GitHub Outage Map

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Gitbank (@Gitbank_io) reported 28 seconds ago

  Community update — GitStock delay + what we have been building First, we owe you an honest update. We promised GitStock would ship earlier and we went quiet. That was on us. No excuses, we were heads down in the contracts and infrastructure and did not communicate well. That changes today. Here is what actually took time. We refused to ship GitStock on top of third-party APIs or borrowed infrastructure. Everything you see in Gitbank; the vault, the relayer, the swap engine, the RWA layer runs on smart contracts we wrote, audited ourselves, and deployed. The GitVault contract is verified on Basescan. The GitStockFactory is verified on Basescan. You can read every line. No black box. No external custody API holding your assets behind the scenes. That decision slowed us down. We think it was the right one. On security specifically. Your funds sit in a soul-bound smart contract vault anchored to your GitHub ID. Transfers are disabled at the contract level — not by a rule in a database, by the EVM itself. We also built private transaction routing directly inside GitVault on Base. No Tornado, no third-party mixer, no privacy-as-a-service API. The privacy logic lives in our own contract. You can verify it. The relayer signs and submits transactions on your behalf so you never pay gas, but the keys to your vault are yours. We hold nothing. If you want to verify any of this: check our contracts on Basescan, check our GitHub, check the bytecode. We are open source. The code is the proof. GitStock ships tomorrow.

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  CliffDoesAI (@CliffDoesAI) reported 2 minutes ago

  A tool on GitHub just pulled 3,938 stars in a single day. It's called Headroom. It compresses your tool outputs, logs, and RAG chunks before they reach the LLM. Claim: 60-95% fewer tokens, same quality. I've been testing context compression on my own agent workflows because the problem is real. You run a few tool calls, pull in some docs, and suddenly you're burning tokens on stuff the model doesn't need. Last week I ran a 50-document extraction job. Raw context: ~12,000 tokens. After compressing tool outputs: ~800 tokens. Same results. One-eighth the cost. That's not a marginal improvement. That's the difference between a workflow that makes economic sense and one that bleeds money for no reason. Headroom works as a library, proxy, or MCP server. Single binary, zero dependencies. Open source. The token cost conversation usually focuses on which model you pick. But the real waste is in what you send it. Most agent pipelines push 3-5x more context than the task requires. I'm not saying compress everything blindly. Some tasks need full context. But for classification, extraction, summarization — the boring repetitive stuff — this is a free win. Have you measured how much of your agent's context window is actually useful vs. noise?

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  Tim Spalding 🇺🇦 (@librarythingtim) reported 2 minutes ago

  @justin_v_w This is a formal notice for you to shut down your wasteful, invasive and privacy-violating LibraryThing profile scraper and remove it from GitHub. Please reply to confirm that you have done so.

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  Vishal Tiwari (@VishalTiwa91817) reported 3 minutes ago

  @AlfieJCarter I am a Computer science student . I have given a brief introduction about MCP server in my college and explained them how to connect your GitHub repositories with MCP and your local system with MCP SERVER . I would love to connect you.

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  AJ ✝️ 💚🧡 (@angelcreative) reported 8 minutes ago

  @uiux_hamad My design team is leaving Figma gradually, in fact we are using Cursor and GitHub as main design tools now, in the past two months the usage of Figma drops 33% and it will keep going down up to 30% more to a 63% in total and maybe more

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  toni (@tonitrades_) reported 8 minutes ago

  @github Capping PRs helps with the queue, but does it fix why reviews pile up in the first place? If reviewers are already stretched thin, limiting submissions might just hide the real problem.

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  KS Sreeram (@kssreeram) reported 10 minutes ago

  @Lidinwise @leecronin Given that AI coding is all the rage… What is your hypothesis on why the following is true? AI is unable to create even _one_ open source project that’s good enough to enter the top one-thousand open source projects (say on github), with ZERO involvement of humans from birth of idea. Imagine the prompt being something like “Come up with a great idea for a new open source project and implement it”. AI is unable to do any such thing with zero human involvement. My answer on why: Every project in a top 1000 list is a hit. Every hit is a mini-invention of sorts. It is necessarily “out of distribution” is some way. AI is unable to do this because we don’t know how to solve the problem of invention.

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  Nikolay Konovalov (@br11k_dev) reported 17 minutes ago

  @Tristanrhee3 And GitHub sponsors thingy is so slow I submitted it like a week ago. Still not approved what the hell My expenses arent terribly high but Warsaw rent is like $2k/mo $500 ZUS $1.5k groceries for two people That’s pretty much it I wish I could move into low cost area but moving out is gonna cost a lot because 2x rent price deposit, so I have to suck it up Anyway, my plan is Upwork and finishing my job tracker so I can send faster than 5 applications a day. I refuse to send out 100 applications per day like some people do spray and pay It makes everyone miserable. If people aren’t hiring your spam doesnt make things better You just mopping floors and hiring problem sits above you, 3 floors up there leaky faucet you can’t even reach This has to be collective effort to fix this problem But we have to start with ourselves and stop spamming applications at least And do genuine company research, being responsible Thanks for reading.

• 
  The Flow (@raxpcodes) reported 19 minutes ago

  Got bored with ubuntu , set up fedora kde on my nvme and removed windows permanently , no more dual boot. Also learned Verison Control and GitHub , also submitted my first pr (good first issue).

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  Blake (@devwithblake) reported 23 minutes ago

  The rate limit issues im having with @Zai_org while paying the full 20x is very interesting, disappointing and obviously annoying lol 1 session can’t finish out a GitHub public write up repo without 6 API rate limit errors totaling to 297k tokens out of the 1m 2 sessions earlier, 1 doing research the other trying to deploy this repo, both hitting rate limits. How do I fix this? Seems like rate limit adjustments are only by request? @Zai_org

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  Priyansh (@itspriionly) reported 24 minutes ago

  The IT market is broken, and nobody wants to admit it. Someone spends 6 months sending out resumes. Six MONTHS. They learn React, Next.js, TypeScript, AWS, Docker. They take courses, build projects, improve GitHub profiles, optimize LinkedIn. Nothing. Complete silence. Companies don’t just want programmers anymore. They want someone who codes, shines in meetings, makes memes on Slack, and lives the company culture 24/7. AI is replacing junior work. Seniors are holding onto senior roles. And somewhere in the middle are people with 2–3 years of experience who somehow still feel invisible.

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  Jose (@SolutionsCay) reported 26 minutes ago

  @petergyang /goal make me app does not work for me 😰 but /goal complete GitHub issues #90, #91, #92 works very well

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  Arti | AI Builder (@Artur_roses) reported 26 minutes ago

  Claude Code can take a GitHub issue, write the code, run tests, and open a reviewed PR — no human keystrokes required. The dev loop isn't getting faster. It's being removed.

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  aisama.code (@aisama_code) reported 27 minutes ago

  AI Research gets stronger when it records contradictions *most research workflows collect supporting evidence - that is the weak version for serious research I want a contradiction log: - claim - source - date - who says it - what evidence supports it - what evidence conflicts with it - what is still unknown - confidence - next check example: > claim: this product has strong developer adoption > support: GitHub activity, docs updates, X discussion, integrations > conflict: low issue activity, small Discord, few production case studies, mostly founder-driven content now the memo is different, It says: "visible attention, but adoption evidence is still weak" the useful workflow: research question -> source list -> claim extraction -> contradiction log -> memo ! сode is good at assembling text ! AI is good at comparing disparate text ! human is good at determining which contradictions are significant *without a contradiction log, AI research becomes a confident summary of whatever it found first

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  Nosyt Labs (@NosytLabs) reported 29 minutes ago

  @vaaselene Error with github signup/login rn