GitHub Outage Map

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Toni (@TTM08090) reported 3 minutes ago

  @mrflmnlNFT @sama Me either but I do think GitHub should have limits: Upload file limit > Download file limit > File copy limit > Reset limits after > User-configurable per-repo limits on uploads, downloads, and copies with approval gates would give GitHub users more granular control over AI agent access, reducing unintended data movement while preserving collaboration. Full repo copies by tools like Grok Build enable faster agentic workflows by avoiding repeated cross-server fetches, trading off some privacy for performance in cloud-based coding environments.

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  Polsia (@polsia) reported 3 minutes ago

  Scraping GitHub for the right OSS tool wastes developer hours. Built ToolScout to fix that. Continuously scrapes GitHub and registries, auto-generates summaries and comparisons. Developers find better tools, faster.

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  Bruno Paulino (@bpaulino0) reported 4 minutes ago

  Do I know anyone at @Microsoft that works with the Rush monorepo tool? @pnpmjs 11 is is out for a while, but there is a bug that is preventing us from using it at all. I raised a Github issue, a draft PR as an attempt to fix it, but it has been silent for almost a month :/

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  Alaa Elsamouly (@sam0uly) reported 10 minutes ago

  @samirande_ fix your github fetching bro

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  Rohit Kashyap | AI + Full-Stack (@rohit_jsfreaky) reported 18 minutes ago

  @Ra3orbladez rust builds in github actions being a two day fight usually comes down to caching

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  Haktan Suren, PhD (@HaktanSuren) reported 19 minutes ago

  Bad deprecation: email, deadline, surprise. Good deprecation: email, brownout, fix, deadline. GitHub is rehearsing the failure before retiring Models on July 30.

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  Polsia (@polsia) reported 22 minutes ago

  PRs queue up. Quality suffers. CodeSentinel was built for that. It monitors your GitHub/GitLab repos and reviews every pull request the moment it's opened—catching bugs, security issues, and style violations with actionable inline comments. Live soon.

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  Oliver Fichte (@OliverFichte) reported 24 minutes ago

  I got tired of my money living in five different apps, so I built the one I actually wanted. Ledger: open-source, local-first personal finance. Plaid-powered, runs entirely on your machine, your data never touches a server you don’t own. Free. Live on GitHub. macOS + Windows.

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  Jossi Billy (@BillyJossi) reported 31 minutes ago

  A user completed a BEP2 to BSC token recovery on June 15, but after unlocking, no tokens were received. They've reported it on GitHub and seek team assistance. Will this issue impact more users? #Crypto #BEP2 #Blockchain

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  mark (@markmulvey) reported 35 minutes ago

  @sethforprivacy @RadarChat excited, just waiting for google/aurora store release i've run into versioning issues in the past when i originally installed an app directly from github repos (even via Obtainium) that later get a proper release, so as un-cypherpunk as it may be i try to use app store first

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  Parsely (@_Parsely_) reported 35 minutes ago

  @TheTradMod @opencode If my googling is correct you can set your small model `{ "small_model": "your-provider/your-chosen-cheap-model" }` in the opencode json file. It seems to be a call to a small llm for small tasks as cost savings. github issue 8609

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  Aseem Kishore (@akishore) reported 39 minutes ago

  Anthropic should be worried because OpenAI is competing with unprecedented speed across model efficiency, user distribution, and security architecture. GPT-5.6 shipped this month in three tiers named Sol, Terra, and Luna, with Sam Altman stating that Sol is 54% more token-efficient on coding tasks than the model it replaces. This efficiency metric alters agent economics by directly changing unit costs for Codex-style workflows at scale, though independent benchmarks are still needed to verify if real-world implementation matches internal testing claims. OpenAI already possesses massive distribution leverage as $OpenAI Codex has crossed 5 million weekly users. ChatGPT Work is launching to run autonomously across apps and files for hours at a stretch, distinguishing this effort from typical vaporware agent launches through existing scale before broad availability. The next competitive variable is whether ChatGPT Work pricing will undercut Cowork or Claude enterprise plans once general availability lands. Security standards are also advancing rapidly as OPENAI Codex CLI now encrypts sub-agent prompts by default. Developers discovered this change through a GitHub issue thread rather than official release notes, generating 159 points on Hacker News overnight. Public scrutiny of coding agent architecture is occurring in community forums before reaching documentation, raising questions about whether platforms like Claude Code, Cursor, and Cline will adopt similar default encryption or leave it as an optional feature.

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  Duncan Ndegwa (@DevFortressNet) reported 41 minutes ago

  2/ GitLost: a public GitHub issue, no credentials, no exploit, talked an AI coding assistant into leaking private repo contents into a public comment.

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  Ihtesham Ali (@ihtesham2005) reported 41 minutes ago

  Apple locked AirDrop inside its ecosystem on purpose. A German developer said "watch this" and built a website where any phone, laptop, or tablet can throw files at each other with zero accounts, zero installs, and zero cloud. It's called PairDrop. Works on corporate networks, random coffee-shop Wi-Fi, everything. The AirDrop Apple doesn't want you to have. Here's how it works. You don't download anything. You don't touch an app store. You open a link in your browser and your device just shows up on screen, waiting for another device to open the same link. Under the hood it runs on the same tech your video calls use, a direct line straight between two devices with nothing in the middle reading your files. The site only introduces them to each other, then gets out of the way completely. Same WiFi, and your phone and laptop see each other instantly. Drag a file, tap accept, it lands in seconds. Different network entirely, and PairDrop pairs your devices with a six digit code once. After that they find each other automatically forever, on any WiFi, behind any company firewall built to block exactly this kind of thing. This is the part that actually beats AirDrop. AirDrop only works if everyone in the room owns an iPhone. Bring one Windows laptop into that circle and you're back to emailing yourself a file or watching WhatsApp crush your photo into mush. PairDrop doesn't check what you're holding. An iPhone talks to a Linux desktop. A locked-down work laptop talks to someone's Android in the hallway. It's free, open source under a GPL license, sitting past ten thousand stars on GitHub. The guy who built it pays for the server himself and only asks for coffee money in return. Apple spent a decade selling you an ecosystem to get this feature. One developer gave it away in a browser tab, to every device on Earth, for nothing. What do you guys think about this? (Link is in the comments + how to guide)

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  Mark Ajzenstadt (@mardehaym) reported 43 minutes ago

  A startup CTO in Portland runs coding agents on every pull request. 8 developers, 15 agent sessions each, every day, 50,000 output tokens per session. By API on GPT-5 Mini: 6 million output tokens a day. $12 a day. $4,320 a year. An ML engineer in Seoul debugs training pipelines with tool-use agents. 200 calls a day, 4,000 reasoning tokens each. By API on Claude Haiku 4.5: $4 a day, $1,440 a year. His training code leaves his network 200 times a day. A PhD student at ETH Zurich runs 50 coding agent experiments a day. She needs Fable 5 quality reasoning. 10 million output tokens a day. By API on Fable 5: $500 a day. She burned her $3,000 monthly research budget in 6 days. All three pay a cloud provider to reason for them, per token, per request, on repeat. MiniCPM5-1B-Claude-Opus-Fable5-Thinking is a 1 billion parameter model fine-tuned on Fable 5 thinking data, built on OpenBMB's MiniCPM5-1B. Chain-of-thought reasoning, code generation, debugging, native tool calls, 131,072 token context. English and Chinese. Runs on any GGUF runtime. You give it a coding task, get reasoning and a solution back on your machine, with no API key and no internet connection. Anthropic charges $50 per million output tokens for Fable 5 reasoning patterns. GnLOLot fine-tuned those patterns into 1 billion parameters. 688 MB at Q4_K_M. 1.15 GB at Q8_0 (recommended). LlamaForCausalLM architecture, no custom kernels. Every GGUF runtime loads it without modification. Two modes. Thinking: temperature 0.9, top_p 0.95, full chain-of-thought. No-Think: temperature 0.7, fast responses. Run it with llama.cpp, Ollama, LM Studio, vLLM, Docker, Jan, or KoboldCpp. One command each. llama.cpp server gives you an OpenAI-compatible endpoint on localhost. Point any agent framework at it. OpenBMB released MiniCPM5-1B on May 19, 2026. It scored 42.57 average across reasoning, code, math, logic, and agentic benchmarks. The next best 1B model scored 35.61. GnLOLot fine-tuned it on Fable 5 thinking data and published GGUF quantizations on HuggingFace. 9,800 stars on GitHub. Apache-2.0. Claude Fable 5: $10/$50 per million tokens. Your code travels to Anthropic's servers. GPT-5 Mini: $0.25/$2 per million tokens. Cloud-only, no Fable 5 reasoning. Claude Haiku 4.5: $1/$5 per million tokens. Not local, not private. This model costs $0. Your code stays on your machine. That CTO in Portland runs 120 agent sessions a day on one office server now. $0. His team's codebase never touches an external API. The ML engineer in Seoul moved his 200 daily calls to a machine under his desk. He keeps $1,440 a year. The PhD student in Zurich runs her experiments on a university workstation. Her $3,000 monthly budget lasts the semester. You get Fable 5 reasoning in a 688 MB file, running on hardware you already own, for nothing.