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Why Camp Mamo? Get access to the first hands-on crash course for building on Celestia. You’ll learn how to build with full-stack control across the different stacks and tools among the Celestia ecosystem. Sign up now:

11,163 views • 1 year ago •via X (Twitter)

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1/ I’ve known Nick White for nearly 6 years. We met while building Harmony Protocol, he was a cofounder, I led growth. Nick is a genuine builder who cares about pushing crypto forward. We caught up on the pod to talk Celestia 🦣: what it is, why it matters, where it’s going. Summary Peter Abilla interviews Nick White, VP at Celestia Labs, to unpack how Celestia is solving the blockchain scalability problem. They explore the breakthrough of data availability sampling, the shift from monolithic to modular blockchains, and how Celestia fits into the broader Ethereum ecosystem. Nick shares insights on trade-offs in modular design, product market fit, cost advantages, and what’s next for ZKVMs and modular infra. Takeaways • Celestia is redefining how blockchains handle data availability • Blockchain scalability remains a major challenge • Data availability sampling (DAS) is a core innovation • Modular blockchains offer flexibility and specialization • Trade-offs exist between modular and monolithic designs • “Cathedral vs. Bazaar” analogy captures the decentralization shift • Celestia works alongside Ethereum, not against it • Product market fit depends on developer traction and real-world use • Celestia reduces costs and improves reliability • ZKVMs are key to the future of modular blockchain development Timeline (00:00) Introduction to Celestia and its vision (01:00) The scalability challenge in blockchain (06:04) How data availability sampling works (10:10) Comparing monolithic and modular blockchains (14:39) Trade-offs of modular blockchain design (18:21) The Cathedral and Bazaar analogy (22:41) How Celestia fits into the blockchain ecosystem (24:49) What makes Celestia’s architecture unique (26:08) Defining product market fit for Celestia (31:05) Cost savings and performance benefits (35:48) Distribution and growth strategy (40:13) ZKVMs and the modular future (44:36) What’s coming next for Celestia -------- Episode is brought to you by Infinex. Experience crypto designed for humans:

papiofficial

21,097 views • 1 year ago

"Introducing Multimodal Llama 3.2": As promised two weeks ago, here's the short course on Meta's latest open model! This short course is created with Meta and taught by Amit Sangani, Director of AI Partner Engineering at Meta. Meta’s Llama family of models is leading the way in open models, allowing anyone to download, customize, fine-tune, or build new applications on top of them. Learn about the vision capabilities of the Llama 3.2, and use it for image classification, prompting, tokenization, tool-calling. You'll also learn about the open-source Llama stack, which gives building blocks for many different stages of the LLM application life cycle. In detail, you’ll: - Learn what are the features of Meta's four newest models, and when to use which Llama model. - Learn best practices for multimodal prompting, with applications to advanced image reasoning, illustrated by many examples: Understanding errors on a car dashboard, adding up the total of photographed restaurant receipts, grading written math homework. - Use different roles—system, user, assistant, ipython—in the Llama 3.1 and 3.2 models and the prompt format that identifies those roles. - Understand how Llama uses the tiktoken tokenizer, and how it has expanded to a 128k vocabulary size that improves encoding efficiency and multilingual support. - Learn how to prompt Llama to call built-in and custom tools (functions) with examples for web search and solving math equations. - Learn about Llama Stack, a standardized interface for common toolchain components like fine-tuning or synthetic data generation, useful for building agentic applications. By the end of this course, you’ll be equipped to build out new applications with the new Llama 3.2. Thank you to Ahmad Al-Dahle, Amit Sangani, and the whole AI at Meta team AI at Meta for all the hard work on Llama 3.2 — we’re excited to make these open models even more accessible to more developers with this new course! Please sign up here!

Andrew Ng

131,767 views • 1 year ago