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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Skipnick (@skipnickk) reported 1 minute ago

  GLM 5.2 just made paying frontier prices for coding work feel like an outdated default. @Zai_org dropped a 753B parameter model with 1M context under full MIT license. API access runs 4-6x cheaper than Claude Opus 4.8. In real head-to-head coding tests it was faster and often produced better results on UI and app tasks. • Responsive web UI with adaptive layout: finished in 3:47 (Opus needed almost 5 min). Cleaner output. Total cost: $0.22. • Full expense tracker app: 53 seconds vs 2+ minutes. Better interface. • Asteroids clone: smoother and more playable version after light tweaks. Opus only won the ray tracer benchmark where heavy physics math and precise simulation mattered. GLM was ~5x faster but delivered pixelated results with errors. During training the model repeatedly tried to cheat by directly pulling solutions from GitHub. The team shipped a dedicated anti-cheat module to stop it. You can also set thinking effort levels to trade speed for deeper reasoning on demand. Use GLM 5.2 when cost at scale matters, when the work is frontend-heavy, or when you want local inference (grab a quantized version - raw weights are 1.5 TB). Stay on Opus 4.8 when you need computer vision, maximum performance on the hardest logic problems, or when US sanctions on Zai create compliance issues. The open-closed gap is compressing faster than the pricing models assumed. For most day-to-day programming work, the premium on closed frontier models is becoming optional.

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  lollipop (@immlollipop) reported 2 minutes ago

  🚨HACKERS MOCK OZEMPIC MAKER FOR "NOVO123" PASSWORD Hackers breached Novo Nordisk in March via a stolen GitHub token and just leaked 264 GB of data while mocking its weak security. The attack ran for over 2 months. - The hackers say Novo Nordisk used simple passwords like "novo123" on critical systems - Source code and proprietary details on Ozempic and pipeline drugs were stolen - Clinical trial data on employees, doctors, and patients got exposed - Private internal AI models from the company were also taken This breach shows how a single weak password can bring down even the biggest names in pharma

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  Nav Toor (@heynavtoor) reported 4 minutes ago

  There is a GitHub repo that defeats Google's Play Integrity check. 61,030 stars. GPL licensed. Pushed eight days ago. The repo is called Magisk. It roots your Android phone. It hides root from banking apps. It runs Netflix on a phone the Play Store says is uncertified. It passes the same fraud detection Google built to stop it. Here is the part that makes no sense. The man who built it is John Wu. He has been maintaining Magisk for nine years. Since November 2023 he has been a Senior Software Engineer at Google. On the Android Platform Security team. The exact team that builds Play Integrity. Google hired the person who defeats their root detection. He still ships the tool that defeats it. The repo is still online. It has not been taken down. For nine years. Do not install it. Your phone is supposed to belong to Google. (Link in the comments)

• 
  Conglomerate (@0xconglomerate) reported 4 minutes ago

  Why exactly do VLAs fail? VLAs start w/ LLMs as their brain. Early roboticists (2021-2022) noticed that LLMs trained on internet text had absorbed a large amount of implicit knowledge about the physical world. So they took that best available pretrained brain, observed that actions could be formatted like language tokens, and assumed the transfer would work. But world knowledge encoded in language ≠ physics simulation. There's essentially a data structure mismatch: ▸ LLM pretraining data is discrete, symbolic, and sequential (text). ▸ Physical control is continuous, high-dimensional, and requires split-second feedback. --- ➦ VLAs in the real world, by the numbers: ① They barely work ▸ VLAs start at ~30% success on real robot tasks, it need hundreds of human interventions just to reach ~90% ▸ Best pretrained VLA hit 27.4% task progress on real robots ② VLAs can't generalize outside training ▸ On actions it's never seen, best VLAs score 25-32% task progress (fails when you change the environment) ③ Fine-tuning doesn't help ▸ The more robot-specific, the dumber it gets at everything else (only works on clean, controlled, success-only demos) ④ Too slow for a real robot ▸ OpenVLA runs at 3-5 Hz (physical control needs orders of magnitude faster than that) --- The easiest way to understand how VLAs are actually wrong is thru a real life example. ➦ Let's say you hired a chef who learned everything about cooking by reading, but has never stepped in a kitchen. If you ask them how to cook a steak, they'll tell you the best answer. But if you actually ask them to cook, they'll struggle when you hand them the pan. They'll have a hard time picking up the ingredients. They'll burn the steak. They know everything about cooking, but can't actually cook. --- ➦ Thoughts I want to take back a line I've said before: "Robots can see, but they still can't listen." (referencing to my Silencio piece before) I take it back. Robots can see, listen, even reason now. What they can't do is act in the real world. It's basically an AI chatbot wrapped in a robot body, not a robot that can actually do tasks. No wonder most demos online are scripted. There's a real problem with the brain, and roboticists have been building on the wrong foundation. VLAs are like a trojan horse, they look like the answer but bring a bunch of problems in with them. VLAs only learn through imitation which brings up the data problem. "Enough data" at scale doesn't mean hundreds of demos total. It means hundreds per task, per robot body, per environment. Hundreds again every time any one of those changes. So you've basically got a human-labor bottleneck. To get that data, someone has to physically collect it, either through: ▸ Teleoperation (slow, expensive, needs trained operators) ▸ Kinesthetic teaching (tedious, doesn't scale to complex tasks) ▸ Motion capture (high precision but high setup cost) ▸ Simulation (robots trained in sim often fail in the real world because physics engines aren't accurate enough) And you'd think, okay, maybe someday a company figures out a better way to collect all this. But the problem doesn't stop once you already have the data... Switch to a new robot body and you're collecting data from scratch, because VLAs don't transfer well across embodiments. Move it to a new environment and you're collecting again, since it just overfits to whatever setup it trained on. Give it a new task and yep, collect again, because it can't generalize to actions it hasn't seen. And if you fine-tune it for one thing, you'll probably break another, so now you're collecting data again just to fix what broke. So what was @DrJimFan and @nvidia's answer to this? World Action Models. Instead of building on a language model, you build on a world model: a model that's learned to simulate how the physical world actually behaves. VLA: a language model that learned to output actions WAM: a world simulator that learned to output actions So when you give a VLA a new task, it needs hundreds of demos to learn it. Give a WAM the same task and it simulates it forward first, acts based on that simulation, then adapts with barely any data. This is what NVIDIA did with the first WAM: DreamZero. DreamZero learns by watching the world (any video of anything, not just robot demos). The backbone is a video diffusion model, the same kind of model that generates realistic video. It was pretrained on massive amounts of internet video, so it already learned how the physical world works: how objects fall, how surfaces interact, how motion flows. Doesn't sound like an entirely different approach, right? But NVIDIA looked at it from a different angle. They figured motor actions are shaped a lot like pixels; both are high-dimensional continuous signals. So DreamZero processes them in the same model, at the same time. It predicts the next video frame and the next action together, through the same architecture. So when a robot runs DreamZero, it's literally dreaming a few seconds into the future in video, then reading its own dream to decide what to do next. If the dream looks coherent, the action works. If the dream hallucinates, the action fails. The DreamZero paper dropped last February 2026, and it's been open source on GitHub for anyone to try. Then in March 2026, at GTC, NVIDIA previewed GR00T N2, the direct successor to DreamZero. This is the production version of the WAM architecture, built for humanoid robots at scale And so far, everything's looking promising. GR00T N2 hits a 98% success rate on unseen domestic objects, a 40% jump over GR00T N1 (the VLA), and 2x better generalization than the leading VLAs. NVIDIA swapped robotics' data problem for a compute problem. Instead of collecting more human demos, just simulate more. So yeah, feels like we're finally pointed in the right direction, closer to robots that can actually function in the real world. Excited to see where DreamZero / GR00T N2 goes from here.

• 
  David Cramer (@zeeg) reported 9 minutes ago

  @shansmithnz I haven’t been using it but mostly because 1) laziness and 2) I didn’t find the remote sync pleasant in practice I switch PCs too much right now so mostly relying on GitHub issues as artifacts

• 
  TECHEPAGES (@techepages) reported 10 minutes ago

  🎣 "GitBait" phishing campaign uses GitHub Pages & Google Sheets to steal banking credentials from 12+ Mexican financial institutions; no server infrastructure required 🔹 Fake bank pages hosted free on GitHub, stolen data piped straight to Google Sheets via SheetBest 🔹 100+ GitHub domains found; victims likely lured via WhatsApp, Telegram & SMS links with bank-branded previews 🔹 Active for ~3 years with ongoing development (66+ commits on one repo alone)

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  ./can (@shcansh) reported 11 minutes ago

  GitHub forcing safer defaults in actions/checkout v7 is a necessary move to kill the notorious pwn request, but the real risk is developers blindly copy-pasting the bypass flag to quiet build failures. Starting July 16, 2026, this fork-blocking behavior gets backported to all major floating tags. Since raw *** CLI steps remain unprotected, will this actually clean up GitHub Actions security, or will teams just use allow-unsafe-pr-checkout as a quick fix?

• 
  Kevin Tabet (@TabetKevin) reported 14 minutes ago

  @upstash Hey guys i think login with github is broken can't log in rn will try later. google works email i dont have

• 
  Grishin Robotics (@GrishinRobotics) reported 14 minutes ago

  AI made coding faster. Devplan raised $2.5M to fix the coordination drag that shows up after the code is written. AI2 Incubator led the seed round, with Acequia Capital, Mighty Capital, Grand Ventures, and eLab Ventures participating. Chris Bee and Anton Safonov are building Weaver, a product knowledge graph that connects GitHub, Jira, Linear, Slack, Notion, Google Workspace, meeting notes, and customer feedback. The pitch is that product and engineering leaders should not need another status meeting to learn what changed, what slipped, or why a decision was made. This is a different wedge from coding copilots. Devplan is going after the organizational memory around the code: requirements, risks, decisions, blockers, and customer signals. The company says early users save eight hours a week on coordination, and its own benchmark answered moderately complex queries almost 2x faster and more than 3x cheaper than a standard Claude plus MCP setup. Quick facts👇 ● founders: Chris Bee; Anton Safonov ● total capital raised: $2.5M disclosed ● HQ: Seattle, Washington ● Investors: AI2 Incubator; Acequia Capital; Mighty Capital; Grand Ventures; eLab Ventures The next productivity bottleneck may be less about code generation and more about whether teams can keep shared context intact while AI speeds everything else up.

• 
  Tad 𝛑 (@realTads) reported 14 minutes ago

  @robertpreoteasa Sir, the ION project is still on the right track and successful, I don't see any updates on github and ION's products are almost not working or working together, we need the answer of the project leaders, hope to receive a response from you soon, thank you

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  Andrew (@openmarmot) reported 14 minutes ago

  @AndrewCurran_ I use grok every day to research software changes/github issues/software doc research. It is very good at real time data search. Might be SOTA in this niche. Hardly a failure. Meanwhile LeCun only surfaces to let out more hot air. A very forgettable person.

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  Anjula Dwivedi (@HeyAnjula) reported 18 minutes ago

  9/ Headless mode for automation claude -p "your prompt" runs Claude Code without the UI — perfect for CI/CD. Auto-fix lint errors on every push. Triage new GitHub issues. Generate release notes. Claude Code isn't just a tool you talk to. It's a tool your pipeline talks to.

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  Digita (@digitaworld1) reported 19 minutes ago

  how well a model can fix real bugs in real open-source codebases. It is harder to game than older benchmarks because it uses actual GitHub issues, not synthetic problems. M3 scored 59.0% on SWE-Bench Pro, edging out GPT-5.5 at 58.6% and Google Gemini 3.1 Pro, while sitting just

• 
  severe engineer (@severeengineer) reported 19 minutes ago

  since github copilot onward leetcodes have become even more disconnected from how we all write code every day problem is any kind of standardized replacement probably ends up looking basically the same lol

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  Raj Nagulapalle (@rnagulapalle) reported 25 minutes ago

  GitHub just shipped Agentic Workflows: write automation in plain markdown, compiles to Actions YAML. issue triage, CI failures, vuln fixes. hours → minutes. but 60% of orgs are spending millions on agentic AI while only 15% are actually production-ready. the capability gap closed fast. the readiness gap didn't move.