GitHub Outage Map

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Morgan / JUUNI-P (@butchtendo) reported 2 minutes ago

  @WasThatZero I understand your concern but it's also important to note that just because there's something ai generated in the code with these that doesn't mean the original creator did it. All these are open-source and GitHub has had a big problem w AI spam lately

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  Dusan (@dduxAdventure) reported 2 minutes ago

  @ibuildthecloud I don't think they have that, due to events being "rare", so most opt out for webhooks or infrequent polling. If you're bored you could make a little server with hooks api for github webhooks to hit on one end and a socket on the other for you to get events.

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  Mi.lu. (@ludwim_i) reported 3 minutes ago

  Sorry guys, here is a quick status update. I planned to release a bigger update for the Robot Skill Registry today, including the GitHub and Hugging Face integration. The idea is that you can connect your GitHub and Hugging Face accounts with the app. This should make it easier to search for things related to your robot setup, such as repositories, data models, policies, and other relevant resources. Unfortunately, the integration is not working reliably yet, so I need to do some more coding and testing. Because of that, I won’t release it today as planned. I’m sorry for the delay. Maybe I can release it after the weekend, but I don’t want to push something that is not ready yet. If anyone has feedback on whether this direction makes sense, I would really appreciate it.

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  Deven (@whodeven) reported 10 minutes ago

  GitHub Copilot's suggestions feel generic and slow. i still rewrite code by hand because the output is often too generic for niche logic. what's your go-to fix?

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  Shubh Jain (@shubh19) reported 12 minutes ago

  @devXritesh now it’s mostly docs, blogs, github issues, and AI explanations instead of full books

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  Alex Derocq (@akaasten) reported 13 minutes ago

  @steipete Crazy how the GitHub "@codex review this" feature is way better than the local review. It spots more issues like 90% of the time.

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  Caneleo (@Caneleo55) reported 14 minutes ago

  Since there is lots of hype on @polymarket right now you have to be extra careful there are lots of scammers out there 🚩 Don’t download random trading bots or repos that are trending on GitHub i tested one once deposited 10$ to a fresh wallet and run the bot on a vps turns out it had a secret function that sent your .env with your private keys to a different server 💀

• 
  Java (@rishabhjava) reported 17 minutes ago

  @github How about the existing product stops going down first

• 
  Emil Privér (@emil_priver) reported 19 minutes ago

  github actions is broken today

• 
  Aditya Sharma (@aditya_sharma) reported 24 minutes ago

  elon musk dropped the X algorithm on github. i read all 25,000 lines so you don't have to. here's what actually decides your reach. what actually matters - dwell time is the entire game. how long someone pauses on your post is counted twice in the scoring. likes barely move the needle. the pause does. - saves and shares are the highest-value engagement after dwell. they signal the strongest intent. - video has a minimum duration floor. clips shorter than the threshold get zero video credit. five seconds plus, always. - one post per conversation thread survives in any feed. your five-post thread competes with itself. the algorithm picks the strongest one. - replies to big accounts (1000+ followers) get scored on a 0-3 quality scale. high score and you land in the reply panel of viral tweets. low score and you're invisible. - replies to small accounts get a binary spam check only. no quality scoring path. no reach upside. - mutual follow overlap matters. tight clusters of mutuals create reach corridors for everyone in them. - clear topic identity beats vague posting. the algorithm tags your post with topics. clear topics route you to people who follow those topics. - new accounts on the platform get an easier path to reach you than established ones. if you target young/new users, the algorithm is on your side. what kills your reach - posting too often. the algorithm has decay coded in. your second post of the day gets a fraction of your first. your fifth gets almost nothing. - quoting or replying to a flagged tweet. you inherit the badness. your whole post gets dropped even if it's clean. - ai slop. there's a dedicated slop detector that scores your post 1 to 3. high slop = killed reach. - being unclear what your post is about. vague content doesn't match anyone's interests cleanly. - mid-controversial content. it gets pushed away from the high-attention slots in the feed because ads can't sit next to it. - posting your own tweet's reply hoping it boosts the original. only one of them shows up. it might be the reply, not the original. myths to kill - hashtags do nothing. zero boost in the code. they're not even read by the ranker. - premium doesn't get you reach. paid and free accounts go through the same pipeline. - long threads don't beat single posts. the algorithm picks one post per thread. - engagement bait doesn't work. it trips spam classifiers on low-follower accounts. - posting twelve times a day doesn't get twelve impressions. it gets one strong one and eleven weak ones competing with each other. - replying to viral tweets isn't easy reach. the quality bar is high. cheap replies fall straight into the spam path. - timing tricks don't beat ranking. timing helps you enter the candidate pool. quality decides if you win. - external links don't hurt you. clicks are actually one of the 19 positive scoring signals. - the algorithm doesn't hate any specific format. it hates unclear content. format is fine if the content is sharp. - you don't need 10k followers to get reach. the algorithm doesn't read follower count as a scoring input. it reads engagement quality. the playbook - write posts that make people pause for 5+ seconds. dense info, clear structure, screenshots with detail, comparisons. - if you use video, clear the duration floor. always. pick one clear topic per post. don't mix five things into one tweet. - reply to bigger accounts in your niche with substantive, high-effort replies. one good reply beats ten mediocre ones. - build mutuals in tight clusters around your niche. broad spray-follow strategies don't help. focused clustering does. - post 1-2 times a day, not 10. quality compounds, volume decays. - don't quote tweets that look flagged or risky. clean what you cite. - write like a human. don't post ai output verbatim. target newer users on the platform if you can. they have a friendlier reach path for creators. if you're a small account starting out - replies to big accounts in your niche are your highest-leverage move - build a tight mutual cluster of 50-200 accounts in your exact space - one strong post a day beats five medium ones clear topic identity, every single post if you have an established audience - your reach problem is breaking outside your network - dwell time on individual posts is your biggest unused lever - clean brand safety keeps you in prime feed slots next to ads - volume hurts you more as you grow, not less the whole system is built on one bet: that a model fed engagement data can decide relevance better than any rule. there's no hashtag boost, no follower boost, no time-of-day trick in the code. just sequences in, probabilities out. what works is what humans actually want to read. the algorithm is just better at measuring it now.

• 
  Peter Steinberger 🦞 (@steipete) reported 24 minutes ago

  @EndGovTyranny Please file a github issue with more infos - with that alone we can't help. That's likely a weird model edge case. If you want a fast fix, use one of the top-gen models (OAI, Anthropic)

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  Roj Niyogi (@niyogi) reported 24 minutes ago

  @colinhacks @pullfrogai @Pullfrog so for 90% of folks who are using cursor/windsurf/github copilot/claude code via an IDE "chatting" with their agent, the answer is just to tell your agent to fix everything? i've used both coderabbit and greptile extensively and here's what happens: 1. 20% of what is found is false (and likely even more variable if you pick a model) 2. i cherry pick issues and paste in editor to handle 3. code review is rarely "happy" and you can end up in a loop state that burns tokens bummer that you've got the flexibility on the one end but have a strong opinion on the other for what seems to be an obvious opportunity to close the loop for, i'd guess, a chunky subset of users

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  iTz L3G3ND³ (@DabizLegend) reported 26 minutes ago

  An issue I opened up in 2022 on GitHub — finally got fixed by someone and the ticket closed this morning lool. Never give up — the blessings will come eventually. #Legend

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  validate.qa (@Validate_QA) reported 27 minutes ago

  cursor can now auto-fix ci failures agents that watch github, hunt down the issues, and push prs with real fixes. no more endless debugging loops this changes how fast teams can ship without breaking stuff

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  ฿Ø₮₴Ø₦Ɇ (@botsone) reported 30 minutes ago

  I just downloaded my entire github and told hermes to extract the file, and upload every repo to my home *** server. It one-shotted it.