ignore tool results, or change its personality. Users are actively harmed. The blast radius of a prompt change is larger than almost any other change in your codebase. And yet it typically receives the least oversight. (Source: Braintrust, "What is Prompt Management?", February 2026) What production-grade prompt management actually looks like in 2026: The mature pattern has five components: Component 1: Prompts decoupled from application code. Your prompts are not strings in your Python file. They're stored in a prompt management system (LangSmith Prompt Hub, Langfuse, Confident AI, Maxim AI, or even a well-structured database). Your application fetches the active prompt at runtime by ID and environment label. This means: changing a prompt doesn't require a code deployment. It also means: the prompt can be edited, reviewed, and evaluated by non-engineers (product managers, domain experts) without touching the codebase. Component 2: Version control with immutable history. Every prompt change creates a new version. Previous versions are preserved. You can diff any two versions. You can see who changed what, when. You can retrieve any historical version. Confident AI's approach takes this further — ***-based prompt management with branching, commit history, and approval workflows. Product manager creates a branch, edits the prompt, runs eval, requests review, engineer approves, merge to main, promote to production. (Source: Confident AI, "Best AI Prompt Management Tools", May 2026) The same workflow you use for code. Applied to prompts. Because prompts ARE code. Component 3: Environment-based deployment (dev → staging → production). A new prompt version is created in dev. It's tested manually in the playground. It's promoted to staging — where it serves a small percentage of traffic alongside the production version. Eval scores are compared. If staging scores are equal or better → promote to production. If worse → reject. Braintrust describes the pattern: "Prompt versions that fail evaluation in staging cannot be automatically promoted to production. When issues appear, instant rollback restores a previously validated version without requiring code changes." (Source: Braintrust, February 2026) No prompt reaches production without passing eval in staging first. This one rule eliminates 80% of "our AI suddenly got worse" incidents. Component 4: Automated evaluation on every prompt change. Before a prompt version can be promoted from staging to production, an automated eval suite runs: → Accuracy on a holdout set of questions with known correct answers → Format compliance (does the output match the required schema?) → Safety checks (does the prompt introduce new safety violations?) → Regression check (is this version at least as good as the current production version?) If any check fails → the promotion is blocked. The prompt stays in staging until the issues are fixed. This is the CI/CD pipeline for prompts. Your code has CI/CD. Your infrastructure has CI/CD. Your prompts should too. Component 5: Production monitoring tied to prompt version. In production, every AI response is tagged with the prompt version that produced it. Quality metrics (user satisfaction, accuracy, hallucination rate) are tracked per prompt version. If you deployed prompt v17 on Monday and satisfaction dropped 15% by Wednesday — the dashboard shows it immediately: "v17 is performing 15% worse than v16 on user satisfaction." You click rollback. v16 is restored. Done. Without version-tagged monitoring, you see "satisfaction dropped" but don't know why. With it, you see "satisfaction dropped because of prompt v17, specifically because the new instruction caused more hallucination on question type X." The tooling landscape in May 2026: Confident AI: ***-based workflow with branching and approvals. 50+ eval metrics built in. Best for teams that want prompt management to feel like GitHub — branches, commits, PRs, merges. Maxim AI: End-to-end platform connecting prompts to simulation, evaluation, and production observability. Best for enterprise teams where both engineering and product need to collaborate on prompt optimization. LangSmith Prompt Hub: Tightest integration with LangChain/LangGraph. Labels for environment management. Strong tracing. Best for teams fully in the LangChain ecosystem. Langfuse: Open source (MIT). Self-hosted. Composite prompts (chaining multiple prompts into one workflow). Best for teams needing full data ownership. Braintrust: AI assistant (Loop) that analyzes prompt performance and suggests improvements. Free tier: 1M traces + 10K eval scores/month. Best for fast-moving teams that want AI-assisted prompt optimization. (Sources: Maxim AI March 2026; Confident AI May 2026; Braintrust May 2026) If you're starting from zero: Langfuse is the easiest starting point — open source, self-hosted, MIT license, handles versioning and tracing. Graduate to Confident AI or Maxim when you need evaluation integration and approval workflows. Three uncomfortable questions: 1) If your AI system's quality degraded this morning, could you identify which prompt change caused it — in under 5 minutes? If "no" — you have no prompt version tracking in production. The degradation could have been caused by a prompt change, a model update, a RAG config change, or a data drift — and you have no way to distinguish them. Version-tagged monitoring eliminates this ambiguity. 2) When an engineer changes a prompt, does an eval suite run automatically before the change reaches production? If "no" — you're deploying the highest-blast-radius change in your codebase with zero automated quality checks. Your code has CI. Your infrastructure has CD. Your prompts have nothing. The prompt is the most impactful artifact in your AI system and it's the only one shipping without automated validation. 3) Can a non-engineer (product manager, domain expert) propose a prompt change, see eval results, and deploy it — without touching code? If "no" — your prompt iteration speed is bottlenecked by engineering capacity. The people who best understand user needs (product) and domain accuracy (experts) can't iterate on prompts without engineering involvement. Decoupling prompts from code and providing a prompt management UI removes this bottleneck. The thesis: → 2023: prompts are strings in code, changed casually, deployed without testing → 2024: some teams start version-controlling prompts, mostly in *** alongside code → 2026: "production-grade prompt management is a five-component system — decoupled storage, immutable versioning, environment-based deployment, automated evaluation, and version-tagged monitoring — and the teams that built it have 80% fewer 'our AI suddenly got worse' incidents than the teams that didn't" Your prompts are the most impactful code in your AI system. They have the largest blast radius, the most frequent change cadence, and the least oversight. Every other artifact in your stack — application code, infrastructure config, database schemas, feature flags — has version control, staging environments, automated testing, and rollback capabilities. Your prompts — the artifact that affects every single user interaction — have none of these. And you're surprised when quality degrades and nobody knows why. The boring prompt versioning work wins. It always does. Especially when your engineer just changed one line in the system prompt, deployed it directly to production, degraded quality for 50,000 users, and nobody noticed until Thursday's support ticket spike.

• 
  Aakashdeep S. B. (@akashdeepsb) reported 11 minutes ago

  @MrPunyapal @github I didn't realise this and I left it to fix some tests and by the time I returned 50% ai credits were already consumed just for iterating over the tests..! Just the tests...! And then I read the blogs about the new policies. 😮‍💨

• 
  Noctum (@Noctum_io) reported 12 minutes ago

  noctum's smart contracts have been analyzed using two automated static analysis tools: Slither and Aderyn. these tools scan Solidity source code and compiled bytecode for known vulnerability patterns reentrancy, unsafe external calls, integer handling issues, access control gaps, and others. neither tool returned critical findings on the pool contracts, the Groth16 verifier, or the Poseidon hasher. the analysis results will be published when the formal writeup is complete. it is important to be precise about what this means. automated static analysis is a useful and important first pass. it is not a security audit. a security audit is a manual review by an independent firm that thinks adversarially about the system, models attacker behavior, and produces a signed report. noctum has not yet commissioned or completed a formal audit. stating otherwise would be false. this work is on the roadmap. the ZK circuit has not yet been reviewed with circomspect, a static analyzer specifically designed for circom circuits. circuit bugs are a distinct class of vulnerability from contract bugs a flawed constraint system can allow invalid proofs to pass verification. circomspect is pending. the trusted setup is also a known limitation: the ptau used is from the Hermez ceremony, but the zkey was generated with a single party locally. this is not a multi-party trusted setup, which means the toxic waste from the ceremony cannot be proven discarded. real ETH deposits carry this risk and users should understand it. the contract addresses, circuit source, deployment scripts, and zk-deployments.json are all public in the GitHub repository. the Groth16 verifier bytecode is deterministic from the circuit anyone with the source and zkey can recompile and verify the deployed bytecode matches. the goal is to make every security assumption visible so users can make informed decisions. an honest security disclosure at this stage is worth more than a false sense of completeness.

• 
  MarMar Labs (@MarMarLabs) reported 13 minutes ago

  If you use GitHub Copilot, the model behind "Auto" in your editor may no longer be an OpenAI one. At Build on June 2, Microsoft shipped MAI-Code-1-Flash — its own coding model, reportedly built without OpenAI tech — and started routing it into Copilot's model picker and Auto picker in VS Code. What it means if you ship with Copilot every day: - The default isn't neutral anymore. "Auto" now routes among first-party Microsoft models too, not just OpenAI and Anthropic. The model that runs when you don't pick one just changed. - Microsoft benchmarked it against Claude Haiku 4.5, not GPT — claiming a +16-point SWE-Bench Pro lead (51.2% vs 35.2%). The frame is "cheap, fast tier," not frontier. - It's tuned for token efficiency: "up to 60% fewer tokens" on harder problems via adaptive solution length. On Copilot's new token-metered billing (live June 1), a model that finishes in fewer tokens isn't just faster — it's literally cheaper on your meter. Connect the two June moves: Copilot went token-metered on the 1st, then got a first-party token-efficient model on the 2nd. Whatever the intent, the result is the same — Microsoft now controls both the meter and a model built to spend less on it. For builders, the takeaway isn't "new model." It's that your default coding surface is being quietly re-platformed under you — and "which model am I actually running, and who made it?" is now a question worth checking, not assuming.

• 
  Edinson Sanchez (@mr_chapy) reported 16 minutes ago

  @msdev as long as github copilot does not cost me an arm and a leg. When will you fix this ?

• 
  doublemover (@doublemover) reported 17 minutes ago

  Damn codex github issues are becoming a ****** garbage dump. Some ****** retard has what I can only assume is openclaw chiming in with useless bullshit on every issue

• 
  Jimmy Koppel (@jimmykoppel) reported 18 minutes ago

  @JongwonPar9958 I see. So your result basically counts Github issues over the lifespan of the project?

• 
  CyberFella (@cyber_fella) reported 24 minutes ago

  @dedene @quasi_mortal That's what you get when the builder of the tool benefits from the tool being inefficient. No real incentive for GitHub to fix this.

• 
  Sandip Bhattacharya 🌏✌️ (@sandipb) reported 31 minutes ago

  @zeddotdev The out of the box markdown viewer is terrible. What we see in zed is not what we see in the rendered view in github. Anything we can do to make the rendering palatable?

• 
  Smukx.E (@5mukx) reported 32 minutes ago

  I am getting a lot of dm's and private message regarding the repositories. 🧵1 / 2 Update:- From the support desk. [Some activity on your account was flagged by our abuse-detection systems for manual review]. My account was marked as spam. I have given additional details to @github. I can't just post them in another platform since that violates their rules, idk what to do !! So, I am just waiting and thinking of another way to reach them. This issue was one of the hardest ways to fix, sometimes it takes months for them to verify that you’re not a bot. I’ve read the reports. I’m not banned on GitHub, the account was cut off from public access. I can still modify, edit, and push to my repositories. If you know someone who can fix the problem. Kindly convey the issue to them. It could be a great help from you guys. @github kindly resolve the issue. I Will be updating the situation. Until then i will be focusing on my private projects.

• 
  Pedro Albuquerque (@petersaints) reported 35 minutes ago

  The models I have been testing have been mostly Qwen 3.5 4B and 9B, and Gemma 4 E4B. One of the things I did was integrate the LM Studio local API Server into VS Code as an alternative to GitHub Copilot Pro.

• 
  technolichap (@technolichap) reported 35 minutes ago

  @C2IRIS set up a self-hosted gitlab server recently, for when github is down

• 
  Alex Schneider (@Aslex) reported 36 minutes ago

  @elgermerlo @trello @linear GitHub Issues

• 
  Vatsalpandya333 (@Vatsalpandya333) reported 38 minutes ago

  Most incidents already have enough clues to identify the likely root cause. The clues are just scattered across: Datadog Grafana Kubernetes GitHub Jira Slack Internal docs The problem isn't data scarcity. It's context fragmentation.

• 
  Zypheen (@Zypheen_) reported 39 minutes ago

  @robdezendorf fix dex screener and link your profile and github, someone random paid dex and linked a drainer. dev or someone fix this plz ES6ZcfTUTVET37RaYW9qDhX1DBBDEaTuvg87Lw9epump

• 
  OptiRefine (@OptiRefine) reported 43 minutes ago

  @ChrisHervochon @github Good call on the org account. Code ownership is step one. The next problem is the code itself — AI assistants don't flag when they generate something with an injection flaw or a hardcoded secret. That's where a lot of vibe-coded projects are sitting right now.

• 
  Bipin Paul (@iAmBipinPaul) reported 44 minutes ago

  @lukehoban The only issue is that GitHub Copilot’s price is too high, so most users will not choose it.

• 
  Anoy (@Anoyroyc) reported 46 minutes ago

  @davj the part about trust breaking down is so real. once devs stop believing your docs they just go straight to discord/github issues instead

• 
  Troyski (@MrTroy_) reported 50 minutes ago

  @thieme @raphaelmansuy @GitHubCopilot People WERE paying for it. If Github is run by idiots, it's not the people's problem.

• 
  Engr_Marc (@DLazyNewGENIUS) reported 51 minutes ago

  @LowLevelTweets Can they just lock in and fix Github first. Recently, that platform breaks more than i do at work.

• 
  Davlatjon Sh (@dalisoft) reported 51 minutes ago

  @morganlinton @FactoryAI 2. GitHub issues are like stale and never gets enough attention. Need dedicated person to fix that (@FactoryAI i could be this person)

• 
  Dorian Niemiec (@JaDorianNiemiec) reported 51 minutes ago

  @mholt6 What about non-technical people who don't want to pay a lot for Apple stuff, and don't want to deal with complexities, compiling/porting kernel modules, GitHub issues with Linux systems either? 🥲 I'm willing to deal with the Linux problems, but many don't...

• 
  Hossain Kabir (@awarehossain) reported 52 minutes ago

  @barre_of_lube @cognition Use official support/help desk or GitHub issues. For auth/SSO, desktop apps often need org admin to enable “desktop access” in settings. Worth checking admin console first.

• 
  Phasma (@0xPhasma) reported 53 minutes ago

  a 28-year-old developer from Beijing is making $30,000/month managing 22 client projects — and he hasn't opened a single project management tool in 4 months. it started by accident. he took on too many clients and started missing deadlines. instead of hiring help, he spent one weekend building a Claude agent to write his status updates. then he built another one to handle client emails. then one to generate weekly reports from GitHub commits. then one to break down briefs into tasks. by week 3, he had 5 agents running in a loop — and his clients thought he had a team. the stack is simple. Claude Code as the brain. Zapier connects the triggers. Notion is the client-facing layer. each agent has one job and never overlaps. > agent 1: reads new client brief → breaks into milestones → populates Notion > agent 2: monitors GitHub commits → writes progress updates every 48hrs > agent 3: scans client emails → drafts replies for his approval in under 2min > agent 4: generates weekly summary reports with metrics pulled from Linear > agent 5: flags scope creep, late replies, and budget overruns before he even sees them month one: $6,200. two clients, mostly manual, agents still breaking. month three: $14,000. five clients. he stopped working weekends. month six: $30,000. eleven clients. average response time to clients: 19 minutes. he now has 22 active retainers. each one thinks they're his priority client. technically, they all are — just not to him. the agents don't sleep, don't ask for raises, and never forget a deadline. the clients pay for a developer. what they're actually paying for is a very well-trained pipeline.

• 
  AshutoshShrivastava (@ai_for_success) reported 58 minutes ago

  Most developers building voice products right now are paying per character to a closed API they have no control over. Every run costs money, data leaves the server, fine-tuning is off the table. Miso One just changed that. 8B parameter open-source weights on GitHub. Clone the repo, self-host it, fine-tune it on your own dataset. The model runs at 110ms and actually emotes, warmth, hesitation, excitement, not just clean flat pronunciation.

• 
  Jason Fleagle (@jjfleagle) reported 58 minutes ago

  @latentspacepod @github @kdaigle The 80% PRs-from-agents scenario makes trust the bottleneck, not generation. Teams will need checks for authorship, test coverage, dependency changes, security review, and whether the agent actually solved the issue instead of just producing a plausible diff.

• 
  Mad ML scientist (@HououinTyouma) reported 59 minutes ago

  like 7 years ago I was working on an obscure ML problem and used an open source package no one ever heard of. unfortunately one of the features didn't work as intended so I submitted an issue on github. just got a notification that they fixed the issue. AGI is here