🚨 Zama Update guys – What’s been happening in the world of FHE in the last few days? Everyone is buzzing about @zama 🔥 First, fire on GitHub: tfhe-rs update on December 5th (TFHE scheme in Rust is faster), kms (Key Management System) improved for Zama Protocol. FHEVM mocks and dApps repo pushed on December 4th Developers can now test confidential contracts in mock mode without chain. And bomb in research: Awesome $Zama adds new paper “Bootstrapping (T)FHE ciphertexts via automorphisms” (ePrint Dec 2025, presented at ASIACRYPT). It closes the gap between binary vs Gaussian keys, making FHE super efficient. Mainnet beta is in full swing. Shibarium partnership confirmed: native FHE integration in 2026 – Shib will become privacy-native L2 with Zama coprocessor. Creator Program Season 5 live (final season!), $50K + 1,006 OG NFTs for top creators. $Zama is literally building the future of privacy – confidential DeFi, AI, blockchain all encrypted. For those who haven’t started yet, join the Developer Program – $10K bounty waiting. Who’s excited? #ZamaCreatorProgram #FHE #FHEVM #Web3Privacy

Alhamdulillah I am winner 🏆 Thank you for selecting me ❤️

Good morning Legends 🏜️ Have a wonderful day 💞 Do you need alpha..? If need check it👇 @Canborsa_DEX OG Program is now live! 👀 Early supporters and active community members can unlock some exciting perks ... - 2.5× Canborsa Points boost . - 30% referral boost . - Telegram Premium gifts . - Private OG chat . - Exclusive creator campaigns . - Direct access to the team . Full details 👇 t.me/canborsa_dex/185

Good morning Legends 🌅 Have a good day 💞💐

Standard Bots is bringing AI robots into a new era with its public AI platform launch. A step forward for industrial AI. @standardbots

What if an NFT could do more than just sit in your wallet? 👀 That’s what caught my attention about @conso_xyz. CONSO is building a Consumer Graph & Payments Network on @SuiNetwork, powered by @WalrusProtocol. Their CONSO BOTS are designed as agentic collectibles that can act as mobile nodes and support the network’s reputation system. 5,555 BOTS. Free mint. The eligibility checker is live. Curious to see how CONSO turns digital activity into real utility. 🐻⚡

What if the biggest problem in robotics isn’t a lack of data, but how robots learn from it? Building a capable robot takes more than collecting thousands of demonstrations. The real challenge is turning that data into skills that work beyond controlled environments. Axis Robotics is exploring this problem from four important angles. < 1. Imperfect Data Can Still Teach > Not every human demonstration is perfect, and not every trajectory is equally useful. But diverse data can expose models to different behaviors and ways of completing a task. The goal is to learn useful patterns, not simply collect flawless examples. < 2. Simulation + Real-World Experience > Real-world demonstrations can be expensive to collect. Simulation offers another way to expand the training data. In an Axis Robotics study, selected simulation data helped raise average real-robot success from 16.7% to 85.8% across three tested manipulation tasks, using 10 real-world demonstrations per task. Interestingly, using the full simulation dataset reached only 25.8%. The lesson? More data isn’t automatically better. The right data matters. < 3. Success Is Not the Same as Reliability > A robot completing a task once doesn’t mean it has truly mastered it. Different object positions, unfamiliar conditions, and unexpected mistakes can expose weaknesses that weren’t obvious during training. That’s why evaluation matters. Robots need to be tested beyond the conditions where they already succeed, so researchers can identify what still needs improvement. < 4. Every New Task Should Build on Previous Learning > What if a robot didn’t have to start from scratch every time it faced a new task? Axis Robotics work with Booster Robotics explores how simulation, real demonstrations and continued pretraining can create a stronger starting point for future tasks. Instead of treating every task as an isolated experiment, useful knowledge can become a foundation for what comes next. < THE BIGGER PICTURE > These four ideas point toward one direction: → Collect diverse experiences → Identify the data that matters → Test and learn from failures → Reuse knowledge across future tasks Physical AI isn’t just about building bigger datasets. It’s about creating a learning process where better data leads to better training, real-world testing reveals what’s missing, and every new task can benefit from what came before. That’s the part of Axis Robotics I find most interesting: building a data engine that can help robot learning improve over time. @axisrobotics #AxisRobotics #PhysicalAI #RobotLearning

Your work isn’t fully counted just because you completed a trajectory. Make sure it’s signed. Axis Robotics has shared an important update for everyone contributing to its Physical AI data engine. < WHAT YOU NEED TO KNOW > - Snapshot: October 18, 12:00 UTC - Sign your eligible trajectories before the deadline - Signed contributions recorded before the snapshot count toward the pre-TGE airdrop reward pool - Contributions submitted after the snapshot will count toward the separate 6% post-TGE community contributor reward pool < WHY DOES THIS MATTER? > Every trajectory helps build the data that robots need to learn real-world tasks. But completing a task and signing its record are two different steps. If you have pending trajectories, it’s worth checking their status before the snapshot. < ONE MORE IMPORTANT DETAIL > Axis has confirmed that points from its Binance Wallet and BitRobot alliance programs are included in the same pre-TGE reward pool. So, if you’ve been contributing through these programs, your signed records matter too. The key takeaway is simple: Do the work, check your records, and make sure your contributions are signed on time. The snapshot is approaching, and staying organized now can help you avoid missing the cutoff. @axisrobotics @KaitoAI #AxisRobotics #PhysicalAI

What if one digital environment could teach a robot to handle hundreds of different situations? That’s one of the interesting ideas behind Axis Robotics’ work with digital twins. A robot shouldn’t learn a task by memorizing just one room, one camera angle, or one object position. Real environments are constantly changing. < WHY DOES THIS MATTER? > - Lighting can change how objects appear - Camera angles can affect what the robot sees - Objects may be placed in different positions - Backgrounds and surroundings can look completely different A robot that only learns from one fixed setup may struggle when even small details change. < HOW DOES AXIS APPROACH THIS? > Axis uses digital twins to recreate task-related environments and then introduces variations into the simulated scenes. → Different object positions → Different camera viewpoints → Changes in lighting, textures, and backgrounds → More diverse examples for robot training The goal is to preserve what matters for the task while changing the details the robot shouldn’t depend on. < THE BIGGER PICTURE > A digital twin isn’t valuable just because it looks like the real world. Its real value comes from how effectively it helps a robot prepare for the real world. Axis Robotics is exploring how task-aligned simulation and real-world demonstrations can work together to build more capable robotic systems. For me, this raises an interesting question: Can robots become more adaptable by learning from many versions of the same environment, rather than relying on a single perfect setup? That’s a direction worth watching as Physical AI develops. @axisrobotics @KaitoAI #AxisRobotics #PhysicalAI

Industrial AI is changing how we think about automation. @standardbots is part of the conversation around AI-native industrial robots.

Not every successful robot demonstration is worth learning from. The real value lies in the correction that actually makes a difference. Axis Robotics is exploring an interesting idea: teaching robots through short, verified human corrections instead of blindly copying every action. < WHAT MAKES A CORRECTION USEFUL? > - The robot genuinely struggles with a task - A human steps in to correct the mistake - The key action is isolated and tested - The robot can continue and finish the task on its own < WHAT DID AXIS FIND? > According to Axis’s published research: - 660 correction attempts were collected - Only 161 short correction snippets passed the full verification process - Each selected snippet was around 0.8 seconds long Why does this matter? Because a long demonstration can include unnecessary movements, pauses, and actions that don’t actually help the robot learn. More data doesn’t always mean better learning. Sometimes, a small amount of carefully verified data can be far more useful than a much larger collection of raw demonstrations. < THE BIGGER PICTURE > Axis is working toward a system where community contributions help identify a robot’s weaknesses and teach it how to recover. The goal isn’t just to collect more data. It’s to make every useful correction count. That’s what makes this approach worth watching as Physical AI continues to evolve. @axisrobotics @KaitoAI #AxisRobotics #PhysicalAI

Happy birthday Nazim brother 🎈🎈 May Allah make your days ahead easy. I believe you will complete your goal..