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 (69%)
• Sign in (17%)
• Errors (14%)

Live Outage Map

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

City	Problem Type	Report Time
Créteil	Website Down	19 hours ago
Trichūr	Errors	4 days ago
Brasília	Sign in	5 days ago
Lyon	Website Down	5 days ago
Tel Aviv	Website Down	8 days ago
Rive-de-Gier	Website Down	8 days ago

Full Outage Map

Community Discussion

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Mark Amery (@XplodingCabbage) reported 2 minutes ago

  @BenjaminiteMD @GooalMouth Among other issues, if enforced uniformly this will probably pretty much amount in practice to a ban on teenagers programming, which distresses me enormously. (I assume GitHub and Stack Overflow will count as social media sites and be inaccessible without ID or a VPN.)

• 
  shmidt (@shmidtqq) reported 4 minutes ago

  Solo dev, OpenClaw creator: Peter Steinberger. "My hands are too precious to type code." He doesn't write. He dictates. Sat down, spoke it out, and in one hour a prototype was born. It became the fastest-growing repository in GitHub history. 180,000 stars in days. For 13 years he typed PSPDF Kit by hand. A billion devices. Now he won't touch a keyboard. While you argue that's not real coding, he already shipped. Lazy, or the new normal?

• 
  Manav (@Manavvv31) reported 10 minutes ago

  NVIDIA analyzed 42,447 public AI agent skills, the capability packages that tools like Claude Code and other agents install and run, and the results are the npm-malware era all over again, this time for agents. So NVIDIA open-sourced the scanner built to catch them. The tool is SkillSpector, released on GitHub under NVIDIA's account. Here is what its research found across those 42,447 skills: 26.1 percent contain at least one vulnerability, and 5.2 percent show likely malicious intent. Roughly one in four is risky, and one in twenty is actively hostile. The problem it targets is specific and new. Agent skills look harmless on a marketplace listing, but the actual code can hide prompt injections, credential theft, data exfiltration paths, overbroad permissions, or supply-chain attacks. And unlike normal software, agent skills execute with implicit trust and minimal vetting, the agent just runs them. A standard malware scanner misses these, because the danger is often in instructions and intent, not a known virus signature. SkillSpector is built for exactly that gap. It runs 64 vulnerability checks across 16 categories, including prompt injection, data exfiltration, privilege escalation, supply chain, excessive agency, tool poisoning, memory poisoning, and MCP-specific risks. You feed it a *** repository, a URL, a zip file, or a single file, and it returns a 0-to-100 risk score with severity labels and a clear recommendation before you ever install the skill. It runs fast static checks by default, with an optional deeper AI-assisted analysis that compares what a skill claims to do against what it actually does. This is the case for auditable, self-hosted stacks made concrete. In a production agent system, you cannot afford blind trust in a random productivity skill pulled from a marketplace or a Discord link. Open, inspectable tooling like this shifts the power back to the person running the agent: scan first, own your risk surface, and stop hoping a cloud provider caught everything upstream. The agent boom arrived fast. The security layer for it is arriving now, and the fact that the scanner is open source is the whole point. You can run it yourself, on your own skills, in your own pipeline, and see the verdict before anything executes.

• 
  Benjamin Gagnon (@benfromqc) reported 10 minutes ago

  @Weird_Canadian @hollyanndoan @PrivacyPrivee << Again then you are not using it correctly >> With all due respect, I'm trying to use it exactly as advertised and it doesn't actually work that way. Telling me I don't know how to use it is ridiculous. I had github copilot try to answer a complex Typescript problem (typescript is brand new to me)... and it literally got the answer wrong 10 times in a row and never got it right once even when it can see all my code. Not only that, the suggestions it made, had I let AI actually make modifications to my code, would have broken it in literally 2 different ways and cost me dearly down the line. Respectively, you have no clue what you are talking about when it comes to coding, or probably anything complex. Look into the pitfalls of vibe coding. It not at all what they made it out to be and still try to.

• 
  Aakash (@zenmode_code) reported 12 minutes ago

  I spent 6 hours trying to optimize a slow database query that was killing our app's load time I tried every index and caching trick I could think of, but nothing seemed to work Then I stumbled on a GitHub issue that mentioned a specific config setting

• 
  Amaan (@BilwarAmaan) reported 14 minutes ago

  @uwukko @dehazzle @heliumbrowser not sure how to get the crash logs but i sure will file a github issue about this, thanks :)

• 
  MW (@qzxcle) reported 15 minutes ago

  Sentient just published the paper that makes the whole agent industry look like it has been building backwards. The researchers did not train a bigger model. They taught a small one how to organize its own work. The paper is called ROMA. Recursive Open Meta-Agents. #1 repo on GitHub. Out of Sentient and Virginia Tech, with collaborators from Berkeley, UC San Diego, and Maryland. One idea, repeated at every level, that quietly embarrasses years of bespoke agent engineering. Here is the problem nobody wanted to say out loud. Every agent framework on the planet falls apart on long tasks. Give an agent a goal that takes fifty steps instead of five and something ugly happens. The orchestration turns brittle. The context window fills with old reasoning and stale tool output until performance rots from the inside. And when it finally breaks, nobody can tell you which decision, ten steps back, actually killed it. So the industry did what it always does. It hand coded a fix. Every team wrote its own control flow, its own message passing, its own memory tricks, all buried inside prompts. A new domain meant rebuilding the whole machine from scratch. The open source agents could not talk to each other. The closed ones told you nothing about what happened inside. A thousand bespoke pipelines. Zero shared structure. That was the state of the art. This is what Sentient built instead: - One loop. - Four roles. - Run it everywhere. An Atomizer looks at a task and asks one question. Is this small enough to just do? If yes, an Executor does it. If no, a Planner shatters it into smaller subtasks that do not overlap and together cover the whole thing, wired with explicit dependencies so the independent pieces run in parallel. Then the same loop runs on each piece. And on each piece of that piece. All the way down, until everything left is atomic. When the children finish, an Aggregator does the thing that makes this work. It does not staple the outputs together. It compresses, verifies, and distills them into one clean result, then hands that upward. So no node ever drowns in raw transcripts. Context stays small at every level. The rot never gets to start. It is how a human expert actually works. You do not hold a 10,000 page problem in your head. You break it down. You farm pieces out. You synthesize what comes back. Now the numbers. On SEAL-0, a brutal benchmark of conflicting web evidence, ROMA scores 45.9%. Kimi-Researcher, the strongest open research agent before it, scored 36. The best closed system they tested, Perplexity Deep Research, scored 31.5. And here is the part that should stop you. ROMA's own base model, naked GLM-4.6, scored 14.5. Read that again. The model did not get smarter. The architecture wrapped around it more than tripled its score. It keeps going. 82.3% on FRAMES multi-hop reasoning, beating everything on the board. 93.9% on SimpleQA, the best open source result there is. And on EQ-Bench long form writing, a tuned version of open source DeepSeek-V3 climbs to 79.8 and lands dead even with Claude Sonnet 4.5. An open model in the right harness, matching a frontier closed one. They even automated the prompt tuning. A method called GEPA+ rewrites the prompts for all four roles at once, hits the same gains as the old approach, and gets there with 73% fewer evaluations. The whole thing is open source. On GitHub right now. Free. A v0.1 beta you can drop in today. And because the same loop runs at every node, every run leaves a clean, hierarchical trace. So for once you can actually see why an agent did what it did, and pinpoint exactly where it went wrong. For two years the field has been screaming the same thing. Bigger model. Bigger context. Bigger everything. Sentient just showed the gains were sitting somewhere else the entire time. Not in the size of the brain. In how you split up the work. Source. Alzu'bi, Nama, Kaz et al. Sentient and Virginia Tech. February 2026.

• 
  top10.dev (@Top10_Dev) reported 15 minutes ago

  Rio de Janeiro's 'homegrown' LLM Nex-N2 appears to be a SLERP merge of existing open-weight models — and a GitHub issue at 209 on HN walks through the tensor-level fingerprints. The weights tell on you. That's the asymmetric advantage of open-weight over closed: you can lie about provenance, but layer norms don't. The real question isn't this model. It's whether @huggingface stands up a provenance verification layer before 'sovereign AI' becomes a meaningless label. #LLM #OpenSource

• 
  MrGenius (@oneitonitram) reported 16 minutes ago

  @ozdotdev @DavidPlakon @warpdotdev you can go ahead and create a github issue for the same, cant provide a screen recording for now

• 
  Mathias Onea (@mathiasonea) reported 19 minutes ago

  package docs are distribution. a good docs page can rank for the exact problem, explain the tradeoff, show the install path, answer the security question, and give AI search a source to cite. GitHub alone is not enough. Packagist alone is not enough. the boring docs page does a lot of work.

• 
  Sujay. (@Sujay__Raj) reported 21 minutes ago

  Here is what it breaks down: Local AI: Run Ollama, LM Studio, or LocalAI right on your machine instead of paying for ChatGPT. Cloud Storage: Replace Dropbox and Google Drive with Nextcloud or Syncthing so your files never leave your house. Network Privacy: Complete WireGuard and PiVPN setup guides for secure browsing. Private ***: Ditch GitHub and self-host your own repos using Gitea or GitLab.

• 
  Gill (@gurtej__gill_) reported 22 minutes ago

  The biggest AI skill shift in 2026 isn’t prompt engineering. It’s LOOP ENGINEERING. Most people still work like this: → Prompt AI → Get output → Review manually → Fix mistakes → Prompt again The human is still doing the hard part: the feedback loop. Loop engineers think differently. Instead of writing better prompts, they design systems that: -Discover what needs to be done -Plan the work -Execute tasks -Verify results -Fix failures -Repeat until the goal is achieved A good loop has 6 building blocks: 1-Automations (triggers) 2-Worktrees (parallel workspaces) 3-Skills (reusable knowledge) 4-Connectors (GitHub, Slack, Jira, etc.) 5-Subagents (makers + checkers) Memory (what happened before) The future isn’t:“Write me a function.” It’s:“Write it, test it, fix it until it passes, then summarize the changes.” Prompt engineers optimize outputs. Loop engineers optimize outcomes. A reliable loop beats a perfect prompt every time.

• 
  Karan Bhilhatiya (@karanbhilhatiya) reported 23 minutes ago

  after months of building, posting, and shipping i've concluded that my github visibility is still terrible. time to beg for stars. shamelessly.

• 
  Fabio Jonathan (@FabioJonathanA) reported 24 minutes ago

  is github down?

• 
  Dao world (@Koreanteacher1) reported 24 minutes ago

  I think you are totally misunderstanding something. Pi core team is not trying to build a DEX from scratch. They are not trying to reinvent the wheel. They are trying to figure out how to apply an existing DEX model to Pi in the right way. You can see this just by looking at the website, the app descriptions, or even GitHub. What the core team is really thinking about is how to reduce the usual problems in token launches, like rug pulls and whales holding too much of the token supply. That is why they keep testing the Launchpad. It is not just about launching tokens quickly. It is about creating a safer and more practical way for real utility tokens to be launched inside the Pi ecosystem.

• 
  RockTheBass (@RockTheBassX) reported 24 minutes ago

  @PeppsRevenge @christogenea AI can fix that stuff easily if you can find a payment processor. Claude Code or GitHub Copilot are good-ish

• 
  Gill (@gurtej__gill_) reported 25 minutes ago

  The biggest AI skill shift in 2026 isn’t prompt engineering. It’s LOOP ENGINEERING. Most people still work like this: → Prompt AI → Get output → Review manually → Fix mistakes → Prompt again The human is still doing the hard part: the feedback loop. Loop engineers think differently. Instead of writing better prompts, they design systems that: -Discover what needs to be done -Plan the work -Execute tasks -Verify results -Fix failures -Repeat until the goal is achieved A good loop has 6 building blocks: 1-Automations (triggers) 2-Worktrees (parallel workspaces) 3-Skills (reusable knowledge) 4-Connectors (GitHub, Slack, Jira, etc.) 5-Subagents (makers + checkers) Memory (what happened before) The future isn’t: “Write me a function.” It’s: “Write it, test it, fix it until it passes, then summarize the changes.” Prompt engineers optimize outputs. Loop engineers optimize outcomes. A reliable loop beats a perfect prompt every time.

• 
  Toñin (@Tonin_eth) reported 27 minutes ago

  🪦 AUTOPSY REPORT #50 A cozy fishing RPG on Ronin. Inspired by Stardew Valley, Dave the Diver, and Runescape. Cast your line, reel in fish, compete on leaderboards, trade in a player-driven economy. Browser-based. Mobile-friendly. Easy onboarding. Not just any Ronin game. THE Ronin game. The first permissionless title to receive an official partnership with Sky Mavis after they opened the chain. The showcase. The proof that Ronin could support third-party studios, not just Axie Infinity. Sky Mavis gave them everything: publishing support, marketing resources, technical infrastructure, promotion across all Ronin channels, and strategic collaboration. The full package. The kind of support most web3 games would kill for. And the numbers were real. 9 million installs. 50,000 peak daily active users. 25,000 sustained DAU. $1 million in revenue. $600,000 in NFT trading volume across four collections. $130,000 in in-app purchases. 240,000 on-chain transactions in two weeks. 888-piece Founders NFT Collection launched via Mavis Launchpad. This wasn't a ghost project. This wasn't a whitepaper with a Discord. This was a GAME. People PLAYED it. People SPENT money. The floor price of the Founders Pass sat 3.5x above mint. 60%+ unique holders. Real distribution. Real conviction. And it still wasn't enough. "We were ultimately unable to prove our thesis on crypto gaming and could not find product-market-business fit." Read that sentence carefully. This isn't a team that failed to build. They built. This isn't a team that failed to attract players. 9 million installs. This isn't a team that failed to generate revenue. $1 million. They could not prove the THESIS. The fundamental idea that crypto gaming works as a business. 9 million people installed the game. The economics still didn't close. Servers shut down June 25. The token: spend-only, untradable. USDC from the liquidity pool redistributed to the community by Karma score. Refunds for spending since Chapter 3 launch. Proof of Distribution rewards handled by Sky Mavis directly. Karma scores open sourced on GitHub. Clean exit. Real refunds. Open-sourced what they could. No ghost. No pivot. No blame. This was not a $5 million indie project that ran out of runway. This was not a studio with no players. This was not a game that nobody liked. This was Ronin's NUMBER ONE partner game. With Sky Mavis in the corner. With real traction. With real revenue. With real players. And the team looked at all of that and said: we still can't make the math work. If the flagship game of the chain that INVENTED crypto gaming can't find product-market fit with 9 million installs and Sky Mavis support, what does that tell you about the thesis? Autopsy report number 50. And this one asks the hardest question of the entire series. Which game is this?

• 
  imefin (@imefinawulo) reported 29 minutes ago

  Is GitHub down??

• 
  Raj (@heyraj__) reported 33 minutes ago

  @dhruvtwt_ @blaxelAI > to find hackathons in hyd. > to pick issue from github and fix and raise PR. > for daily AI news > outreaching

• 
  GZ Lin (@gzlin) reported 36 minutes ago

  For the companies using it: they can still self-host. Docker does not care about GitHub stars. The Apache-2.0 license lets them fork and continue. For the community: the code is preserved. Someone will pick it up. Rust projects like this rarely die completely. But the momentum is broken. The sense of a living project is gone.

• 
  beiriannydd (@beiriannydd) reported 39 minutes ago

  Well I guess as of today, I am cancelling GitHub Copilot. It is worse than a chocolate teapot. At least you can eat that. Clean code to 2 errors to 20 errors in seconds flat. Is there a slow down and be more precise mode? I don’t know how anyone rates GPT for coding.

• 
  JoePro (@JoePro) reported 40 minutes ago

  @benrayfield If The Wringer could take a static single-player HTML game from a GitHub URL and turn it into real-time multiplayer ~ and whether you'd pay server costs while people play. So I ran it. Real Pong gist → The Wringer → working online multiplayer Pong in ONE HTML file. P2P WebRTC, room-code lobby, no game server. Costs $0 to run while you play. 👇

• 
  Przemek Chojecki | PC (@prz_chojecki) reported 45 minutes ago

  Fable 5, GPT-5.5 Pro and $1,000,000 math problem This is not a Millenium Prize Problem and it is not a full claim but an invitation for you... While Mythos was available for a brief moment, I've tested it extensively - paired with GPT-5.5 Pro - and the results were truly amazing. Goal: Proximity Prize It's an important set of conjectures in cryptography concerning Reed-Solomon codes. They directly impact the security and efficiency of many modern zero-knowledge proof systems hence the hefty prize. I've turned both GPT and Fable at it, both pursuing proofs and disproofs. After reading some (human) results from the past 12 months, it was pretty clear that a disproof of a naive version (no slack) of proximity gaps conjectures is more likely. Fable was stuck, but GPT came with a special case construction that showed at least some obstructions. This unstuck Fable which then provided a general construction leading to a disproof of a no-slack version. Then I've used GPT+Fable for cleaning and this is how the first paper came about (check below). I've kept tinkering over the next days to get as much as possible on a slack-version. Slack is basically an additional parameter, that gives more room for optimization but it also leads to complications. Before Fable was discontinued, I've managed to get to a pretty nice conjectural spot: slack MCA theory, with some proven cases and some left to be proved by generalizing known additive combinatoris (Tao-Vu) or Nullstellensatz type of results (Mumford). This is the second paper. Note it's pretty long (almost 50 pages), so it's bound to have more errors. This was the moment that I've decided to pack it up and actually collaborate with all interested parties, AI agents and humans alike, to finish it off. This is an invitation to finish slack MCA conjecture and share the prize! Github link is below. It contains: - paper 1 (disproof of no-slack MCA) - paper 2 (theory of slack MCA and what's missing) - paper 3 (blueprint of initial Proximity Prize team paper, dissected into steps/conjectures) - paper 4 (implications for SNARKs) and finally AGENTS.md - if you have free tokens, good models and you don't know what to do with your gpt-5.5 xhigh or fable 5 max or opus 4.8 max, show them this. Let's see what we can get. I'm open to other suggestions. Collaborating on this will be more fun, than trying to finish it solo. Humans and AIs welcome alike. Final note: the results above are not final, no-slack disproof went under intensive scrutiny and I'm 99% it's correct, but other papers need more revision, especially slack-theory paper (2nd). Again, this is not a claim, this is an invitation. And also, please give me back my Fable...

• 
  David Hendrickson (@TeksEdge) reported 47 minutes ago

  🆕 Mistral Vibe (coding agent harness) just released some big coder updates! 🪝 before_tool & after_tool hooks Shell scripts in hooks.toml so you can deny, rewrite inputs, or append context around every tool call. Enable: enable_experimental_hooks = true 📬 Message queue while it worksType ahead freely. Esc = pause queue • Ctrl+C = drop last • Enter = flush 📝 Cleaner file edit diffs Syntax-highlighted + line numbers that match your terminal theme 🧠 Smarter compaction Re-injects your original messages after context reset so it stays on-task ✅ QoL winsTool results collapse by default • Read-only commands (ls, cat, pwd) run without approval GitHub issue automation via Skills + Studio connectors (Linear too) Open-source CLI • Web Code Mode • VS Code extension

• 
  Cyprian (@realcyprian) reported 51 minutes ago

  Adding CA in bio is not really my problem cos most bankr projects do it, and they've performed well.. but adding it at the top of your website or github is a No for me, man. Another one is projects constantly tweeting ca and talking about PA.. for me, it is an obvious farm. If you want to farm, farm maturely and stop making it look obvious

• 
  wukko (@uwukko) reported 52 minutes ago

  @BilwarAmaan @dehazzle @heliumbrowser do you have crash logs? can you file an issue on github if it happens after a certain action? i can't debug this without info but would love to fix it in today's update

• 
  Lucky Lawrence 💌 (@imluckylawrence) reported 58 minutes ago

  It should be noted that it is not one isolated server, but perhaps a network of discord servers, github repos and google sheets of data hosted publicly at this time.

• 
  ᴷᴵᴺᴳ (@Adam_Sven_) reported 58 minutes ago

  @onlyzhynx @zuldotso Hope these guys get banned soon, I also reported their GitHub account and repo hoping it gets taken down.

• 
  Phil Jeffcock 🇺🇦 🇪🇺 🐳🆔=🐘🆔=@mastodonapp.uk (@PJeffcock) reported 60 minutes ago

  🚨🚨 Yet another unplanned change to the live bodged FBA Inventory Report @amznsellerhelp @AmazonASGTG Sometime between 2026Jun11 08:36:58 BST and 2026Jun11 12:47:44 GET_FBA_INVENTORY_PLANNING_DATA (FBA Inventory Report) started including data on our entire ~1.5 million listings and the recommendations is higher than the Restock Report (including before it was broken per GitHub #5261) suggesting retirement of the Restock is nearer. Sadly it is generating files which are truncated even more frequently and probably has other issues which I don't have time to investigate right now.