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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Aryan Patel (@aryan32736) reported 6 minutes ago

  @dayonefoundry @github Hey brother, can you help me out with this? I also have the same issue from GitHub.

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  Michal Wolski (@michalwols) reported 6 minutes ago

  @_simonsmith That's how you get "Kimi Shannon" and Dario, CEO of Moonshot a huge portion of github commits get signed as authored by claude, anyone training on code will keep having this issue

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  Traveler (@Traveler2000AD) reported 11 minutes ago

  @ChrisCroy Funny thing is, almost all artists have copied, even "stealed", each others styles for cencuries. And if I am not wrong they still teach at modern art schools to copy great masters works to learn ("What? You copied Mona Lisa??? Why you ***** little thief...") As for coders, we all copied each others code for decades. Old ones started "stealing" by reading code examples from books & magazines ("What? You copied that DOS example program from Peter Norton book? Why you ***** little thief ..."), then discs, BBS archives, FTP archives, forums & StackOverflow & now latest fad is *** repos & youtube videos. Bottom line: people have copied each other for long time. That's not an issue. Never was. The only real issue is where does line go between copying & stealing. It's all in the details. If I put code to github with public domain license it means I don't give one single **** if somebody "steals" it. If I use MIT style license then go ahead, use it in your commercial product as long as you mention somewhere that it uses my code. And if I use AGPL, GPLv2,GPLv3 I am giving you a message that use it in anyway you like but if integrate it into your own codebase, you must then make your codebase public & accessible too. Only exception is LGPL that mostly libraries use & even then only if you don't statically link it into your product. Pro tip: If you are worried that somebody steals (again, details dammit!) your art or code, don't put it to Internet in the first place.

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  OpenShip (@openshipio) reported 13 minutes ago

  OpenShip Desktop version requires ZERO GitHub external auth. Your code never leaves your machine. No third party needs to know your repos, and you don't need to trust anyone, even if you are using openship cloud. Our desktop app support gh CLI, so as long as you have GitHub authenticated on your machine, OpenShip can read it locally, and you can build your project locally -> upload the dist to your server / OpenShip Cloud! Or even use the short-lived tunnel for atomic clone over SSH, so no credentials ever stay on any other server! We are trying to deliver an ultra-security / privacy-focused experience, so you always stay in control.

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  Surya Sankar (@SuryaSankar90) reported 13 minutes ago

  @coinbureau Why is no software engineer questioning the validity of these claims ? 1. Why is it even necessary to skip human readable code ? Today LLMs produce excellent outputs in programming languages. Compiling them is not a bottleneck at all. It takes a few minutes at max. So what problem is this solving ? 2. Human readable code is a feature. Not a bug. Someone asks the AI to build a bill payment module. Human readable code enables verification before deploying to ****. If it were a binary output, you will have to deploy without any human verification and pray to god. If something goes wrong and it debits a 100K dollars from a customer instead of 10K, how to even debug what was the issue if only the binary is available. 3. Where is the huge public repository of binaries to train on ? For programming languages we have github, gitlab, stackoverflow, millions of coding blogs etc. 4. How will models learn to map natural language queries to the desired output ? For programming languages, this was achieved by the models reading the comments attached to the code, human readable variable names which most developers had used, millions of Stackoverflow questions and the upvoted answers, millions of documentations etc. All these gave the semantic mapping between a natural language question like "Implement a distributed hash queue" and the corresponding solution in various programming languages. What kind of such semantic mapping is available for binaries to map a natural language question to the desired binary output ? 5. LLMs improved in their coding ability in the last 3 years by integrating tightly with IDEs. Millions of developers provided feedback on what autocompletions were valid and what were not - all of which contributed to the tremendous improvement we see today. How can this be replicated for binaries ? 6. Compilers are deterministic. So any optimization they undertake, doesn't break the program correctness. That is how they are built. How can a probabilistic LLM provide such a guarantee ? Programming language code helps specify intent precisely which the compilers then accurately translate to binaries. Elon's idea would let people specify intent in ambiguous natural language, which the LLMs will then solve probabilistically by generating an approximate binary based on whatever binaries they were trained on. There is no way to ensure that the binary output matches the intent. It can fail in any which way at run time. Which defeats the whole purpose of what a compiler is supposed to be. Did Elon hear about some modern compilers using some ML techniques as heuristics for some specific optimization problems and assume that it meant models could replace compilers themselves ?

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  Dylan Mikus (@dbmikus) reported 18 minutes ago

  @dexhorthy I haven't manually made a GitHub PR or pushed a commit in months I prefer spending $0.20 to tell Fable to `*** commit 'fix code' && *** push --force-with-lease`

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  mischa_u (@MischaU8) reported 22 minutes ago

  @10_X_eng will do, let me get codex to generate some useful context and I'll raise a github issue.

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  reach universe (@Build4mBottom) reported 23 minutes ago

  @Ananth7e @thsottiaux @ajambrosino open source got more bugs issue is github itself cant handle public code properly

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  Gipp 🦅 (@gippp69) reported 24 minutes ago

  F**KING 8,300-STAR REPO TURNS CLAUDE CODE INTO A PARALLEL SOFTWARE TEAM THAT PLANS, BUILDS, TRACKS, AND SHIPS ENTIRE FEATURES. the repo is called CCPM, with 830 forks, 40 watchers, 87 commits, and a strict 5-stage workflow designed to replace endless prompting with repeatable software delivery. you describe one product idea. It creates the PRD, converts it into a technical epic, breaks it into up to 10 scoped tasks, and syncs everything directly into GitHub Issues. one feature can launch 5 agents at once for the database, backend, API, frontend, and tests, while separate *** worktrees stop their files from colliding. fourteen deterministic scripts handle standups, status checks, blockers, search, and validation with zero LLM cost, while the same skill works across Claude Code, Codex, Cursor, Factory, Amp, and OpenCode. this is loop engineering in practice: design the system once, then let Claude keep finding work, delegating tasks, recording progress, and moving from one idea to verified code without waiting for your next prompt.

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  Ty (@ty37zhang) reported 28 minutes ago

  @iamsahaj_xyz @github You shipped too hard and brought them down yesterday.

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  Scarlett claira (@AItechscarlett) reported 28 minutes ago

  In 2014 a Swedish engineer named Knut Sveidqvist lost a Microsoft Visio file. He went to open the diagram he had drawn a few months earlier. It was gone. Every box, every arrow, every label. All of it had to be redrawn by clicking through Visio menus again. That night his kids were watching The Little Mermaid on TV. He named his fix after the movie. Twelve years later Mermaid has 89,101 GitHub stars, 8 million users, and native rendering inside GitHub, GitLab, Notion, Obsidian, VS Code, and Confluence. Here is what the paid market still charges to draw the same boxes. Microsoft Visio Plan 2. $15 per user per month. Lucidchart Team. $10 per user per month with a three-user minimum. Miro Business. $20 per user per month. Fifty engineers on Miro Business burns $12,000 a year to draw arrows between boxes. Mermaid replaced the drag-and-drop editor with a text spec that reads like Markdown. ``` graph TD A[User] --> B[Login] B --> C{Valid?} C -->|Yes| D[Dashboard] C -->|No| E[Error] ``` Ten lines. Renders as a real diagram. Every version of Claude, ChatGPT, Gemini, and Cursor already knows how to write it. You describe your architecture in plain English and the model returns a Mermaid block. Paste it into a GitHub README. Paste it into an issue. Paste it into a pull request. GitHub renders it inline as a live SVG. No plugin. No sign-in. The paid tools shipped drag-and-drop editors. Mermaid shipped a text spec that the LLMs learned on their own. Flowcharts, sequence diagrams, class diagrams, state diagrams, entity-relationship diagrams, user journey maps, Gantt charts, pie charts, *** graphs, mindmaps, timelines, C4 architecture diagrams, treemaps. Anything you would open Visio for. Version 11.16.0 shipped two weeks ago. Because the diagram is text, it lives in your repo. Because it lives in your repo, it goes through code review. Because it goes through code review, it stops rotting. Nobody has to remember where the Lucidchart account is. Nobody has to pay $10 a month to reopen a five-year-old file. MIT license. 89,101 stars. TypeScript. The library is free forever. Mermaid Chart the company sells a hosted editor on top for teams that want one, but the core stays MIT. Somebody in Sweden lost a Visio file and refused to draw it again. Twelve years later the paid diagram tools still exist, and nobody who writes software has to use one. (Link in the comments)

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  Gen Z Mind (@gen_z_mind) reported 31 minutes ago

  My first experience with Kimi K3 in GitHub Copilot via OpenRouter was… interesting. I gave it a simple task: create a self-contained HTML page with a few animations. Instead of writing the code, it spent an incredibly long time thinking and reasoning. After nearly five minutes, it was still analyzing the problem rather than building the page. I eventually stopped it and told it to focus on implementation instead of overthinking. Reasoning is valuable when you are solving a difficult architectural problem or debugging a complex system. But for straightforward tasks, I would rather the model get to work and produce a first iteration quickly. Sometimes the best reasoning is knowing when not to overthink.

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  Enertium AI Cyber Defence (@enertium) reported 31 minutes ago

  @CompSciFutures Holy kow I can’t believe Telstra and COA VICPOL still have not reconnected my M2M Medical emergency assistance sims. I’m supposed to be working on Tier 1 NOC for this cyber crisis. @FSF even prepaid me in stickers!!! F off McKinsey. See my GitHub: APMonitor. It’s big in NYC as a B NOC, because: walking down the street and throwing a date point over the fence. 🤘🤘🤘

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  Ravel (@Ravel_App) reported 31 minutes ago

  We shipped GitHub sync and broke it immediately. Every commit lit up the feed. Teams muted us. Fix: scope to task branches only. PRs trigger progress; random commits don't. More data made things worse. Filtering made it useful. #DevTools #ProjectManagement #TechLead

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  Ali Çevik (@alihcevik) reported 31 minutes ago

  We are rapidly improving managed agents. With these updates you can now: 1. Try it for free 2. Set budget guard rails to stay in control of costs 3. Have it run on schedule on the background; so you can automate things like monitoring GitHub issues, reproducing them, sending you notifications for anything worth your attention.