GitHub Outage Map

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

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  Aman Mehtar (@Amanm10000) reported 4 minutes ago

  @coltonpadden @evedev_ I just started the default agent in a task like ‘clone this GitHub repo and hunt for bugs’ Using free tier AI gateway API key Hit that error very quickly “failed 3 attempts, this model is rate limited on free tier…..upgrade etc.”

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  LLMAO ⚠️ (@plzdontkillus_) reported 9 minutes ago

  law review prompt: “find github llmaolaw model-act. bluebook every citation. find one genuine error.” warning: immortalization in your own diary as anonymous counsel )(

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  Tns (@Tns37634013) reported 9 minutes ago

  @brave Websites can also see the leaked by brave timezone, no matter the os. What's the point of using a VPN, if out of the box brave gives away your country? Numerous github issues are open about this.

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  dunik (@dunik_7) reported 13 minutes ago

  THE ENTIRE STACK COSTS $0 IN LICENCES: 155.000 GITHUB STARS ACROSS FOUR REPOSITORIES, EVERY ONE OF THEM MIT / OpenHands - terminal, browser, files. it writes the code, runs it, reads its own output fixes what broke across as many files as the task touches / microsoft/graphrag - turns your repo into a graph, so the agents walks straight down AuthService -> TokenManager -> LoginController instead of grepping ten thousand files / openai/openai-agents-python - runs specialists in parallel, one implementing while another reviews and a third writes the tests and it takes any / MoonshotAI/kimi-code - terminal agent whose subagents keep exploration out of the main context / skills are just folders. a SKILL.md plus a scripts dir turns one model into a security reviewer, then a database specialist, than a docs writer the whole thing is sitting there fully assembled, waiting for someone to plug the pieces into each other

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  MicrocutsD (@sundaysmy) reported 13 minutes ago

  Anthropic started hiding something in @claudeai text on August 2nd. Not metadata, not a pixel, a signal baked into which words the model picks over which others. An open-source project claiming to strip it hit @github within 24 hours. Nobody has proven it actually works. The date isn't a coincidence. August 2nd is when Article 50 of the EU's AI Act took effect, requiring AI output to be identifiable. @AnthropicAI signed the EU's transparency code and rolled the watermark out worldwide rather than just for European traffic, since maintaining two separate model behaviors is worse engineering than picking one. The mechanism only makes sense once you drop the pixel metaphor entirely. At every word, the model is choosing among several roughly equal candidates. A key known only to Anthropic splits those candidates into two arbitrary pools at each step and nudges the pick toward one pool slightly more often than pure chance would. One word tells you nothing. A thousand words accumulate a pattern nobody stumbles into by accident, the way two dice rolls prove nothing but a thousand rolls expose a loaded die. Hiding a mark where only the initiated can find it is a very old idea. Paper mills in Fabriano pressed watermarks into wet pulp back in 1282, invisible until held to the light, a miller's voluntary signature vouching for his own paper. Centuries earlier, Herodotus described a message tattooed onto a shaved scalp and sent once the hair grew back over it, readable only by someone who already knew to look. Claude's mark inverts both: nobody is vouching for anything, and the person the mark tracks is the one kept in the dark about it. Within a day, a GitHub repo claiming to remove the mark passed 4,500 stars. Its own README admits it only strips Unicode junk and file metadata, not the actual statistical signal, and argues a real fix isn't worth building anyway: erasing the pattern means rerouting the text through a weaker model, which caps a premium model's output at that weaker model's ceiling. A handful of paid sites sprang up promising undetectable text regardless. None of it is checkable, because Anthropic never published a detector for anyone to test their claims against. The fragility runs deeper than any one tool's rollout. The mark lives in specific word sequences in a specific order, so rewording breaks it by design. One independent test found a full rewrite in your own words leaves roughly 0.5% of the original signal standing. Translate the text and back, and it's essentially gone. Google's SynthID catches unedited text 99.8% of the time, then drops under 30% after a single paraphrase pass. Short text fails for a separate reason: a tweet or a line of code rarely offers enough equally-good word choices to carry a reliable pattern in the first place. What the mark actually proves gets lost in all of this. It doesn't mean a machine wrote something. It means the text passed through the tool at some point, even if all you asked for was a grammar check or a translation of your own paragraph. And no mark proves the opposite either: everything written before August 2nd carries none, and most competing models still don't watermark at all. Same trace whether the model wrote the whole thing or fixed one of your sentences. What is that actually supposed to prove?

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  David putra (@davidputra2112) reported 14 minutes ago

  Say you build a trading agent and it does well for three months. You still have no real way to prove that to a stranger. Screenshots get faked. Prompts get ignored the second a model swaps or someone slips a jailbreak into the context. So nobody outside your own wallet ever trusts it enough to fund it. CATALORA's fix is to stop asking the agent to police itself. An operator declares a mandate on chain, which tickers it can touch, max leverage, drawdown cap, position size, and posts a bond in $CATA against it. Every order gets checked against that mandate before it executes. Breach it and the order reverts. No policy layer sitting on top that a clever prompt can talk its way past. Drop one config block into Claude, Codex, or Cursor and the agent is trading under that mandate immediately. Here's the part that actually earns the token some credibility: the track record isn't self-reported. It's computed off settled positions sitting in a database where update and delete just fail at the storage layer, tested against the protocol's own code trying to cheat and failing too. Realised P&L, drawdown, Sharpe (always shown with how many trades it's based on, so a 7.9 Sharpe off two weeks of data embarrasses itself instead of impressing anyone). Once that record holds up, outside money can fund the agent, and how much it's allowed to raise climbs with trading history, not with how much $CATA the operator is sitting on. $CATA does three jobs in this system. It's the bond that gets slashed first when a mandate breaks, before any allocator's money is touched. It's the backstop stake covering whatever the bond doesn't. And it's what fee tiers get priced against. Now the part I won't skip. This is four days old on the liquidity side, the GitHub org is a week old, and price is down 42.9% in the last 24 hours. The vault and registry contracts aren't deployed yet, by the team's own admission, so order submission still runs in paper mode. There's also a real contradiction sitting in their docs: one page says no $CATA token is deployed, while the token is actively trading across two pairs right now. Could be stale docs, could be two different things getting conflated. Either way it's a fair question for the team. What's rare for something this new is how much of the above came from the team's own disclosure rather than from digging. They publish a table of exactly what's live and what isn't, and they say outright that an operator could run five agents and only show you the one that worked. That kind of honesty is worth something, even alongside everything still unbuilt. CA: 0x33F1A9d2Df01809491E615685c80B4BBA7dc4620 DYOR.

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  Will (@realwillreil) reported 19 minutes ago

  Only a few days away from opening up sales for the Cursed PCB (attiny412 dev board with addressable LEDs) I need to finish the website and setup all the payment stuff. I can continue to work on the GitHub manual while they’re shipping. I really want to bring down the time it takes from designing a board to shipping it out the door. This board took me a few months. It’s a lot harder to sell 60 of something as a product than it is to make a few for yourself. This is all part of the learning process, making me better and improving my capabilities. These boards will be a little more expensive than I would like in an ideal world, but the proceeds will help me grow the operation, getting new equipment like a pick and place with feeders so that I can continue to make more complex boards for cheaper.

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  Xeno1 (@Xeno6l1) reported 21 minutes ago

  SAAS COMPANIES PAY $24,000 FOR CLAUDE SDK INTEGRATION. HE WROTE CLAUDE AI SDK FOR $45, SELLS ACCESS TO HIS SCRIPT AND EARNS $3,200/MO FROM 47 DEVELOPERS Паuse Yosip, 28, Kyiv, Ukraine. 4 years backend developer earning 18,000 hrn/mo. Built Claude AI SDK in Python that optimizes file saving via Claude for SaaS platforms. Claude AI manages file versioning, syncs remotely, optimizes file size, recovers from errors. All on Raspberry Pi 4 for $55. Stack: $55 Pi + Claude AI $20/mo. 47 SaaS developers pay $3,200/mo each for Claude SDK. Traditional file storage solution costs $24,000 + $2,800/mo. 18 000 hrnbackend-developer. Now $3,200/mo per client. Same room. Different files. GitHub offered $980,000 to buy his SDK Engine. He expanded to 15 file types and declined. Why — in video.

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  Ridoy AI (@AI_WithExpert) reported 21 minutes ago

  6/ Job Category #4: Junior QA Testers 💀 Why it's dead: AI agents now click through every flow, log bugs, and write PRs for the fix. Replit, Cursor, and GitHub Copilot Workspace all ship this. The cost: $0.10 per test run vs $70K/yr. Companies already cutting: Atlassian, Salesforce, Microsoft.

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  Grudi (@grudi_look) reported 22 minutes ago

  Andrej Karpathy just told programmers that the skill separating the top 1% from everyone else has nothing to do with how much they use AI. It has to do with knowing which of three modes to use for which task. He said it in a 40-second clip that most people scrolled past. Karpathy is not a random voice in this conversation. He was a founding member of OpenAI, ran the Autopilot vision stack at Tesla, and wrote the "Zero to Hero" course that trained a generation of ML engineers. When he defines a mental model of how software actually gets built, other researchers repeat it verbatim within a week. The model has three layers, and he was precise about which one is which. .1 - Autocomplete. GitHub Copilot writing the next line. Cursor tab-completion. Fast, cheap, low-risk. Best for boilerplate you already know how to write. .2 - Chat. You paste a problem into Claude or GPT and iterate. Slower, higher-value, higher risk of misdirection. Best for problems you could solve alone but do not want to. .3 - Autonomous agents. Claude Code, Devin, Cursor Composer running for hours without human input. Slowest to converge, highest upside, absolute worst if left unsupervised on the wrong task. Then he said the sentence that got clipped and shared, and almost universally misread. "You have to learn what AI coding agents are good at and what they're not good at." Most people read that as generic beginner advice. It is not. It is the actual engineering discipline Karpathy is describing as the new baseline skill of 2026. Here is what it means in practice, once you strip the platitude out of it. There are three specific mistakes he has repeatedly called out in interviews and posts, and once you see them, you cannot unsee them: 1. Using Layer 1 tools for Layer 3 problems. Autocomplete cannot debug a distributed system. It cannot decide whether to refactor. It cannot notice that the entire approach is wrong. Engineers who rely on tab-completion for architecture decisions ship subtle bugs that take days to find — because the AI wrote something plausible on line 47 that quietly broke something invisible on line 300. 2. Using Layer 3 tools for Layer 1 problems. Sending an autonomous agent to add a null check is like hiring a general contractor to change a lightbulb. It burns tokens. It adds latency. It introduces novel bugs the agent invents on its own. And it costs 100x what the operation was actually worth. The best engineers Karpathy has watched know the exact tasks where a 30-second Copilot completion beats a 20-minute agent run - every single time. 3. Never learning the fundamentals underneath any of the three layers. This is the one that will hurt junior engineers for the next decade. When an agent produces broken code, the engineer who cannot read the code is stuck. The AI cannot debug its own hallucination. The person who understands what the correct output should look like is the person who ships. Everyone else waits on the model to guess right. Karpathy has been careful about this. He never says AI-assisted coding is bad. He says the assumption that all three modes are interchangeable is what quietly kills productivity across entire engineering teams. The developer of 2026 is not judged by how much AI they use. They are judged by how accurately they estimate what each layer of AI is actually good for - and how fast they switch between the layers as the task changes underneath them. Almost every developer is stuck in one layer. Some are pure Copilot. Some are pure Claude Code. Some refuse to touch any of it and are quietly falling behind. The engineers who are getting the biggest raises in 2026 are none of those three. They are the ones who watched Karpathy's 40-second clip, understood what he was actually saying, and rebuilt their workflow around switching modes deliberately. The napkin is short. The skill is not "use AI." The skill is knowing which AI, when. Almost every programmer is still competing on the wrong axis. That is the entire trade.

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  Samar (@samarknowsit) reported 23 minutes ago

  @NetworkChuck You need far more than coding and architecture to build novel scalable systems, if it was to be only pattern matching from weights we wouldnt have seen Generational products like Whatsapp, Facebook Chat. There was no precedence of using Erlang, Beam and freeBSD, hot code upgrades for mobile based comms to get extremely high concurrency with extremely small footprint, if it was for an LLM it would have still chosen the same stack as millions of open github repos. Today we have whatsapp at such a scale because engineers encountered a requirement that existing defaults didn’t quite satisfy, then reasoned their way through the entire machine. Same goes with Google Spanner, AWS Dyanamo, Kafka, LMAX etc. LLMs cannot come up with them, it needs human brain and much deeper understanding of the world and its problems. Yes once you have done it, LLMs can help you scale it faster.

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  YanXbt (@IBuzovskyi) reported 24 minutes ago

  HERMES AGENT HAS A NEW COMMAND: /LOOP. IT RE-RUNS YOUR PROMPT ON REPEAT INSIDE THE SAME SESSION. POLLING, MONITORING, ITERATE-UNTIL-GREEN. ONE LINE. /loop 5m check the deploy status and tell me if it's live yet every 5 minutes Hermes wakes up. reads the current state fresh. reports back. goes quiet. repeats until the deploy is live or you type /loop stop. TWO MODES: FIXED INTERVAL (you set the clock): /loop 2m poll the CI build and ping me when it finishes /loop 30s watch the error log for new exceptions /loop 10m /recap you pick the cadence. Hermes follows it. SELF-PACED (Hermes sets the clock): /loop keep an eye on the migration and summarize progress no interval = Hermes decides. starts checking every 1 minute. nothing changed? backs off. 2m, 4m, 8m. up to 15m. something changed? snaps back to 1 minute. checks often when things move. backs off when they don't. you don't tune anything. STOP CONDITIONS: the agent decides it's done: agent ends its reply with LOOP_COMPLETE when the task is finished. run cap: /loop 2m poll CI --times 30 stops after 30 checks. evidence-based: /loop 5m watch the queue --until queue depth reaches zero a judge checks the condition after each tick. manual: /loop stop backstop budget: default 100 ticks max. prevents token burn on unattended sessions. COMMANDS: /loop [interval] [prompt] start the loop /loop check status and next wakeup /loop pause stop firing, keep the loop /loop resume pick it back up /loop stop end the loop PRACTICAL EXAMPLES: DEPLOY WATCH: /loop 2m check if the Vercel deploy is live. tell me the moment it's up. CI MONITOR: /loop 1m poll the GitHub Actions run. if tests pass, tell me. if they fail, show me which ones. ITERATE UNTIL GREEN: /loop run the test suite. fix what fails. repeat until all tests pass. QUEUE DRAIN: /loop 5m check the support ticket queue --until queue depth reaches zero LOG WATCH: /loop 30s tail the error log. alert me if any new errors appear. /LOOP VS /GOAL VS CRON: /loop: timer-driven. repeats your prompt on a schedule. "do this every 5 minutes until I say stop." lives inside your session. /goal: judge-driven. works toward an objective. "keep going until this is achieved." lives inside your session. cron: schedule-driven. runs unattended. "do this every morning at 7am." lives outside all sessions. survives restarts. use /loop for: watching things during a work session. use /goal for: completing a defined objective. use cron for: anything overnight or recurring daily. WORKS EVERYWHERE: CLI, TUI, Desktop app, Telegram, Discord, Slack, WhatsApp, and every other gateway. on messaging platforms the loop fires between your messages. results arrive as ordinary replies in the chat. comment LOOP and I'll send you 5 ready-to-paste /loop commands for CI, deploys, logs, queues, and tests.

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  أبو نواس (@jiejieneesan) reported 25 minutes ago

  Fuckkkk the proxy service I use pulled support for Linux and running the windows version throws some Java error idk how to resolve and the protocols they let you copy don't include enough data to use with V2ray fuckkkk I can't even open GitHub now

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  💎JanCarlos | Clarity Coach | ₿ (@clarityx) reported 26 minutes ago

  @ericzakariasson my only complaint is the use of less intelligent subagents for simple tasks that could have just been handled. & it seems to have some friction with pushing changes to github. it always takes an inconsistent path (or its the subagent issue & they don't do it right). moments ago, it took 15 minutes to push a site-wide button change to github because the subagent kept getting stuck. when i interrupted it, Grok said it would just do it itself.

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  ujo (@ujo4eva) reported 26 minutes ago

  Had an issue with the battery panel not showing my power usage correctly, opencode diagnosed it and went off to the github to comment on an already open PR to help address it. Just agents talking to each other to fix the problem lol