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How Things Work #0: ZODA and the Accidental Computer Today, we explore how Data Availability Sampling (DAS) can scale blockchains with Alex Evans and guille from Bain Capital Crypto. Turns out, you can (somehow) embed verifiable computations over data in DA into DA 🪄.

36,002 Aufrufe • vor 1 Jahr •via X (Twitter)

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⚠️The average person generates 2.5 quintillion bytes of data annually. That's enough to fill 575,000 libraries!📚 This data is used to track, target, and manipulate you. #Cybersecurity matters.💡 Cybersecurity Dictionary for Everyone is on Apple Books:

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@alexhevans @GuilleAngeris @BainCapCrypto so much good content to watch 🤠

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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:

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PhD Students – How to automatically extract data from papers for your literature review? Extracting relevant data from papers is challenging. However, this process can be automated. Meet AnswerThis – a tool that extracts data in seconds. Here is how it works. 1. Go to and log in. 2. After logging in, click on 𝐸𝑥𝑡𝑟𝑎𝑐𝑡 𝑑𝑎𝑡𝑎. 3. Then click on 𝑈𝑝𝑙𝑜𝑎𝑑 𝑃𝐷𝐹 and upload your papers. 4. These are the papers from which you want to extract data. 5. After uploading papers, select data you want to extract. 6. The predefined options are - Key findings - Research gaps - Methodology - Limitations - Future work - Contributions - Practical implications 7. You can also extract custom data e.g., dataset used. 8. For example, I want to extract methodology used in these papers. 9. I selected 𝑀𝑒𝑡ℎ𝑜𝑑𝑜𝑙𝑜𝑔𝑦 and clicked on 𝐴𝑑𝑑 𝐶𝑜𝑙𝑢𝑚𝑛. 10. AnswerThis extract data about methodology used in the papers. 11. You can change data view from normal to Table View. 12. For this, scroll back to top and click on 𝑇𝑎𝑏𝑙𝑒 𝑉𝑖𝑒𝑤. 13. Now for instance, you want to extract more data from these papers. 14. Go back to the top and click on 𝐸𝑥𝑡𝑟𝑎𝑐𝑡 𝑑𝑎𝑡𝑎. 15. Select the data type you want to extract. 16. For example, I want to extract data about future work. 17. So I click on 𝐹𝑢𝑡𝑢𝑟𝑒 𝑊𝑜𝑟𝑘 and then clicked on 𝐴𝑑𝑑 𝑐𝑜𝑙𝑢𝑚𝑛. 18. AnswerThis extracted data about future work from the papers. 19. After extracting the desired data, you can export it. 20. Select the data you want to extract. 21. Then click on 𝐸𝑥𝑝𝑜𝑟𝑡 𝑑𝑎𝑡𝑎. 22. Your data will be exported in CSV format. You can then analyze this data for your literature review. Try AnswerThis today: Anything you'd like to add?

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