AI Builders and Learners SF

​https://github.com/karpathy/autoresearch

​​​Resources

• ​​​GitHub: https://github.com/sagecodes/ai-build-and-learn
• ​​​Events Calendar: https://luma.com/ai-builders-and-learners
• ​​​Slack (Discuss during the week): https://slack.flyte.org/
• ​​​Hosted by Sage Elliott: https://www.linkedin.com/in/sageelliott/

​​In this stream

• ​​​AutoResearch
• ​​​Hands-on demo
• ​​​Community Discussion + practical examples

​​​Community challenge (optional)
​​​Try spending 30–90 minutes during the week learning or building something related to the topic, then share what you’re working on in Slack.

​​​Note on Flyte / Union
​​​You may see Flyte used in some demos. Flyte is an open-source AI orchestration platform maintained by Union (where I work) for building scalable, durable, and observable AI workflows. You do not need to use Flyte to participate.

• ​​​Union: https://www.union.ai/
• ​​​Flyte: https://flyte.org/

​​​Drop a comment with ideas for future topics (agents, RAG, MLOps, robotics, frameworks, and more).

Want to discuss the contents during the reading week? Join the Slack Flyte MLOps Slack group and search for the "ai-reading-club" channel. https://slack.flyte.org/

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About the book:
Title: Designing Data-Intensive Applications, 2nd Edition
Authors: Martin Kleppmann, Chris Riccomini
Published: February 2026

https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/

Chapters:
1. Trade-Offs in Data Systems Architecture
2. Defining Nonfunctional Requirements
3. Data Models and Query Languages
4. Storage and Retrieval
5. Encoding and Evolution
6. Replication
7. Sharding
8. Transactions
9. The Trouble with Distributed Systems
10. Consistency and Consensus
11. Batch Processing
12. Stream Processing
13. A Philosophy of Streaming Systems
14. Doing the Right Thing

####

Book Description
Data is at the center of many challenges in system design today. Difficult issues such as scalability, consistency, reliability, efficiency, and maintainability need to be resolved. In addition, there's an overwhelming variety of systems, including relational databases, NoSQL datastores, data warehouses, and data lakes. There are cloud services, on-premises services, and embedded databases. What are the right choices for your application? How do you make sense of all these buzzwords?
In this second edition, authors Martin Kleppmann and Chris Riccomini build on the foundation laid in the acclaimed first edition, integrating new technologies and emerging trends. You'll be guided through the maze of decisions and trade-offs involved in building a modern data system, learn how to choose the right tools for your needs, and understand the fundamentals of distributed systems.

• Peer under the hood of the systems you already use, and learn to use them more effectively
• Make informed decisions by identifying the strengths and weaknesses of different tools
• Learn how major cloud services are designed for scalability, fault tolerance, and consistency
• Understand the core principles upon which modern databases are built

https://learning.oreilly.com/library/view/designing-data-intensive-applications/9781098119058/

Want to discuss the contents during the reading week? Join the Slack Flyte MLOps Slack group and search for the "ai-reading-club" channel. https://slack.flyte.org/

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About the book:
Title: Build a Text-to-Image Generator (from Scratch)
Authors: Mark Liu
Published: December 2025

Manning ():https://www.manning.com/books/build-a-text-to-image-generator-from-scratch

O'rielly platform: https://learning.oreilly.com/library/view/build-a-text-to-image/9781633435421/

Chapters:
Part 1 Understanding attention and transformers
1 A tale of two models: Transformers and diffusions
2 Build a transformer
43% complete
3 Classify images with a vision transformer
4 Add captions to images
Part 2 Introduction to diffusion models
5 Generate images with diffusion models
6 Control what images to generate in diffusion models
7 Generate high-resolution images with diffusion models
Part 3 Text-to-image generation with diffusion models
8 CLIP: A model to measure the similarity between image and text
9 Text-to-image generation with latent diffusion
10 A deep dive into Stable Diffusion
Part 4 Text-to-image generation with transformers
11 VQGAN: Convert images into sequences of integers
12 A minimal implementation of DALL-E
Part 5 New developments and challenges
13 New developments and challenges in text-to-image generation

####

Book Description
Build a Text-to-Image Generator (from Scratch) takes you step-by-step through creating your own AI models that can generate images from text. You’ll explore two methods of image generation—vision transformers and diffusion models—and learn vital AI development techniques as you go.

Build a Text-to-Image Generator (from Scratch) teaches you how to:

• Build and train models to generate high resolution images based on text descriptions
• Edit an existing image based on text prompts
• Build and train a model to add captions to images
• Build and train a vision transformer to classify images
• Fine-tune LLMs for downstream tasks such as classification, text or image generation
• Better differentiate real images from deepfakes