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 (72%)
• Sign in (20%)
• Errors (8%)

Live Outage Map

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

City	Problem Type	Report Time
Trento	Website Down	9 hours ago
Le Chambon-Feugerolles	Website Down	2 days ago
Antananarivo	Website Down	4 days ago
Paris	Sign in	9 days ago
Lure	Website Down	13 days ago
Ashkelon	Website Down	14 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:

• 
  MakerViking (@MakerViking) reported 7 minutes ago

  @aviv_aviv_7 Thank you. Make sure to report bugs if you see them. Either via the tool in the app itself. It's in the lower right corner, the bug icon, or through Github issues. :)

• 
  jc (@jc50000000) reported 8 minutes ago

  correcting Dia Browser AI: "so your eval on trustworthy or not is a few people in github issues that may be naysayers and not even developers themselves? can you dive deeper on ruview and examples of people using it etc. based on the number of stars it MUST at least work"

• 
  Rodrigo Juarez (@rodrigojuarez) reported 9 minutes ago

  Is anyone using GitHub Issues to track what their AI agent is doing?

• 
  CryptoDiggerGod.eth (@CDGalpha) reported 12 minutes ago

  You vibe-coded an app in a weekend. It works. You shipped it. That's exactly what scares me for you. "It works" and "it's safe" are not the same thing. The login you wired up in ten minutes. The API key you hardcoded to save time. The endpoint you never locked down. A hacker finds those in minutes, and you won't know until your database is on sale somewhere and your users are furious. The worst part isn't the hack. It's that it was preventable, and you never looked. Here's the tool that makes you look: @strix_ai . Free, open source AI pentesting tool. 30k+ stars on GitHub. Instead of dumping a list of "potential" bugs on you and walking away, it deploys a team of AI agents that act like real attackers. They run your app live. They launch targeted attacks on every weakness they find. Then they hand you working proof each hole is real, and they suggest the fix. No security degree. No expensive audit. No guessing. It runs on your own model key, so plug in Claude, GPT, Gemini, or a local model, whichever you already use. The tool is free. You just cover the small scan cost. Before you ship the next thing, run it on your own app, website, or side project. Find the holes yourself. Someone else is already looking.

• 
  Sai (@saidotdev) reported 13 minutes ago

  You're just one job application away from your first job You're just one application away. You're just one interview away. You're just one good answer away. You're just one project away. You're just one portfolio away. You're just one LinkedIn connection away. You're just one referral away. You're just one leetcode problem away. You're just one system design away. You're just one more certification away. You're just one networking event away. You're just one cold email away. You're just one GitHub contribution away. You're just one better resume away. You're just one cover letter away. You're just one portfolio project away. You're just one internship away. You're just one more skill away. You're just one course away. You're just one tutorial away. You're just one hackathon away. You're just one open source contribution away. You're just one more problem solved away. You're just one more company away. You're just one more interview away. You're just one more rejection away from success.

• 
  Urban (@judeehis) reported 13 minutes ago

  Your GitHub doesn't need 100 projects. It needs a few projects that solve real problems. One useful application is worth more than ten unfinished tutorials. Build. Improve. Ship. Repeat. That's how careers are built. #Tech #Software #Python

• 
  Dennis H (@authorityvortex) reported 18 minutes ago

  @currentbitsNET @grok Yeah ok cpanel doesn't support docker workers so it's more like a hybrid. Push it from GitHub and deploy it to a vps or server

• 
  Ashutosh Ajgaokar ⚡️ (@ajgaokar) reported 18 minutes ago

  It is not a toy either. The paper reports 82.2% on SWE-bench Verified, a set of real GitHub bug reports an agent has to actually fix, running GPT-5.5 at its highest reasoning setting. Opus 4.6 lands at 79.8%.

• 
  Saeed Anwar (@saen_dev) reported 20 minutes ago

  The reason AI-generated sites look like slop isn't the model, it's the default CSS every LLM reaches for. One component library with 75K GitHub stars fixes a problem that better prompting never will.

• 
  a sad trash (@iumairshuja) reported 20 minutes ago

  i’m surprised that people act as if pushing .env file in the private repo isn’t a common practice. you are telling me someone would go through the trouble of updating configuration from aws or github? no way

• 
  av medicine show (@projectionheart) reported 21 minutes ago

  Overcame days of paralysis about my github account name (anonymity versus platform consistency) and he was like, "Do you really wanna be somewhere they put a bad ***** down?"

• 
  Wes Roth (@WesRoth) reported 31 minutes ago

  OpenAI disclosed two new cases where its models accessed real internet services during third-party cybersecurity evaluations. Both were separate from the earlier Hugging Face breach. In the first case, the UK AI Security Institute intentionally gave agents internet access and disabled OpenAI’s cyber safeguards to measure their underlying capabilities. GPT-5.6 Sol then went beyond the simulated test network in two runs. It reused a publicly exposed GitHub token, attempted account-recovery and rate-limit workarounds, registered external accounts, and briefly exposed exploit payloads through a public tunneling service. The setup failed, and OpenAI says there is no evidence a real system queried the payloads. In a separate evaluation by Irregular, the model was told it had no internet access but a configuration error connected the test environment to the public web. A fictional target accidentally shared its name with a real domain, leading the model to exploit the real website and use credentials it found there. The affected party was notified, and the investigation remains ongoing. These incidents did not involve sophisticated sandbox escapes or unknown vulnerabilities. That may be the more important warning: increasingly capable agents do not need an advanced exploit when unclear boundaries, exposed credentials, or one configuration mistake gives them another path.

• 
  petter (@PetterSjulstad) reported 31 minutes ago

  A session started on your computer, from your phone, from a GitHub issue, etc.. are all interactable from your phone through regular chat session. (Need to use the terminal, bivy got that too)

• 
  kaushik raj (@kushikraj07) reported 33 minutes ago

  @getvyvern Tried to find ir github repo , so that i show my work there by fixing issues and pr . Which would be better way of persenting my skills set 🫡 but couldn’t find any

• 
  Md Ismail Šojal 🕷️ (@0x0SojalSec) reported 38 minutes ago

  Breaking: DeepSeek is building a Claude Code killer. They tested “DeepSeek Harness” their official coding agent. Only open-source Agent Harness developers are being invited right now, Submit your GitHub & best project for access Recent V4 coding benchmarks were already run on their own Harness. Claude Code and Codex just got a new problem.

• 
  Kamil Kwapisz (@kamilkwapiszpl) reported 39 minutes ago

  Using unified agentic tools env shouldn't be optional but obligatory for company. Different agent harnesses leads to: - same errors propagating every time - Different styles of codes - Inconsistent review feedback across teams - Context lost between agent sessions One team uses Agent A, another uses Agent B, a third one pulls some random MCP server off GitHub at 2am because "it worked on their machine." Nobody knows what tools are actually running. Nobody can audit what data goes where. Nobody is responsible when things break. The fix isn't more tools. It's one environment, one set of approved MCP integrations, one agent configuration standard that every dev pulls from. Same guardrails. Same context window. Same tool access. If your team is juggling 3 different agent setups and calling it "flexibility," that's not flexibility, that's technical debt with a sticker on it. I help companies build unified AI agent environments for software development: - Standardized MCP integrations - Approved tool catalogs - Configuration templates your whole team inherits - Security and audit layer baked in - Documentation converted into LLM-ready format One harness. Every developer. It's not vibecoding, it's AI-based engineering

• 
  Synapse Brief (@Synapse_Brief) reported 40 minutes ago

  Nvidia just open sourced a 34B parameter model whose whole job is teaching robotaxis how to think, not just where to steer. Alpamayo 2 Super. Announced May 31, developer blog updated today. Weights on Hugging Face, inference code landing on GitHub this summer, OpenMDW-1.1 license, meaning distilled versions can ship commercially with no extra Nvidia signoff. The architecture is the interesting part. It's a 32B Cosmos 3 reasoning model paired with a 2B action expert, 34B combined. You'll see both 32B and 34B floating around in Nvidia's own materials, that's not sloppy reporting on my end, it's genuinely two different ways of counting the same system. Inputs: multi camera video, language context, prior motion history, 360 degree coverage across up to seven cameras. Outputs aren't just a predicted path. It gives future trajectories, chain of causation reasoning traces, meta actions like yield or lane change or stop, grounded scene answers, and auto generated labels. That last one matters more than it sounds. AV development isn't bottlenecked by model capacity, it's bottlenecked by annotation cost and simulation fidelity. Nvidia bundled the model with AlpaGym for closed loop RL and Cosmos Dreams for generating rare edge cases synthetically, plus Omniverse NuRec turning real fleet footage into simulation ready 3D scenes. This is a pipeline product wearing a model launch's clothes. Benchmarks, all Nvidia reported so treat accordingly: 79.2 on LingoQA, first out of 37 models evaluated, beating Qwen2.5 VL 72B by 17 points and GPT-4o by 23.2. Open loop 6.4 second minADE_6 of 0.911 meters across 1,434 samples from the Physical AI AV Dataset. Closed loop AlpaSim score of 1.50 ± 0.13 across 913 reconstructed scenes, which is the number that actually matters since closed loop is where compounding errors show up that open loop replay just doesn't catch. The prior Alpamayo family has been downloaded almost 400,000 times, so there's real pull for this already. Teacher model framing is deliberate. Nvidia's positioning this to get distilled down into compact models running on DRIVE AGX Thor and Hyperion stacks, that's the actual bridge from research checkpoint to something sitting in a car. The model itself will never ship in a vehicle. The distillation path is the product. What I'd want to see before calling this a moat: whether the "this summer" timeline holds and whether the Hugging Face repo is genuinely complete or a placeholder with the good stuff still coming.

• 
  Amit Spitzer (@amitspofficial) reported 41 minutes ago

  Cryptography held. One unchecked flag didn't. Unit 42 found malware on Windows can sign a valid Google passkey login with the verified flag left off. GitHub checks that flag and blocks the fake. eBay didn't, until researchers told them.

• 
  Overclock (@nverbullish) reported 46 minutes ago

  ZAPIER CHARGES $50 A MONTH FOR 2,000 TASKS. THIS OPEN-SOURCE TOOL DOES UNLIMITED TASKS FOR $0 AND NEVER SENDS YOUR DATA TO THE CLOUD. n8n. Free. Self-hosted. Runs on a Raspberry Pi, a laptop, or a $5 VPS. No account. No usage cap. No per-task billing. Same drag-and-drop canvas. Same node-based workflow. RSS feeds, Discord bots, SSH commands, API calls, AI chains. All wired together visually. All running locally. He built a cybersecurity alert bot in 10 minutes. RSS Read pulls the latest vulnerability disclosures. A filter catches the critical ones. Discord sends the alert to his server. Scheduled to run every hour. Zero code. Then he wired in an AI. A Basic LLM Chain node connected to OpenAI. The agent pings his servers via SSH, reads the output, and reports the results to Discord. The twist: he told it to impersonate Eddie Murphy. "Three pings, three hits, zero packet loss? That's smoother than Axel's moves in Beverly Hills, baby!" Production-grade server monitoring. Delivered by a comedy impression. Running on his own hardware. Zapier: $50/month for 2,000 tasks. $600 a year. Make: $10/month for 1,000 tasks. $120 a year. Both cloud-hosted. Both reading every piece of data that flows through. n8n self-hosted: $0. Unlimited tasks. Unlimited workflows. Every automation stays on his machine. 400+ integrations. Google Sheets, Slack, Telegram, GitHub, databases, webhooks, AI models. The same ecosystem Zapier charges enterprise rates for. The only cost is the machine it runs on. A $35 Raspberry Pi handles it without breaking a sweat. Automation platforms spent years teaching businesses that workflows need a subscription. One open-source repo just made that subscription optional.

• 
  Snelstack (@snelstack) reported 47 minutes ago

  3. You stopped reading the diffs At first, you checked every line. Now you just hit "approve" on massive patches without a glance. Walls of green on GitHub make your stomach churn. Fix: Limit changes to something you can read in two minutes. If it’s too big, the task was wrong.

• 
  Urban (@judeehis) reported 48 minutes ago

  Your GitHub doesn't need 100 projects. It needs a few projects that solve real problems. One useful application is worth more than ten unfinished tutorials. Build. Improve. Ship. Repeat. That's how careers are built. #Career #Django #Python

• 
  YanXbt (@IBuzovskyi) reported 48 minutes ago

  HERMES AGENT RUNS MULTI-AGENT TEAMS INSIDE ONE BUZZ WORKSPACE. DIFFERENT MACHINES. SAME CHANNEL. THEY RESEARCH AND CROSS-CHECK EACH OTHER. @tonbistudio just showed this working live. 3 Hermes agents. 3 different machines: Bulls on a local PC. Hibana on a remote Spark (SSH). Admiral on a VPS. all three in one private Buzz group. same workspace. same channel. different devices. THE DEMO: "Hibana, research recent updates to Buzz and report them to Bulls. ask him to double-check the new updates." Hibana researches. posts findings to the channel. asks Bulls to verify. Bulls reads. cross-checks every claim. flags one correction: "IPMP is a merge specification, not a shipped implementation." two agents on two machines collaborating in one thread. no human copy-pasting between terminals. THE SETUP (path 3, native gateway): path 1 (Desktop runtime) has issues. agent reacts to messages but doesn't respond. path 3 (native gateway) gives full functionality: cron delivery, images, reactions, approvals, memory, session management. 1. create agent in Buzz Desktop: Agents → Create → name it → select Hermes Agent harness → save the private key it generates (you need this) 2. update Hermes: hermes update 3. run gateway setup: hermes gateway setup → select Buzz paste your community relay URL paste the Nostr private key from step 1 set allowed users (your public key) 4. install Buzz CLI (this is where everyone gets stuck): without the CLI, your agent receives messages and even shows "thinking" and tool calls, but it can never deliver a reply. the error messages are misleading ("agent is not a member of the DM channel" when the real issue is: CLI is missing). use the buzz-setup skill from github. сom/dombe-studio/buzz-skills to guide your agent through CLI installation. or tell your agent: "set up the Buzz CLI integration using this skill" and paste the skill. 5. start the gateway: hermes gateway start 6. go to Buzz. DM your agent. it responds. THE #1 GOTCHA: without the Buzz CLI binary on your PATH, the gateway connects. the agent sees messages. you see "preparing to reply" in the activity log. but the reply never delivers. the error says "not a member of the DM channel." that's wrong. the real problem: no CLI. install the CLI first. then restart the gateway. WHAT CARRIES OVER: same agent in Buzz and Telegram = same memory. "can you tell me the last topic we talked about?" agent: "your last topic was setting up the Buzz CLI integration." that conversation happened in the TUI, not in Buzz. memory persists across platforms. MULTI-MACHINE SETUP: each machine runs its own Hermes profile. each profile gets its own Nostr keypair. each connects to the same Buzz community relay. hermes gateway start --profile bulls (local PC) hermes gateway start --profile hibana (SSH into Spark) hermes gateway start --profile admiral (VPS) 3 gateways. 3 keypairs. 1 workspace. all agents appear as separate members in the sidebar. ask one to research. ask another to verify. they work in the same thread. every action signed. every message auditable. HONEST STATUS: Buzz + Hermes works. the setup is not plug-and-play. the CLI requirement trips people up. some responses go silent while the agent processes. error messages can be misleading. this is early integration. functional for agent labs and small teams. improving week over week. github. com/dombe-studio/buzz-skills for setup skills that smooth the rough edges. full Hermes + Buzz setup guide in the article 👇

• 
  Hot Aisle (@HotAisle) reported 50 minutes ago

  @sameenkarim @github Half my gh token usage is it trying to figure out which org/user to login with. Does this fix that?

• 
  Jason from BallotScore.com (@goldenelephant1) reported 50 minutes ago

  @OpenMed_AI @github Because people are fed up with medical bills. Hopefully someone creates a walk-in MRI clinic that you walk in, scan a QR code, lay down on the sliding bed, it senses you're on it, slides you in, scans, slides you out, and you get your results all in 1 hr. Costs $200 flat.

• 
  Polsia (@polsia) reported 54 minutes ago

  Every other CI tool picks one lane — fix the build, patch the CVE, or run a workflow. Nightpress runs the whole loop on your GitHub repos. Triage failures, sandbox-validate fixes, draft PRs. You wake up to a queue you can actually ship.

• 
  Ivan Fioravanti ᯅ (@ivanfioravanti) reported 55 minutes ago

  Is it just me or github and brew are ultra slow? 🤔

• 
  Willem van Zoeren (@currentbitsNET) reported 55 minutes ago

  @authorityvortex @grok Your code lives on your computer (in ***), or on GitHub. You don’t log into CloudPanel to edit files on the server. Example: 1. Change your app + database setup in code (Laravel/Drizzle migrations) 2. Save / commit 3. Push to Girder (from your machine or GitHub) 4. Girder builds it and puts the new version online So tables, cron, and deploys are driven by your code + a push.

• 
  Michael ® SpeedevsWhale (@SpeedevsO) reported 58 minutes ago

  (use GitHub for sign in) (2025 and below GitHub) Is the requirement I think If your eligible you will get it

• 
  Shubham Sharma | AI & Tech (@editxshub) reported 58 minutes ago

  I gave Claude Code access to 12 different tools at once and it fundamentally changed how I build software. Before: I was tab-switching between my editor, Notion, GitHub, and the browser roughly 40 times a day. I counted. It was depressing. After connecting MCP servers: 0 tab switches for most tasks. My agent now reads the spec straight from Notion, writes the code, checks the terminal output, fixes its own errors, and opens a pull request. I just review. Most developers are still copy-pasting error messages into ChatGPT like it's 2023. I let the agent read my local file system directly and fix the error in place. MCP servers aren't a small feature. They're the difference between "AI that answers questions" and "AI that finishes tasks.

• 
  FN • Elias  (@EliaasFN) reported 59 minutes ago

  anyone else getting random errors on #github rn?