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For music creators of "Black Myth: Wukong," reaching global audiences does not mean changing the music to please everyone. It means offering a sincere, contemporary expression of an Eastern myth. #ChinaCool# #ChinaSeen# #BlackMythWukong#

Consistency matters most when it makes the founder smarter, not merely busier. You can publish every week, send outreach, build features, or attend events consistently. But repetition alone does not mean the business is learning or improving 🤯 💡 Choose one repeated activity and ask yourself: “What should this teach me?” Outreach may reveal which customers respond. Sales calls may show which problem matters most. Product trials may expose where customers lose interest or become confused. The useful loop is simple: act, observe, compare, adjust. Consistency creates momentum when every repetition makes your next founder move more informed, before time, money, and attention run out 💨 👉 Follow @bMightie for more founder intelligence for the solo and bootstrapped business journey from day zero to takeoff. #startupshowup# #entrepreneurship# #founderlife# #productivity#

Elon Musk is not merely building companies He is building the infrastructure humanity needs to move entire civilization forward • SpaceX — making life multiplanetary • Tesla — accelerating sustainable energy, autonomy and robotics • 𝕏 — defending free speech and protecting the global town square • SpaceXAI— pursuing truth and understanding the universe • Starbase — building the launch gateway to humanity’s multiplanetary future • Starlink — connecting humanity everywhere, from the world’s largest cities to its most remote regions • The Boring Company — building three-dimensional transportation networks for three-dimensional cities • Neuralink — restoring communication, mobility and human capability Different companies, different missions, one goal One man pushing every frontier forward Elon Musk has dedicated his entire life to expanding the boundaries of what humanity can achieve Elon is on Team Humanity

When your business is growing slowly, it is easy to think all your effort is going nowhere. You look at the handful of customers, enquiries, sales, or followers and wonder whether the business is actually working. After everything you have invested, the progress can feel disappointingly small 🤯 👉 But early progress is not measured by numbers alone. It is also measured by what the market is teaching you. Every customer conversation, objection, question, and purchase helps you understand who truly values your offer, what matters most to them, what is holding them back, and what gives them enough confidence to move forward. 💡 Instead of asking, "Why isn't this growing faster?", ask yourself: → What customer pattern am I starting to notice? → What objection keeps coming up? → What message seems to capture attention? → What can I improve before speaking to the next customer? Then use that insight to sharpen your messaging, positioning, or offer before chasing more visibility. The founders who make the most of the early stage are often the ones learning the fastest from every market interaction 💨 👉Follow @bMightie for more solo-founder and bootstrapped business strategies, pitfalls, and realities for the journey from day zero to takeoff.

AI Is Moving Beyond “Generating Videos” — Toward “Generating Worlds” Over the past two years, AI video models have advanced at an astonishing pace. From Runway and Pika to Sora and Veo, AI-generated videos have become increasingly realistic and more consistent with the physical laws of the real world. Many people believe the next objective is simply to generate videos that are longer, sharper, and more lifelike. But if we take a step back, we can see that the real transformation is not happening in video itself. It is happening in world models. What Is a World Model? In 1943, psychologist Kenneth Craik proposed an idea that would influence artificial intelligence research for decades. He argued that the human brain does not merely react to the outside world. Instead, it maintains an internal model of how the world works. Because we have this internal model, we can predict the outcome of an action before we actually take it. Before crossing a road, we estimate whether a car will pass by. Before catching a ball, we predict its trajectory. These abilities come from continuously simulating the world in our minds, rather than relying entirely on trial and error. This idea later became known by a more formal term: World Model. A world model does not describe a single image or a fixed video clip. It is an internal representation capable of continuously simulating the rules and dynamics of the real world. Why Is AI Research Turning Toward World Models? Because predicting “what comes next” is becoming increasingly central to how AI systems work. Language models predict the next token. Image models predict the next step in the denoising process. Video models predict the next frame. A world model, however, attempts to predict something broader: What should the world look like in the next moment? In 2018, David Ha and Jürgen Schmidhuber proposed in their paper World Models that an intelligent agent could first learn a model of the world, and then use that internal model to plan its actions. The Dreamer series later demonstrated that many complex tasks could be learned by training agents inside an “imagined world.” At the same time, the development of video models such as Sora and Veo led researchers to another realization: A model capable of continuously generating video has already learned, at least implicitly, many of the rules governing the real world. As a result, these two research directions have gradually begun to converge. But Video Is Not Yet a World This is where the distinction is often misunderstood. For a world model to support meaningful real-time interaction, it must solve several critical problems. Most video models today are essentially answering one question: What should the next frame look like? A true world model needs to answer much more: What happens if I take one step forward? If I walk behind a building and then return, will the building still be there? If I suddenly change the camera angle, will the entire space remain consistent? If I enter a command such as: “Summon a dragon.” Will the world respond immediately? In other words, a world model must do more than generate content. It must understand space. It must understand time. It must understand causality. And it must understand interaction. Moving from watching to participating is where the real difficulty of world models begins. World Models Are Entering the Interactive Era One of the latest attempts in this direction is Alaya World, recently open-sourced by Alaya World, or @alayastd. Instead of generating a fixed video clip, it generates a world that users can explore in real time. Users can begin with text, an image, or a video, enter the generated scene, move freely through it, and introduce new prompts at any moment during generation. The world responds immediately. According to the publicly released information, Alaya World provides: Real-time streaming generation at 720p and 24 FPS Stable continuous exploration for more than one minute The ability to switch prompts and trigger skills or events during generation Model weights and inference code released under the Apache 2.0 License Training code and datasets planned for future release What makes these capabilities important is not simply the technical specifications. It is that the generated “world” can now support continuous interaction. The official demo shows that users can genuinely control, transform, and explore the generated environment. AI Is Evolving From a Tool Into an Environment Over the past few years, most discussions around AI have focused on content generation. Generating text. Generating images. Generating videos. But world models raise a fundamentally different question: Can AI generate an environment that people can inhabit, explore, and continuously evolve? If the answer is yes, the impact will extend far beyond video generation. Game development, robotics training, embodied intelligence, digital twins, virtual production, and many other fields could be transformed by the development of world models. World models are still at a very early stage. Yet from Craik’s proposal of an internal mental model more than eighty years ago to the emergence of today’s interactive world-generation systems, a clear evolutionary path is beginning to take shape. Perhaps what AI is ultimately learning has never been limited to images, videos, or language. Perhaps it is learning the world itself. References GitHub: Technical Report:

This is the capital of robotics! 🌁 Silicon Valley is home to so many physical AI companies that you could spend a month visiting them and still not see even 10% of them (trust me, I tried). It took me 3x to create this map as it did to create any other. And the truth is, it's still incomplete. Let’s see why this is the case. So, the Bay Area is the world's leading ecosystem for robotics startups, bringing together top AI talent, top universities, experienced founders, and unmatched access to capital. The region is anchored by Stanford University and University of California, Berkeley, two of the world's top universities for AI and robotics. They produce a constant stream of researchers, founders, and breakthrough technologies. The Bay Area is also home to many of the companies shaping the future of robotics and AI, including @Figure_robot, @physical_int , and major AI labs such as @OpenAI. This concentration of talent makes it easy for startups to recruit experienced engineers and collaborate with leaders in embodied AI. Not mentioning that it  is also home to leaders such as @NVIDIARobotics , whose headquarters and leadership in AI chips power much of today's robotics revolution, and @Tesla, whose work on autonomous driving and humanoid robots has created a deep pool of robotics, AI, and manufacturing talent. Perhaps its biggest advantage is access to capital and ambition. The Bay Area has the world's deepest network of venture capital firms, serial entrepreneurs, and technical leaders who are willing to fund bold, long-term robotics companies. In the comments I'll post the companies from the ecosystem. ‼️ Note that Bay Area has >300 robotics companies, research labs, and innovation hubs, so this is a curated selection of the notable product companies, not an exhaustive census! P.S. I'm constantly working on improving these maps, so if your company is missing, please DM me with basic info about the co, and I will include it in the next release. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →