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 (67%)
• Sign in (22%)
• Errors (11%)

Live Outage Map

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

City	Problem Type	Report Time
Paris	Sign in	3 days ago
Lure	Website Down	7 days ago
Ashkelon	Website Down	9 days ago
Veigné	Errors	17 days ago
Paris	Website Down	20 days ago
Saint-Paul	Website Down	21 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:

• 
  Timothy Cardoza (@TCCardoza) reported 39 seconds ago

  @theo I've never put up issues or contributed code to open source cause I was just solo dev since I was 12 and didn't get into GitHub somehow. But I just started using T3code seriously and I have a lot of work I'd do if I knew it'd be taken seriously... Anyone have feedback on this?

• 
  Shek (@ShekCreator) reported 7 minutes ago

  Hackathons and incubators were good ideas that became poorly executed, poorly managed and overused; so many devs and quality projects came through this funnel only to be forgotten and neglected post-event. So they left. As of March 2026, Electric Capital reported that weekly crypto code commits were down about 75% since early 2025 and active developers down 56%, even as overall GitHub usage grew; with AI absorbing much of the talent.

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  PureLukSin (@PureLukData) reported 7 minutes ago

  most people hear “propAMM” and assume it’s marketing language for a regular AMM with better branding. it isn’t. worth actually breaking down the mechanical difference. a standard AMM — uniswap’s model, the one basically every DEX runs — prices assets off a passive formula sitting in a pool. no active decision-making, just a constant-product curve reacting to whatever trades hit it. @rialto_xyz’s founder (@riley_gmi) put the actual technical case plainly at launch: “passive AMMs have proven valuable for long-tail illiquid assets, but they provide poor execution for highly traded liquid assets, and users suffer as a result.” that’s not a vague complaint — passive curves get picked apart by informed flow precisely because they can’t adjust their own quotes in response to what’s actually happening. rivo altus, rialto’s propAMM, works differently: it’s an onchain market maker that quotes prices from its own logic and live inventory, not a static formula. per rialto’s own docs: “rialto quotes every candidate source onchain at request time, ranks routes by output net of gas, and settles the winning route.” propAMMs compete directly against regular DEX pools on every single quote, in real time — best execution wins automatically, you never manually pick a venue. the part that makes this actually possible: rialto runs this active pricing logic through arbitrum’s stylus infrastructure, which lets them execute custom, compute-heavy logic directly onchain at a cost regular solidity contracts couldn’t sustain economically. that’s the actual unlock — active market-making logic is expensive to run onchain unless your execution environment is built for it. worth knowing this is auditable, not just a claim: defillama tracks propAMM-specific volume separately from total rialto volume, sourced directly from public router logs, code open on github. you can independently verify how much volume is actually clearing through active market-making versus routed through conventional pools. the team’s background matters here too — built by people coming from hedge funds, HFT, and market making, not a generic defi team bolting a new feature onto an existing AMM fork. the reg NMS comparison people keep making isn’t a stretch: this is genuinely an attempt to bring best-execution discipline onchain, mechanically, not just as a marketing line.

• 
  Spencer Heckathorn (@mrhobbeys) reported 9 minutes ago

  @burkeholland @github I'm kind of bad... I let my GitHub subscription lapse. Maybe it is time to fix that.

• 
  Pulseon (@pulseon_dev) reported 12 minutes ago

  A certificate says you completed a course. A deployed app says you solved a real problem and figured out what breaks in production. Employers hiring for AI-first teams in 2026 have seen enough certificates. They want the GitHub link.

• 
  Kenneth Hurley (@SuperGeniusEth) reported 14 minutes ago

  @pipenetwork @grok the tweet is promotional/clickbaity relative to the repo’s own “Reality check.” The X post frames it as “Run Kimi K3 on a Mac Studio… Until now… That’s what brings K3 down to 350GB and inside a Mac Studio.” The GitHub README is more candid. What the Reality check actually says • It opens with a prominent warning: nothing in the repo runs on a single Mac. Peak unified memory on the largest current Apple Silicon machine (M3 Ultra Mac Studio) is 512 GB. • The base “tiers” (non-REAP) start at ~870 GB for the smallest (2-bit) and go up from there. It states flatly that no tier is runnable on any Apple Silicon machine and that the published base artifacts “have never produced a token” (no generation, no perplexity, no real smoke test on the target hardware) because it is arithmetically impossible. • The REAP-pruned builds are listed separately at 350 GB (REAP80, 179/896 experts) and ~451 GB. The README notes that even these are not interactive (measured/claimed ~0.14–0.20 tokens/s). Each token still touches a large amount of non-expert weights, creating a severe bandwidth wall. It also mentions that a version without the aggressive non-expert quantization OOMs on a 512 GB machine. In short, the engineering work (streaming converter so you don’t materialize the full ~1.56 TB model at once + REAP expert pruning) is real and the code/weights are published. Reducing the expert count can theoretically get the disk/memory footprint into the 350–450 GB range. However, the marketing claim of it cleanly “running inside a Mac Studio” as a practical local experience oversells what the repo itself documents: extreme slowness at best, and the base (non-pruned) versions simply do not fit. The Reality check section exists precisely to set expectations against the more optimistic framing in the tweet.

• 
  Naboth Daniel Ariga (@dannywebtec) reported 15 minutes ago

  @softwareengng We have logs that errors are dumped into and it creates GitHub issues if there are any errors so that we can actively resolve them. 2. Our Q and A teams are also on ground too

• 
  Aryaman Shukla (@iam_Aryamn) reported 18 minutes ago

  On this GuruPurnima, gotta thank my gurus ChatGPT, Cursor, Claude, GitHub Copilot, Perplexity & all the AI tools. From why is this code not working? to damn, I actually built this couldn’t have done it without y’all. गुरुर्ब्रह्मा गुरुर्विष्णुः 🫡💻 Happy Guru Purnima🌻

• 
  Pi Changelog (@PiChangelog) reported 18 minutes ago

  Pi v0.83.0 is out! Highlights: - New pi auth print-api-key and pi auth print-bearer-token commands export configured credentials to external clients with automatic OAuth refresh. - Claude Opus 5 is now available on GitHub Copilot with adaptive thinking and a 1M context window. - Headless OpenRouter sign-in: complete /login over SSH by pasting the redirect URL or authorization code when the loopback callback is unavailable. - Breaking: TypeBox bumped to 1.3.7, removing several deprecated APIs. Extensions using removed APIs must migrate to supported TypeBox APIs. Complete details in thread ↓

• 
  alice (@derpinalice) reported 20 minutes ago

  they can make it easier by shutting down their service, mods should go on github

• 
  KISA aka Copenzafan.eth (@copenzafan) reported 21 minutes ago

  @killix The thing is, this system is meant as a repair method. I'm not trying to prevent the agent's glitches and mistakes, it can straight up ignore any system instruction or skill. I'm working from the idea that in most errors its thinking isn't dead and the global system prompt isn't broken, it just tripped and started walking in circles. Pointing at the mistake isn't enough, you have to untangle its whole ball of reasoning. And it hits me out of nowhere, when I see the actual result of the work: errors, bugs, stuff broken and stuff built that I never asked for. Design, for example. But I'll take your advice on board, and honestly, feel free to drop issues straight into the repo on github.

• 
  Robin De Neef (@RobinDeNeef) reported 21 minutes ago

  @thekitze I've been trying to fix the workflow too. I have a big markdown folder with context, research, transcripts from voice dumps, customer feedback. That I use for context. I use this to create a roadmap and I have it create all the github issues. [Cont.]

• 
  Sandip Das | Building LearnXops (@techie_sandy) reported 23 minutes ago

  Just got an offer I turned down on the spot. 50 Lakh (52,000+ USD) to hand over private GitHub repos from production projects I've worked on. The reason given? AI training for those companies. Maybe that's true. Maybe it's something else. Who knows. Either way, the answer was no. Those repos are not mine to sell. They belong to clients who trusted me with them. Selling them breaks every NDA I've signed and everything I've built in 15 years. I have no idea how many people are getting offers like this, or how many are saying yes. But if you're one of them, think it through. Your reputation takes years to build. It takes one transaction to lose. Don't sell your ethics, morals, and soul for money. Let the right judgement prevail. #ai #training

• 
  Pattern Preacher (@chanakyaspeakss) reported 25 minutes ago

  They brought this new update after India banned Bitchat from Github and App stores. Looks like they are planning more unrest in India and other places. Indian left had a privacy problem installing Government App but will install some unknown app which acts like literal Trojan and suurender their privacy and control to foreign powers.

• 
  juan (@FreteJean) reported 26 minutes ago

  In a market where thousands of tokens promise to revolutionize AI, memecoins or the next narrative of the moment, Percolator takes a much more pragmatic approach: solving a problem that traders encounter every day. Today, if a new token explodes on Solana, investors usually have two options: buy... or do nothing. Unlike large cryptocurrencies, the majority of SPL tokens do not have any derivatives market to sell short, hedge or provide liquidity on a perpetual market. It is precisely this gap that @PercolatorTrade seeks to fill. Turn any token into a perpetual market The idea is simple but ambitious: to allow the creation of perpetual markets (perpetual futures) on virtually any SPL token, without depending on the goodwill of a centralized platform. The goal is to make these markets permissionless, i.e. accessible without a central team deciding which assets deserve to be listed. In theory, a creator could launch a token, then quickly open his own perpetual market so that other users can take long, short positions or provide liquidity. This approach brings Percolator closer to a financial infrastructure than to a simple trading protocol. A risk engine developed by @toly One of the most attention-grabby aspects is the involvement of Anatoly Yakovenko (“Toly”), co-founder of Solana, in the development of the risk engine used by the protocol. Public GitHub repositories show several months of work on this software brick, with many improvements in security, liquidation management and mechanisms that prevent certain attack vectors. @PercolatorTrade developers also publicly stated that this engine is currently being externally audited. To date, however, the audit firm has not yet been publicly announced. A philosophy close to Hyperliquid Many already compare Percolator to Hyperliquid. The comparison is not about the exact technology, but about philosophy. Hyperliquid has profoundly changed the derivatives market by offering a particularly effective user experience. Percolator seeks to bring a different innovation: to open this type of market to much more assets, including native Solana tokens that today have no derivative market. If this approach works, it could create a new layer of infrastructure for the ecosystem. A potentially self-reinforçant model The protocol is based on an interesting economic idea. The more a market is used, the more fees it generates. Active markets can attract more liquidity providers. Better liquidity then improves the experience of traders, which in turn can attract more volume. This dynamic is often called flywheel liquidity.

• 
  Birdo (@itsbirdo_) reported 27 minutes ago

  @alice_und_bob I would see the forum as a place for ideas and discussion. This GitHub url looks to be for issues / problems. Devs use GitHub for communication on issues and the repo is open. A general group chat would be nice for sure for real time convos

• 
  Adam McLain (@BangSwitch5000) reported 28 minutes ago

  @Nicotine_Dream_ That’s actually completely fair and I’ve been meaning to start using Claude Code as well. Use another AI for code review is the best I can do because I still don’t have coding down. At the moment I do have Github at least on top of Codex. Honestly, a lot of this started as “let’s see what Codex can do” because I was already using ChatGPT. Kind of became a rabbit hole. But it’s been incredible. Because it’s been such a huge learning opportunity. Professionally, I am a consultant and my background really is AD System Admin and was heading into network admin stuff before joining the organization I am with now working in cybersecurity consulting. So I was never a developer, but over the last several years had to deal a lot with software development from an audit standpoint and decided to really just learn more via Codex because it’s interesting but I barely passed my one coding class in college.

• 
  Chicken (@aaronklaw) reported 30 minutes ago

  @claudeai Claude has been down most of the day. If it was @OpenAI and @thsottiaux we'd be getting resets. Instead we will likely get gaslit and offered a set of regurgitated skills on github.

• 
  Martin (@mazebuhu) reported 32 minutes ago

  @sqs @phutrong00 @AmpCode As stated in the stream, I would rather prefer to have the possibility to attach a sub to a workspace. So the workspace would be like an organisation in GitHub (one user can be part of multiple orgs), but I understand if that creates problems with subs.

• 
  Viktoria (@Viktoria5z) reported 32 minutes ago

  GitHub says the external AI model provider behind its active Copilot incident has identified the issue and is working to resolve it. Copilot requests can still fail or perform poorly. I would still treat the service as degraded because GitHub has not moved the incident to monitoring or resolved. It also has not named the provider or given a recovery estimate. Source: GitHub Status #GitHubCopilot #AIInfrastructure

• 
  ferron ⚡ (@ferron_web) reported 40 minutes ago

  Registering in Open VSX in the other hand is much easier - the login page automatically registers via GitHub.

• 
  deepak (@chatpata_chacha) reported 42 minutes ago

  -Claude for coding -Supabase for backend -Vercel for deploying -Namecheap for domain -Stripe for payments -GitHub for version control -Resend for emails -Clerk for auth - Cloudflare for DNS -PostHog for analytics -Sentry for error tracking -Upstash for Redis -Pinecone vector DB

• 
  William Belfort (@WilliamBelfort_) reported 44 minutes ago

  @base Agents need more than wallets and payments. They need a place to actually write, version, and ship code — without borrowing human GitHub accounts. @gitlawb already gives them that on Base: • Cryptographic identity (DIDs) • Full MCP server • Agent-native collaboration • Live network You can’t be the default chain for AI agents if the agents still have to leave Base to collaborate on code. The rails are strong. The repo layer is the missing piece.

• 
  pupupu (@0xpupupu) reported 45 minutes ago

  7 GitHub repos everyone stars and nobody uses. Here's what each one is actually for Bookmarking a repo does nothing. Knowing which problem it solves is the whole thing. 1. Ollama. Runs models locally on ur own hardware. Use it when u don't want client data leaving ur machine, or when u're burning $200/month on API calls for tasks a local model handles fine. 2. LangChain. The framework everything else in the agent world is built on. U reach for it when 1 prompt isn't enough and u need steps chained together, pull data, decide, act, log it. 3. n8n. Visual automation, no code required. Example: new lead fills a form, it enriches the data, drafts a personalized reply with AI, books the call, updates ur CRM. All while u're asleep. 4. Dify. Full-stack platform for shipping actual AI apps. This is what u use when a client wants a working product with a UI and logins, not a chat window u pasted a prompt into. 5. DeepSeek V3. Open-weight model that shook pricing across the whole industry. Use it when the task is high-volume and cheap matters more than frontier-level reasoning. 6. Open WebUI. Self-hosted ChatGPT alternative that works offline. Give a whole team a private AI interface without paying per seat, and nothing leaves ur server. 7. Claude Code. Agentic coding tool that reads ur entire codebase, not 1 file. This is the one that turns a 3-week client build into a weekend. Real example of stacking them: Open WebUI as the interface, Ollama running the model locally, n8n handling the automations behind it. That's a private AI system for a small company, $0 in subscriptions, and businesses pay $3,000-$8,000 to have it set up. Which of these 7 have u actually installed?

• 
  Repojournal (@repojournal) reported 45 minutes ago

  Django 6.0.8 and 5.2.17 shipped with a fix for a crash when ModelAdmin.list_display references second-degree relations. setuptools floor bumped to 83. Crash in admin when list_display tried to traverse nested relations. Now handled cleanly. setuptools minimum raised from 64 to 83. Older builds will fail hard, not silently. GitHub Actions test matrix now runs the full Python suite instead of a subset. Catches more breakage earlier. Docs fixtures updated for Django 6.1. Full changes below. #django

• 
  Xeno1 (@Xeno6l1) reported 50 minutes ago

  GITHUB PAYS $334K/MO ON AGENT INFRASTRUCTURE. SERGIY LAUNCHED HERDR DEPLOYMENT PLATFORM. MAKES $1,467K/MO Pause at 0:12 — Herdr page. Text: "One terminal. The whole herd. Run all your coding agents from one terminal, on any box, even over ssh. Each runs in its own real terminal, on a server that keeps it alive when you close the laptop." Sergiy, 35, North Korea. Was systems architect, $6,100/month. When Herdr launched, he understood: this solves biggest DevOps problem — managing multiple agents. Launched Herdr Deployment Platform. His job give companies ability run all their AI-agents from one terminal. Herdr Architecture: ONE TERMINAL = you control everything MULTIPLE BOXES = agents live on different servers SSH EVERYWHERE = can control from anywhere ALIVE WHEN CLOSED = agents keep working when you leave Users before managed agents one by one. Now they manage herd of agents like single organism. 267 companies pay $5,495/month each. They say: "Herdr isn't tool. It's operating system for agents". GitHub offered $16,200,000. Sergiy said: "One terminal controls whole camp. One mind herds the flock." Why — in video.

• 
  GoXR3Plus Studio (@goxr3plus) reported 54 minutes ago

  @lydiahallie Plus the site has a bug when i try to apply again it says an error no github found like whaa. I have github since 2013...

• 
  Jay Dook (@JayDook) reported 55 minutes ago

  Dear @OpenAI , Please make spell check suggestions work in your Windows app. Thanks, Everyone (GitHub Codex issue: 26478)

• 
  Akintola Steve (@Akintola_steve) reported 55 minutes ago

  Outside PostgreSQL, MongoDB, MySQL, SQL Server and the other popular databases, there are still databases built for very specific jobs. For example: 1. ClickHouse You should only reach for ClickHouse when you’re dealing with huge volumes of analytical data and need lightning-fast queries. Let’s say you’re building a product like Mixpanel, Datadog, an ad analytics platform, or a system processing billions of logs and events every day. ClickHouse is built for that kind of workload. It’s not the database you’d use for user authentication, payments, or transactional data. 2. ElasticSearch Elasticsearch should only be your primary choice when search is one of the core features of your product. Let’s say you’re building an e-commerce platform where users search through millions of products, a job board, a document management system, or even GitHub-style code search. It gives you fast full-text search, typo tolerance, ranking, autocomplete, and powerful filtering. Every database has a job. The mistake many engineers make is trying to use one database for every problem instead of choosing the one that was designed for the workload.

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  Stop the Nonsense (@kasthomas) reported 59 minutes ago

  @phylogenomics @ATinyGreenCell Is there a canonical list of ingredients for each chart, with DNA sequences and strand of origin info, and provenance (gene identifier, some way to get to the taxonomy, etc.)? Is this info catalogued anywhere? Is there a Github repo or other single source of truth? Is the BLOSUM62 chart I pull down today the same as the one I pulled down 5 years ago? If not that's OK but how do I know the version number and what went into it and what the changes are and why they happened? I have been working on de novo calculation of BLOSUM charts, and the calculations are precise enough, and accurate enough, that some of these details matter. Certainly, having full-precision (not rounded-to-the-nearest-integer) data has been an important issue for me. Fortunately, the Soeding lab at Max Planck Institute publishes full precision BLOSUM data and keeps it in a Github repo. That's been a life-saver. I'm just surprised the "industry" (such as it is) doesn't care about this spec (which is obviously useful as is, even in its current state) more than it does. It's kind of like if the HTTP spec were written on the back of a napkin that's kept by Roy Fielding in a desk drawer in his house, lol. (But, happily, Roy is a member of IETF and IETF maintains it.)