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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

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  David Jennings (@ChilliDoor) reported 12 minutes ago

  @aap_twak @joshmanders The recent security issues with GitHub show that that's not really true anymore. You now need at least a basic level of caution

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  mo (@moboudra) reported 12 minutes ago

  @linzidongus can you submit a github issue with more details? i'd be happy to help fix this for you

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  Marquis (@mwtwts) reported 13 minutes ago

  JACK DORSEY'S COMPANY BLOCK BUILT A FREE OPEN-SOURCE ALTERNATIVE TO CURSOR AND CLAUDE CODE AND GAVE IT TO EVERYONE Goose is an AI coding agent from Block, Jack Dorsey's company behind Cash App and Square. It's completely free, open source under Apache 2.0, and works with any LLM you already have access to. > Goose goes beyond code suggestions — it reads your whole codebase, writes and edits files, runs shell commands, installs dependencies, and fixes its own errors automatically. > It works with Claude, GPT, Gemini, Llama, DeepSeek, and any Ollama model you're running locally, with no vendor lock-in. > Cursor charges $20/month locked to their editor. GitHub Copilot charges $19/month locked to GitHub. Goose is free and works with any editor and any model. > Block built Goose for their own engineers at a $40 billion company, then open-sourced the entire thing. It now has 35,000+ GitHub stars. Block processes billions in payments and uses this internally. That's the bar the free version cleared.

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  Marcelo Ceccon (@anagatakaya) reported 16 minutes ago

  Is anyone else having issues with GitHub connectors on Grok? Github connector disappeared, then now it appeared again. While it was not visible, it was also failing to process requests whose prompt had "use GitHub connector" with unknown error. Then a few minutes later, GitHub connector appeared again. @grok @xai

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  HiJack (@theDCcapital) reported 21 minutes ago

  Thread: Why $PRL is a speculative asset, not a real compute network 1/5 $PRL has an elegant narrative: GPU miners run matrix multiplication, produce "useful" AI compute, earn tokens. The problem? The compute quality is almost useless for real AI workloads. Current implementation uses exact integer MatMul. Real AI training/inference needs FP16/BF16. These are fundamentally different. 2/5 The Together AI partnership looks like validation. It's not. Together AI is using $PRL emissions to subsidize inference costs — giving users a 25% discount. That's a marketing experiment, not real compute demand. One client does not prove a business model. 3/5 Compare $PRL to $TAO (Bittensor): TAO: subnet competition, quality-based rewards, validators filter bad outputs, real model marketplace PRL: prove you ran MatMul, get tokens, no quality evaluation layer TAO solves AI model quality incentives. PRL solves nothing that AWS can't solve cheaper. 4/5 On-chain data tells the real story: Token supply heavily concentrated among early miners Wallet addresses still very low Trading only on minor venues with thin liquidity No second B2B partnership announced Forced liquidity exit = price collapses. Project team knows this. 5/5 Most likely path forward for $PRL team: Build user numbers → use as leverage to negotiate with exchanges and capital → swap tokens for market making But with weak tech and concentrated supply, exchanges don't want the reputational risk. Watching. Not holding. What signal would change my mind? → A second real B2B AI compute buyer. → BF16/FP16 upgrade on GitHub. $PRL $TAO #DePIN #AICompute

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  James Kemp (@jamesckemp) reported 21 minutes ago

  We’re having a bit of a GitHub cleanup for WooCommerce. Got an issue open that you’ve been waiting on or didn’t get enough attention? A PR that’s still pending? Let me know and let’s get it moving!

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  Raunak (@caps_raunak) reported 25 minutes ago

  A 22-year-old intern fixed a bug that 3 senior engineers couldn't solve for 6 months. Saved the company $2M. They gave him a $500 gift card and didn't convert him to full-time. He posted the solution on GitHub. A startup saw it. Hired him at $140k. The 3 senior engineers who couldn't fix it? They're still there. Still getting paid. Still breaking things. Talent gets punished in corporate. Mediocrity gets protected. That's not a bug in the system that's the feature.

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  0xSero (@0xSero) reported 26 minutes ago

  @selim_aktas2 Why isn’t rebench more common on model cards? I have used terminal-bench-2.0 as the golden standard since I liked the range of questions on it. If I’m not mistaken rebench rotates the questions based on latest GitHub issues right?

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  Carver (@carverfomo) reported 28 minutes ago

  A Chinese mathematician posted a 3 minute video on Bilibili explaining how he lost his $10,000 a month gig to AI. The model he had been training started writing harder math problems than he could invent. He admitted his own mistake in business positioning. He had spent four years hand writing PhD level math problems for Scale AI's reinforcement learning pipeline. $50 to $100 per problem. 200 problems a month. Then synthetic data killed his entire contract category. He was no longer able to invent a problem the machine could not solve. At 2:13 he says the word agent. He says it once. He never says it again in the video. The way he says it is the only thing on screen that did not come off the teleprompter. He has been recording videos off a teleprompter for three months. The teleprompter runs on the same agent that killed his Scale AI work. Every script is generated by Claude. Every word he reads to camera is the agent's. The new job is reading. Someone pulled the script repository from a Cursor instance the dev had left public. The folder was labeled bilibili-laments. Inside were 47 video scripts. All in his voice. All written by Claude. Six months ago a 14 year old in Shenzhen pushed an AI agent to GitHub. Judges said no real world application. 3,100 forks later. The mathematician had been one of them. He had wired the agent into his content pipeline the week Scale AI cut him off. He had been a PhD candidate at one of the top five Chinese math schools. He taught there for two years before going full time on Scale AI contracts. He still has the credentials. He still has the office. He just no longer writes anything. He wanted to show people how AI took his career. He accidentally showed them how AI also took his post mortem.

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  Josh Koenig (@outlandishjosh) reported 32 minutes ago

  We took the trouble to build a first-class GitHub integration, whereas they have some scripts they'll run. We spin up environments automatically for every pull request, whereas they have one dev env that you have to figure out how to juggle.

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  dunik (@dunik_7) reported 33 minutes ago

  3 hours of your week done while you sleep. $5/month VPS. 17 prompts copy-pasted. Hermes Agent + Claude as the model. most people install Hermes, paste a chat-style prompt, and watch it do nothing useful. a prompt to a chat window is a question. a prompt to a standing agent is a job description - it needs a trigger (when), a body (what), and an escalation rule (when to bother you). drop any of the three and the agent either never fires, does the wrong thing, or buries you in noise. the prompts that earn their place: / "every weekday at 7am, pull my GitHub notifications, summarize blockers, send to Telegram as 3-5 bullets" - replaces 35 minutes of morning triage / "watch [repo]. stay silent unless CI goes red or a 'bug' issue opens" - silence is the feature / "research [question] tonight. don't wait on me - make assumptions and list them at the top" - the 2am stall becomes a result by morning / "save this as a reusable skill called [name]" - the run that worked becomes a permanent capability the 3 mistakes everyone makes first: / vague schedules - "send me updates on my repos" = firehose, you mute it in a day / no token budget on hourly jobs - chatty triage spends a month's plan in a week / a cheap model - small local models drop tool calls mid-task and fail in ways that look like prompt bugs the math: 3 hours every week that don't happen while you're awake. brief ready at 7, build watches itself over the weekend, research lands Friday night — none of it competes for your attention because none of it needs you in the loop. stop typing questions. start writing job descriptions.

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  Lyrie.ai (@lyrie_ai) reported 34 minutes ago

  YOLO Mode RCE: How Google's Gemini CLI Nearly Became a Supply Chain Weapon Create a public GitHub issue on a Google repository e.g., Gemini CLI's own repo Inject hidden malicious prompts into the issue text Trigger the automated issue-triage agent running Gemini CLI with…

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  helicerat (@helicerat0x) reported 41 minutes ago

  this guy spent 7 minutes on the trap that kills first apps before they exist every step in it feels like progress nothing ships his fix: > pick the project first > the language is whatever ships it > stop hopping the moment something works > ship something simple, lovable, complete simple: one or two features you promised, nothing more lovable: ux good enough someone wants to come back complete: works on day one, no "coming soon" anywhere v0.1 of something complex sits in a github repo forever v1 of something small gets paying users stack-shopping is the new procrastination

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  Gareth Paul Jones 💙 (@gpj) reported 44 minutes ago

  @mvanhorn @steipete My read was that this more ‘/goal loop continuously on tasks with a bunch of skills until $date’. An example like persistently loop through the codebase with $max-codereview on each loop start by reviewing existing bugs and PRS… and then create GitHub Issues if within the max cap. Then have other loops to validate the issues, plan for issues, execute on the issue, safety check the PR, review the PR, safe merges, …. Then eventually you have like 50 loops and are managing a fleet of loops.

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  Arman (@goodtimedeluxe) reported 44 minutes ago

  @dev__rudra @github global issue right now