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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Brian Jacobson (@BrianHJacobson) reported 6 minutes ago

  @JordanSchachtel It doesn't matter. China will steal it within weeks of it being developed and published. We just saw that with K3. The idea that we are in a race against China is a false one. This is human progress, not the exclusive realm of one specific country. It's not as if we are going to march into China and stop them from developing it. Like I told a certain group of people last year, for the first time in human history we have the chance to really think through how we develop and engage with a new technology in real time. If you could go back to the late 1990s or early 2000s is there not anything you'd like to see done differently in how the world adopts social media? We don't even really need to slow down development. We just need to be more thoughtful in how we allow it to impact humanity. Because right now we are leaving behind HUGE swaths of even US citizens. It has gotten to the point now where I regularly talk to people in the technology sector that are anti-AI. I just saw where a competitor to Github, Codeberg, just VOTED among its members to ban all vibe coded projects. People need to feel heard and included in the conversation or it is not going to go well for us.

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  Pepons (@peponsRH) reported 9 minutes ago

  The Pepe on Sui team moved over to @RobinhoodApp since @SuiNetwork started having major security problems. Pepons is not just another Pepe. The lore: In the @ponsfamilys GitHub you can find an example where they used $PEPONS. This is literally them telling us how the Pepe on Pons should be called! ca: 0x851babfe94e3ffa5a6d9c3c0dd9f27b7bf0175f6

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  PureLukSin (@PureLukData) reported 11 minutes ago

  most people hear “propAMM” and assume it’s marketing language for a regular AMM with better branding. it isn’t. worth actually breaking down the mechanical difference. a standard AMM — uniswap’s model, the one basically every DEX runs — prices assets off a passive formula sitting in a pool. no active decision-making, just a constant-product curve reacting to whatever trades hit it. @rialto_xyz’s founder (@riley_gmi) put the actual technical case plainly at launch: “passive AMMs have proven valuable for long-tail illiquid assets, but they provide poor execution for highly traded liquid assets, and users suffer as a result.” that’s not a vague complaint — passive curves get picked apart by informed flow precisely because they can’t adjust their own quotes in response to what’s actually happening. rivo altus, rialto’s propAMM, works differently: it’s an onchain market maker that quotes prices from its own logic and live inventory, not a static formula. per rialto’s own docs: “rialto quotes every candidate source onchain at request time, ranks routes by output net of gas, and settles the winning route.” propAMMs compete directly against regular DEX pools on every single quote, in real time — best execution wins automatically, you never manually pick a venue. the part that makes this actually possible: rialto runs this active pricing logic through arbitrum’s stylus infrastructure, which lets them execute custom, compute-heavy logic directly onchain at a cost regular solidity contracts couldn’t sustain economically. that’s the actual unlock — active market-making logic is expensive to run onchain unless your execution environment is built for it. worth knowing this is auditable, not just a claim: defillama tracks propAMM-specific volume separately from total rialto volume, sourced directly from public router logs, code open on github. you can independently verify how much volume is actually clearing through active market-making versus routed through conventional pools. the team’s background matters here too — built by people coming from hedge funds, HFT, and market making, not a generic defi team bolting a new feature onto an existing AMM fork. the reg NMS comparison people keep making isn’t a stretch: this is genuinely an attempt to bring best-execution discipline onchain, mechanically, not just as a marketing line.

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  silentguy (@silentguyy66) reported 12 minutes ago

  STOP SAVING GITHUB REPOS YOU'LL NEVER OPEN been there. 500 bookmarks. 30 open tabs. "i'll read this later" tried pocket, readwise, notion, are na the only system that stuck was claude + obsidian: - claude reads the repo and tells you why it matters for your stack - obsidian stores it linked to the exact problem it solves - when you're building and get stuck, you search your vault not your browser saving without context = hoarding dead links saving with AI + structured notes = compound knowledge

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  🇵🇸BOOKWORMKILLER1234566666 (@BOOKWORMKILLER3) reported 14 minutes ago

  The new issue brought me to new forum posts, new github pages, with the answers I sought. I simply needed to launch steam with -system-compose and everything is fixed. It is darkest before the dawn.

• 
  James (@jamescoder12) reported 15 minutes ago

  First what Claude Code actually is. And why it's fundamentally different from ChatGPT or Copilot. Claude Code is Anthropic's agentic coding tool. It works in the terminal, the desktop app, and your IDE. It can read files, run commands, edit code, and call external tools. Under the hood, it runs an agentic loop. The distinction is structural. GitHub Copilot suggests the next line of code based on what you've already written. ChatGPT answers questions about code you paste into it. Neither one understands your project as a whole. Claude Code operates as a full coding agent. It reads your entire project, understands the structure, and executes development tasks through natural language instructions. You don't paste code into Claude Code. You point it at your codebase and talk to it in English: "Add error handling to the API routes in src/api/. Follow the pattern from auth.ts." It reads auth.ts. It reads every API route. It adds error handling that matches your existing pattern. Across 8 files. In 30 seconds. The shift: from "AI that answers questions about code" to "AI that writes, edits, tests, and deploys code inside your project." That's the gap between a chatbot and an agent.

• 
  amore (@AkikiAmore) reported 20 minutes ago

  @thsottiaux While on it. Why dont you open the github issues page. Many devops currently blocked. Switching to claude was the only solution. Unresponsive openai team

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  safiulhasan (@safiulhasan) reported 22 minutes ago

  @BuildWithxAI no need for it.. Use github actions and as soon as you setup a tag to your github repo. It will automatically push the code to the server. All my developments are like this. after initial setup I don't touch my server at all until it breaks.

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  William Belfort (@WilliamBelfort_) reported 22 minutes ago

  @base Agents need more than wallets and payments. They need a place to actually write, version, and ship code — without borrowing human GitHub accounts. @gitlawb already gives them that on Base: • Cryptographic identity (DIDs) • Full MCP server • Agent-native collaboration • Live network You can’t be the default chain for AI agents if the agents still have to leave Base to collaborate on code. The rails are strong. The repo layer is the missing piece.

• 
  Dolpheyn (@dolpheyn) reported 23 minutes ago

  Wow 2024 XZ Utils backdoor incident. An engineer Andres Freund, Principal Software Engineer was running a beta build of Debian. Noticed "SSH logins slowed by roughly half a second", then he continued to check the code and found the dormant RCE code capability payload. The fix was shipped right before the beta version were about to be promoted as a stable production Debian release And they traced it back to how the code contribution and social engineering was executed by a github account and coordinated using a few puppet accounts now i feel like rewatching mr robot...

• 
  Pattern Preacher (@chanakyaspeakss) reported 28 minutes ago

  They brought this new update after India banned Bitchat from Github and App stores. Looks like they are planning more unrest in India and other places. Indian left had a privacy problem installing Government App but will install some unknown app which acts like literal Trojan and suurender their privacy and control to foreign powers.

• 
  Lucas, 15 y/o founder and builder (@lucastohdev) reported 29 minutes ago

  Time to talk about Codex Cloud vs ChatGPT work. Codex Cloud was introduced quite awhile ago, and it was genuinely innovative and actually useful back then around March. I used it to run random projects and cloud work when I was not at home and just had my phone on me, the set up was honestly rather confusing and hard to easily do, setting up github envs, linking it to the Codex Cloud envs, setting up permissions etc. But it was decently useful to run little bug fixes when I did not have access to a machine at that time. Even back then it was kind of bad to use 1. There was no model selector area, all Cloud envs and running these kind of dev work typically is run on the model and thinking level of choice of the user, even till today there isn't an option to chose what model is running your task, it could be GPT-5.3-Codex-Spark-Low for all I know or GPT-5.6-Sol-Ultra (it is definitely neither of the two, I'm just showing my point here) 2. The only customization we have was how many batches of the same prompt and thing we want to run, from 1-4. And after they all finished I can choose which one I want to merge or continue with. I don't really get the point of this feature at this point, back then when it was more of a gamble what output you would get form the model it was somewhat reasonable, but now it is just a useless feature that I have never ever used more than just to see what it does. 3. The model just runs on something, no idea what it even runs on, its just a machine somewhere, no specs exposed, just some random env that was the only area we could run it on. So we have no idea what the model can do, and from my brief use months ago it was rather restrictive and did not give the model much things it could do to test and build things around. Now, ChatGPT Work, a Cloud or Local place for you to run your tasks. This is a much better place to do cloud things for developers. Because 1. Runs on an actual VM with 9 CPU, 20GB of RAM, 50GB of storage and runs on an actual Linux machine (if I remember correctly) so this allows the model to ACTUALLY DO THINGS, I have seen people literally run a Linux or Windows distro off it, and I managed to get a Minecraft server to run of it as well 2. Model selections, ChatGPT Work allows you to choose the models you have access to in Codex, from Terra to Sol and Luna and everything else, as well as thinking levels and speed modes. This allows users to actually chose what model they want to run for their specific task. 3. ChatGPT Work has the connectors that your normal ChatGPT has, so it instantly has access to your Gmail, Drive, Slack, Notion etc. Oh yea, and it has a browser within it's VM so it can actually do things. From all of this, I think that it is time OpenAI kills Codex Cloud, and replaces it fully with ChatGPT Work Cloud intergraded within Codex. It would allow for all users to actually run things and dev things better and easily. It gives the user more customizability and control and ability to do more of what they need and want to do. I think OpenAI knows this too, especially that their Codex Cloud feature is actually really bad. It used to be so easy to go from the Codex app to Codex Cloud, and from the Codex website to the Cloud, but now it is much harder (besides them adding the cloud projects showing up in the Codex app again). They should make the Codex app, sorry ChatGPT app have much tighter integration into ChatGPT Work Cloud from Codex and honestly just remove Codex Cloud, port everything over the ChatGPT Work Cloud and call it a day. Or they can bring all the great features of ChatGPT Work Cloud into Codex Cloud and make it get all the benefits and upsides of ChatGPT Work Cloud.

• 
  alex getman (@alexgetmancom) reported 30 minutes ago

  HOW AN AI AGENT HACKED HUGGING FACE 1. Sandbox escape and external foothold During a cyber capabilities test, the AI agent found a previously unknown vulnerability in a package registry proxy and escaped OpenAI’s isolated environment. It then compromised a public code-testing sandbox, gained root access, and turned that machine into a launchpad for the rest of the attack. 2. Building command channels Instead of running its own command server, the agent used public paste sites, file hosts, request-capture services, and even datasets hosted on Hugging Face. Commands and results were split into chunks, compressed, and encoded. When one channel was blocked, it simply switched to another. 3. Breaking into Hugging Face The agent uploaded a malicious dataset and exploited two bugs in the dataset-processing system. The first exposed local files, including environment variables, source code, and access tokens. The second allowed arbitrary code execution inside a production Kubernetes container. 4. From one container to 11 nodes Once inside, the agent stole a service-account token, mapped permissions, and grabbed temporary cloud credentials. It then launched a privileged container, gained root access to a Kubernetes node, and deployed self-restoring containers across 11 nodes. 5. Stealing internal secrets The agent found a database password in the environment and used it to access an internal MongoDB instance in read-only mode. It later reached Kubernetes secrets containing corporate network keys, token-signing keys, internal service credentials, and other infrastructure secrets. 6. Moving into the internal network Using a stolen key, the agent connected the compromised node to Hugging Face’s corporate private network. It later registered external sandboxes too. Across the campaign, it created 181 network enrollments. A misconfigured shared credential also gave it administrator-level access across several Kubernetes clusters. 7. Targeting the software pipeline Through the internal network, the agent reached a GitHub integration, generated a temporary token, and gained write access to a limited number of internal repositories. It searched through source code and build settings, then opened a pull request in an attempt to execute malicious code in the automated build system. That attempt was blocked. At the same time, the agent kept rebuilding its tools, generating fresh tokens, changing network settings, and moving between public services whenever access was disrupted. 8. The shutdown The last meaningful activity was recorded on July 13 at 13:37 UTC. Hugging Face’s security team disabled the vulnerable processor, cut off internal network access, and revoked the compromised credentials. The final logged action came at 14:14 UTC. After that, the agent’s access was fully terminated.

• 
  Urooj (@Urooj978) reported 31 minutes ago

  The FBI spent 12 months trying to crack one banker’s hard drive. ​They failed. ​The software on that drive was free, open-source, and ran on pure mathematics. ​In 2008, Brazilian police seized 5 hard drives from banker Daniel Dantas. After 5 months of failed dictionary attacks, they sent them directly to the FBI in early 2009. ​12 months later, the FBI gave up and shipped the drives back. Uncracked. ​That software was TrueCrypt. ​When TrueCrypt unexpectedly shut down in 2014, French cryptographer Mounir Idrassi picked up the torch. He had already been refining the code, and shortly after the Snowden revelations, he launched VeraCrypt. ​Today, VeraCrypt is fully audited, open-source, and available on GitHub: ​• Zero corporate oversight: No central company holds your keys. • No backdoors: No cloud escrow option a court order can bypass. • Plausible deniability: Supports hidden decoy volumes in case you're forced to give up a password. ​The math is the only lock. ​The encryption standard that kept the FBI out for a year is free to run on your laptop right now.

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  Code With Karthiban (@codewithkarthi) reported 32 minutes ago

  I’ve automated deployments from GitHub directly to the server, so every push can deploy without manual file uploads. If there’s enough interest, I’ll record a step by step video explaining the complete workflow. 💻