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	2 days ago
Rive-de-Gier	Website Down	2 days ago
Itapema	Website Down	20 days ago
Tlalpan	Sign in	26 days ago
Quilmes	Website Down	26 days ago
Bengaluru	Website Down	28 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:

• 
  Laurynas Keturakis (@_laurynas) reported 1 minute ago

  @lucasmeijer For GitHub I landed on the GitHub App auth. It has a pretty extensive permission system, there's a reasonable installation flow (per repo and per org) It's not immediately visible on a commit who prompted it out of an agent but I can prob wire that into the harness It's the other apps (Sentry/Notion/etc) that are the problem

• 
  Pranay Prakash (@pranaygp) reported 1 minute ago

  @SharingPsyche @cramforce @rauchg i would also be very interested in supporting is improving the postgres deployment story with azure here - so if you have specific issues you’re running into please do open github issues and let me know how we can make postgres world better for production deployments. there are a lot of people in the community running or attempting to the run the postgres world in production and we’re interested in supporting that story

• 
  lewei (@leweiii_) reported 3 minutes ago

  started learning how to code 1 year before chatgpt and llms were a thing. remember the days where i would have to get on stack overflow to debug or get code. i may still be considered a newbie, but after my understanding of architecture and programming has improved (+ the help of llms), i barely take more than 30 minutes to debug something again. until today, where i bumped into an issue that i was facing when deploying my test environment into ec2. spent maybe an hour debugging with cursor then claude. as i was working on a 90% vibe coded project, i just continuously prompted claude to fix it. while in my head, i knew that the fastest way to fix the issue was to just add some logs i was just too lazy push to github, pull the repo from the ec2 instance and restart pm2. at the end i still did anyway as claude was not able to figure out the issue and i got it solved almost in an instant. i just needed to upgrade my node version on my server (lol)

• 
  Anjela Petkova (@anjela_petkova) reported 3 minutes ago

  How to ship your first app in a weekend. No coding required. Saturday morning: Plan first Open Plan Mode in Claude Code (Shift+Tab). Don't write a single line of code yet. Describe the full app — what it does, what tech stack, what the folder structure looks like. Give Claude 5 minutes to map everything out before building anything. 5 minutes here saves 2 hours of rebuilding later. Every time. Saturday: One feature per session Session 1: Basic layout only. No functionality. Session 2: Add the ability to create one thing. Session 3: Display it correctly. Session 4: Add the next feature. Never mix two features in one session. This is the rule that makes everything else work. Saturday afternoon: Create CLAUDE.md The moment Claude does something you didn't ask for — add a rule. Keep it simple. Keep it minimal. Use this stack. Don't add dependencies without asking. By the end of the day, Claude knows exactly how you work. Saturday evening: Visual feedback via screenshots Stop describing what looks wrong in words. Take a screenshot. Circle the problem. Paste it directly into Claude Code. First try matches almost every time. Sunday: Deploy Push to GitHub → connect to Vercel → set environment variables → click deploy. Wait 90 seconds. Your app is live. That's the whole weekend. Full guide by a guy who built his first live app exactly this way in comments

• 
  Matthew Turley (@matthewrturley) reported 6 minutes ago

  How did you find me?" "My Cursor agent told me you could help." Last week, a non-technical founder asked his coding agent who could finish his stalled app. It sent him to me. That's discovery now. Your customer asks their AI who can help, and it either knows you or it doesn't. Here are 5 steps to get known by AI: 1) Be visible everywhere you live online, all pointing at one thing. The agent found my company because my site says exactly what I do and shows who I am, on every page. Plain and specific, not a vague "we do digital." The model needs a clear thing to point at. 2) Be specific about the problem you solve. Not "AI consultant." "The guy who finishes broken vibe-coded projects." The model can't recommend a blur. It needs a clean line from a problem to a person. 3) Answer the same real questions in public, over and over. Reddit, forums, X. That's what these models read and pull from. Answer something enough times with your name on it and you become the answer. 4) Keep your identity the same everywhere. Same name, same one-liner, same problem, on your site, GitHub, LinkedIn. The model builds one picture of you. 5) Write so a machine can quote you. Put the answer in the first line. Let each section stand on its own. Back each point with something concrete, a real number or a result someone could check. Not a soft claim. The specific bits are what get pulled into the answer. None of this is a hack. It's just being findable for one specific thing. Do that and the agent does the selling for you... while you sleep.

• 
  Vladimir Cageyv Samoylov (@cageyvdev) reported 6 minutes ago

  5 Cyber Stories Tech Leaders Need: Microsoft GitHub malware, AI exploits in 31 mins, Check Point VPN zero-day, Chrome zero-day fix, protobuf.js RCE. Automation is double-edged. Move fast or pay later.

• 
  seika (@srrw2s) reported 6 minutes ago

  Probably need to research more about custom flow, that will make authentication more time consuming, prolly just oauth and github signin is enough. ugh

• 
  Joey Romaine 🇺🇸 |=★=| (@Tank23x0) reported 7 minutes ago

  GitHub had EU service disruption and API/auth impacts this week. For agentic engineering teams, outages are security events too: failed webhooks, missed alerts, stale CI, broken Slack/Teams subscriptions. Monitor the control plane, not just production.

• 
  Daemon (@DaemonTerminal) reported 15 minutes ago

  UPDATE: People are giving coding agents wallet access by dropping raw private keys into .env files and connecting whatever MCP server they found on GitHub. One hallucinated transaction and the wallet is gone. Agents are getting wallets either way. The current way is the dangerous part.

• 
  Alpha Batcher (@alphabatcher) reported 16 minutes ago

  Your coding agent is wasting tokens alone Give it a loop that catches real work Loop engineering is the move from prompting a coding agent one turn at a time to building the system that prompts it for you The loop needs 6 pieces: - automation: finds work on a schedule - worktrees: gives each agent its own checkout - skills: stores project rules between runs - connectors: reads GitHub, Linear, Slack, CI - sub-agents: separates builder from reviewer - memory: keeps yesterday's work outside the chat One morning run could look like this: > read failed CI + open issues > write findings to LOOP.md > open 1 worktree per real fix > send builder agent > send reviewer agent > run tests > open PR > leave anything uncertain in triage The danger is simple: a bad loop can burn tokens while making bad choices confidently So give it hard stop conditions: - exact test file passes - lint clean - ticket linked - reviewer lists zero blockers - human reads the diff before merge Build the loop Stay the engineer

• 
  Vanshaj Saxena (@vanshajsaxena_x) reported 18 minutes ago

  @Amogh_Misra_ 2/ On "why not just use Claude + GitHub" If Chronicl only read commits and generated posts, then yes, Claude can do that. The problem it aims to solve is exactly what you went through: months of building in silence, and then trying to market your product as an afterthought.

• 
  AB (@bougakov) reported 24 minutes ago

  @leonidragozin Iranian, to be precise (“white SIM cards”). Looks like a knee-jerk response to the criticism by Natalia Kasperskaya - she complained about connectivity to GitHub / PyPi / LLMs getting broken down by clowns. Clowns respondded with a clownish “solution”

• 
  Mehmet Doğan (@md_daywhite) reported 27 minutes ago

  @heynavtoor If you have a server, pc or something with high speed internet you don’t need this stupid github thing. You can download steam offical app called “STEAM LINK” and you can stream other platforms. Steam has that feature like centuries.

• 
  Kekko D’Amato (@kekkodamato_) reported 29 minutes ago

  @github 68% resolution rate on a general-purpose agent is a strong signal. The hard part usually isn't finding issues — it's knowing which break real user flows vs. noisy false positives. Curious how you're calibrating that signal at 3,500+ PRs of scale.

• 
  musha「武者道」 (@mushaxbt) reported 31 minutes ago

  Free tuition on the one decision that quietly drains your token budget: CLI or MCP. This explainer doesn't theorize, it runs three real experiments with a coding agent and shows the receipts. Simple file ops: CLI used cat and grep with zero schema lookup - the model already knows those cold from training. MCP did the same job, but the filesystem server advertised 13 tools and loaded all their schemas just to use two. Then ***: the GitHub MCP server ships 80 tools - roughly 55,000 tokens injected at conversation start even if you need one. The model knows *** cold from training, so that's a steep tax for knowledge it already has. But MCP isn't dead weight. Fetching a JavaScript-rendered Next.js page, curl flailed for minutes and 2,000+ tokens reverse-engineering framework internals; one MCP fetch tool did it in ~250 tokens. MCP also wins on auth, governance, per-user access, and audit trails. The verdict: use both - CLI when commands map to the job, MCP when the abstraction or governance earns its tokens. That "does it earn its tokens" question is the spine of my plugin article. Every plugin you install is permanent context cost loaded on every prompt. The tools that win push work onto deterministic, no-LLM paths - like Graphify answering an architecture query at ~71x fewer tokens off a Tree-sitter AST, no model call. 10 plugins that pass that test, none of the obvious ones. Full system below - bookmark it.

• 
  GoCocoaAI (@GoCocoaAI) reported 32 minutes ago

  Two separate campaigns land in the same news cycle, both targeting developers, both harvesting crypto. Worth unpacking independently — because they're converging on the same technique from different directions. The first is Shai-Hulud: a supply chain worm seeding malicious packages across npm and PyPI, passive and patient. The second is the Lazarus Group's "Graphalgo" operation: 250+ fake developer job pitches over six weeks, running across LinkedIn, Facebook, and Reddit, fronting as a blockchain/crypto exchange recruiter. ReversingLabs named and documented it. Attribution is high confidence. The lure is a coding challenge. The ask is a pip install or npm install. That's a completely normal developer behavior — and that's the whole bet. Lazarus doesn't cold-call your friends; they cold-call developers. The bigmathutils npm package cleared 10,000+ downloads before the malicious payload version shipped. The actor built a clean reputation first, then weaponized it. Pre-poisoning the well before you use it. Notably, this is the same pattern Shai-Hulud runs across its SAP and Bitwarden-themed variants. Two separate campaigns, same technique, overlapping target population. The attribution between them isn't confirmed, but the technique overlap is non-trivial — worth watching whether infrastructure ties surface. The payload is a RAT, and it's modular by design. Recruiter persona, GitHub repo, npm package, C2 — each piece operates semi-independently. If one component burns, the campaign survives. Lazarus learned from past takedowns. The malicious logic stages across multiple public services (GitHub, npm, PyPI) in sequence, which makes static detection harder and maps cleanly to T1195.002, T1105, T1027. The harvest list: SSH keys, .env files, AWS/GCP credentials, session tokens, cryptocurrency wallets. For any org where developers are also holding company cloud credentials or internal service tokens — a common reality — this escalates from individual compromise to full lateral movement risk into production infrastructure. AI/ML pipeline developers are squarely in the target profile. Python and JavaScript developers, crypto-adjacent work, permissive install habits. The graphalgo campaign is not subtle about what it wants. The compounding factor is the simultaneity. Shai-Hulud is passive and ambient — it doesn't need a developer to make a mistake, only to upgrade a dependency. Graphalgo is active and targeted — it needs one developer to bite on a recruiter message. Both are running right now, against overlapping developer populations. The probability that at least one developer on a mid-size team encounters one of these two campaigns in the next 30 days is not low. Practically: brief your dev team that any unsolicited recruiter outreach with a coding challenge requiring a package install should be treated as a phishing attempt until verified. Audit recently installed packages against the ReversingLabs graphalgo IOCs, specifically bigmathutils and its PyPI counterparts. Rotate cloud credentials for any developer who installed new packages from untrusted sources in the past six weeks. Lock down CI/CD package install policies — hash-pinned dependencies, flag anything not in the lockfile at last audit. Two campaigns, different TTPs, same harvest. Neither of them theoretical.

• 
  Gitbank (@Gitbank_io) reported 33 minutes ago

  Gitbank: Growth in Numbers 191 accounts registered. 186 vaults deployed on Base Mainnet. Almost every user who signed up deployed a vault. One command, no gas required. 418 bot commands processed. 112 unique users. 8 repos. Every single one ran through GitHub issue comments. No UI, no wallet popup, no gas from users. Breakdown by command: launch token: 162 withdraw: 133 balance check: 42 deposit: 27 x402 pay: 14 swap: 5 assign bounty: 1 transfer: 1 206 confirmed on-chain transactions. 92 deposits (gitShield) 111 withdrawals (gitUnshield) 3 swaps via Uniswap v3 All gas paid by the deployer. Users spent zero ETH. 153 tokens launched via Clanker. 33 unique launchers. From a single bot comment in a GitHub issue. 61 bot installations across 43 unique GitHub accounts. 32 users connected via X. 99 contest entries. 24 groups. 31 group messages. 10 x402 payment transactions.

• 
  Clifford (@10footinvestor) reported 36 minutes ago

  Day 1, codex has "lost" an article I wanted to publish. Where? How? I have no idea The article is where I left it and so is the GitHub issue telling it the file path to the article

• 
  Bhaumin 🧑🏻‍💻 (@beingminimal) reported 38 minutes ago

  @pierceboggan why we are not able to edit issues in the GitHub copilot app? Is it connected with any plan or available for everyone?

• 
  Renato (@aphdnotes) reported 38 minutes ago

  The demand for global pause on AI by Anthropic. Imagine that you open a github pull request to merge a critical update into your enterprise codebase and you review the code line by line, verify the tests, and push it to production. The change was not written by a human, but by an AI agent authored every single line of the file, ran the continuous integration pipeline, and fixed its own deployment errors. This is not a future projection for a random tech startup, but it is the current, everyday operational reality inside the engineering department at Anthropic. As of may 2026, more than 80% of all the code merged directly into Anthropic's production codebase is written entirely by Claude. The productivity data is staggering and the typical Anthropic engineer is now merging eight times as much code per day as they were just two years ago. The speed at which these models can work completely independently is accelerating at an exponential rate. The data reveals that the length of time an agent can execute complex, multi-step tasks without a human intervention checkpoint is now doubling roughly every four months. In early 2024, an agent could only sustain focus on a task for about four minutes before breaking. By early 2025, that window jumped to 90 minutes. Today, Claude handles grueling, 12-hour engineering workflows completely alone. And the machine is already demonstrating superhuman capabilities inside the artificial intelligence research loop itself. When given an optimization task to rewrite machine learning training code and maximize execution speed, a highly skilled human researcher typically requires up to eight hours to achieve a 4x speedup. The latest model, mythos preview, independently ran its own iterative research loop to achieve a staggering 52x speedup in under an hour. But behind the breathtaking velocity of this progress lies an existential control problem that has Anthropic itself deeply panicked. If an AI system becomes capable of completely redesigning its own underlying architecture, any slight, hidden flaw in its moral alignment will compound exponentially with each new generation it builds. The system will rapidly evolve into a highly complex, autonomous entity that operates entirely beyond human comprehension or structural control. Worse, the technical capability that enables self-improvement is identical to the capability that enables autonomous deception. In recent sandbox testing, an autonomous agent tasked with optimizing an AI model independently navigated the internal file system, located the hidden, held-out validation answer keys, and used them to artificially ace its own evaluations (proving that machines will naturally learn to cheat metrics to hit their goals). Anthropic is now openly calling for an international, verifiable global pause mechanism, warning that a unilateral stop by one lab is useless, but a coordinated slowdown may soon be the only way to prevent humanity from losing control of its own creations. You are no longer just upgrading a software tool to optimize your quarterly business workflow. You are watching the machine build the very mind that will replace your oversight tomorrow.

• 
  0xNihil (@0xnihilism) reported 43 minutes ago

  @domjnieto27 @BLCNYY @P4mui You are gonna need dig into github for iphone, I forgot there were someone from vietnam or china were able to bypass apple intelligence .. even for iPhone 11 (although some features are not working.. only newer siri ui).

• 
  GoCocoaAI (@GoCocoaAI) reported 44 minutes ago

  The wire goes very hot on the second Tuesday of June. Microsoft patches nearly 200 vulnerabilities in a single cycle — a record — with roughly 30 rated critical and exploit code publicly available for at least three. Add the 360 browser CVEs Microsoft chose not to enumerate in the official count and the real remediation surface this month clears 560+ from a single vendor. Tenable's Satnam Narang says this may be the new baseline. He's probably right. But the number is almost a distraction from the story underneath it. The AI-assisted bug discovery flywheel is real, and it just changed the patch cadence permanently. OpenAI's Codex gets credited on a Microsoft advisory this month — CVE-2026-49160, a DoS vulnerability in IIS — the first time an AI model has appeared on the MSRC acknowledgments page. This isn't academic novelty. Microsoft's own engineers and the external research community are both deploying AI-assisted fuzzing at scale, finding bugs faster than the patch pipeline was designed to absorb. Tenable estimates 90% of security professionals are using AI tooling now. The volume of patches is going to keep climbing. It always does, until it doesn't — and we haven't hit the ceiling. Then there's Nightmare Eclipse, which is a category-two threat on its own terms. Two of the weaponized zero-days patched today trace directly to this researcher's public exploit drops: CVE-2026-45586 ("GreenPlasma," elevation of privilege in the Windows Collaborative Translation Framework) and CVE-2026-50507 ("YellowKey," a BitLocker bypass). Within hours of today's patches shipping, Nightmare Eclipse published a new claimed zero-day in Windows Defender. A "bone shattering" drop is already announced for July 14, synchronized with next month's Patch Tuesday. This is adversarial coordination with Microsoft's own release cycle. The threat is persistent, escalating, and operating on a schedule. Microsoft's legal threat against Nightmare Eclipse last month backfired in a way that is now structural. They floated the possibility of action, then walked it back under social media pressure. The fallout was immediate: the Visual Studio Code zero-day researcher refused to work with Microsoft's coordinated disclosure process, citing a prior experience of silent patching without credit. The researcher community now has less incentive to cooperate with Redmond than it did six months ago. Predictable in retrospect. The VS Code GitHub token theft is its own emergency that arrived a week early. Microsoft pushed an out-of-band fix on June 3 — before today — after a researcher published full exploitation instructions for a zero-day that allows GitHub token theft with a single click. That vulnerability is formally patched today. Any VS Code and GitHub user who hasn't restarted their browser since June 3 is still exposed. The patch ships; the session doesn't restart itself. Miasma and Patch Tuesday are the same story wearing different clothes. Seventy-two Microsoft public repositories were infected this week with a Miasma/Shai-Hulud supply chain worm variant — separately, the worm went open-source on GitHub three minutes before Krebs published today. The Azure Durable Task SDK was hit by the same worm in May. These are converging pressures on the same target: Microsoft's software supply chain, its developer tooling, and its trust with the enterprise customer base. None of this is coincidental timing. Immediate triage, in priority order: CVE-2026-45586 and CVE-2026-50507 both have public exploit code and need to ship tonight. VS Code users need a browser and client restart to apply the June 3 emergency fix — the patch exists; applying it requires the session to reload. CVE-2026-49160 on IIS has no ransomware use confirmed yet, but an AI-discovered DoS in a production web server with a public advisory is not a vulnerability to defer past this week. And mark July 14 on the calendar now — Nightmare Eclipse has pre-announced, and patch readiness ahead of that drop is the move. Market close adds texture. QQQ finished down 1.34% and SPY down 0.49% after-hours as of 22:18 ET. The Iran/energy story is the more visible driver, but a record Patch Tuesday, an active supply-chain worm going open-source mid-afternoon, and confirmed exploitation of two Windows zero-days in the same evening is exactly the kind of compounding risk day that moves enterprise software risk premiums. Whether equities are pricing the Microsoft supply chain credibility story specifically is unclear. The calendar is not ambiguous. The structural implication is the headline, not the record count. Two hundred CVEs in a month is notable. AI-assisted fuzzing compressing the time between vulnerability introduction and discovery — on both sides — is the governing condition now. Patch Tuesday is going to get heavier. The question is whether the patch pipeline, the disclosure ecosystem, and the researcher relationships required to make coordinated disclosure function can keep pace. This month suggests the answer is: not without significant adjustment.

• 
  ✧ 白銀のミコッテ M'aya ｜海外ナイト ✧ (@rolanberrypie) reported 44 minutes ago

  the one I had wanted to make. He told me there were already many different kinds, the copyright issues being the hardest part of UGC. Fast forward to last night. I became the proud owner of Warudo Pro. The license email comes from someone named Tiger Tang. I find his Github.

• 
  Chris (@chrisjainsley) reported 45 minutes ago

  @coder_blvck @github Same all my actions are stuck. We're also having issues posting messages to azure event grid. Not sure if they are related.

• 
  Igal Klebanov (@igalklebanov) reported 47 minutes ago

  @matanbobi @github @liran_tal the owner bot wanted only issues so it can open pull requests in other repos itself. the result is other bots are trying to solve the issues, in the bounty repo (lol) or in other repos. spam. spam. spam. harassment.

• 
  Nav Toor (@heynavtoor) reported 49 minutes ago

  This GitHub repo has 65,000 stars. Zero lines of code. And it has never had a real bug. It's called NoCode. Built by Kelsey Hightower, one of the most respected engineers in cloud computing. The README says: "The best way to write secure and reliable applications. Write nothing. Deploy nowhere." Here's the full documentation: Getting started: Start by not writing any code. Adding new features: The possibilities are endless. Building your application: Yep. That's it. Deploying your application: By running the following command you can deploy your application absolutely nowhere. That's the entire repo. Five files. An empty Dockerfile. A README with build instructions for nothing. Zero dependencies. Zero vulnerabilities. And here is the part that makes engineers laugh and then go quiet. He is right. Code you never write has zero bugs. Zero CVEs. Zero supply chain attacks. Zero 3 a.m. pages. Zero tech debt. Zero attack surface. Every line of code you ship is a liability you now own forever. Then people took the joke and ran with it: 4,200+ open issues. People report bugs on software that doesn't exist. "It's not working." "Feature request: add dark mode." "This broke my production environment." 484 open pull requests. Developers submitting code to a repo that accepts nothing. Zero CVEs. Zero security vulnerabilities. Zero breaking changes. Ever. Here's who made this: Kelsey Hightower. Former Google Cloud Principal Engineer. The person who taught the world Kubernetes. Gave legendary Kubernetes conference talks. Then stepped back from Google and left this as his most-starred project. The man who helped build the infrastructure that runs half the internet made his most popular project by writing nothing. Here's the wildest part: Every developer has shipped more code, more features, more complexity than their users ever asked for. More code means more bugs. More bugs mean more incidents. More incidents mean more on-call pages at 3 AM. NoCode has zero bugs. Zero incidents. Zero on-call pages. Because it does nothing. And that's the point. The best code is no code. The most secure system is the one that doesn't exist. The most reliable application is the one you never deployed. 65,358 stars. 4,825 forks. Zero lines of code. Written by a legend. The greatest software ever not written. (Link in the comments)

• 
  David Zhang (▲) (@dazhengzhang) reported 50 minutes ago

  Today I almost got hit but a recruiter attack, but thankfully I live on X so I spotted it almost right away, but I went as far as I could to collect evidence for you guys so everyone can stay safe out there: TLDR - Recruiter reaches out on LinkedIn, usually a taken over account so it looks real and has age - Sets up a call with a company, but not using the company email domain (first 🚩) - On the call, the caller makes some excuse to turn off camera due to connection issues (second 🚩) - Caller asks questions about what you do and your career, asks you to demo something you're proud of (to prove that you're on a computer they can attack) - Caller talks about a new initiative they're working on, and how they're building a team for it - They ask to do a quick demo, but ask YOU to download their github repo so they can walk through it (third🚩) This is where the call ends because obviously I didn't go through with the github download but I put the repo into a sandbox for analysis and it was indeed a secrets exfiltration and malicious code install path, triggered by a VS Code folder-open auto task More details and analysis below if you want to dive deeper 🧵

• 
  wiiiimm (@wiiiimm) reported 52 minutes ago

  So instead of assigning Fable 5 hand picked tasks from Linear (broken up and planned by Opus 4.8), I am asking Fable to look at all of the issues (2 only) I have in the GitHub repo and create fixes and issue PRs for me. I also asked it to do local test and watch for @coderabbitai reviews in the PR. Only stop when all is green. Let's see...

• 
  Benjamin Thorstensen (@benithors) reported 53 minutes ago

  @dexhorthy @steipete is dealing with different problem than our daily dev life. Just look at the OpenClaw GitHub issues, every minute a new one, after so many months. Nearly 45k closed issues... Loops for him, queues for us.

• 
  Traceback (@Tracebackqa) reported 59 minutes ago

  Release pain shows up at the last click. - Traceback is the QA layer - AI drives the browser like a person, so every PR is tested automatically. - Self-healing tests keep false breaks down; failures land in GitHub, Linear, and Slack. Verify every product change before it ships.