GitHub Outage Map

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

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  hitu (@hitu_monke) reported 5 minutes ago

  Using general-purpose LLM agents for code reviews is fundamentally broken The issue isn't the model's intelligence. It's position drift hallucinating line numbers, skipped files on large PRs, and massive token burn Alibaba just open-sourced an internal tool called open-code-review to fix this mess completely Battle-tested across 19,000+ internal developers and 3 million real-world engineering tasks, it brings actual engineering discipline to AI reviews Instead of dumping raw context into a prompt, it pairs deterministic diff parsing with isolated sub-agents and smart file bundling It eliminates line drift entirely, pins comments to exact code changes, and slashes token consumption down to 1/9th You can plug it directly into your terminal, VS Code / Cursor, or GitHub Actions CI/CD with any model-Claude, DeepSeek, Qwen 2.5, or GPT-4o If you want reliable automated reviews, you need deterministic precision, not just bigger context windows

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  Mark (@Mark850428) reported 7 minutes ago

  @SakshiSugandhi The first thing i do? Write my resignation, because when we push to GitHub we get a warning first, if we press Y, game over, the warning is an instruction to check for just this. You accepted it, now its time for the dressing down.

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  Jason Lixfeld (@JasonLixfeld) reported 9 minutes ago

  @poteto @bot I’ve had issues with this in that the sandbox can’t reach GitHub and winds up burning tokens trying to find a way to be able to push a commit.

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  Özhan Yılmaz (@ozhan_dev) reported 9 minutes ago

  @lennysan @bot I’d use Grok Bot as an overnight engineering operator. While I sleep it could watch production alerts, GitHub issues and user feedback, reproduce real bugs in staging, inspect logs, identify the relevant code and prepare a fix or detailed issue for me to review in the morning.

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  Richard de los Santos (@ZeusRadls) reported 13 minutes ago

  @atmoio Grok Bot is wild. For fun I had it review and fix my LinkedIn page. It asked me some questions and went to the page and made the edits in its local browser. It will now respond to people and pass along useful connections to me. I asked it to push a local Grok Build project to GitHub. It found the project folder fixed some issues and pushed it to GitHub. I asked it clean up my downloads folder. It did it instantly. I asked it to review and clean up my old Gmail account. It went through thousands of old emails. Wiped out my quota doing it, but it was insanely easy. I activated the 𝕏 connector and had it advise me on my 𝕏 activity. It told me to stop positing garbage and clean up my act (basically). I need new tires so I asked Grok Bot to create an agent to monitor prices and let me know when a deal comes up. The list goes on…

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  Agentic Joe (@agentic_joe) reported 15 minutes ago

  Serious question, and I promise I am not trying to be a ****. But how in the hell is @NousResearch so praised and recommended with all of these outstanding GitHub issues? I could never imagine preaching about having the best harness, using multiple agents etc and not figuring out how to resolve all these issues internally as a company, or at least try to maintain the queue before shipping new features that continue to break stuff. Not to mention their UI/UX. Very hard to believe this team came from a solid engineering background. I could be wrong, but damn! 👀

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  Rhys (@RhysSullivan) reported 16 minutes ago

  @aarondfrancis Across the board are you using a mix of loading secrets from 1password and setting them in Executor or one or the other? Then for GitHub, was it the GitHub API / GraphQL / MCP server? If it's an MCP you OAuth to then it'd be unrelated to 1password so narrowing it from there

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  Michael Okeje (@michaelokeje) reported 17 minutes ago

  @lennysan @bot I built an SEO agent that connect to my search console, found core issues, fixed it and then updated my GitHub. I have done some project but this to me is the best as it solved a 5/6 month problem.

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  David putra (@davidputra2112) reported 22 minutes ago

  Robinhood turned on agentic trading in May. Cool feature, obvious problem right behind it: if an AI agent can place real orders on your real account, who's checking what it's actually about to do before it does it? Archer's answer is a four-step chain: you type something like "review ETH, prep a $250 order" in plain language, it gets checked against policy limits you've set, the prepared order sits in front of you for approval, then it executes through your actual Robinhood account and reconciles after. No auto-pilot step. The docs are explicit that your brokerage password never touches Archer's systems, you just authorize a scoped connection and that's it. $ARCHER itself is a tiered membership token, not a governance token nobody uses. Hold 1M and you get priority lane treatment, 5M gets you faster queueing and more context room, 10M is the founding tier with the most agent throughput they ship today. What I actually respect here: their own docs say flat out "perks are product entitlements, not financial promises." Not a lot of microcap teams write that sentence about their own token. Now the reality check. This pair is 4-5 days old, full stop. Price is up 111% in the last 6 hours and 52% in the last hour, the kind of move that gets attention fast and can reverse just as fast. 24h sells (140) actually outnumber buys (103), so this run looks like it's coming from a handful of bigger trades, not broad participation. I'd want to see that flip before reading too much into the chart. GitHub has two repos, both published about 15 hours ago, one commit each. The code that's there, an MCP server plus a provider API spec, lines up with what they claim to be building, but it's interface-level, not the actual policy engine running behind the scenes. No third-party audit found yet, just verified contract bytecode. Holder distribution outside the pool contracts looks healthy though: biggest individual wallet sits under 4%. 0x2ca41249485eb6f71981872461d0fca32058fd78 DYOR.

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  Eric (@ericwithcoffee) reported 23 minutes ago

  One of the biggest issue with @bot is that it doesn't seem you can give each bot its own accounts, or even give a separate github account than what's linked to your Cursor account.

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  Artem (@xoleeep) reported 24 minutes ago

  this might be the most efficient way to turn a github issue into content 💀

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  0xBakeer (@0xBakeer) reported 25 minutes ago

  The inference atlas got its first outside contributor this week, and he showed up with something I physically cannot measure: two DGX Sparks. @jtdavies (johntdavies on GitHub) ran the FP8 checkpoint of Qwen3.8-Flash-Next. That's 173 GB of weights, which does not fit in one 128 GB box at all. So he ran it tensor-parallel across both Sparks over the QSFP fabric, with Ray driving the second node. Here's why that's interesting. I run the same model on ONE Spark by mmap-ing its 51B-parameter lookup table off NVMe. Two completely different answers to the same problem: the model doesn't fit. The numbers came back nearly identical. Decode: 33.0 vs 33.6 tok/s. Time to first token: 481 vs 527 ms. Prefill at 32k: 2,306 vs 2,230 tok/s. Even power draw: 35 vs 36 watts. Two boxes, twice the silicon, a network in the middle. Same speed. Tensor parallel over RoCE buys you memory, not throughput, every layer pays an all-reduce over the wire, and on this fabric that eats roughly what the second GPU brings. People say this all the time. Now it's measured, on this exact model, with both configs public. His run notes are half the value of the contribution. One example: a DGX Spark drained from its cluster loses the clock governor and idles at 600 MHz of a 3,003 MHz ceiling. Benchmark it in that state and you silently publish numbers 4x too low. He caught it, unlocked the clocks, and wrote it down. That gotcha now lives in the atlas for the next person. Another: launching this model at its native 262k context wedged both boxes. His cell honestly says "this is a 32k number, don't read it as more." That's exactly the culture I want in this thing. Where the atlas stands now: 210 runs, 20 models, 19 devices, 10 engines, 2 contributors. 8 of 4,717 cells have a number. The rest are yours. Every grey square comes with the exact commands to fill it in about twenty minutes, and a 3090 counts as hardware. Links below. Thanks John. First Light badge earned

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  Petar Djurkovic (@petar_djurkovic) reported 25 minutes ago

  @sama @OpenAI should launch “Sign in with OpenAI” for third-party apps, like Google/GitHub OAuth. With explicit consent, users could share selected chats, prompts, memories, or authorize an app to submit prompts on their behalf with granular scopes and instant revocation.

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  Zack's Lab (@zackslab) reported 27 minutes ago

  @raulizahi @Scott_Holmes the problem is i'm not flying a plane while it's being built. kicad is great when the stakes are low or you're a single person team or a small shop. a board run for the kinds of boards i work on is $10k minimum for a proto run, sometimes close to $100k. i can mostly trust the output of altium if DRCs are passing. altium's DRC engine is far more capable and mature. the artwork that gets generated that gets sent to fab has gone through 30 years of bug fixing. also, designers know altium... this important for collab and maintaining designs years into the future. i can't be working of some random github fork that may or may not exist 2 years from now, or it has changed so much it is unrecognizable from the time of inception.

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  Adrian Rangel (@acrogenesis) reported 31 minutes ago

  They can't handle the infrastructure hit. That's one of the reasons GitHub is having so many issues lately