• 3 min read

Claude Agent SDK vs. Google ADK: for Completely different Usecases

Despite similar names, Anthropic’s Claude Agent SDK and Google’s Agent Development Kit (ADK) are fundamentally different tools addressing distinct problems.

They aren’t just two flavors of the same thing. They represent fundamentally different ways to solve the problem.

  • Claude Agent SDK is a local runtime for autonomous execution. Think "Super-Intern" on your laptop.
  • Google ADK is a framework for AI system orchestration.
  • 6 min read

Building AI Agents in Google Cloud: Choose the Right Approach for Your Needs

TL;DR

  • ADK → Google-developed open-source framework for building complex multi-agent systems with maximum control and modularity
  • Conversational Agents (Dialogflow CX) → Omnichannel customer conversations with structured flows and open-ended playbooks
  • Open-Source Frameworks → Leverage specific framework capabilities (LangChain's integrations, LangGraph's state management, CrewAI's collaboration) on GCP's managed infrastructure
  • Agentspace → Enterprise search platform and self-serve agent creation for automating everyday knowledge work tasks

Key decision point: Choose your building framework (ADK/Conversational Agents/Open-Source), then optionally deploy to Agentspace for enterprise wide access.

  • 6 min read

The Language of Agents Decoding Messages in LangChain & LangGraph

Ever wondered how apps get AI to chat, follow instructions, or even use tools? A lot of the magic comes down to "messages." Think of them as the notes passed between you, the AI, and any other services involved. LangChain and LangGraph are awesome tools that help manage these messages, making it easier to build cool AI-powered stuff. Let's break down how it works, keeping it simple!

  • 9 min read

Building Personal Chatbot - Part 2

Enhancing Our Obsidian Chatbot: Advanced RAG Techniques with Langchain

In our previous post, we explored building a chatbot for Obsidian notes using Langchain and basic Retrieval-Augmented Generation (RAG) techniques. Today, I am sharing the significant improvements I've made to enhance the chatbot's performance and functionality. These advancements have transformed our chatbot into a more effective and trustworthy tool for navigating my Obsidian knowledge base.

  • 6 min read

Building an Obsidian Knowledge base Chatbot: A Journey of Iteration and Learning

As an avid Obsidian user, I've always been fascinated by the potential of leveraging my daily notes as a personal knowledge base. Obsidian has become my go-to tool for taking notes, thanks to its simplicity and the wide range of customization options available through community plugins. With the notes and calendar plugins enabled, I can easily capture my daily thoughts and keep track of the projects I'm working on. But what if I could take this a step further and use these notes as the foundation for a powerful chatbot?

  • 3 min read

Quantized LLM Models

Large Language Models (LLMs) are known for their vast number of parameters, often reaching billions. For example, open-source models like Llama2 come in sizes of 7B, 13B, and 70B parameters, while Google's Gemma has 2B parameters. Although OpenAI's GPT-4 architecture is not publicly shared, it is speculated to have more than a trillion parameters, with 8 models working together in a mixture of experts approach.