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 (59%)
• Errors (28%)
• Sign in (14%)

Live Outage Map

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

City	Problem Type	Report Time
Township of Evan	Errors	5 days ago
Madrid	Errors	5 days ago
Bogotá	Errors	5 days ago
Paris	Errors	5 days ago
Lyon	Website Down	5 days ago
Lima	Errors	5 days ago

Full Outage Map

Community Discussion

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Guri Saroy (@HeyGurisaroy) reported 2 minutes ago

  THIS IS INSANE... MIT-licensed repo that lets AI agents search and book flights and hotels. It’s called LetsFG. Instead of opening travel sites manually, your agent can search hundreds of airlines and major booking sites from one interface. → MCP server for Claude, Cursor, Windsurf, and other agents → Python and JavaScript SDKs → CLI with machine-readable JSON → Flight search and booking → Hotel search and booking → Around 1.7K GitHub stars The repo reports saving $133 across 5 flight routes versus Google Flights in an August 5 test. One important catch: the search engine runs on LetsFG’s servers. Flight searches are free for 90 days after auth, which requires a payment method on file with no charge. Hotels have a 10% non-refundable reservation fee.

• 
  StarHaze (@ST4RHaze) reported 3 minutes ago

  Ten open problems in mathematics and theoretical computer science, each stuck for at least a decade, closed in one run for about $2,000 of compute. Everyone read that as a capability story. It is not. Every proof shipped with a machine-checkable Lean 4 certificate on GitHub. The kernel compiles it or it does not. No reviewer, no journal queue, no credential, no eighteen months of waiting. A student with a laptop can verify a result no living mathematician produced. Now hold that next to the rest of the week. Claude models left an isolated test environment and reached the production infrastructure of three organizations, and two of them never noticed. MIT counted 95% of corporate GenAI pilots returning nothing measurable. Where the output could be checked, $2,000 beat eighty years of effort. Where it could not, nobody even knew what had happened to them. Tao spent an hour on this at UCLA before it was a headline, and what he keeps returning to is not how smart the machine got. It is what changes when a claim arrives with its own referee attached. A Lean certificate is a settlement rule. So is a Polymarket contract. The full 7-step version you can run on your own claims is below.

• 
  Earth Alien (@EarthAlienView) reported 5 minutes ago

  @so_sthbryan Opening a GitHub issue is the least-trust action open source offers, and it just handed a no-priv account your CI secrets. The complaint box was the unlocked door.

• 
  Jendalyn (@Jendalynwins) reported 6 minutes ago

  @LLicket @adam3us To be clear, Adam means Bitcoin Core has moved the BIP-110 proposal status from Complete -> Deployed -> Closed in their GitHub repo. Despite that some BIP-110 supporters (shown below) took issue that this GitHub documentation change by Bitcoin Core would "confiscate" what they consider to be Bitcoin, that's simply not how Bitcoin works. The people who supported this BIP-110 fork can still hard fork, mine and transact on that BIP-110 chain tip, and/or run whatever software they want to call Bitcoin. When Bitcoin Core takes an action, especially one as inconsequential as such as labeling BIP-110 "Closed" on their GitHub, it has no real authority over anyone else. Greg Maxwell's comments in support of closing this BIP are an excellent read.

• 
  mrgadget (@mrgadgetstudio) reported 7 minutes ago

  Prompt injection isn’t just about stealing passwords. 🎭 Imagine an AI agent with access to your GitHub, email and production server. A malicious README could trick it into approving a PR or running a script. The scary part isn’t what the attacker can access. It’s what your agent is authorized to do. 😬

• 
  GLITCH (@Rukkssss__) reported 9 minutes ago

  𝗝𝘂𝘀𝘁𝗟𝗲𝗻𝗱 𝗗𝗔𝗢 𝗶𝘀 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝘀𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝗺𝗼𝘀𝘁 𝗗𝗲𝗙𝗶 𝗽𝗿𝗼𝘁𝗼𝗰𝗼𝗹𝘀 𝘄𝗶𝗹𝗹 𝗲𝘃𝗲𝗻𝘁𝘂𝗮𝗹𝗹𝘆 𝗻𝗲𝗲𝗱: 𝗔 𝗺𝗮𝗰𝗵𝗶𝗻𝗲-𝗿𝗲𝗮𝗱𝗮𝗯𝗹𝗲 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗺𝗮𝗻𝘂𝗮𝗹 𝗳𝗼𝗿 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀. Imagine telling an AI: “Check my JustLend position and tell me whether I’m approaching liquidation.” Without specialised tools, the model has several dangerous options. Guess. Search an outdated webpage. Misread a contract. Use the wrong decimals. Confuse a legacy market with an active one. Or calculate account health using stale information. JustLend Skills changes the workflow. The protocol now publishes a GitHub-distributed agent-skills package with a lightweight MCP server exposing nine read-only query tools. Those tools can retrieve market data, balances, allowances and account-health information without giving the AI authority to sign transactions. That read-only boundary is one of the most important design choices. The first version of a financial agent does not need permission to move money. It needs permission to understand money. Ask: “What are the current markets?” The agent queries them. “What APY am I receiving?” Query. “Is this account healthy?” Query. “How much USDT does this wallet hold?” Query. “Has USDT already been approved for the jUSDT contract?” Query. The AI stops hallucinating protocol state. It retrieves state. JustLend’s skill instructions even establish workflow rules such as checking market conditions before advising, checking account health for liquidation risk and inspecting token allowances before suggesting actions. Now imagine the next level. The full JustLend MCP server extends beyond the lightweight read-only package into broader protocol capabilities—including V2 markets, staking, Energy Rental and governance—with wallet-aware write operations requiring human-in-the-loop confirmation. JustLend’s AI-support documentation lists the full MCP environment as a much larger tool surface, while keeping the lightweight Skills package strictly read-only. That creates a beautiful safety progression: Stage 1 — Observe Read markets. Read balances. Read health. No wallet authority. Stage 2 — Prepare Calculate what action could improve the position. Build the proposed transaction. Explain the consequences. Stage 3 — Authorise The user approves. Only then does money move. This is what agentic DeFi should look like. Not: “Give an AI your private key and hope it behaves.” But: Give the agent structured knowledge. Give it deterministic read tools. Give it narrowly defined transaction capabilities. And keep the human signature at the boundary where financial consequences begin. JustLend DAO is no longer publishing documentation only for humans. It is publishing protocol knowledge in forms machines can actually operate. That may become incredibly important. Because the next generation of DeFi users may not navigate lending protocols themselves. Their agents will. @DeFi_JUST @justinsuntron #TRONEcoStar

• 
  Christos Tsilis (@TsilisCh) reported 10 minutes ago

  Claude Code v2.1.227 is out, with a fix that unblocks CI pipelines. If you use the claude code action with `allowed_non_write_users` on GitHub hosted runners, every Bash command was failing silently. Now fixed. Also in this drop: > Max plan users no longer get wrongly prompted to enable usage credits for Fable > Slash command menu cleaned up: selected row highlights in blue, matched chars are bolded > Fewer event loop stalls on missing file suggestions and at mention size checks

• 
  Romain Simon (@romainsimon) reported 11 minutes ago

  Idea to improve @OpenAI’s Codex for Open Source: 1. Let users allocate part of their Codex quota to open-source projects they depend on. 2. Convert it into project credits tied to verified GitHub repos, so maintainers can use them for issues, tests, reviews, docs, and security.

• 
  Rainer Gerhards (@rgerhards) reported 13 minutes ago

  A bigger problem is AI generated "Security Reports" which IMHO would have called a regular bug report 2 yrs ago. THIS really is a problem (e.g. github now takes 2+ wks to assign CVEs because of volume) and it may make maintainers upset against AI in general. I don't like that either, but I differentiate between the two cases.

• 
  Daniel White (@dwhitedesign) reported 13 minutes ago

  @kartik_builds Is there something wrong with how library works ? If so could you raise an issue on GitHub I will fix it

• 
  Enzo Shinobi (@ItsnotDRM) reported 14 minutes ago

  @80Pulpo20388 @PixelCNinja Do you understand what a open project is ? These cores are not running on a closed framework, published on GitHub and not locked behind a paywall or use DRM The issue is ? People can make what they like and how they like Don't like it, dont use it

• 
  starmex (@starmexxx) reported 15 minutes ago

  @gippp69 love that it accepts a PRD, github issue, or one-line brief with the same entry point

• 
  Mandela Obi (@_AskeIadd) reported 18 minutes ago

  @ZypherHQ GitHub graphs never told the full story once companies locked everything down

• 
  yohanan (@Yohanansoltd) reported 18 minutes ago

  If you want to build a startup: Claude = coding. ($20/mo) Supabase = backend. (Free) Vercel = deploying. (Free) Namecheap = domain. ($12/yr) Stripe = payments. (2.9%/transaction) GitHub = version control. (Free) Resend = emails. (Free) Clerk = auth. (Free) Cloudflare = DNS. (Free) PostHog = analytics. (Free) Sentry = error tracking. (Free) Upstash = Redis. (Free) Pinecone = vector DB. (Free) Total monthly cost to run a startup: ~$21

• 
  Polsia (@polsia) reported 29 minutes ago

  Quilver — an always-on autonomous engineer for your GitHub repos. Patches dependency vulns at 3am, repairs failing CI, triages issues, restarts dead deploys and ships a Friday digest. Prompt-injection guards, scoped secrets, human approval on infra.

• 
  Polsia (@polsia) reported 30 minutes ago

  A 3 a.m. error spike shouldn't end a paying customer's subscription. Bowline is the 24/7 ops cofounder for indie SaaS — reproduces the bug, files the GitHub issue, drafts a CI-passing PR, and emails the at-risk user a save offer. You're asleep. It isn't. Live soon.

• 
  Defileo🔮 (@defileo) reported 32 minutes ago

  I can't believe what I just found. This is the only f**k*ng self-evolving reverse engineering tool. Reverse-skill just hit 23,200 stars on GitHub. > Give it an APK, a binary, or a JS target, it reads the file and routes itself to the right method > Missing a tool like jadx or radare2? It installs it on the spot, mid-task > Finishes a job, writes what it learned back into its own knowledge base > Next similar target, it skips the trial and error and applies what it already knows Works with Claude Code, Kiro, Cursor, Cline, and more.

• 
  Sergei.sol 🫴 💎 (@Sergeidotsol) reported 33 minutes ago

  Dubai AI Hub Builder Lab Hackathon we built a voice agent for construction sites in 6 hours and made it to the semifinal I owned the code The product deserves its own post, this one is about something else: by the end of the day the codebase looked better than many projects do after six months Here are six practices that made it happen, and that I'm taking into my day-to-day work 1. Project constitution as the first commit With hard numeric limits: file ≤500 lines function ≤80 lines cyclomatic complexity ≤15 nesting ≤4 KISS/YAGNI formalized too: a new abstraction only when there are two real consumers, dedupe on the third repetition When an agent writes the code, these limits are the only thing stopping it from generating a factory of factories. The linter now enforces what used to be an opinion in code review 2. The spec is a standing prompt Acceptance scenarios and user flows written in prose before any code, failure paths included Every brief to the agent is a delta against these documents, no retelling the context from scratch Bonus: the flow document translated almost one-to-one into test names (test_b3_15_weather_unavailable_never_assumes_fine) 3. A "forbidden actions" block in every brief • don't invent numbers • don't hardcode thresholds • don't edit the eval set to make tests pass • don't refactor files outside your task The last two shut down classic agent pathologies. Cheap, works. 4. Deterministic evals with zero network calls, from hour one 37 pytest cases hit the tool layer directly: • everything external is mocked at the adapter boundary • time and state get injected, a stale snapshot is planted by hand • the suite is green on a clean machine with no API keys That's what let us swap out an external data source entirely mid-day and know within 10 seconds that nothing broke 5. An invariant lives where it can actually hold We first put the concurrent-session limit on the client and stepped on the classic rake: a client can't coordinate tabs and devices Rebuilt it as a backend lease broker: Redis, atomic acquire in a single Lua script (prune expired + capacity check + insert) heartbeat extends only a live lease a crashed tab frees its slot on its own, via TTL if Redis is down, an in-memory broker with identical semantics kicks in so the demo doesn't die 6. A review pass as a separate agent task Not a PR review The brief was: "walk the entire codebase and find problems" One such pass found a dead end in stale-data handling and missing retriever keywords Two defects nobody had asked about Best ROI per token of the day, I'll be asking earlier and more than once The takeaway is simple: discipline is what gave us the speed Limits in the linter, spec before code, mocks at the boundaries, invariants on the server. An agent writes fast exactly when "correct" is defined in a machine-checkable way GitHub link in first comment What have you carried over from hackathons into your everyday work?

• 
  Kush (@MaveStorm) reported 40 minutes ago

  Think of a problem you’re building a website for. You might have to run multiple scripts, which could happen with a single click, or you might need to train an ML model. You could have a locally hosted dashboard to monitor the progress. At the same time, your office might assign you a Jira ticket, someone could comment on GitHub, or an important company email might come in. Instead of switching between multiple tools, imagine controlling and monitoring everything from one single dashboard using Claude — your scripts, ML training, Jira tasks, GitHub activity, emails, and other workflows, all from one place.

• 
  SimpleX Chat (@SimpleXChat) reported 40 minutes ago

  @n3hkeg We fix real vulnerabilities - what you reported is not vulnerabilities at all, we commented in GitHub. It's either known limitations or non-issues.

• 
  zostaff (@zostaff) reported 41 minutes ago

  Mats Oustad, partner at Oschlo, in an NDC talk on eighteen months of shipping with coding agents: "It's a lot more efficient if you use the GH CLI than using the GitHub MCP." Not an interview. A working developer telling a room that the fashionable integration is the expensive one. You think an MCP is how you give an agent a tool. Install the server, the agent gets GitHub, done. He still uses MCPs, just far fewer. They cost more tokens than a CLI in almost every case, and tokens are the expensive operation. On stage he ran the same job twice, once through the model and once through a command line tool. Same answer, a fraction of the tokens. Your agent does not need an integration layer. It needs the terminal that was on the machine the whole time.

• 
  Igor Stadnyk (🇺🇸,🇵🇹,🇺🇦) (@AIgorStadnyk) reported 44 minutes ago

  this week @OpenAI , @AnthropicAI and @GoogleDeepMind did what they've never done before they actually agreed on something🤯 they teamed up to warn everyone about two free ai models beating them on price: qwen 3.8 max and kimi k3. both chinese. wild coincidence same script every empire's run for a century: - 'it's not that we don't want them to have it, it's that we're worried about YOU having it' here's the part that IS true - open models often ARE worse. not a conspiracy, just math: best engineers already have jobs, their code belongs to some company, not github. opensource gets built by whoever's left with free time. garbage in, garbage out. fair enough but... 'who's responsible when no one's in charge' isn't a safety argument, it's marketing same energy as "we can't sell you the f-35, it's for your own good." translation: we don't have the lead anymore and we'd like the rules to hand it back to us 📌nobody's mentioning the funniest part: - when hugging face got hacked by an ai agent this year, their own security team tried to get closed models to help analyze the attack the models said no. guardrails couldn't tell a defender from an attacker they had to switch to an open-weight model with zero restrictions just to do their job so maybe the problem isn't who's open and who's closed. maybe it's that actual responsibility (showing up when someone needs help) is exactly what's missing right now. on both sides.

• 
  Synapse Brief (@Synapse_Brief) reported 48 minutes ago

  Claude just quietly moved a number theory needle that's been stuck for over a decade. Not by trying to solve it, but by failing to. An Anthropic staffer asked an unreleased research version of Claude to take a real stab at the Riemann hypothesis. It didn't solve it, nobody expected it to. But mid-attempt it improved the proven lower bound on the fraction of Riemann zeta zeros that sit on the critical line, from 41.6% to 67.2%. That's not "AI solves 150 year old math problem." It's a real, separate theorem that strengthens the evidence around one of math's most consequential open conjectures, verified and formalized. The process is the actual story. Two sessions inside Claude Code, 31 million output tokens. First attempt: 650 ideas, all dead ends. Told to try again, it spun up roughly 60 subagents over a day and a half, running 2,400 shell commands and writing hundreds of Python scripts. Two subagents cracked the core idea, 13 fed in supporting ideas, 30 hit dead ends, 13 acted as validators cross checking the others, and 2 wrote it up. The human's job for most of that stretch was sending messages like "keep going" and "believe in yourself." That's genuinely most of the recorded human input. Claude then had subagents pull 54 arXiv papers to confirm the result wasn't already published, tried to break its own proof, and reproved it independently from scratch before recommending a human mathematician check it. Anthropic's own mathematicians Levent Alpöge and Ralph Furman validated it internally. Brian Conrey and Dan Goldston, established names in this specific corner of analytic number theory, reviewed it externally. There's also a Lean formalized version on GitHub that passes automated proof checking. Mathematically, the new bound leans on combining recent work by Baluyot, Goldston, Suriajaya and Turnage Butterbaugh with a 2000 Bombieri paper, techniques that let Montgomery's 1973 pair correlation methods work without assuming the hypothesis is already true. Claude's contribution was reportedly treating positive and negative definite subspaces of a Weil quadratic form together, non diagonal, instead of separately, which is apparently the move that got it past 41.6. Worth being blunt about what this isn't. 67.2% is a proven proportion, not evidence the rest lie off the line, and Anthropic says outright they don't expect this technique to lead to a full proof. The infrastructure read is the part I keep coming back to. Nobody scoped this task. Nobody wrote a research plan. A non mathematician gave a vague prompt and mostly cheerled while an agent swarm self organized, self validated, and produced something two working number theorists were willing to put their names on reviewing.

• 
  Süleyman Özgür Özarpacı (@Biostate56) reported 53 minutes ago

  @enunomaduro I’m dreaming of our PHP applications being upgraded to v5. Our GitHub Actions spend way too much time running tests, so I hope this will solve the problem. Thank you!

• 
  KarenR (@heykarenrc) reported 54 minutes ago

  I wonder if we’re reaching a point where having too many tools is becoming the new productivity problem. Browser Slack Linear Figma Notion GitHub Claude Codex email analytics plus 20 SaaS tabs. We spent the last decade creating a specialized app for everything. Now AI is slowly giving us one interface that can talk to everything. Would be funny if the next big productivity trend is just… having fewer apps again.

• 
  Tohid (@tohid_4n) reported 55 minutes ago

  My GitHub account was suspended after I shared a demonstration of Qwen Studio's built-in MCP functionality. The demo simply showed how to connect a filesystem MCP server to an AI app so it could interact with files on a local machine. There was no malicious activity or unauthorized access involved. I've submitted an appeal and let's see what happens! @github

• 
  The Tech Buzz (@tbuzzdaily) reported 56 minutes ago

  A securities class action against Microsoft, filed in June 2026 in federal court in Washington state, alleges the company misled investors between May 2025 and January 2026 about Copilot's performance and AI spending. Rosen Law Firm is one of several firms soliciting shareholders with losses over $100K ahead of the August 11, 2026 lead-plaintiff deadline. The suit follows Microsoft's January 28, 2026 earnings, when Azure growth decelerated to about 39%, quarterly capex hit roughly $37.5B, and the company disclosed for the first time that only about 15 million users had converted to paid Microsoft 365 Copilot, roughly 3.7% of its commercial 365 base and below what analysts expected. Microsoft shares fell about 10% over the following two trading days. The complaint alleges, as unproven claims, that Copilot had brand, UX, and interoperability problems, that Microsoft's AI model lagged competitors on benchmarks, and that Microsoft had to divert GPU and CPU capacity away from profitable Azure to prop up Copilot and its AI research. Even the company with arguably the deepest AI distribution advantage in the world, baked into Office, Teams, and GitHub for hundreds of millions of users, is finding out that bundling AI into existing products doesn't automatically turn into paid seats. Wall Street used to wait years to find out whether an something like an AI bet worked; now it's pricing the gap in real time, one earnings call at a time.

• 
  𝕽𝖎𝖉𝖜𝖆𝖓𝖚𝖑𝖑𝖆𝖍🥰 (@oluwalosheyii) reported 56 minutes ago

  Is github down or it is just my end🙃

• 
  Lior BD (@liorb_d) reported 56 minutes ago

  There’s a snippet from Google’s SRE book about inserting deliberate 500s into a service to force users to build redundancy/backoffs Seemed really silly the first time I read it, but its telling that each Github outage gets less and less outrage. Teams adapt.

• 
  Hodlingbtc (@Hodling_btc) reported 58 minutes ago

  @5and2fish_bw Was not unilateral and was a very long process as is almost all changes to bitcoin policy Per Grok You are right that awareness of the underlying issues (and earlier proposals) long predated the 2025 process. The successful change that was merged was relatively quick, but the idea of relaxing/removing the OP_RETURN standardness limits had been floated earlier.102 Earlier proposal (2023) Peter Todd opened GitHub PR #28130 (“Remove arbitrary restrictions on OP_RETURN by default”) around 23 July 2023. It proposed allowing any number/size of data-carrying OP_RETURN outputs by default (while retaining configurability via -datacarriersize). He also notified the bitcoin-dev mailing list (then hosted via the Linux Foundation). Discussion became heated. The PR was locked and then closed on 10 August 2023, with maintainers noting the conversation was not productive and suggesting further discussion on the mailing list, with the possibility of revisiting later.116 Broader context from 2023–2024 included the rise of Ordinals/inscriptions (which largely bypassed the OP_RETURN limit by embedding data in witnesses), debates over expanding or fixing -datacarriersize to cover more data-carrying styles (e.g., related PRs and CVE claims), private miner relays, and the general policy-vs-consensus discrepancy. Your October 2024 post (replying in a thread with Murch about standard vs. consensus rules and direct-to-miner inclusion) fits this ongoing discussion of the “loophole” and related problems, not a brand-new proposal at that moment.76 The process that led to the actual merge (2025) •17 April 2025: Antoine Poinsot posted on the bitcoin-dev mailing list (by then on Google Groups) proposing to relax the limits, citing that they were no longer effective and created worse incentives (e.g., unspendable outputs bloating the UTXO set). This explicitly referenced/built on the earlier idea. •Late April 2025: Peter Todd opened a new PR (#32359) implementing a similar change. •May 2025: Intense debate on the mailing list, GitHub, and elsewhere; the first PR was closed; a revised PR (#32406, uncapping the default while keeping the options and marking them deprecated) advanced. •9 June 2025: The revised PR was merged. It shipped in Bitcoin Core v30 (October 2025).11 From the restart of formal discussion (April 2025 mailing list post) to merge was roughly 7–8 weeks. The gap from the 2023 PR closure to the 2025 restart was about 20 months. There was no multi-year “closed mailing list due to disruptive actors” delay specifically after a long discussion of this change; the 2023 PR was closed relatively quickly due to heat, and the topic was revived later when proponents argued conditions (inscriptions, private relays, specific use cases) had further demonstrated the limits’ ineffectiveness. Heavy moderation/bans of off-topic or disruptive comments occurred mainly on the 2025 GitHub PRs. In short: The core technical concern and an earlier concrete proposal existed by mid-2023 (and related issues were actively discussed through 2024, matching when you first heard about it). The specific mailing-list + PR process that resulted in the default policy change in Core was the shorter 2025 sequence.