Mastering Blockchain Integration: Chapter 26

Weights & Biases · Intermediate ·🧠 Large Language Models ·2y ago

Key Takeaways

Mastering blockchain integration with LLMs using Weights & Biases' open-sourced Wandbot application, exploring its capabilities and implementation on Discord and Slack.

Full Transcript

[Music] in the previous videos we build a very simple application using L chain and we expose it on the web using radio as you develop your application uh you will need to move Beyond uh the scale of the example that we presented in this lesson and to give you some inspiration we are also sharing and we've open sourced the onebot that we are using um internally with we Andes employees and that we also exposed externally on our Discord server and where you can go and play and interact with it so you can find this um application under 1db onebot and here you can find all of the source files uh you can uh see documentation on how this uh bort was developed you'll be able to see both the slack and the Discord client implementation in this repo you can go and review the source files uh which is a bit more comprehensive than the examples we covered in the in the lesson and I encourage you to to play with this uh onebot if you have any questions about it its implementation you can ask it on our Discord server you can also uh create issues or pull requests here in this repo and we definitely want to make it better we want to make it mature application and hopefully this gives you some inspiration on how to build potentially a simil app yourself we also encourage you to open source your applications so as a community we can learn together and develop really nice and useful llm based applications

Original Description

🏫 Advancing Your LLM App Development Skills in Chapter 26. Learn blockchain integration and explore Wandbot's capabilities in this insightful chapter. 🧑🏾‍🎓 Full course with certification and class materials available free at http://wandb.me/building-llm-powered-apps 🏆 Daily swag draw and grand prize Airpods draw from Dec 1 and 31, 2023. Details at http://wandb.me/llm-apps-contest 🗣️ Join the course conversation on our Discord channel at http://wandb.me/course-discord *Episode Description* Join Darek Kleczek, Machine Learning Engineer at Weights & Biases, for this next chapter of our "Building LLM-Powered Apps" course. Dive into the advanced stages of application development using blockchain and understand the real-world implementation with Wandbot. 🌟 Chapter Highlights Advanced Application Development: Transition from simple examples to more sophisticated LLM applications. Exploring Wandbot: Discover the intricacies of Wandbot, a versatile application used internally at Weights & Biases and available on Discord. Open Source Insights: Access the open-source repository of Wandbot for a comprehensive understanding of its development and features. Interactive Learning: Engage with Wandbot on Discord and explore its functionalities firsthand. Community Collaboration: Learn how open sourcing your applications can foster community development and enhance LLM applications. 🎓 Enroll for Free: Join us on this educational journey to master the art of building LLM-powered applications. Enroll at http://wandb.me/building-llm-powered-apps. 👉 Next Chapter Sneak Peek: Up next, delve into the final quiz and project assignment, wrapping up our comprehensive course.
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0. What is machine learning?
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Weights & Biases

Learn how to integrate blockchain with LLMs using Weights & Biases' open-sourced Wandbot application, and explore its implementation on Discord and Slack. This lesson provides inspiration for building similar apps and encourages open-sourcing for community learning.

Key Takeaways
  1. Explore Wandbot's source files on GitHub
  2. Review documentation on Wandbot's development
  3. Play with Wandbot on Discord
  4. Create issues or pull requests for Wandbot's improvement
  5. Open-source your own LLM-based applications
💡 Open-sourcing LLM-based applications can facilitate community learning and development of useful apps.

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