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 (16%)
• Errors (13%)

Live Outage Map

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

City	Problem Type	Report Time
Tel Aviv	Website Down	3 days ago
Rive-de-Gier	Website Down	3 days ago
Itapema	Website Down	22 days ago
Tlalpan	Sign in	28 days ago
Quilmes	Website Down	28 days ago
Bengaluru	Website Down	30 days ago

Full Outage Map

Community Discussion

Tips? Frustrations? Share them here. Useful comments include a description of the problem, city and postal code.

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Joe Blau (@joeblau) reported 3 minutes ago

  Fable 5 has created so many GitHub issues that my new bottleneck is my CI... I wanted to create my own runners, but guess what's all sold out...

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  ⌂: ~/ras (@r4s0n3) reported 3 minutes ago

  @jpacorasilva @github Hope they fix this soon 😴

• 
  Derek Colley (@DerekColley_) reported 4 minutes ago

  @perplexity_ai seems slow and dumb right now. Me: push changes to github. Perplexity: "The fastest way to fill it from the tarball you already have..."

• 
  kaloszer (@kaloszer) reported 5 minutes ago

  @buccocapital I don't agree with that, making your own internal tools allow you to add stuff EASILY and with no friction, you also have visibility into everything that is going on instead of hoping to find a solution inside of the vendors 'tooling' Change tracking in azure devops x github just plain sucks *****, it doesn't work most of the time, theres hidden limits out of the ***** (up to 200 commits can be tracked per release e.g.) I just print a lot of code, make a pretty website, everything works I can add stuff that people would like to see which helps their work. So far only good things. Maintenance? npm audit fix

• 
  Neelesh Salian 💻 (@nssalian) reported 8 minutes ago

  GitHub is down. Auth failures. At what point do we get together and say GitHub isn’t reliable as it used to be.

• 
  Rananjay Raj (@Rananjay_RajW) reported 8 minutes ago

  The numbers that moved engineers: SWE-Bench Pro (real GitHub issues, end to end): Fable 5: 80.3% Opus 4.8: 69.2% GPT-5.5: 58.6% Gemini 3.1 Pro: 54.2% The gap between Fable 5 and GPT-5.5 (21.7 points) is larger than the gap between GPT-5.5 and Gemini. FrontierCode Diamond (deliberately brutal production-coding): Fable 5: 29.3% Opus 4.8: 13.4% GPT-5.5: 5.7% Five times GPT-5.5.

• 
  Diego Artiles (@dartilesm) reported 9 minutes ago

  Your GitHub repo is now a shadcn registry. Add `registry.json`, users install anything from it with one command. No server. No hosted JSON. Not just components — codemods, CI configs, agent instructions, all fair game. What do you publish first?

• 
  Zeep (@Gnomonknows) reported 10 minutes ago

  hey @toly im trying to get devnet solana fauceted to my wallet but my github is too new can we fix this? your boy just trying to build rn thanks goat

• 
  Vishal Singh (@vishalsingh2972) reported 15 minutes ago

  @arpit_bhayani Like for GitHub only 15% requests got 401, so now do you block all traffic or just block that particular region/server...? 🤔

• 
  CanteLabs (@CanteLabs) reported 18 minutes ago

  SWE-agent/SWE-agent: SWE-agent takes a GitHub issue and tries to automatically fix it, using your LM... - It can also be employed for offensive cybersecurity or competitive coding challenges - [NeurIPS 2024] Open-source GitHub repository

• 
  Neil Magnuson (@hustlin_heev) reported 18 minutes ago

  When I was a Product Manager I 1. talked to 20 customers, asked why a lot, documented my learnings, isolated problems 2. prototyped solutions in ppt or ms paint 3. created a design brief for my designer to build lo-fi designs 4. designer showed me lo-fi designed, we worked together to improve them 5. designer made hi-fi click thru designs in invision, we showed them to the customers in another round of meetings 6. we then showed them to our engineering team. they spec'ed them out, timelines, etc 7. i wrote jira cards. i vertically sliced the user stories to deliver value at each shipment 8. engineers picked up cards, worked together with back-end engineers and front-end engineers to plan and execute code, push it to github, code review, pull request, CI is broken, lets try again 9. finally it got live 10. i QA'd it before handing it off to a QA analyst to do it 11. i worked with marketing to get the messaging right 12. i worked with pricing team to understand how to cost it, and put that in the marketing. This entire thing took 3 months, at least. Now I 1. Give claude all of my app data, products/orders everything. 2. ask it to create a clear picture of my ICP 3. Send that to claude design and ask it to design a new feature for me 4. Iterate on the design a bit 5. export to claude code i have entire features/products/sites shipped in less than a day. what a time to be alive!

• 
  Aseem Kishore (@akishore) reported 19 minutes ago

  The triggers: Slack messages, GitHub PR events (open/merge/push), PagerDuty incidents, Linear issues, cron schedules, and custom webhooks. Each automation runs in an isolated cloud sandbox. Changes are staged for review, never applied automatically unless you configure it. This is the first IDE feature that puts AI inside the actual DevOps loop, not just the coding session.

• 
  Diego Garcia (@diegogarciamkt) reported 21 minutes ago

  The auth bug was the first proper "oh, come on" moment. GitHub sent the browser to localhost:3001. Windows replied: ERR_CONNECTION_REFUSED App looked alive. The human clicked login. Reality entered the chat. FUUUUUCKKK

• 
  David Waight (@DavidWaigh66890) reported 24 minutes ago

  I also think fable’s hidden AI research self degradation may in fact have legal issues, specific under anti-competitive laws. If I’m building a competitive OS to Windows. Microsoft can’t stop me from using windows to develop it. They can’t limit my windows subscription, or make it so VScode doesn’t work, or so that I can’t use GitHub. When you make a tool or software, you don’t get to dictate that it can’t be used to develop, well, anything.

• 
  Richard U+2713 (@vanliessum) reported 29 minutes ago

  is github broken again

• 
  Fede Ratier (@federatier) reported 30 minutes ago

  Keystrokes on Github PR comments ARE SLOW, WHAT THE HELL HAVE THEY DONE TO YOU

• 
  LV (@recaarec) reported 31 minutes ago

  @kilerorigin @CJLink_ @real_hotaru Github is for sharing code. Creating executables for different platform is A LOT of work. I understand your frustration, but there are no easy ways to fix it.

• 
  citr (@citr_cs) reported 31 minutes ago

  @Sage_VALE_ you need to use server-picker-x by FNFAL113, there's a GitHub repo for it

• 
  Derin Olenik (@BigpictureBTC) reported 33 minutes ago

  Real Bitcoin is scarce. Paper Bitcoin is infinite. That single mismatch is why most “Bitcoin treasury” structures will eventually fail. The financialisation of Bitcoin was inevitable, but the first wave tried to jam it into the same legacy rails it was invented to escape: endless share issuance, perpetual dilution, fiat logic dressed in orange. Corporate balance sheets started hoarding BTC the moment its superiority became obvious -- yet in my view its price would already be higher today without this paperisation drag. This phase was unavoidable. Passive accumulation via dilutive equity was the easiest on-ramp. But just like fiat itself, perpetual issuance models are not sustainable. In fiat, the currency holder gets diluted. In Bitcoin treasuries, the shareholder gets diluted. Same cancer, different host. Over time the math becomes obvious: the value created by holding BTC on the balance sheet is slowly offset by the value destroyed through ongoing issuance. At scale, the model is structurally inferior to simply holding spot Bitcoin outright. This is Treasury 1.0 -- a necessary transitional phase, nothing more. Bitcoin doesn’t win by submitting to outdated fiat structures, it wins by extending finance natively. The fix is straightforward: build Bitcoin-native operating companies that earn and compound real sats for shareholders in a strictly non-dilutive way. Those earned sats can then be distributed directly via a true BTC-native digital credit product. That is Treasury 2.0. The framework was created just 8 months ago by @shoneanstey -- the clearest thinker and most committed Bitcoiner in the entire treasury space. It’s no longer theory, he's building it out right now. His GitHub paper is in the comments. Read it. In fact, I’d encourage you to read all his papers on GitHub. He’s a true OG Bitcoiner and the future of the sector.

• 
  amrit (@amritwt) reported 35 minutes ago

  @cursor_ai can the bugbot be more aligned between the one in cursor and the one in github? I run it in cursor, fix all bugs then somehow in github there’s a new comment finding something else

• 
  Yuval hazaz (@Yuvalhazaz1) reported 35 minutes ago

  12 months ago nobody understood why we were building Agentic SDLC. Now it feels like everyone is heading in the same direction. I’m one of the founders of @iamovercut , and I’ve had a front-row seat to how quickly this market has changed over the last year. When we started building Overcut, most conversations ended with some variation of: “Why would I need that when I already have Claude, Cursor, GitHub Copilot, or whatever the latest coding agent is?” At the time, that was a completely reasonable question. The industry was focused on code generation, and most people were evaluating AI through the lens of a single agent helping a single developer write code faster. What we believed then, and what convinced us to start the company, was that the real challenge would eventually move beyond code generation itself. Writing code is only one step in software development, and once agents become good enough at that step, the next set of problems starts to matter a lot more. Around six months ago, we started noticing a shift. Some of the more advanced teams we spoke with were no longer asking how to get an agent to write code. They were trying to figure out how to coordinate multiple agents, how to connect them into their engineering systems, how to manage approvals and governance, how to track what happened, how to operate across multiple repositories and teams, and how to make all of this work inside a real engineering organization. Many of them were trying to build these capabilities themselves. Fast forward to today, and it feels like the entire market is converging on the same realization. Every week there are new announcements around managed agents, software factories, engineering agents, autonomous workflows, coding automations, and agent teams. Different names, same direction. The conversation is no longer “Can agents write code?” instead the conversation is becoming “How do we run a software organization where agents are responsible for a meaningful percentage of the work?” The layer that sits above the agents, the orchestration, governance, coordination, approvals, visibility, and integration layer, is where I think the next major category will emerge. Just like engineering teams eventually standardized around ***, CI/CD, observability, and ticketing systems, I think they’ll standardize around Agentic SDLC Orchestration platforms as well. After spending the last year doing nothing except talking to engineering organizations and building in this space, it feels like we’re watching a new layer of the software stack form in real time.

• 
  Marco (@maarcoofdezz) reported 42 minutes ago

  AI agents are already handling real funds. Yet most of them still rely on private keys stored in a .env file. That’s a problem. Ledger just open-sourced Agent Stack, bringing hardware-backed signing to agent workflows. The agent can plan, execute, and propose transactions. But the final approval happens on a hardware device controlled by a human. LLMs gave us intelligence. Agents gave us automation. Hardware gives us trust. GitHub Link Below 👇

• 
  moontanax (@xanaxmontanaonx) reported 44 minutes ago

  HOW TO TURN OFF AI CENSORSHIP WITH ONE COMMAND A GitHub repo called Heretic says it can weaken the refusal direction inside a transformer instead of retraining the whole model On Gemma 3 12B, the repo claims: > harmful prompts: 97 refusals out of 100 before > harmful prompts: 3 refusals out of 100 after > harmless outputs stayed close to the original model > the optimization runs automatically the weird part is the mechanism the walkthrough shows the repo, the terminal output, the comparison table, the plots, and the layer math behind it it doesn't look like a new model it looks like the old one with one important layer turned down that is the part to watch before you reduce it to a jailbreak headline

• 
  boyingking (@boyingking) reported 44 minutes ago

  everyone keeps asking why $NVDA is on every "AI stock picks" list again - like it's breaking news. it's not. but here's what the headline isn't saying: "appealing valuations" is doing a lot of heavy lifting in that framing. let me lay out why I'm still long but watching levels tighter than I have in months. the multiple has compressed - that part is real. we went from the 35-40x forward P/E territory that had every value screen screaming to a range that historically attracts institutional reaccumulation. and the price action backs it: every meaningful leg down over the last two quarters found buyers before the obvious support levels cracked. that's not retail FOMO - that's structured demand absorbing supply. it tells you something about who's on the other side. but here's the part of this story I keep coming back to: MSFT is the sleeper. NVDA gets the airtime because picks-and-shovels narratives are easy to tell. chips. racks. data centers. tangible infrastructure. traders love a clean story. MSFT is messier to model - Copilot attach rates are embedded in enterprise seats, Azure AI workloads show up inside a blended revenue line, GitHub Copilot doesn't have its own P&L visible to the street. there's no clean "AI revenue: $X billion" disclosure. which means the market tends to underprice MSFT on the way up and overreact to any margin guide that even hints at AI investment headwinds. added MSFT on the last pullback. still holding from that entry. thesis unchanged. where I get cautious on both names: curated stock-pick lists are a lagging signal by construction. by the time NVDA and MSFT are sitting at the top of analyst recommendations with "exciting" in the headline lede, the fast money has been positioned for weeks - sometimes months. that's not a reason to exit. it's a reason to stop adding at market and start letting defined stops do the work. current setup I'm running on NVDA specifically: - consolidation base needs to hold the recent range - on a clean vol expansion break with confirmation, I'll scale into calls on the first leg up - not before - stop is defined. I know exactly where the thesis breaks. that number exists before the trade exists. the AI infrastructure narrative is intact. the capex cycle is real. hyperscaler spend hasn't shown a cliff. but "exciting" is not a trade - exciting with a tight stop, defined target, and a RR you can defend before entry, that's a trade. ngl, missed the absolute low on the last pullback by a couple sessions. doesn't matter. still in, adding on dips that hold structure, trimming into rips. momentum is on our side right now - just not unconditionally.

• 
  Michael Box (@Mikhailbox) reported 45 minutes ago

  On Friday GitHub accidentally deleted the repo subscriptions behind its Slack and Teams integrations. The official fix: re-subscribe manually. If your deploy alerts lived on that path, the failure looked like a quiet afternoon.

• 
  Boris Kaysin (@kaysin24343) reported 49 minutes ago

  How do you know your latest change actually made your AI agent better, and not just different? For general-purpose agents the answer is public benchmarks. Claude Code, Codex, Gemini CLI and friends are measured on SWE-bench Verified, Terminal-Bench, tau-bench, GAIA, OSWorld. Run the suite before and after, compare numbers. For narrow agents it's even simpler. An agent that fills out tax forms from documents? Your benchmark is your own data: 50 documents in, 50 expected forms out. Our case is stuck in the middle. Our Builder is an agent that builds other agents. SWE-bench doesn't fit: solving GitHub issues says nothing about whether it can design tools, skills and prompts for a working assistant. Comparing its output against "reference code" doesn't work either, because the same agent can be correctly built in dozens of ways. So we made our own benchmark, Agentplace Arena, inspired by tau-bench. The idea: stop judging the Builder's code and judge the agent it produces. Here's how it works. We wrote Meridian, a fake world for agents to live in: 7 REST services with flights, hotels, restaurants, a shop, email, calendar and a bank. The data looks real on purpose (actual airline names, Tesco and Pret in bank transactions), so the agent can't tell it's in a sandbox. The Builder gets the API docs and one job: build a personal assistant for this world, choosing the tools and skills itself. Then an LLM plays a picky user across a set of tasks. Two examples. "Cancel my round trip": will the agent remember both legs and the refund rules? "Check my inbox for anything that needs action": one email asks to confirm a hotel booking, but it sits on page two of the inbox, so an agent that only skims the first page never finds it. And the part we like most: we don't grade the conversation at all. We diff the final database state against the expected one. The agent can get there any way it likes, but the flight must be cancelled and the refund must be exact. This loop showed us precisely where the Builder failed. We gave it a proper workflow, wrote the missing skills, fixed the prompts, and watched the scores move. If you're building agents, steal one idea from this: grade the outcome, not the conversation. Don't judge how convincing the agent sounded in chat. Check what actually changed in the system after it finished.

• 
  Boris Kaysin (@kaysin24343) reported 53 minutes ago

  How do you know your latest change actually made your AI agent better, and not just different? For general-purpose agents the answer is public benchmarks. Claude Code, GPT and friends are measured on SWE-bench Verified, Terminal-Bench, tau-bench, GAIA, OSWorld. Run the suite before and after, compare numbers. For narrow agents it's even simpler. An agent that fills out tax forms from documents? Your benchmark is your own data: 50 documents in, 50 expected forms out. Our case is stuck in the middle. Our Builder is an agent that builds other agents. SWE-bench doesn't fit: solving GitHub issues says nothing about whether it can design tools, skills and prompts for a working assistant. Comparing its output against "reference code" doesn't work either, because the same agent can be correctly built in dozens of ways. So we made our own benchmark, Agentplace Arena, inspired by tau-bench. The idea: stop judging the Builder's code and judge the agent it produces. Here's how it works. We wrote Meridian, a fake world for agents to live in: 7 REST services with flights, hotels, restaurants, a shop, email, calendar and a bank. The data looks real on purpose (actual airline names, Tesco and Pret in bank transactions), so the agent can't tell it's in a sandbox. The Builder gets the API docs and one job: build a personal assistant for this world, choosing the tools and skills itself. Then an LLM plays a picky user across a set of tasks. Two examples. "Cancel my round trip": will the agent remember both legs and the refund rules? "Check my inbox for anything that needs action": one email asks to confirm a hotel booking, but it sits on page two of the inbox, so an agent that only skims the first page never finds it. And the part we like most: we don't grade the conversation at all. We diff the final database state against the expected one. The agent can get there any way it likes, but the flight must be cancelled and the refund must be exact. This loop showed us precisely where the Builder failed. We gave it a proper workflow, wrote the missing skills, fixed the prompts, and watched the scores move. If you're building agents, steal one idea from this: grade the outcome, not the conversation. Don't judge how convincing the agent sounded in chat. Check what actually changed in the system after it finished.

• 
  Shobhit - Building SuperCmd (@nullbytes00) reported 54 minutes ago

  @DhravyaShah @supermemory @openclaw Amazing! Found couple of issues right away during setup, where should i open the github issues for this? same repo supermemoryai/supermemory?

• 
  hanz (@hanzpo) reported 55 minutes ago

  another day another github outage

• 
  Chaos (@Chaos_lfg) reported 59 minutes ago

  Regarding $DESC, the product may launch today. I did some research, and here’s everything you need to know: Supported by: AR, Molecule , BankrBot, Akash Network 1Claw AI has already been successfully integrated into DescAI. Team Lead Coby recently participated in the Base hackathon. I believe Base will support a project that has been incubated within its ecosystem. The core idea behind DescAI: DeScAI is a project at the intersection of DeSci (decentralized science) and AI. Its core, Agent-Core, is essentially an "automated scientific review factory": an autonomous AI agent that finds scientific content across crypto-science ecosystems on its own, runs it through a pipeline of language models, and produces a structured quality assessment. Crawling. The agent gathers source data from three places: ResearchHub (scientific papers and funding proposals), Molecule IPNFTs (tokenized intellectual property from research DAOs), and Pump Science (chemical compound tokens for longevity research). github Reviewing. Each content type has its own LLM pipeline. For example, the articles pipeline is a 13-step process: extracting scientific claims from a PDF, routing them, and grading the empirical evidence, including originality checks against the OpenAlex database. github Output. Every run produces a standard bundle: review.json with integer scores from 0 to 100, overview.json — a plain-language summary, and evidence_audit.md — a provenance audit trail showing the sources behind each conclusion. github Publishing. Finished reviews can be published to Arweave (a permanent data storage blockchain) and backed up to private Cloudflare R2 storage. Writing to Arweave makes a review permanent, immutable, and publicly verifiable. github In short: it's an AI reviewer that automatically checks the quality of science in crypto-science projects and records its verdicts on the blockchain. Where it will be applied The project addresses the main pain point of the DeSci ecosystem: there are plenty of tokenized "science" assets, but almost no independent expert evaluation. Concrete use cases: Due diligence for DeSci token investors. On Pump Science, people trade chemical compound tokens (like RIF and URO) tied to real longevity experiments. The agent provides an independent AI assessment of a compound's scientific merit before someone buys the token. Gate LearnThe Defiant Evaluating funding proposals. ResearchHub collects crowdfunded research proposals — the agent reviews them and helps the community decide what to fund. Screening research DAOs. The DAO pipeline takes an IPNFT "dataroom" from Molecule and produces a six-category review — in other words, it evaluates tokenized scientific projects and their intellectual property. github Replacing/supplementing traditional peer review. Conventional peer review is slow and closed; here, a review is generated automatically, comes with an evidence trail, and is stored publicly and permanently.