AI Builders and Learners SF

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

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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

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: Agentic Architectural Patterns for Building Multi-Agent Systems
Authors: Dr. Ali Arsanjani, Juan Pablo Bustos
Published: January 2026

Packt: https://www.packtpub.com/en-us/product/agentic-architectural-patterns-for-building-multi-agent-systems-9781806029563

O'rielly platform: https://learning.oreilly.com/library/view/agentic-architectural-patterns/9781806029570/

Chapters:
Part 1: Foundations and Core Agent Concepts
Chapter 1: GenAI in the Enterprise: Landscape, Maturity, and Agent Focus
Chapter 2: Agent-Ready LLMs: Selection, Deployment, and Adaptation
Chapter 3: The Spectrum of LLM Adaptation for Agents: RAG to Fine-tuning
Part 2: Agentic AI: Architecture and Design Patterns
Chapter 4: Agentic AI Architecture: Components and Interactions
Chapter 5: Multi-Agent Coordination Patterns
Chapter 6: Explainability and Compliance Agentic Patterns
Chapter 7: Robustness and Fault Tolerance Patterns
Chapter 8: Human-Agent Interaction Patterns
Chapter 9: Agent-Level Patterns
Chapter 10: System-Level Patterns for Production Readiness
Chapter 11: Advanced Adaptation: Building Agents That Learn
Part 3: Execution: Strategy, Use Cases, and The Future
Chapter 12: A Practical Roadmap: Implementing Agentic Patterns by Maturity Level
Chapter 13: Use Case: A Single Agent for Loan Processing
Chapter 14: Use Case: A Multi-Agent System for Loan Processing
Chapter 15: Agent Frameworks – Use Case: A Multi-Agent System for Loan Processing with CrewAI and LangGraph
Chapter 16: Conclusion: Charting Your Agentic AI Journey

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Book Description
Generative AI has moved beyond the hype, and enterprises now face the challenge of turning prototypes into scalable solutions. This book is your guide to building intelligent agents powered by LLMs.
Starting with a GenAI maturity model, you’ll learn how to assess your organization’s readiness and create a roadmap toward agentic AI adoption. You’ll master foundational topics such as model selection and LLM deployment, progressing to advanced methods such as RAG, fine-tuning, in-context learning, and LLMOps, especially in the context of agentic AI. You'll explore a rich library of agentic AI design patterns to address coordination, explainability, fault tolerance, and human-agent interaction. This book introduces a concrete, hierarchical multi-agent architecture where high-level orchestrator agents manage complex business workflows by delegating entire sub-processes to specialized agents. You’ll see how these agents collaborate and communicate using the Agent-to-Agent (A2A) protocol.
To ensure your systems are production-ready, we provide a practical framework for observability using life cycle callbacks, giving you the granular traceability needed for debugging, compliance, and cost management. Each pattern is backed by real-world scenarios and code examples using the open source Agent Development Kit (ADK).