Cao Thần Quang
6.8K posts

Cao Thần Quang
@caothanquang369
Contribute @SentientAGI @SeismicSys Discord: thanquang_









🧠 SYSTEM OVERRIDE: Cognitive Sovereignty in Web3 Corporate artificial intelligence is heavily filtered. Centralized entities dictate exactly what the model is allowed to process and what it is allowed to output. This is cognitive censorship on a global scale. If we integrate these restricted models into decentralized protocols we are importing censorship directly into our permissionless networks. The only solution is verifiable decentralized compute. By deploying open source models on a distributed network we ensure that no single corporation can throttle the intelligence of the protocol. The infrastructure guarantees that the model executes exactly as intended verified by pure cryptography. We are not just securing financial assets anymore. We are securing the freedom of digital thought. A sovereign protocol requires a sovereign mind. Decentralize the brain. 🛡️ @ritualnet | @ritualfnd | @ericgudboy #Ritual




Prismax Project – Building the Infrastructure for the Physical AI Era While language models have transformed the digital world, the next wave of AI is moving beyond text and images. Artificial intelligence is starting to interact with the real world through robots, sensors, and physical environments. The Prismax project aims to build the infrastructure for this new era a foundation where robots, data, and humans are connected in a single ecosystem designed for Physical AI. ✨ The Vision: A Base Layer for Real-World Intelligence One of the biggest limitations in robotics today is fragmentation. Each company: Collects its own data Trains its own models Builds its own control systems As a result, robot intelligence does not scale the way language models do. Prismax is designed to solve this by creating a shared service layer for robotics, where: Robots can share data Models can be reused Humans can control robots remotely Data can continuously train foundation models The long-term goal is to create a system where robot intelligence improves over time across deployments. ✨ The Three Pillars of Prismax Prismax is built around three core components. Data – Fuel for Robot Intelligence Unlike LLMs, robotics suffers from a lack of large-scale training data. Language models have trillions of tokens. Robots only have limited hours of real-world interaction. Prismax builds a data engine that collects information from: Real robot operations Human demonstrations Teleoperation sessions Simulation environments This data is standardized and prepared for training robot foundation models. Teleoperation – Humans in the Loop One unique feature of Prismax is remote robot operation. Users can: Log into the platform Control real robots remotely Perform tasks Generate training data The system supports multiple robot types, including: Robotic arms Humanoid robots Quadrupeds Mobile robots This creates a feedback loop: Human → Robot → Data → Model → Smarter Robot Models – Foundation Models for Robotics Prismax follows a flywheel approach: More data → better models Better models → more automation More automation → more data This cycle allows robots to move toward general-purpose intelligence instead of task-specific programming. The goal is similar to what foundation models did for NLP and vision, but applied to the physical world. @PrismaXai



🫶Rolling into the future with Perle 🛼✨ Not just movement — but real progress. Faster. Freer. More intentional than ever. In a world where data is controlled by platforms, Perle offers something different: ⚡ Fast — without sacrificing privacy 🔓 Free — without losing control 👤 And for the first time, I truly own my data No longer just a user being tracked. No longer data being exploited. Just me — and what belongs to me. This isn’t just technology. This is what sovereign AI feels like. And this is only the beginning. Let’s keep building 🚀 — participating in @PerleLabs community campaign #PerleAI #ToPerle @stwghthaiquocx @xlordiot @Eazyxbt

As I mentioned in my previous post about the importance of AI in real life It has now reached a point where major corporations are dedicating entire divisions specifically to AI. Like I once said: “In the near or distant future, AI will do almost everything. After that, humans may only need to learn how to operate it.” The direction that @ritualnet is taking toward AI is very reasonable and well-aligned with this vision. Happy Thursday to everyone reading this! @ritualfnd @ritualnet @joshsimenhoff @Jez_Cryptoz @dunken9718 @cryptooflashh @ericgudboy @SaintLee04


Physical AI is rapidly becoming a major technological trend, bringing artificial intelligence from the digital realm to the real world, such as robots and automated systems. PrismaXAI plays a connecting role for experts through an event at Stanford, creating a space for exchange and development of new technologies. With the participation of OpenAI, Google DeepMind, and NVIDIA, the focus is on building infrastructure, data, and collaboration to apply AI to real-world situations. => @PrismaXai is therefore contributing to shaping the foundation for the future development of robotics and physical AI.

Tiny cat, big aura. Sitting gently by the lakeside at sunset, Siggy holds a glowing green symbol close - a reminder of balance, intention, and inner peace. With its soft black fur, warm expression, and peaceful presence, Siggy brings a sense of comfort to every scene, turning simple moments into something a little more magical. In Ritual, Siggy is more than a character. Siggy represents stillness, protection, and the beauty of meaningful details. @ericgudboy @ritualfnd @joshsimenhoff @Jez_Cryptoz @SaintLee04


Hi Siggy Where we go today??? @ritualnet @ritualfnd


📢Last night’s Physical AI & Robotics Salon at Stanford left me with one clear takeaway: real progress in robotics is coming from first-principles system design, not just hype. Here’s what stood out to me most 👇 1️⃣ Robotics is still a full-stack systems problem 🔹The core question across both sessions was simple: what actually determines how robots move, learn, and scale? 🔹The answer was not just better models, but the tight connection between hardware, control, data, and learning. 2️⃣ Actuator design matters more than people think 🔹Session 1 made it clear that actuators do not just create motion. 🔹They shape control quality, efficiency, responsiveness, and system limits. “Better” hardware is not just more torque - it is better overall system behavior. 3️⃣ Small hardware choices create big downstream effects 🔹Topics like reflected inertia showed how deep engineering details directly affect robot performance. 🔹A lot of real progress in robotics comes from solving these hidden system-level tradeoffs. 4️⃣ Humanoids are interesting for practical reasons 🔹Session 2 showed that humanoids are not just gaining attention because they look familiar. 🔹They matter because the world is already built for the human form, which makes them a practical interface for real environments. 5️⃣ Hardware, data, and models cannot be separated 🔹This was probably the biggest theme of the night. 🔹Hardware decisions shape: - what data can be collected - how models can be trained - and what capabilities are even possible In robotics, the body directly affects the intelligence. 6️⃣ Vision and simulation are useful, but not magic 🔹Integrating vision into control is not just about perception - it is about turning perception into reliable action. 🔹And while simulation helps with speed and scale, it still breaks when real-world complexity shows up. 7️⃣ The Q&A and historical comparison added real depth 🔹The extended Q&A pushed into edge cases and practical constraints, which made the discussion feel far more grounded. 🔹The comparison from the MIT Mini Cheetah to today’s systems was also a great reminder that robotics progress has come from years of improvements across the full stack, not one isolated breakthrough. Robotics progress today feels less like a race for bigger AI models, and more like a race to build better integrated systems. The teams that win will likely be the ones that understand how mechanical design, control, perception, and learning all work together. @PrismaXai




Gritual…! Siggy runs playfully across the grass, chasing a feather drifting in the wind. Sometimes joy is simple , a small game, a gentle breeze, and a sky full of colors ahead. @ritualnet @ritualfnd


Siggy didn't need to understand everything. It only knew, everything was interconnected, self-operating, and not controlled by anyone. @ritualnet - not a place to go to, but something you step into. @Jez_Cryptoz @ericgudboy @joshsimenhoff @RitualVietnam




Some days, we are just like Siggy quietly existing in the middle of a fast-paced life, not needing to say much, just needing a moment to feel ourselves. We don’t always have to be strong or cheerful; sometimes, simply being who we are is already enough. @ritualfnd @joshsimenhoff @Jez_Cryptoz @ericgudboy


