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The Houston Rockets announced earlier this week “ClutchBot,” a generative AI-powered mascot as part of a partnership with Theta Labs. The collaboration marks the NBA’s first use of artificial intelligence to improve fan interaction. The project wants to change how fans follow the team through real-time updates, statistics, and ticketing information through the Rockets’ website and app. The AI platform is built on Theta Labs’ decentralized EdgeCloud infrastructure. It takes advantage of cutting-edge AI to provide personalized, always-on support for fan inquiries. It is trained specifically on Rockets and NBA data so it can provide accurate answers for basic questions like “when is the next game?” to historical performance stats and lineup details. Just like Jalen Green’s hot hands to tie the series against the Golden State Warriors, the Rockets aren’t just launching threes, they’re launching into the future. Powered by Theta’s Layer-1 blockchain and decentralized GPU network, ClutchBot delivers fast and secure interactions backed by more than 80 PetaFLOPS of compute power. With advanced NLP models, integrated APIs, and dynamic fine-tuning, ClutchBot sets a new standard for interactive sports entertainment. Below is our Q&A with Mitch Liu, CEO of Theta Labs. crypto.news: What natural language processing capabilities allow ClutchBot to understand nuanced fan questions like historical stats or player comparisons? Mitch Liu: ClutchBot leverages state-of-the-art NLP techniques, including agentic AI workflows and LLM fine-tuning on realtime and domain-specific data. This allows it to interpret the intent behind user queries, retrieve the relevant data, and generate coherent answers accurately and quickly. CN: What challenges did you encounter adding API data with the Rockets’ proprietary database for ClutchBot’s? What other kinds of challenges did you face? ML: Integrating API data from the Rockets’ proprietary data sources posed several challenges. For example, we had to build a robust data aggregation system to unify and reconcile realtime data from different APIs, which returned data in different formats and contained inconsistent information. We also had to minimize hallucination as much as possible, particularly with important game, team and player information. We implemented advanced prompt engineering techniques along with a continuous model finetuning framework which would quickly incorporate new information as data are generated in realtime. CN: What role did Theta’s EdgeCloud play in powering ClutchBot? Why use EdgeCloud? Why not use centralized compute alternatives like AWS or Azure? ML: Think of EdgeCloud as an intelligent orchestration software that can route specific AI jobs to the most efficient and cost-effective option. Sometimes you don’t need A100 or H100 GPU to handle a request that a commercial grade Nvidia 3090/4090 GPU can, run by our Theta community stakeholders. They get rewarded with TFUEL tokens and run on the underlying Theta blockchain, and partners benefit from flexibility and peak time scaling which is critical in live sports entertainment. CN: How are you going to collect feedback from users/fans to improve ClutchBot and troubleshoot issues post-launch, especially during the 2025–26 NBA season? ML: The AI fine tuning and improvement process occurs continuously behind the scenes and in the future through direct user feedback via incentive user engagement tokens. As we expand the number of sports partners, we begin to get the benefit of improved issue troubleshooting as one fix would benefit all partners. In the end, we end up with a far more accurate, and engaging conversational AI agent.

🚨 Bitcoin has officially entered its most BULLISH cycle phase — the same one that preceded every major parabolic rally. The recent breakout to $94,000 confirms it. The next stop? $250,000 BTC. And this time, Altseason will deliver once-in-a-cycle gains — if you're holding the right low-caps. The 4-Year Cycle Is Repeating (Again) Every U.S. election year, both stocks and crypto follow a pattern: ♦️ ✅ Post-Election Pump ♦️ 📉 Correction Phase ♦️ 🚀 Post-Inauguration Mega Rally This year is no different — and Q2 is historically the strongest quarter: ♦️ Bitcoin average Q2 return: ~26% ♦️ Ethereum Q2 return: ~66% ♦️ Altcoins: Often exceed 66%+ returns in Q2 BTC Dominance Will Fall — That’s the Altseason Trigger ♦️ Bitcoin leads first, then slows ♦️ Capital rotates into Ethereum ♦️ Then low-caps begin exploding That moment is now approaching — and you need to position early. Top 6 Low-Caps With 1000x Potential in 2025 I’ve filtered hundreds of projects. These are real builders, not hype tokens. They’re in AI, ZK, cloud computing, metaverse, and modular L1s — the hottest narratives of this cycle: 🔹 $ARC – ARC Protocol Narrative: AI Infrastructure ♦️ Decentralized AI layer powering next-gen infra ♦️ Price: $0.08 ♦️ Market Cap: ~$40M 🔹 $ATH – Aethir Narrative: DePIN / AI Cloud ♦️ Decentralized GPU-as-a-service platform ♦️ Built for AI & ML scalability ♦️ Price: $0.03586 ♦️ Market Cap: ~$250M 🔹 $VIRTUAL – Virtuals Protocol Narrative: Metaverse Infra ♦️ Bridges real & virtual assets seamlessly ♦️ Driving Web3-native virtual economies ♦️ Price: $0.63 ♦️ Market Cap: $400M 🔹 $ZKJ – Polyhedra Network Narrative: Zero-Knowledge Proof / ZK Infra ♦️ Cutting-edge zk solutions for scaling & interoperability ♦️ Price: $2.05 ♦️ Market Cap: ~$222M 🔹 $SNX – Synthetix Narrative: DeFi / Derivatives ♦️ On-chain synthetic asset protocol ♦️ Deep liquidity for decentralized futures ♦️ Price: $0.86936 ♦️ Market Cap: ~$292M 🔹 $SONIC – Sonic SVM Narrative: Modular Gaming Chain ♦️ Solana Virtual Machine Layer-1 for gaming dApps ♦️ High-speed infra for sovereign Web3 economies ♦️ Price: $0.21464 ♦️ Market Cap: $77M Final Takeaway: Altseason is Here — Don’t Blink ♦️ $BTC is entering macro breakout territory ♦️ Altcoins will take the spotlight as dominance falls ♦️ These low-caps are not just hype — they’re backed by utility and trend alignment The next 90 days will define the next 3 years of your crypto wealth.

The crypto market constantly evolves — and this year, one trend is making serious noise: AI meets blockchain. Tokens like $FET ,$AGIX, $RNDR, and $TAO are no longer just hype names.They're at the frontlines of a technological revolution where decentralized infrastructure fuels artificial intelligence in powerful new ways. The convergence of AI and Web3 is opening doors to: Decentralized compute marketplaces Privacy-preserving AI training Tokenized access to machine learning models Incentivized data-sharing ecosystems These aren’t just buzzwords — they’re new business models built on blockchain rails. And they’re solving major pain points in the centralized AI world. $FET (Fetch.ai): Building autonomous agent networks where AI bots perform tasks like delivery, trading, and logistics. $RNDR (Render): Powering decentralized rendering for AI/ML and graphics. Big names like Apple and Nvidia are showing interest. $TAO Creating a decentralized network of AI models that learn from one another and get rewarded with TAO tokens. $AGIX Enabling a global marketplace for AI services — fully decentralized. These projects are attracting attention not just because of price movement, but because of real-world use cases and ecosystem growth. So If you’re here only for the pumps, you might miss the real opportunity. AI tokens represent infrastructure-level innovation, much like Ethereum did in its early days. Understanding these tokens means: Looking beyond speculation Learning what each protocol actually does Watching for developer and partner traction While AI narratives are booming, not every "AI token" is legit. Some ride the wave with no utility behind them. Take your time to: Read whitepapers Join community discussions Track roadmap delivery Use on-chain analytics for insights In crypto, attention moves fast — but trends like AI + Blockchain could define the next bull cycle. They’re building the backbone for how data and intelligence are exchanged in the future. The future of Web3 won't just be decentralized it’ll be intelligent. Make sure you’re not just watching the charts, but watching the shift. Follow me to stay ahead of the curve. I’ll be posting more on this and other valuable trends reshaping the crypto space from AI to DeFi and beyond. DefiMohk✍️

Here are some trending AI game development areas and tools: AI-Powered Game Development - *Procedural Content Generation*: AI generates game content, such as levels and environments, reducing development time and increasing game replayability. - *Personalized Gaming Experiences*: AI tailors gameplay to individual players' preferences and behavior, enhancing engagement and enjoyment. - *Automated Game Testing*: AI-powered testing tools identify bugs and optimize game performance, streamlining the development process. Emerging Trends - *Neural Radiance Field (NeRF)*: A technique generating 3D representations from 2D images, enabling immersive AR and VR experiences. - *Voice Synthesis Technology*: AI-generated voices enhance gameplay, allowing smaller studios to create voice-acted content without extensive recording sessions. Game Development Tools - *Unity's Muse Animate*: Allows developers to animate characters without coding, leveraging AI for streamlined development. - *Unity 6*: Upcoming update with enhanced AI support, mobile features, and VR device compatibility. - *AutoML*: Automated machine learning tools, like AutoWeka and Auto-sklearn, make ML accessible to non-experts, improving game development efficiency ¹.

As AI becomes more embedded in daily life, concerns about data privacy grow. Fully Homomorphic Encryption ($FHE ) is emerging as a key enabler of **Confidential AI**—a new paradigm where AI models can be trained and run on encrypted data, preserving privacy without sacrificing functionality. What is Confidential AI?** - AI that operates on sensitive data while ensuring that neither the data nor the model parameters are exposed. - Confidential AI is powered by privacy-enhancing technologies like FHE, secure enclaves, and differential privacy. How FHE Enables Confidential AI** - **Inference on Encrypted Data**: A user can send encrypted input to an AI model, which returns encrypted predictions—without ever seeing the raw input. - **Training on Encrypted Data**: Sensitive datasets (e.g., medical or financial) can be used to train models securely. - **Secure Model Sharing**: AI models can be deployed without leaking proprietary or sensitive intellectual property. --- ### **Example Use Cases** - **Healthcare AI**: Predicting disease outcomes using encrypted patient data. - **Finance AI**: Risk scoring and credit analysis on encrypted customer information. - **Smart Devices**: Encrypted voice commands processed securely in the cloud. - **Federated Learning**: Multiple entities collaborate to train AI models without exposing their data. --- Tech Landscape & Tools** - **Zama’s Concrete ML**: An open-source framework for running neural networks on encrypted data. - **IBM HELayers**: Designed to accelerate encrypted AI workloads. - **Microsoft SEAL**: A popular $FHE library supporting encrypted machine learning applications. - Growing integration with PyTorch and TensorFlow for FHE-ready AI. --- ### **Challenges** - **Performance**: $FHE operations are still slower than plaintext equivalents. - **Model Accuracy**: Some encryption schemes limit precision, affecting AI model outputs. - **Tool Maturity**: Development tools are still emerging and often complex to use. Why the Buzz?** - **Trust in AI**: With $FHE , companies and governments can adopt AI without fearing data leaks. - **Privacy Regulations**: Confidential AI helps comply with laws like GDPR, HIPAA, and CCPA. Business Advantage**: Enables cross-institutional collaborations without sharing raw data. Conclusion** FHE is unlocking a future where AI and privacy are no longer at odds. While challenges remain, the ability to run encrypted AI workflows is a massive leap toward responsible, confidential computing.