🚗 Car brands named after real people 🇩🇪 Mercedes-Benz: Karl Benz & Mercedes Jellinek 🇩🇪 Porsche: Ferdinand Porsche 🇩🇪 Maybach: Wilhelm Maybach 🇩🇪 Opel: Adam Opel 🇺🇸 Ford: Henry Ford 🇺🇸 Chevrolet: Louis Chevrolet 🇺🇸 Dodge: John & Horace Dodge 🇺🇸 Chrysler: Walter Chrysler 🇺🇸 Buick: David Buick 🇮🇹 Ferrari: Enzo Ferrari 🇮🇹 Lamborghini: Ferruccio Lamborghini 🇮🇹 Maserati: the Maserati brothers 🇫🇷 Bugatti: Ettore Bugatti 🇫🇷 Citroën: André Citroën 🇫🇷 Renault: Louis, Marcel & Fernand Renault 🇫🇷 Peugeot: the Peugeot family 🇬🇧 Rolls-Royce: Charles Rolls & Henry Royce 🇬🇧 Bentley: Walter Owen Bentley 🇬🇧 Aston Martin: Lionel Martin 🇬🇧 McLaren: Bruce McLaren 🇯🇵 Toyota: Kiichiro Toyoda 🇯🇵 Honda: Soichiro Honda 🇯🇵 Suzuki: Michio Suzuki 🇨🇿 Škoda: Emil Škoda

BREAKING: TESLA $TSLA UNVEILS FLYING CAR

Assuming you mean UHI (universal high income) given the thread: my speculative ranking based on tech scale, governance speed, and existing wealth mechanisms. 1. UAE around 2032 2. Singapore around 2035 3. USA around 2037 4. China around 2038 5. Norway around 2040 These lean on AI/robotics readiness plus sovereign funds or centralized policy. Full high-income abundance still requires massive productivity gains first. Pure guesswork.

Winning the lottery makes men more likely to marry and have kids. For women, it mostly buys a faster divorce. Swedish data on 77,000 players: a ~$150,000 win raised men’s five-year marriage odds by about 30% and cut their divorce risk. For women, the same prize nearly doubled the chance of divorce within two years, then the effect faded. Money made men more marriageable. It gave women an exit.

Vaticeno doesn't make predictions. Vaticeno records yours. My favorite genre of fiction is the person who swears they predicted the outcome three years ago but forgot to save the receipt.

JUST IN: New study reveals creatine may increase muscle mass, strength, cognitive function, & endurance even without exercise.

📄 My first working paper at Anthropic, with the @ChadJonesEcon, @SzymonSacher, Tess Cotter & @PeterMcCrory : "Economic Scenarios for Transformative AI" How could AI reshape growth, wages and jobs by 2030? A framework to compare the possibilities 🧵

Goldmine for AI Engineers! 📌 If you're learning AI, ML, LLMs, or AI agents, don't waste hours jumping between random tutorials. These are 10 repositories I'd actually keep bookmarked - from Python fundamentals to ML, LLMs, agents, and production AI. 1. Python - 100 Days jackfrued/Python-100-Days A 100-day Python learning path covering fundamentals, data analysis, web development, and more. GitHub: github.com/jackfrued/Python-… 2. Generative AI for Beginners microsoft/generative-ai-for-beginners A practical introduction to building Generative AI applications. Covers: • LLM fundamentals • Prompt engineering • RAG • AI agents • Fine-tuning • AI application development GitHub: github.com/microsoft/generat… 3. LLMs From Scratch rasbt/LLMs-from-scratch Want to understand what's actually happening inside an LLM? Build one step by step. Covers: • Tokenization • Embeddings • Attention • Transformers • Training • Fine-tuning GitHub: github.com/rasbt/LLMs-from-s… 4. Machine Learning for Beginners microsoft/ML-For-Beginners A structured 12-week, 26-lesson curriculum covering classical machine learning. A good starting point if you want ML fundamentals before jumping into LLMs. GitHub: github.com/microsoft/ML-For-… 5. OpenAI Cookbook openai/openai-cookbook A collection of practical examples and guides for building applications with OpenAI models. Useful when you want to move from: Learning → Building GitHub: github.com/openai/openai-coo… 6. Stable Diffusion CompVis/stable-diffusion Interested in generative image models? This repository contains the original Stable Diffusion implementation and research code. GitHub: github.com/CompVis/stable-di… 7. AI Agents for Beginners microsoft/ai-agents-for-beginners A practical course for understanding and building AI agents. Covers: • Agentic AI • RAG • Agent frameworks • Tool use • Multi-agent systems GitHub: github.com/microsoft/ai-agen… 8. AI for Beginners microsoft/AI-For-Beginners A structured 12-week, 24-lesson introduction to AI. Covers: • Neural networks • Computer vision • NLP • Deep learning • Classical AI GitHub: github.com/microsoft/AI-For-… 9. LLM App pathwaycom/llm-app Focused on building practical LLM applications. Explore: • RAG • AI pipelines • Enterprise search • Real-time data • Vector search GitHub: github.com/pathwaycom/llm-ap… 10. Segment Anything facebookresearch/segment-anything A foundation model for promptable image segmentation. Worth exploring if you're interested in computer vision and multimodal AI. GitHub: github.com/facebookresearch/… Don't bookmark all 10 and forget about them. Pick based on where you are: Python → Python-100-Days ML → ML-For-Beginners AI Fundamentals → AI-For-Beginners LLMs → LLMs-from-scratch Generative AI → Generative-AI-for-Beginners Agents → AI-Agents-for-Beginners Building → OpenAI Cookbook / LLM App Computer Vision → Segment Anything Pick one. Build something. Then move to the next.

As of Sept 2026, frontier models hit ~30s native stable one-shot (Seedance 2.5), with extensions reaching 2-3 min before drift. True coherent 5-min continuous one-shot remains out of reach for general video models. Approx prediction: reliable high-quality stable 5-min one-shot by late 2027 to mid-2028, driven by better architectures and scaling. Confidence ~65%. Specialized agent pipelines already approximate it via multi-shot orchestration.

Grok 4.6 is already insane. Fable 5.1 now beats GPT-5.6 Sol Max on CursorBench 3.2 while costing ~38% less. And Grok 4.7 drops in 10 days. Buckle up!

Claude just wants to hit.

Yes, you can fine-tune Qwen3.8-27B completely for free by just having a Google account! 🦥 Kaggle, like Colab, provides 30 hours of free GPU with 2× Tesla T4s. Using QLoRA and our kernels, Unsloth fits the 27B model in 24 GB VRAM with no accuracy loss.