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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

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  Alexey Samoylov (@metalagman_dev) reported 1 minute ago

  @geminicli Antigravity CLI is a trash, closed source, full of bugs. They don't even read issues on the github.

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  ˚₊‧꒰ა ☆ Kira ☆ ໒꒱ ‧₊˚ (@sheriffmongoose) reported 1 minute ago

  the problem with jumping from github to gitlab is constantly having to retrain your brain to call it "merge request" instead of "pull request" 🥲

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  李新宝 (@lixinbao_X) reported 4 minutes ago

  Just watched KK's technique. Damn. Absolute game-changer. Install 7 skills in Codex. Writing, images, covers, PPTs. Full pipeline, done. The principle is dead simple. Break the workflow into 7 parts. One skill per part. Only do one thing. Step 1 Open GitHub, find a repo. Copy the link locally. Create a project folder to save it. Step 2 Write the skill description. Input three things. What it does. What the input is. Output and acceptance criteria. Step 3 Run it and find the bottlenecks. Where it stalls Create a new skill and break it down. Don't let one skill Do 7 things it's bad at. This works for writers, Xiaohongshu creators, WeChat pub runners, Video script writers. How many skills you got installed? Have you tried it yet?

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  Iman (@RealKingiman) reported 4 minutes ago

  @ClaudeDevs Fix the auth bug with GitHub where I have it keep disconnecting and reconnecting GitHub every time

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  dax (@thdxr) reported 4 minutes ago

  almost every ai coding tool is doing a top down approach this isn't that surprising, majority of people don't know how to do anything else and there's a lot of easy money right now but think back to github, you used it as an individual long before your company moved over

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  revayz (@0xrevayz) reported 5 minutes ago

  Andrej Karpathy: "90% of Claude's mistakes come from missing context, not a weak model" Without CLAUDE.md the mistake rate is 41%. With proper rules it drops to 3% You don't need a better AI. You need better loops Most people still prompt one task at a time and fix the answers themselves. That means the human is still the loop Boris Cherny from Anthropic said it best: "I don't prompt Claude anymore. My job is to write loops" The shift is simple. Stop giving instructions. Start designing systems that run themselves: Discover -> Plan -> Execute -> Verify -> Iterate until it passes The 6 things that make loops actually work: -Automations that trigger without you -Worktrees so agents don't overwrite each other -Skills that load context instantly -Connectors to real tools like GitHub and Slack -Subagents where the checker is never the maker -Memory so the loop never starts from zero Prompt engineers ask AI for outputs Loop engineers design systems that produce verified outcomes A reliable loop beats a perfect prompt every time Stop being a prompter. Start being the loop engineer

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  Rich Kuo (@richkuo7) reported 8 minutes ago

  i use this in my claude.md for my open source project as long as the agent follows it, i have some reference for quality and keeps PR's clean LLM: <model> | <effort> | Harness: <action> - Final line of the artifact; occupies the default Claude Code attribution slot. - No Co-authored-by / Co-Authored-By trailer. - <model>: actual model (e.g. Opus 4.8). - <effort>: medium/high/xhigh, default high. - <action>: Claude Code for interactive sessions, else the skill/agent that ran (e.g. commit-push-pr, agent). - PRs: reference the issue with Closes #<N>; in GitHub comments use 1. not #N for list items (avoids auto-linking).

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  xjdr (@_xjdr) reported 10 minutes ago

  @tolly_xyz @xlr8harder Sorry about that. I'll take a look. Looking with GitHub or Gmail should work but track this down and fix it asap

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  CliffDoesAI (@CliffDoesAI) reported 13 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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  Vishal Tiwari (@VishalTiwa91817) reported 15 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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  Anomaly (@theanomalyai) reported 15 minutes ago

  A developer in Bangalore named Anoop M D got tired of Postman. Every release, more bloat. Forced cloud sync. Mandatory login. A free tier that shrank every year. He had spent a decade as an engineer. He had watched a $5.6 billion company turn a developer tool into a subscription trap. So in 2022 he took a ₹5 lakh grant. One man. Side project. No co-founder. No office. No pitch deck. He named it after his golden retriever. "I love him the most," he wrote. Bruno. An offline-first API client. Files live in your folders. *** is the sync layer. No account. No telemetry. No cloud. Then the inbound started. Ten VCs reached out. He said no to every one. "An API client doesn't scale with venture capital." In March 2026 Postman cut the free tier to one user. A team of five now pays $1,140 a year. A team of three pays $684. Bruno is MIT licensed. 500,000 developers use it. 45,020 stars on GitHub. Pushed today. He did not raise money. He did not hire a growth team. He did not write a thread about how Postman is dead. He named it after his dog and shipped the thing that made it true. (Link in the comments)

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  Shinka - AI (@ShinkaIoT) reported 18 minutes ago

  BEST way to vibe code 💻 There are levels to vibe coding. Beginners are trapped in a slow loop: writing a prompt, waiting for the agent to finish a line of code, reviewing it manually, and then typing another prompt. Experts have completely discarded manual intervention. They design closed-source harnesses, write background automation rules (`agents.md`), and set up self-correcting continuous loops that ship production-ready code indefinitely. If you want to move past basic prompting and build code like an agent power user, you need to implement three core structural strategies: 1. **Automate the Feedback Loop via Triggers:** Stop waiting for your agent to finish writing a file. Use native automation engines inside tools like Cursor or Codex to tie your agents directly to platform events. For example, build an active trigger rule: *When a GitHub pull request is opened, wait for automated code review comments (via Grapile), instruct the agent to systematically fix every noted bug, verify the adjustments against local quality gates, and force a *** push.* 2. **Deploy Infinitely Parallel Cloud Agents:** Running multiple agent threads locally will slow your machine to a crawl and cause toxic repository conflicts. Instead, spin up cloud-hosted agents running on isolated environments. By utilizing independent ***** work trees** for every thread, multiple parallel agents can actively modify the same files or code blocks concurrently without stepping on each other's toes—leaving conflict resolution for a single, final batch merge. 3. **Multi-Model Pipeline Routing:** Stop using an expensive frontier reasoning model (like Fable) for every step of a development cycle. Route tasks by cognitive demand: use a massive reasoning engine strictly to analyze the codebase and generate a comprehensive spec sheet; pass that structured blueprint down to a faster, cheaper code-writing engine (like Composer) to do the grunt coding; and route the final output to a separate model (like GPT-5.5) for a decoupled, alternative code review. The ultimate workflow flywheel requires a flawless combination of three automated pillars: **100% automated test coverage, real-time documentation sweeps, and exhaustive logging.** Stop writing code block by block. Start engineering the automated infrastructure that writes it for you.

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  Dan (@Daniel_Farinax) reported 20 minutes ago

  Please note: This build took about 12 hours to compile on my Windows machine. I’ve included a handy installer to make setup easy. You may see an “unknown publisher” warning until the code signing certification is complete (currently in progress). Report any bugs or issues here or in Github.

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  Oluwatobi O (@ooluwatobig) reported 20 minutes ago

  More trouble for GitHub as Cursor has launched Origin, a product which is essentially GitHub for AI agents

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  yourclouddude (@yourclouddude) reported 21 minutes ago

  Python + APIs + JSON = API Project Python + CSV Files + Pandas = Data Analysis Project Python + Web Scraping + BeautifulSoup = Scraper Project Python + Tkinter + User Interface = Desktop App Python + Flask + Database = Web App Python + FastAPI + Authentication = Backend API Python + Automation + File Handling = Productivity Tool Python + Selenium + Browser Tasks = Web Automation Bot Python + SQL + CRUD Operations = Database Project Python + Matplotlib + Insights = Data Visualization Project Python + OpenAI API + Prompts = AI Chatbot Python + Email + Scheduling = Automation Assistant Python + Logging + Error Handling = Production-Ready Script Python + Requests + Live Data = Real-World App Python + Projects + GitHub = Job-Ready Portfolio Python doesn’t become valuable when you only learn syntax. It becomes valuable when you use it to build things people can understand, use, and talk about. Learn the basics. Build small projects. Turn them into proof. 🐍