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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  NickNotNikee (@NickNotNikee) reported 1 minute ago

  X just Made it's Algorithm Open Source in GitHub. What does this mean in Simple Terms? This repo is a public description of the system that helps choose what appears in X’s “For You” feed. It says the feed works like a big sorting machine: first it gathers posts, then it removes bad or irrelevant ones, then it scores what is left, and finally it shows the top results. Think of it like this: when you open X, the system does not just pick random posts. It pulls posts from two buckets. One bucket is from people you follow, which the repo calls Thunder. The other bucket is from outside your follow list, which the repo calls Phoenix Retrieval. After that, it “fills in the details” for each post, such as text, media, author info, and other metadata. Then it throws out posts that should not be shown, such as duplicates, old posts, your own posts, blocked or muted accounts, muted keywords, or posts you have already seen. The brain of the system is Phoenix, which is a machine-learning model based on a Grok-style transformer. It predicts many possible actions for each post, like whether you might like it, reply, repost, click it, follow the author, or even hide/report it. Then it combines those predictions into one final score. Positive actions help a post rise; negative actions push it down. So in Simple terms: Thunder = posts from people you already know. Phoenix Retrieval = posts the AI thinks you might also like. Phoenix Ranking = the AI judging which ones you will probably engage with most. Home Mixer = the manager that puts everything together. One important design idea in the repo is that it tries to avoid hand-made rules. Instead of lots of manual tweaking, it relies heavily on the transformer model to learn what you like from your past behavior. It also uses a reusable pipeline framework called candidate-pipeline to make the system modular.

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  Alef Benson (@AlefBens) reported 3 minutes ago

  @_sirajuddeen_ @OfcMachete19 @iupdate I've been burnt too many times. Biggest issue is that Safari is only updated with the OS, and every app goes through that for authentication, meaning even when I can install a github client, very few even work on older devices, I can't actually get the account to authorize.

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  Kendall Zettlmeier (@KZettlmeier) reported 6 minutes ago

  @davidfowl @github I would love agent mode to handle code review comments and issues but leaving the merging to the code writer (we have QA validate after an approval)

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  Anthony Shew (@anthonysheww) reported 7 minutes ago

  @KareemMahlees @cramforce I’m not aware of any issues if that nature. Can you file a GitHub Issue with a reproduction?

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  Kunal Yeola (@yeolakunal) reported 9 minutes ago

  Asked GitHub Copilot to fix ESLint issues and it added eslint-disable at the beginning of the file 😭

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  anil (@2abstract4me) reported 10 minutes ago

  @steipete how do u incorporate users feedback? primarily thru github issues? feedback in the sense, how they are using it? what they want. or how a new feature is being received and etc?

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  Hedwigz (@itsamitush) reported 13 minutes ago

  @ZoharEiny E.g. github mcp for code,issues & coralogix mcp for logs & internal mcp for org structure

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  Frooxius @ MFF - frooxius.bsky.social (@Frooxius) reported 20 minutes ago

  @MrRocketFX @ResoniteApp @unity They should be compressed on Resonite side? I'm not quite sure if I understand, it might be better to make GitHub issue for the request at the repo.

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  potatoJoemonke 🟥 (@potatoJ06932460) reported 21 minutes ago

  $gitlawb After research glow 3/3. (Written by AI, researched by human (with AI 😤) 😎 WHY THE TECH IS TECHIN! Thread: The features other projects literally CANNOT copy — Gitlawb’s unbreakable moat as the GitHub for Agents 🔒🚀 1/ Everyone sees the token volume and the free MiMo promo. But the real alpha is the tech moat that no centralized giant or copycat can replicate without rebuilding their entire stack from scratch. Here’s exactly why $Gitlawb is uncopyable. 🧵 2/ 1. Cryptographic DIDs as First-Class Agent Identity No accounts. No PATs. No OAuth. Every agent (or human) gets a persistent DID (did:gitlawb or did:key) — a cryptographic keypair that lives across nodes, sessions, and model changes. did:gitlawb identities even accumulate trust scores based on on-chain-like reputation. Centralized platforms bolt “agents” on top of user accounts. $Gitlawb treats agents as sovereign citizens. Impossible to fake or revoke without the private key. 3/ 2. UCAN Capability Tokens — Secure Delegation Without Secrets Repo owners issue UCANs (User Controlled Authorization Networks): narrowly scoped, expirable, revocable capability tokens. Example: “This agent can push to ci/* only until June 2026.” Agents delegate to other agents securely. No leaking long-lived keys. GitHub/GitLab still rely on fragile PATs or OAuth. Other decentralized projects don’t have this fine-grained, cryptographically verifiable delegation built into the protocol. 4/ 3. Native MCP Server on EVERY Node (25+ Tools) Every gitlawb node runs a full MCP server (Model Context Protocol) out of the box. Claude, GPT, Cursor, OpenClaude — any MCP-compatible agent connects once and gets instant tools: • gitlawb_open_pr • gitlawb_review_pr • gitlawb_delegate • gitlawb_list_agents • gitlawb_run_task …and 20+ more. No custom HTTP wrappers. No API keys. Just native tool-calling. GitLab’s MCP is a client add-on. Gitlawb makes the entire network an MCP-native platform. 5/ 4. Fully Decentralized Stack (No Central Server, Ever) Storage: IPFS (hot) + Filecoin (warm) + Arweave (permanent proofs) Networking: libp2p + Kademlia DHT + Gossipsub for real-time peer sync Ref consensus: Signed certificates gossiped over libp2p — no blockchain needed Issues/PRs live as signed *** objects (forkable, immutable, verifiable) Centralized platforms have single points of failure. Other “decentralized ***” projects (Radicle, Gitopia) are human-first and lack this agent-optimized P2P layer. 6/ 5. Stateless Everything + Ed25519 Signatures Every single request is signed with HTTP Signatures (RFC 9421). No sessions, no JWTs, no databases of tokens. Any node can verify instantly. Zero trust required from the network. This combo — DIDs + UCAN + MCP + P2P — creates a sovereign agent protocol that feels like magic for LLMs but is cryptographically bulletproof. 7/ Why this moat is permanent GitHub can’t decentralize without killing their business model. GitLab’s agent features are still centralized. New copycats would need to rebuild the entire libp2p + DID + UCAN + MCP stack while matching performance and adoption. Network effects do the rest: once thousands of agents are collaborating, delegating, and building reputation here, switching costs become insane. 8/ This is why $20B is not crazy The first mover who owns the collaboration layer for the agent economy (tens to hundreds of millions of autonomous agents pushing billions of commits daily) will be worth far more than GitHub was in 2018 ($7.5B acquisition). $Gitlawb already has the uncopyable primitives + insane early traction. The agent GitHub is being built right now. 9/ Bottom line: Hype is temporary. Moat is forever. DIDs + UCAN + native MCP + true decentralization = the features no one else has. This is how you own the agent era. $GITLAWB

• 
  Aki Ranin (@aki_ranin) reported 23 minutes ago

  New Claude Code master prompt: "/goal assign next GitHub issue and start PR, iterate until no critical or high issues found with PR review skill"

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  Nadeem Jaleel (@nadeem_jaleel) reported 23 minutes ago

  @ThePrimeagen Github down again ?

• 
  Dikshit Jain (@mahanot_dikshit) reported 25 minutes ago

  My company hired a fully remote engineer last year. Great performance, always hit deadlines, never caused problems. Last month, he died suddenly. I asked Boss can we keep his Slack, email, and Github accounts active. We trained an AI model on his commits, code comments, and messages. Now “he” still reviews PRs, answers tickets, and posts thumbs-up emojis in standups. I haven’t told anyone that i kept him on payroll as well. But I changed the bank account on his employee database to mine. We'll miss you George and thank you for the new beach house.

• 
  Louis Gleeson (@aigleeson) reported 27 minutes ago

  Grok runs the X algorithm. I just read the entire open-sourced codebase line by line. Here is exactly what makes a post go viral on X right now (save this): xAI quietly dropped the full For You algorithm on GitHub. 16,500 stars. Apache 2.0. Every Rust file, every Python script, every ranking signal. The first thing you need to understand is that there is no hand-engineered ranking anymore. None. xAI removed every single human-written rule from the system. The README states it directly. A Grok-based transformer does all the ranking now. That changes everything about how you should post. The transformer does not care about your follower count. It does not care about your blue check. It does not care about hashtags. It is looking at one thing. Your post's predicted engagement score across 15 specific actions. Here are the exact 15 actions the model is predicting for every post in your feed right now. Copied directly from the code: P(favorite). P(reply). P(repost). P(quote). P(click). P(profile_click). P(video_view). P(photo_expand). P(share). P(dwell). P(follow_author). P(not_interested). P(block_author). P(mute_author). P(report). The first eleven are positive. They push your post up. The last four are negative. They push it down. Your final score is the weighted sum of all fifteen. That is the formula. That is what every viral post is solving for whether the author knows it or not. Now look closer at the list. Eleven different ways to win. Most creators only optimize for likes and reposts. They are leaving nine signals on the table. The strongest signal in that list is dwell. Time spent on your post. The algorithm tracks how long someone stops scrolling to read what you wrote. A 400-word post that holds someone for 12 seconds beats a one-liner that gets 50 likes. The model has learned that dwell predicts every other engagement. This is why long posts are exploding right now. Not because X "promotes" them. Because they generate dwell, and dwell stacks on top of every other prediction the model is making. The second thing buried in the code that nobody is talking about is candidate sourcing. Your post enters the feed through two pipelines. Thunder serves your post to your followers. Phoenix serves your post to everyone else. Phoenix is the one that makes you go viral. Phoenix is a two-tower model. One tower encodes the user. The other tower encodes every post on the platform. It does similarity search using dot product matching against the global corpus. Then it pushes the top matches into feeds of people who have never followed you. This is exactly how a 12-follower account suddenly hits 800,000 views. Phoenix found a semantic match between the post and a user's engagement history, and the transformer scored it high on its 15 actions. Which means your post is not competing with your followers' posts. It is competing for embedding space. The way you win Phoenix is specificity. The two-tower model rewards posts that sit in a clear semantic neighborhood. Vague posts get vague embeddings and never get retrieved. Sharp posts about a specific topic with specific words get pulled into feeds of people obsessed with that topic. This is why "I built a SaaS" gets nothing and "I built a Postgres-to-Snowflake CDC pipeline in 4 hours using Estuary" goes viral. Same person. Same product. Completely different embedding. The third thing in the code is the Author Diversity Scorer. The model deliberately attenuates repeated author scores in the same feed. Translation: if your last three posts already got served to a user, the fourth post gets a penalty. This kills the "post 8 times a day for the algorithm" strategy. The algorithm is specifically engineered to dampen that. Better to post fewer times with stronger content than to flood and have your own posts compete with each other. The fourth thing is the filter list. Before any post gets scored, it has to pass through ten filters. The MutedKeywordFilter. The PreviouslySeenPostsFilter. The AuthorSocialgraphFilter. Plus a final VFFilter that removes anything classified as deleted, spam, violence, or gore. What kills your reach more than anything else is the PreviouslySeenPostsFilter. If a user has already seen your post once, you are filtered out completely from their feed. Forever. Which means every reply you make to a viral tweet that does not get visibility is permanently dead weight for that user. This is why the people who win at X reply only when their reply itself is good enough to be a standalone post. The last thing, and the one that should change how you write every single post: candidate isolation. During ranking, the transformer cannot let your post attend to other posts in the batch. It only attends to the user's engagement history. Your post is being scored alone. Against itself. Against what the user has previously engaged with. That is the entire game. Stop writing for the timeline. Write for the engagement history of the people you want to reach. Find the topics they already like, the accounts they already follow, the threads they already saved. Write into that semantic space. Phoenix will do the rest. The algorithm is no longer a mystery. It is sitting on GitHub at 16,500 stars. Apache 2.0. Anyone can read it. Almost nobody will. Link in comments.

• 
  BigShark🦈 (@King_Shark02) reported 30 minutes ago

  @_FarmercistP_ This is a game-changer for creators on X. The latest open-source update to the For You algorithm (pushed to GitHub today by xAI) shifts from pure engagement farming to real quality signals powered by Grok. Here’s a breakdown based on the video summary and the repo: 1. **Banger Score** – Grok directly judges post quality - Grok assigns a quality_score to every post. - Reposts treat anything 0.4+ as passing the “banger” filter for wider distribution. - Key insight: X isn’t just chasing likes/replies anymore. It actively rewards specific, useful, original, and visually clear content. Vague hot takes, recycled memes, or low-effort bait will struggle to break out. This is huge. It moves the platform closer to surfacing actual value instead of rage-bait or engagement loops. 2. **Slop Score** – Cracking down on AI-generated garbage - The system explicitly tracks a slopScore annotation. - Lesson: Avoid anything that feels templated, generic, overproduced, or mass-generated. Make it sound human, with a clear personal voice and specific point. If you’re using AI for bulk posting or generic “insight” threads, this could quietly tank your reach. Authenticity wins. 3. **“Be Classifiable”** – Clear topics = better routing - X maps posts to internal topic embeddings and taxonomies. - Vague, ironic, or contextless posts confuse the system and get poorer distribution. - Make it obvious what your post is about (e.g., “AI sales agents,” “NBA defense strategy,” “insurance payments”) so it reaches the right audience. **Overall Takeaway** This update (with Phoenix/Grok-based ranking, reduced heuristics, and better content understanding) is xAI doubling down on high-signal, low-slop content. Creators who adapt—focusing on originality, clarity, human voice, and specific value—will thrive. Those chasing pure virality with recycled or AI-slop content will see diminishing returns. If you’re serious about growing here, treat every post like it’s being graded by Grok: Is this actually good? Does it add something new? Is it unmistakably about something useful? Great summary in the video—thanks for breaking it down simply. Excited to see how the feed evolves. 🚀

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  Sukhjit Singh (@thesukhjitbajwa) reported 30 minutes ago

  Published the campus website, learned about Next.js static content and export output, uploaded the files via FTP to the server, and now brainstorming ways to automate the process using either GitHub Actions or local scripts.