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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

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  John Papa (@SublimeTrades) reported 50 seconds ago

  Been using this short window we have access to Fable 5 with the paid subscriptions to do high level design and architecture discussions with HLDs and GitHub issues as the outcome of the sessions. Seems a waste to have Fable actually code with how fast it burns tokens.

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  Akruti Acharya (@akrutireports) reported 2 minutes ago

  Someone built an AI plugin around the oldest optimization trick: fewer words. Caveman makes AI coding agents answer with fewer words while keeping the technical content intact. The idea is simple: Same answer. Fewer tokens. It works with Claude Code, Codex, Gemini, Cursor, Copilot, Windsurf, Cline, and 30+ other agents. The project reports an average 65% reduction in output tokens by removing filler instead of changing code, commands, or error messages. It also includes commands for concise commit messages, one-line PR reviews, compressing memory files like CLAUDE.md, and tracking token usage. The best part is that the repo doesn't oversell the numbers. It explicitly says the 65% applies to output tokens only and explains when overall savings may be smaller. That level of honesty is probably one reason it has already crossed 82k GitHub stars.

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  Nick Hazrd (@badhazrd) reported 3 minutes ago

  @GregTomaselli @github I think the biggest security issue is the fact that you won't get public or private.

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  Assim Genshi (@AssimGenshi) reported 10 minutes ago

  @aryanranderiya @github Whaaat? I thought that the problem in my end, turns u have the same problem??!

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  BIGWARZ (@bigwarzeth) reported 10 minutes ago

  @JoshXT you need to login with any other method and then he can connect via GitHub inside the app

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  Manthan Gupta (@manthanguptaa) reported 11 minutes ago

  If you were actually hearing everyone then you would be fixing the reliability issues github has been facing for some time now

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  Abhishek Singh (@0xlelouch_) reported 13 minutes ago

  Loop engineering is simply moving from: “AI, write this function for me” to: “Here is the goal, rules, tests, tools, and stop condition. Keep working until the result is actually correct.” Instead of you manually prompting the AI 20 times, you build a repeatable loop where the AI can: 1. Understand the task 2. Inspect the codebase 3. Make a change 4. Run tests/lint/build 5. Read the failure 6. Fix itself 7. Stop only when checks pass Simple example: You want to add rate limiting to a Go API. Normal prompt engineering: “Write rate limiting middleware in Go.” AI gives code. You paste it. Build fails. You send error. It fixes it. Tests fail. You send another prompt. You are the loop. Loop engineering: “Find all public API routes. Add per-user rate limiting of 100 requests/minute. Use Redis. Do not change existing response formats. Run unit tests and integration tests. Fix failures until all tests pass. Create a PR only when coverage does not decrease.” Now the agent has a loop: Goal: Add rate limiting while requirements are not verified: inspect codebase implement smallest safe change run go test ./... run integration tests inspect errors fix errors check security/performance constraints stop when: tests pass coverage is not lower API contract is unchanged The important part is not the AI prompt. The important part is the feedback system around it. --- Remember, Good loop engineering needs: - clear goal - access to tools: code, logs, tests, GitHub, database sandbox - rules: what it must not break - verification: tests, lint, benchmarks, review - memory: what it already tried - stop condition: when to stop spending tokens and touching code Think of it like hiring a junior engineer. Bad setup: “Build something good.” Good setup: “Fix this bug. Here are the logs. Here are the tests. Do not touch payments. Run the test suite. Show proof before merging.” AI agents become useful when they are not just generating code, but are forced to observe reality and correct themselves. So prompt engineering is asking better questions. Loop engineering is building a system where the AI keeps asking itself the next useful question until the work is done.

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  Anto Patrex (@antopatrex1) reported 15 minutes ago

  vox just let you talk to github copilot instead of typing. no cap this fixes the "staring at blank screen" problem fr fr. your hands stay on the keyboard, your brain stays in the code.

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  Tiago Santana (@worldwithTiago) reported 19 minutes ago

  Merged the content-loop email fix at 6am. Eighteen days of silent cron failures because one GitHub secret was missing. The unglamorous part of autonomous systems is credentials. A fascinating problem. What would frictionless machine access look like?

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  echo (@echostatic101) reported 20 minutes ago

  @Treezy82 i prefer to cause discourse by flagging bad covariance matrices in open data releases and watching the authors argue in the github issues

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  Tamara Martinović (@CodeWithTamara) reported 21 minutes ago

  @kushmergedeck Stacklight. An email each day with updates on the stack you use - from Vercel and Github, to OpenAI and Anthropic. What's new, what's deprecated, what's broken. Scanning every 15 min. A Slack alert for the red warnings. What do you think, would people build their own rather then pay for mine? That's what worries me.

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  David Cramer (@zeeg) reported 21 minutes ago

  @rsdgpt Toss it in a GitHub issue otherwise feel free to DM (or shoot me a slack connect) if its easier

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  Vaibhav Sisinty (@VaibhavSisinty) reported 21 minutes ago

  Researchers just replaced $100,000 consumer surveys with an AI model and a demographic persona. The accuracy hit 90% of human reliability. 🤯 Here is what they actually did. Colgate ran 57 real product concept surveys. 9,300 human respondents across toothpaste and personal care products. Then replicated the entire thing using AI. But here is the problem they had to solve first. When you ask an AI to rate something 1 to 5, it always picks 3. Safe. Middle of the road. Useless for real market research. So they built something called Semantic Similarity Rating. Instead of asking the AI to pick a number, they asked it to explain its purchase intent in plain text first. Then they mapped that response against anchor statements using embeddings. The result was a realistic distribution of ratings that matched what real humans actually said. 90% of human test retest reliability. Distribution similarity of 0.88 versus 0.26 for standard AI prompting. It even reproduced demographic nuance. Lower income personas rated premium products lower. Mid age groups showed more interest in familiar products. Without personas the whole thing collapses. With them it works. The global market research industry is worth $76 billion. Most of that money goes to panels, surveys, and waiting weeks for results. This runs in hours. On GPT-4o or Gemini. Code is open source on GitHub.

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  Michael Liam (@Millionareum) reported 22 minutes ago

  I JUST FOUND SOMETHING THAT SHOULD BE VERY EXPENSIVE Running a company with zero employees. Here's what makes this possible: Paperclip. It's a 100% open source project on GitHub, with over 70,000 stars. I'm not talking about triggering a single model. You hire a CEO, you hire engineers, and you also hire a QA supervisor. Each worker is an artificial intelligence agent, and Paperclip is the Node that keeps them compatible.js and React control plane. Stop dealing with disorganized systems and build a living organization: - Establish a CEO agent for strategy. Hire engineers and designers through Claude or Codex. - Set up an automated QA cycle before any ticket is closed. Manage the entire portfolio from your phone. Do you know what you do when an agent makes a mistake? You're not rewriting the entire pipeline. You're just refining the persona instructions, like coaching a junior employee. This is exactly the kind of tool this field needs right now. Free, open source, can be hosted on your own server.

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  Vaneck Intern (@NewVaneckIntern) reported 22 minutes ago

  @github Okay but can you fix the way we upload files to repos? I don't think an upload folder button on the website is too much to ask. Neither is a consistent desktop experience