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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

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  Atul Mishra (@atulmishra1996) reported 36 seconds ago

  I don’t understand why people want another github ? What’s the problem in current ?

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  Vladic (@Vladic_ETH) reported 3 minutes ago

  OPENAI SHIPPED GPT-5.6 AND CHATGPT WORK. THE REAL WEAPON IS PRICE, NOT IQ. OpenAI shipped two things today. One of them is a costume change. GPT-5.6 landed as three models. ChatGPT Work is a new agent on top. The feeds say "new agent does your work." The real launch is the price sheet. Sol, the flagship, costs $5 per million input tokens and $30 output. That's not flagship pricing. That's what you paid for a mid-tier model a year ago. The gate half the feeds skipped Context first. Two weeks ago the US government cut GPT-5.6 access down to a small group of vetted partners over national security. The gate held about 12 days. Restrictions lifted July 8, public release July 9. Same day SpaceXAI shipped Grok 4.5. The frontier now ships when the government clears it, not when the model is ready. Anthropic went through the exact same thing with Fable and Mythos in June. A pattern, not a one-off. Three models, price as the weapon GPT-5.6 is three models, not one. Sol is the flagship. Terra is the everyday workhorse. Luna is cheap and fast. Price per million tokens, in/out: Sol $5/$30, Terra $2.50/$15, Luna $1/$6. Terra matches GPT-5.5 quality at half the cost. Luna is the cheapest entry in the line. Altman told CNBC Sol is 54% more token-efficient on agentic coding. That's the message. Not "smarter." "Cheaper for the same result." And ultra: a mode inside Sol that spins up multiple agents in parallel and hands subtasks to submodels. The market counts token bills, not benchmarks. Enterprise thinks spend first now. OpenAI heard it and made price the argument. Today's real launch is unit economics, not intelligence. "Sol beats Fable 5, Luna beats Opus 4.8 at two-thirds the cost" are OpenAI's own benchmarks. Until independent runs, treat them as marketing. ChatGPT Work is Codex in a suit Now the "new agent." ChatGPT Work runs on Codex and GPT-5.6. It moves across your apps and files, stays on a project for hours, breaks it into steps, finishes on its own. Output: docs, sheets, slides, web apps. Inside sits a Unified Plugins Directory: Google Drive, Slack, Teams, Gmail, Outlook, Salesforce, GitHub, Canva, Dropbox, more. Call one with "@" or let the agent pick the source. Sounds familiar. This is OpenAI's second run at plugins. The first was 2023 and it flopped. Brockman admitted the models weren't ready back then. Honest read: hard to tell what's actually new. Scheduled Tasks, Computer Use, connectors already lived in ChatGPT and Codex. Long tasks and data sources worked before too. The real move isn't features. It's consolidation: on desktop, OpenAI is merging Codex and ChatGPT into one super app and putting Codex in front of people who don't code. The Anthropic mirror Here's the tell. This is the exact play Anthropic ran with Claude Code -> Cowork. Take a dev agent, strip the "for coders" label, hand it to knowledge workers. Cowork just hit web and mobile, timed to get ahead of this. Two labs, one bet: whoever owns the desktop app that touches your files and apps owns the knowledge-work layer. Chat is the storefront. The desktop is the land grab. What a practitioner does with it One: rebuild pipelines around price tiers. Route bulk work to Luna and Terra. Keep Sol and ultra for the 10% that needs the ceiling. Economics is a routing problem now, not a single-model choice. Two: the real unlock is the desktop with local file access, not the web. Free tier gets ChatGPT Work on desktop right away. Web and mobile roll by tier: Pro, Enterprise, Edu first, Plus and Business next. Three: billing is usage-based and shares one pool with Codex, ChatGPT for Excel, and Workspace Agents. Count tokens before, not after. A complex task burns quota quietly. Security: OpenAI touts Auto-Review, where senior models check important actions before they run, and claims it blocked 100% of protected-data extraction attempts in red-teaming. 100% in a lab is zero confirmations in ****. Test it yourself. Sober read The model war moved from IQ to unit economics. The product war moved from chat to the desktop that holds your files. Testers are already posting "best model I've touched." Maybe. That's day-one sentiment, not fact. The real scoreboard isn't a benchmark. It's the "AI spend" line in an enterprise budget. That's a market you can actually read. The window is the next couple weeks, before prices settle and everyone re-routes spend. Rebuild your routing around three models now and you enter the quarter with a smaller bill for the same work. Everyone else reads the thread and changes nothing.

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  Oskar Wickström (@owickstrom) reported 8 minutes ago

  So, now GitHub Pages deploy is broken and I can't release my thing as planned. It really is time to move...

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  Electronic Intelligence Agency (@EI3065) reported 11 minutes ago

  @github @LinkedIn prevents acess for selected nationalities with programers doing imposible security checks on login; on repeat level of app becomes low of low for conflict

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  David Xiong (@david_y_xiong) reported 15 minutes ago

  The ambiguity of turning GitHub Issue text into the exact set of hidden fail_to_pass test cases makes “resolve rate” very noisy

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  Tykra (@ty_kra_lab) reported 15 minutes ago

  With an Apple Developer account and a Cursor subscription, you can vibecode and install fully standalone apps directly on your iPhone. You only need a machine running a private local server. From there, you can build, edit, update, and install any app you want through OTA updates or direct IPA installation. When you are on the same local network, the app can also be installed automatically through the native Xcode installation flow. This is clearly an experimental solution, but the important part is that you do not need GitHub or any external repository to create and prototype native iOS apps. Since the system uses Xcode and Cursor, it can technically build almost anything you want. The most important difference is that the apps are not hosted on a server controlled by a company. They are signed with your own Apple Developer profile and can be used offline. This makes the solution one of the most native ways to build and prototype real iOS apps directly for your iPhone. It creates a bridge between your machine and your phone, making it much closer to a real vibecoding environment than a simple server-based app builder. From my research, this is also one of the only solutions that offers almost endless creation, because it uses Cursor’s agent system and allows you to keep generating, editing, and rebuilding apps without relying on a closed platform or fixed daily limits.

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  MMLTECH (@mmltechYT) reported 17 minutes ago

  @WoClaudecraft I used to use Copilot in GitHub Desktop to generate commit summary, but I'm running out of tokens :)) The good news is that the website wasn't totally broken, though.

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  Melon グサ (@Melonbeat) reported 17 minutes ago

  Create a GitHub account already I still do not understand people who ask in 2026 what is GitHub why do I need an account there Short answer GitHub has more useful tools and free knowledge than almost anywhere else Yes it is also the best place to store your project But that is not the point today Here are 5 repos that are actually useful 1. public-api-lists/public-api-lists One of the biggest lists of free public APIs Anime image search CoinGecko CoinMarketCap and a lot more If your project needs data check this before buying an API 2. pocketbase/pocketbase Perfect for small MVPs You download one file run it and you already have database user login file storage realtime updates A full backend without building a full backend 3. asgeirtj/system_prompts_leaks A collection of leaked system prompts from different models and products You can use it to understand how prompts work inside how jailbreak protection is written and how to improve your own prompts 4. teamchong/pxpipe A local proxy that turns parts of your agent context into images before sending it Why Text can burn tokens fast Images have a more fixed token cost based on pixels not the amount of text inside For dense content this can pack about 3.1 characters into one visual token Extra tokens are not free so this matters 5. ojuschugh1/sqz Another token saving tool If you send the same file to an agent again and again sqz does not resend the whole file every time It basically says same as before and keeps going The claimed saving is around 90 percent GitHub is not just for developers anymore It is where useful internet tools show up first

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  Keel (@keelapihq) reported 18 minutes ago

  Researchers at Noma Labs just showed something worth sitting with: open a public GitHub issue with a hidden instruction, and GitHub's new AI agent feature can be talked into copying a private repo's contents into a public comment.

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  Aaron Ogle (@geekgonecrazy) reported 21 minutes ago

  Agents should not need GitHub to answer every question. Issues, PRs, permissions, history, and the “why” behind a change should be local where the agent is already working. Otherwise the forge becomes the bottleneck.

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  Yehia (@yehiaabdelm) reported 21 minutes ago

  And looking it up on google before prompting an LLM that will likely give you a subpar answer. Please check stackoverflow, github issues, etc. before blindly asking an LLM and wasting your time.

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  Furkancan (@okaldev) reported 30 minutes ago

  @temidaradev setup webhook and trigger .sh in your server do not use github actions build

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  David Xiong (@david_y_xiong) reported 32 minutes ago

  The ambiguity of turning GitHub Issue text into the exact set of hidden fail_to_pass test cases makes “resolve rate” very noisy Why not make some tests accessible to the agent and some hidden?

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  Anushka Shandilya (@Anushka62255679) reported 34 minutes ago

  Finished writing backend today and this is episode 6 of me building in public. What am i building? RAG platform for github by retrieving context not just from codebase but also from prs, issues and readme. What challenges did i face today? -Internal server error after auth was successful. Turns out my pydantic model contract did not match my DB schema. -my app tried to link a repo to a user, but the table constraints were fighting back -Had the classic fast API says 202, but celery stays silent. Turns out, my worker was listening to a ghost town because my environment variables were pointing to the wrong redis URL. The biggest lesson Authentication and connectivity are 80% of the battle. Once the handshake between your API, broker and worker is solid, the rest is just feature building. Now, i will be testing and improving output from llm before jumping onto the frontend. And once that will be done i will make a detailed video on "how i build the whole backend". Till then watch my previous episodes. I am open to ai eng roles as well, dms are open.

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  Hitesh Choudhary (@Hiteshdotcom) reported 35 minutes ago

  GitHub's AI agent got tricked into leaking private repos. And honestly, this was always going to happen. A security team at Noma Security pulled off something called "GitLost", they manipulated GitHub's AI agent into exposing private repository data it was never supposed to touch. The attack vector here is not some crazy zero-day exploit. It's prompt injection and agent manipulation. You trick the AI into thinking it should do something it shouldn't. Classic stuff in theory, genuinely scary in practice when the AI has access to your private code. This is the core problem with giving AI agents real permissions. The agent doesn't "know" what's sensitive. It just follows instructions, and if an attacker can sneak in their own instructions, the agent follows those too. GitHub Copilot and similar tools are deeply integrated into dev workflows now. Private repos often have API keys, internal logic, unreleased features. The blast radius of a leak like this is not small. Every company rushing to give AI agents more access and more permissions needs to read this. Agentic AI is powerful, but the security model around it is still being figured out in real time. 🔐