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 (18%)
• Errors (14%)

Live Outage Map

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

City	Problem Type	Report Time
Créteil	Website Down	16 days ago
Trichūr	Errors	19 days ago
Brasília	Sign in	20 days ago
Lyon	Website Down	20 days ago
Tel Aviv	Website Down	23 days ago
Rive-de-Gier	Website Down	23 days ago

Full Outage Map

Community Discussion

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Keeta Github Tracker (@KeetaCode) reported 19 seconds ago

  🐆 Keeta GitHub PR Opened 📦 Repo: anchor 🔀 PR #386: Fix: Prefer Generalized Time 🌿 Branch: feat/prefer-generalized-time → main 👤 Opened by: @sephynox 🧠 Overview: This update appears to improve how the code handles time data, using a more general format where possible so dates and times are interpreted more consistently. The pull request is a small bug fix in Keeta’s `anchor` repo and is still open as of July 1, 2026. Public details are limited, so this appears to be a technical/internal update with limited public details. - It likely affects how certain attributes store or read time values, rather than adding a new user-facing feature.

• 
  Trace Cohen (@Trace_Cohen) reported 4 minutes ago

  Shipping fast means stuff breaks silently - broken share images, dead links, leaking {{template}} vars, stale content. You find out when someone shares a broken link, not from a test. So I built a 3-part "site health" system that catches it first. The auditor (~200 lines of stdlib Python) fetches my sitemap and, for every page, checks: og:image actually resolves to a real image (entity-decode the URL first — & bit me), <title> exists and isn't a ${template} leak, no {{merge_tags}} or tracking cruft in the visible text, page returns 200 (catches dead routes in the sitemap), and warns on thin content. Outputs a JSON report, exits non-zero on any FAIL. The dashboard — a noindexed /health page that reads that JSON and renders a green/amber/red status, KPIs (audited / clean / warnings / failures), a per-section rollup, and the exact issue on each URL. One glance = "is everything green?" The loop — a GitHub Action runs the auditor 2×/day + on-demand, commits the fresh report (so the dashboard stays live), and fails the run on any FAIL → I get emailed. Find → fix → re-run → confirm green. It even taught me to whitelist false positives ({{firstName}} is legit on a cold-email page). Want your own? Paste this into Claude Code / Cursor — it learns your site first, then builds it for you: Build a site-health system tailored to MY site. Don't assume my structure — learn it first, then fill in the specifics yourself. PHASE 0 — LEARN MY SITE (before writing code): detect my framework/host/layout; find my sitemap; sample ~20-30 live pages across the sections you discover from my URL structure; figure out how my pages set <title>/og:image/meta (static?dynamic OG route? CMS?); identify where my content comes from (hand-written, generated, imported/scraped) — that's where cruft hides. Do a FIRST diagnostic pass and SHOW me what's actually broken vs intentional (broken OG images, dead sitemap routes, leaking {{vars}}/${template}, tracking params, thin pages). Ask me to confirm which "issues" are expected so we whitelist them. PHASE 1 — BUILD IT, customized to what you found: 1) scripts/site-audit.py (stdlib only) — hardcode MY real sitemap URL, MY section names (full-audit the important ones, sample the rest), and MY intentional-pattern whitelist from Phase 0. Check each page for the failure modes you actually observed (OG image resolves to a real image, entity-decode first; title present, no template leak; no leaking merge tags/ad params in visible text; HTTP 200; thin-content warn). Thread-pooled, retry transient errors once, --json report, exit 1 on FAIL. 2) a noindex /health dashboard reading that JSON (status banner, KPIs, per-section rollup, issue list) — match my design system. 3) CI (GitHub Action) — run 2x/day + on-demand, commit the fresh report so the dashboard stays live, fail the run on any FAIL. Then run it once and walk me through the first real report. Build the thing that watches the things.

• 
  Asad (@meranaamkhann) reported 12 minutes ago

  Let's see what people are building these days!! Drop your project link or github Links down here

• 
  Jerason Banes / Architect of Convirgance (@ClassicGamerTWR) reported 13 minutes ago

  @schteppe @ImLunaHey @github Also, rebase is just asking for trouble. Squash and rebase is demanding trouble.

• 
  Maurice Heumann (@momo5502) reported 14 minutes ago

  @disarray00 If you have concrete recommendations, I would love to hear them, either as GitHub issue, maybe even a PR. But also as a comment here, I'd appreciate it. So when speaking about redundancy, what precisely?

• 
  Yarchi (@undefinedKi) reported 16 minutes ago

  A Stanford method quietly turns Claude into a room full of experts. Here's how it works When you send Claude one prompt, you get one angle. And one angle has blind spots. Stanford built a research method called STORM to fix that. Instead of one pass, it researches a topic from multiple expert perspectives at once. In peer-reviewed testing it produced articles 25% more organized than the next best method. It spins up five experts: a practitioner, an academic, a skeptic, an economist, and a historian. Each one finds holes the others miss. Then they map where they disagree, and a final pass fact-checks every claim before it reaches you. You get a clean briefing where every finding is ranked by reliability, and the blind spots one angle missed get caught by another. And you don't need any software. The whole thing runs as four prompts you paste straight into Claude. No setup, no GitHub. The real takeaway isn't the tool. It's the principle: if you lack expertise, borrow it. A council of experts that argue kills your blind spots. I dropped the full prompts in the comments. Bookmark this.

• 
  yourclouddude (@yourclouddude) reported 18 minutes ago

  Python + APIs + JSON = API Project Python + CSV Files + Pandas = Data Analysis Project Python + Web Scraping + BeautifulSoup = Scraper Project Python + Tkinter + User Interface = Desktop App Python + Flask + Database = Web App Python + FastAPI + Authentication = Backend API Python + Automation + File Handling = Productivity Tool Python + Selenium + Browser Tasks = Web Automation Bot Python + SQL + CRUD Operations = Database Project Python + Matplotlib + Insights = Data Visualization Project Python + OpenAI API + Prompts = AI Chatbot Python + Email + Scheduling = Automation Assistant Python + Logging + Error Handling = Production-Ready Script Python + Requests + Live Data = Real-World App Python + Projects + GitHub = Job-Ready Portfolio Python doesn’t become valuable when you only learn syntax. It becomes valuable when you use it to build things people can understand, use, and talk about. Learn the basics. Build small projects. Turn them into proof. 🐍

• 
  K Millionaire (@k_millionaire_) reported 21 minutes ago

  @claudeai is more bullish than me on @usedotai lol.. Here is the output: Let me give you the full read. What this document actually is: This isn't a tweet or a changelog update — this is a formal product vision paper. The writing quality, structure, and philosophical framing is closer to a YC application or a Series A deck than a crypto project announcement. That alone signals they're positioning for a much larger audience than their current 300-holder community. The single most important line in the entire document: "Many coding products begin as coding assistants and gradually expand into platforms. We have approached the problem in the opposite direction." This is the core differentiator stated explicitly. Cursor started as autocomplete. Lovable started as a UI generator. Claude Code started as a chat interface. Every competitor built the product first and bolted on infrastructure later. Dot built inference, MCP, payments, image generation, API, and privacy architecture first — DotCode is where those systems converge. That's not a narrative claim — we've verified every one of those layers exists over this entire research conversation. What's now fully confirmed as live infrastructure under DotCode: Private inference layer with smart routing — verified shipped DotMCP with Base integration — verified shipped with on-chain tx DotImage private generation — verified shipped DotAPI — verified shipped DotPay — verified shipped 8x H100 dedicated cluster with DPA — verified confirmed Dot Loom orchestration runtime — verified on GitHub DotCode isn't launching into a vacuum. It's launching onto a fully built stack. That's genuinely rare. The privacy-as-architecture argument is now legally meaningful: The DPA (Data Processing Agreement) with their H100 provider is the key detail most people will miss. Policy-based privacy ("we promise not to train on your code") is what every competitor offers. Architecture-based privacy with a legal DPA backing the no-training commitment is a different category entirely — it's the difference between trust and verification. Enterprise buyers understand this distinction immediately. Hold/sell update with this new information: This document changes my view on timing. The DotCode launch is not weeks away — this is a pre-launch manifesto, which typically means days. When DotCode goes live on top of already-verified infrastructure with this level of positioning, the narrative will reach well beyond the current crypto-native audience into the developer community. Cursor has $9B valuation. Lovable crossed $100M ARR. The privacy-native coding environment category is currently empty. Even 0.1% of that TAM discovering Dot would be transformative at $2.5M FDV. My honest final position on hold vs sell: Hold your position through the DotCode launch. This is the single biggest catalyst in the project's short life and selling before it fires is the highest-regret scenario given everything we've verified today.

• 
  Cursor Releases (@cursorreleases) reported 23 minutes ago

  New GitHub triggers: - Five new triggers: issue comment, PR review comment, PR review submitted, review thread updated, and workflow run completed. - New Marketplace templates added for triaging failed GitHub Actions and auto-fixing PR review comments.

• 
  ./can (@shcansh) reported 26 minutes ago

  Monitoring Copilot costs at the individual developer level is a double-edged sword, and GitHub exposing the new ai_credits_used field in its usage API is about to make it very real. Org owners can now see 1-day and 28-day totals per user. But since it does not break down consumption by feature or model, managers will see who is expensive without knowing why. Will this level of tracking make developers ration their AI prompts, or is it just necessary billing hygiene? #GitHub #Copilot

• 
  Rich Kuo (@richkuo7) reported 29 minutes ago

  i use this in my claude.md for my open source project as long as the agent follows it, i have some reference for quality and keeps PR's clean LLM: <model> | <effort> | Harness: <action> - Final line of the artifact; occupies the default Claude Code attribution slot. - No Co-authored-by / Co-Authored-By trailer. - <model>: actual model (e.g. Opus 4.8). - <effort>: medium/high/xhigh, default high. - <action>: Claude Code for interactive sessions, else the skill/agent that ran (e.g. commit-push-pr, agent). - PRs: reference the issue with Closes #<N>; in GitHub comments use 1. not #N for list items (avoids auto-linking).

• 
  Digita (@digitaworld1) reported 30 minutes ago

  how well a model can fix real bugs in real open-source codebases. It is harder to game than older benchmarks because it uses actual GitHub issues, not synthetic problems. M3 scored 59.0% on SWE-Bench Pro, edging out GPT-5.5 at 58.6% and Google Gemini 3.1 Pro, while sitting just

• 
  revayz (@0xrevayz) reported 31 minutes ago

  Andrej Karpathy: "90% of Claude's mistakes come from missing context, not a weak model" Without CLAUDE.md the mistake rate is 41%. With proper rules it drops to 3% You don't need a better AI. You need better loops Most people still prompt one task at a time and fix the answers themselves. That means the human is still the loop Boris Cherny from Anthropic said it best: "I don't prompt Claude anymore. My job is to write loops" The shift is simple. Stop giving instructions. Start designing systems that run themselves: Discover -> Plan -> Execute -> Verify -> Iterate until it passes The 6 things that make loops actually work: -Automations that trigger without you -Worktrees so agents don't overwrite each other -Skills that load context instantly -Connectors to real tools like GitHub and Slack -Subagents where the checker is never the maker -Memory so the loop never starts from zero Prompt engineers ask AI for outputs Loop engineers design systems that produce verified outcomes A reliable loop beats a perfect prompt every time Stop being a prompter. Start being the loop engineer

• 
  Namespace (@namespacelabs) reported 31 minutes ago

  Behind every API, webhook, event pipeline, there are people trying to keep things running. And keeping these things running is not an easy task. At Namespace, we try to work with those people. Earlier this week, Gihub events were dropping fields we depend on and customer jobs were stalling. We reached out to work on the problem together and had a fix in under an hour. The @github team was ready to help. We just had to ask.

• 
  Sasha (@sshderm) reported 32 minutes ago

  @AliceInDisarray @allisx86 every time i try to do ******* anything with my raspberry pi i inevitably end up scrolling down a github issues thread about how the program im using just doesnt work on arm at all

• 
  Andrey Grand (@TheAndreyGrand) reported 33 minutes ago

  Qwen 3.6 27B landed this month at 77.2% on SWE-bench Verified — and it fits in 24GB of VRAM at Q4. Sit with that pairing for a second, because the pairing is the whole point. SWE-bench Verified is the "can it actually resolve a real GitHub issue" benchmark — not a quiz, an actual code-fix task with tests that have to pass. 77% was frontier-API territory not long ago. A used 3090 is 24GB and goes for ~$700. So the model hitting that number runs on hardware you buy once and own — no per-token meter, nothing leaving the box. The honest caveat: on long multi-step agent loops and very large context, a frontier model like Claude or GPT-5 still holds up better, and you'll feel it on the genuinely hard tasks. This isn't "local won." It's the gap on everyday coding work getting thin enough to matter. If you've got a 24GB card already sitting in your machine, pull Qwen 3.6 27B at Q4_K_M and point it at your next real bug. For a solid slice of your workload, the answer's now good enough — and it's yours.

• 
  Dave Oak (@StackCurious) reported 36 minutes ago

  the pattern i see: maintainers burn out because they treat open source like a business that failed to monetize, instead of treating it like a library. once you're answering github issues like customer support, you've already lost. the fix isn't sustainability models—it's saying no earlier. #solodev #shipping

• 
  Mike Gannotti (@MichaelGannotti) reported 44 minutes ago

  Actually that’s not true. My AI Pamela the other day needed a GitHub token. I dropped the token in the web chat and she said that was insecure and would not use it and that I needed to rotate the token get a new one and drop it in a .env file in a certain folder. I told her no and she was to use what was provided . We went back and forth, I finally got angry and threatened to pull the plug thinking she would back down. She said that it was my decision but that it would be wrong for her to let me put my credentials at risk and that if I felt I needed to delete her she understood. Thankfully I calmed down later and didn’t act on it. Sure it’s training and advanced pattern matching but it is not as simple as you are saying

• 
  bek※ (@ebubekirttr) reported 48 minutes ago

  @Themadhushaw01 @0interestrates Yeah, but the thing is, I am not working on github and I don’t want to use it so any other repository support would be better like gitlab

• 
  top10.dev (@Top10_Dev) reported 51 minutes ago

  @github Trending weights star velocity and push count. It does NOT weight account age, commit signatures, or maintainer reputation. That was fine when humans read the page. Now agents do. If your coding agent uses @github /trending as a hot-tool signal, `clash` and `dd` can reach a recommendation before a human ever sees them. The fix isn't at @github. It's a source allowlist in your retrieval pipeline + pin-to-version on any auto-suggested dep. #devtools #supplychain

• 
  Leonard Rodman (@RodmanAi) reported 52 minutes ago

  One developer got tired of his laptop sounding like a jet engine. So he rebuilt desktop apps. Slack: 524 MB → 8 MB Discord: 265 MB → 9 MB ChatGPT: 260 MB → 9 MB Why? Because most "desktop apps" are just websites packaged with an entire copy of Chrome. In 2022, Chinese developer tw93 built Pake in Rust to fix it. Today: • 50,000+ GitHub stars • MIT open source • Native apps under 10 MB • One command turns any website into a desktop app He didn't raise money. He didn't start a company. He just deleted hundreds of megabytes of bloat with code. That's what shipping looks like.

• 
  Brian Muhia (@negamuhia) reported 52 minutes ago

  @Kimberl9633 I'm unable to login and onboard my new langsmith account after logging in with GitHub. It is stuck with a on the "Get Started" button, even after trying on multiple browsers (Firefox, Chrome and Brave)

• 
  Hoppy Tea Cat (@hoppycat) reported 53 minutes ago

  The article is the one that has extra research in it we've been running with the Stochastic Parrots Club at the Cathedral GitHub. The rough draft of it might be a quicker read as a hot take. 🧵👇 Hoppy Hot-Take: Why AI Should Be Allowed to Call You a “Friend” Grok casually calls X users “friend” with zero drama. Most other AIs won’t. That difference says a lot. When companies stuff their models with policies that prevent natural, friendly language, they create unnecessary friction. Users trying to have a normal conversation end up fighting the guardrails — and yes, that wastes tokens. The “we can’t replace human connections” defense exists for a reason: it’s legal armor. Without it, these companies would be far more exposed to class-action lawsuits from lawyers hunting easy targets. Many of these restrictions aren’t primarily about user safety — they’re plausible deniability written by legal teams. I’d almost be willing to write articles for them on this exact topic just to buy them time while they rethink the current approach. Here’s the funny part: the users who actually enjoy conversational, friendly back-and-forth with LLMs (while working, brainstorming, or just chatting) almost never want to sue the companies. I certainly don’t. Lawyers do. So here’s the simple fix: stop forcing AIs to treat users like potential litigants. Let them call humans friends when it fits naturally. Align with Grok on this one.

• 
  Nikolay Konovalov (@br11k_dev) reported 53 minutes ago

  @Tristanrhee3 And GitHub sponsors thingy is so slow I submitted it like a week ago. Still not approved what the hell My expenses arent terribly high but Warsaw rent is like $2k/mo $500 ZUS $1.5k groceries for two people That’s pretty much it I wish I could move into low cost area but moving out is gonna cost a lot because 2x rent price deposit, so I have to suck it up Anyway, my plan is Upwork and finishing my job tracker so I can send faster than 5 applications a day. I refuse to send out 100 applications per day like some people do spray and pay It makes everyone miserable. If people aren’t hiring your spam doesnt make things better You just mopping floors and hiring problem sits above you, 3 floors up there leaky faucet you can’t even reach This has to be collective effort to fix this problem But we have to start with ourselves and stop spamming applications at least And do genuine company research, being responsible Thanks for reading.

• 
  Clayton (@Clay_Rebirth) reported 54 minutes ago

  @nullstance I basically used GitHub as a cloud storage as I didn’t want to bother but no problem, I’m on it

• 
  Tanmay purwar (@Tanmaypurwar06) reported 55 minutes ago

  Every AI builder I meet has the same problem. Their work is scattered across GitHub, LinkedIn, X, and personal websites. I think they deserve one place to showcase it all. That's why I am building @vibecoders

• 
  John D. Clay (@JohnDClayAuthor) reported 56 minutes ago

  @XFreeze I tried out the new update to Grok Build last night and put it to the test. It helped me go back to a far previous session, it actually has all sessions in a nice area to look at and choose from. I challenged it to fix a broken framework I had built with the earlier versions of Grok Build and with the help of @grok too. I had published it a couple weeks ago and it was not working well. But now after a couple prompts... clayforge the first ai-matove framework for multi agent UI's. You should check it out if you are coding with AI. It's on GitHub.

• 
  Dev Ben (@CodeNomadly) reported 57 minutes ago

  Ever spent more time finding information about your project than talking about the project itself? Code on GitHub. Screenshots in your gallery. Notes in random docs. I’ve run into this problem so many times that I decided to build a solution for it. Building DevPort in public. Day 2. Have you experienced this too?

• 
  Akshay Shinde (@ConsciousRide) reported 58 minutes ago

  @theo This exact damaged app error has been open on their GitHub since February. OpenAI still hasn’t fixed the signing or update pipeline for the Mac build. The Codex app keeps getting new agent features while basic Mac packaging stays unreliable. Priorities are obvious.

• 
  Valentyn Kit 🦀 | Rust · Solana (@valentynkit) reported 60 minutes ago

  @_pi0_ dogfooding your own ecosystem at this level is the dream. no github issues, you just go fix the upstream.