🌐 OpenAI pushes ChatGPT Atlas update amid growing AI browser risks

OpenAI has rolled out updates to ChatGPT Atlas, its AI-powered browser, after researchers highlighted persistent security risks tied to prompt-injection attacks. The company says it’s reinforcing safeguards, while admitting the problem may never be fully eliminated.

🔸 Atlas blends a web browser with an AI agent that can read pages, summarize content, and perform actions on a user’s behalf dramatically expanding what a browser can do.

🔸 Security researchers have shown that hidden instructions embedded in websites can manipulate the AI, potentially causing it to leak data or take unintended actions.

🔸 OpenAI says it now uses automated “AI attackers” to simulate prompt-injection attempts and stress-test Atlas before vulnerabilities spread in the wild.

🔸 Despite the updates, OpenAI has acknowledged that prompt injection is a structural weakness of agentic AI systems, not a bug that can simply be patched away.

🔸 Experts warn that AI browsers introduce a new attack surface, since the model effectively acts with the user’s permissions inside the web environment.

The Atlas update highlights a deeper shift: as AI agents move from passive assistants to active operators, security becomes less about fixing exploits and more about redefining trust on the web.

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🧠 Promptschat is building a library of ready-to-use AI prompts

A growing collection called Promptschat is positioning itself as a one-stop archive of prompts for virtually any neural network and use case from serious technical work to pure creativity.

🔸 The library contains hundreds of prewritten prompts, covering coding, analysis, writing, design, marketing, and experimental use cases.

🔸 Some prompts are long, structured “meta-instructions” that effectively turn an AI into a specialist coder or domain expert, not just a general assistant.

🔸 Others focus on creative tasks, storytelling, ideation, tone shifts, and unconventional experiments with generative models.

🔸 The core value is speed: instead of reinventing prompts, users can copy proven frameworks that already produce strong results.

Promptschat reflects a broader shift: as models become more powerful, prompt engineering is turning into reusable intellectual infrastructure, not just a personal skill.

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⚠️ Microsoft wants to eliminate C and C++ and replace them with Rust by 2030

Microsoft is outlining an ambitious long-term goal: phasing out all C and C++ code across its software stack and moving to Rust, a safer systems programming language designed to prevent entire classes of security bugs.

🔸 A senior Microsoft engineer said the internal objective is to remove every line of C and C++ by 2030, replacing them with Rust wherever possible including core systems software.

🔸 The motivation is security: memory-safety issues account for the majority of serious vulnerabilities in large codebases, and Rust eliminates many of these problems by design.

🔸 The plan is not a manual rewrite. Microsoft is betting on automation, tooling, and AI-assisted code translation to migrate massive legacy codebases at scale.

🔸 Microsoft has already been introducing Rust into parts of Windows, Azure, and security-critical components, making this a continuation rather than a sudden pivot.

🔸 Important nuance: this is a long-term engineering goal, not a guaranteed company-wide mandate. Some teams and legacy components may keep C/C++ where Rust isn’t practical.

This isn’t about fashion or developer preference, it’s about reducing systemic risk. If Microsoft succeeds, it could redefine how the entire software industry thinks about legacy code and security.

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🗣️ Sam Altman on competing with Google

Sam Altman argues that OpenAI’s window to challenge Google opened because the incumbent moved too slowly and now the gap is harder to close.

🔸 In 2023, Google could have easily crushed OpenAI if it had treated the startup as a real threat and fully focused on AI. That moment passed, and catching up is now much harder.

🔸 Google’s distribution advantage is enormous, but Altman believes OpenAI can still win on product quality and speed of iteration, where smaller, more focused teams move faster.

🔸 He calls Google’s ad-driven search business the best business model in tech but also a trap. It makes it extremely difficult for Google to disrupt itself with a radically new AI-first product.

🔸 OpenAI, by contrast, has little to lose. It can afford to rethink interfaces, workflows, and pricing from scratch without protecting legacy revenue.

🔸 Despite this, Altman says Google remains the most dangerous competitor. To stay ahead, OpenAI plans to build a full-stack AI platform: models + infrastructure + devices and interfaces.

This isn’t a fight about who has more data, it’s about who’s willing to cannibalize their own business first.

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⬇️ One tool to download videos from almost anywhere

A new free utility makes it possible to download videos from nearly the entire internet locally, fast, and without relying on cloud services.

🔸 Supports 1,000+ platforms, including YouTube, TikTok, Twitch, Vimeo, Kick, and more.

🔸 Lets you download only specific segments, not the full video.

🔸 Handles multiple downloads at once, no need to queue manually.

🔸 Includes quality selection, from low-res to the original source.

🔸 Can download subtitles alongside the video.

🔸 Supports auto-downloading new videos from a feed or channel.

🔸 Runs entirely locally, no accounts or cloud processing.

🔸 Has a browser extension, one click and the video is saved.

This feels like a “Swiss army knife” for video downloading: simple on the surface, but surprisingly powerful under the hood.

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🗣️ Jeffrey Hinton says the era of AI scaling is over

Jeffrey Hinton argues that traditional scaling is hitting a wall, not because of compute, but because the free internet data used to train models is largely exhausted.

🔸 According to Hinton, the most valuable remaining data now lives inside closed corporate systems, inaccessible for large-scale training.

🔸 This makes simply training bigger models on more scraped data increasingly ineffective.

🔸 His bet is on synthetic data, models generating their own training data as part of the reasoning process.

🔸 Hinton points to AlphaGo, which surpassed humans by playing against itself rather than relying on human examples.

🔸 He believes LLMs can follow the same path, using self-generated data to improve reasoning and exceed human intelligence.

The next leap in AI won’t come from more data or bigger models, but from models that learn by thinking.

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📚 Hugging Face’s free ML courses, perfect for New Year learning

Hugging Face has expanded its library of free machine learning courses, collecting 12 updated offerings in one place, a great way to dive into AI over the holidays.

🔸 Courses include:
• LLM Course (highly recommended)
• Robotics
• MCP
• Post-training techniques (small course)
• Agents
• Deep Reinforcement Learning
• Computer Vision
• Audio
• Open-Source Cookbook (not exactly a course, but very useful)
• ML for Games
• Diffusion Course
• ML for 3D

Whether you’re a beginner or looking to specialize, these hands-on, updated courses provide a structured way to learn state-of-the-art ML techniques for free.

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Stone Age

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🐬 Disney’s quiet bet on aquatic robots

Disney has been developing robotic aquatic creatures for years and what started as simple underwater tech is now starting to look like a full replacement for live-animal shows.

🔸 Disney’s work on aquatic robots began over 20 years ago, initially as basic underwater drones with motors for vertical and horizontal movement closer to radio-controlled vehicles than lifelike robots.

🔸 Around 2020, Disney shifted toward biomimetic designs, unveiling dolphin-inspired robots that used fins instead of propellers, dramatically improving fluidity and realism.

🔸 By 2025, Disney introduced near-autonomous hydrofoil-based aquatic robots equipped with GPS, ultrasonic sensors, and self-balancing systems.

🔸 These robots are currently designed for live shows inside Disney parks, where predictability, safety, and choreography matter more than full autonomy.

🔸 Disney says the designs are inspired not only by real marine animals, but also by fictional creatures from Avatar: The Way of Water, blending biology with cinematic imagination.

🔸 The implication is hard to miss: realistic robotic animals offer spectacle without ethical backlash, training costs, or regulatory pressure.

What you’re seeing isn’t just a theme-park experiment, it’s an early glimpse of a future where dolphinariums are replaced by code, sensors, and storytelling instead of live animals.

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🧠 A 33,000-skill “brain” for AI agents is emerging

A massive, structured skill database is being built for Claude Code, OpenAI Codex, and ChatGPT, allowing neural networks to be explicitly taught new capabilities instead of relying on ad-hoc prompts.

🔸 The database contains 33,000+ distinct skills spanning automation, software development, research, data workflows, and operational tasks.

🔸 Each skill comes with clear instructions, constraints, and expected behavior, making them reusable rather than one-off prompt hacks.

🔸 A search engine and filters make it possible to quickly find the right capability by domain or use case.

🔸 Skills are linked to real sources on GitHub and documentation, grounding them in practical implementations.

🔸 The system is designed to plug directly into agentic workflows for coding agents and autonomous AI systems.

This marks a shift from prompt engineering to capability engineering where AI systems are upgraded by importing skills, not rewriting prompts.

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When the robot rebellion begins, no one will know, but there will be signs

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🧠 Andrey Karpathy says he feels like a lagging programmer in the AI era

Former OpenAI and Tesla AI leader Andrey Karpathy shared a candid reflection on how AI is radically reshaping what it means to be a programmer and why even top engineers now feel behind.

🔸 Karpathy says human contribution to code is becoming sparse and fragmented, with large parts delegated to AI systems rather than written directly.

🔸 He believes he could be 10× more productive if he fully mastered the new AI tooling stack that has emerged just in the last year and calls his inability to do so a skill issue.

🔸 A new abstraction layer has appeared on top of traditional software engineering: agents and subagents, prompts, contexts, memory, modes, tools, plugins, skills, hooks, MCP, LSP, slash commands, IDE integrations, and workflows.

🔸 Developers now need a mental model of stochastic, error-prone, constantly changing systems very different from deterministic software yet deeply intertwined with classic engineering.

🔸 Karpathy compares AI to a powerful alien tool with no manual, where everyone is forced to experiment in real time while the profession undergoes a magnitude-9 earthquake.

🔸 His advice is blunt: roll up your sleeves, because the pace of change means standing still is falling behind.

Karpathy’s post reads less like a complaint and more like a warning: programming isn’t dying but the old mental models are, and even the best engineers now have to relearn the craft from scratch.

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