AI + Blockchain can sound abstract. Let’s make it concrete. When people hear “AI on blockchain,” it often sounds like a buzzword salad. In practice, it’s clear and practical: • Distributed compute marketplaces (Blockchain + AI): Blockchain enables a marketplace where anyone

🧩 Quiz time: What is one real limitation of using centralized compute for AI workloads? A) It is fully decentralized by design. B) It prevents the use of custom or sensitive models due to restrictions or censorship. C) It uses layered neural networks. D) It provides free

What do you believe in ?

🚀 What Would You Build with Decentralized AI? Imagine a world where decentralized AI is embedded in everyday life: 🌱 Farmers receive hyper-local weather predictions powered by decentralized AI, running directly on nearby compute, no gatekeepers required. 🎨 Artists generate

🌱 Building in public means growing in public. Here are a few of our favorite resources shaping how we think about decentralization, AI, and the future of compute: 🔹 Lilypad Litepaper – Our vision for a decentralized AI compute network. docs.lilypad.tech/lilypad/resear… 🔹 “Attention

What would the DeAI world look like without Lilypad?

🧩 Quiz time: How do blockchains record transactions? A) By erasing old data regularly. B) By using transformer models to store data. C) By adding new blocks linked to previous records, preserving history while updating data. D) By editing previous records to update data.

Everyone wants to “get” AI and blockchains. Few actually do. Here’s what to master if you want a real foundation: ✅ Explain AI + its evolution. Not just “ChatGPT.” Understand the journey from symbolic AI → machine learning → deep learning → foundation models. ✅ Understand

AI isn’t one thing. It’s a whole map. From generating video to interpreting emotion, AI models operate in wildly different ways and understanding those functions matters if you're running compute, designing workflows, or choosing models. Here’s a high-level view of the most

Lilypad leverages idle computing power globally, making AI cheaper and more accessible.

The most familiar AI models? They talk back. Text-to-Text models take a prompt and generate more text in response making them the foundation of modern AI workflows. Whether you're chatting, summarizing, translating, or coding, you're using this class of model. 🔧 Common uses:

Not All Models Want to Be Famous While GPT headlines dominate the news, the AI quietly transforming science, law, finance, and the physical world? Specialized models. These aren't general-purpose chatbots. They're fine-tuned, domain-specific, and often mission-critical: 🔬

🤔 Quiz: Why might combining blockchain with AI be useful? A) It eliminates the need for compute in AI. B) It creates a central authority for compute jobs. C) It allows users to access decentralized GPU compute while using blockchain for tracking, verification, and payment. D)

Understand Tokens. Control Cost. Most people only care about what a model can do. Smart builders also care about how it does it and what it costs. That starts with tokens. 🧠 A token is a chunk of text the model reads, not always a full word. 📏 Token count affects memory