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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Victor Chidera Ugwu (@_VictorUgwu) reported 6 minutes ago

  2. Sharing only code, not insights A GitHub full of notebooks nobody reads = invisible. Businesses hire analysts to drive decisions, not to flex window functions. Fix: For every project, write a one-page summary in plain English. "We should raise prices on product X because..."

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  kkkfasya (@kkkfasya) reported 10 minutes ago

  they should hang every github engineer upside down and tickle them with feathers until they DIE

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  Jeromy Sonne (@JeromySonne) reported 11 minutes ago

  @TJ_Bongiorno None of them. MTAs fundamentally are broken technology not worth it. Claude can do a proper lift study DIY or using an open source framework from GitHub. Build don’t buy and save the $$$

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  D Mack (@dmack1012) reported 13 minutes ago

  @fanteLiscio fym open source am i supposed to put down github,com/dmack6464/relationship or some bs

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  atomicbot.ai (@atomicbot_ai) reported 15 minutes ago

  Hermes Agent vs OpenClaw using Qwen 35B Local Model We asked agents to scrape GitHub star history for both tools, find what caused the growth spikes, build a live dashboard in the browser. MacBook Pro M5 Max 64Gb OpenClaw: 203k tokens, 12m 01s - wrote a bash script Hermes: 257k tokens, 33m 01s - wrote a SKILL.md OpenClaw hit GitHub API, got truncated responses, paginated through contributors, pulled star-history JSON, found a security incident in OpenClaw's history, fetched SVGs, fixed broken HTML from trimming, rewrote it clean. Hermes parallel tool calls across GitHub API, web search, and browser. Hit Google rate limit, auto-switched to DuckDuckGo. Fetched article contents, mapped viral moments, then built the dashboard. Both shipped a live dashboard with star growth charts and spike annotations

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  NickNotNikee (@NickNotNikee) reported 16 minutes 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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  Ashish Ranjan (@Ashish_050488) reported 16 minutes ago

  build on laptop (3 secs), upload only the dist folder. 500kb. server just serves files now, doesn’t build anything. deploy went from 15 mins to 5 secs. turns out big companies do this exact thing, just automated. github actions next so i never think about

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  ジョン (@jcinjpn) reported 18 minutes ago

  It’s abundantly clear that GitHub is going to burn itself down in its own land grab for AI. We need an open alternative or one done by a player that isn’t in the model game.

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  Atul (@atulcode) reported 18 minutes ago

  @GithubProjects Github is down

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  Roj Niyogi (@niyogi) reported 22 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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  Shubh Jain (@shubh19) reported 23 minutes ago

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

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  Moe Sbaiti (@MoeSbaiti) reported 30 minutes ago

  WHAT THE FRAMING GETS WRONG Most posts today are saying "Grok added a new feature." That framing is backwards. What happened is that an agent framework with over 110,000 GitHub stars, the number 1 ranking on OpenRouter, and an NVIDIA endorsement just got native access to one of the most capable models available through a simple OAuth login. xAI made the announcement. Not Nous Research. Hermes Agent also self-improves. When it solves a hard problem, it writes a skill file for that solution and saves it. The longer it runs on your specific workflows, the more capable it becomes for your specific context. That is not how people are talking about this today. The memory layer and the self-improvement loop are the actual product. Grok is the engine.

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  Ferbin (@Ferbin08) reported 39 minutes ago

  @cyrilXBT every github trend: week 1 stars explode, week 3 issues mount, week 5 nobody touches it. where are routing and agent memory in that cycle?

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  青川一 (@worigoule) reported 39 minutes ago

  @ZooL_Smith And then you google solutions and found yourself ended up in a github merge request or more likely a issue page written in like, 2 years ago

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  YiboInsights (@yiboinsights) reported 41 minutes ago

  What's the most annoying thing about AI agents? My guess: the moment you open a new session, it forgets everything about you. There's a GitHub project gaining 2,000 stars a day right now — built specifically to fix that. It's called Hermes Agent, and it's the first agent I've seen with a real learning loop 👇