AI

1. October 21, 202631 min read

   Iceberg Is Getting a Vector Type, and Parquet Decides What It Looks Like

   Iceberg's proposed vector type enforces fixed dimension, non-null, and finite values. What it promises, how Parquet shapes it, and what to do now.

2. September 21, 202639 min read

   Fast Classification Models, LLMs, and the Apache Iceberg Lakehouse

   How fast classification models like Jev alongside open alternatives such as GLiClass compare with LLMs, and how to run both together inside an Apache Iceberg lakehouse.

3. September 21, 202631 min read

   Vector Search Directly Over Iceberg Tables, and When You Still Need a Vector Database

   Embeddings are just columns. When exact vector search over Iceberg scans beats a vector database, when it does not, and how to lay out tables.

4. September 2, 202625 min read

   The Data Team of the Agentic Era: Generalists Owning End-to-End Workflows

   The case for generalists owning end-to-end data workflows with agents, the counterargument, and how to make the transition work.

5. September 2, 202624 min read

   Will AI Replace Data Engineers?

   What the evidence shows about whether AI replaces data engineers, which parts of the job compress, and which parts do not.

6. August 24, 202630 min read

   Iceberg Row Lineage: The Feature AI and CDC Workloads Will Eventually Depend On

   Iceberg row lineage gives rows a durable identity across rewrites. Why CDC pipelines and AI workloads will eventually depend on it.

7. July 24, 202631 min read

   The Filters We Build: Scams, Slop, and the Search for Signal, From Radio Ads to AI

   How every media era from radio to AI forces humanity to build new cognitive filters, the shield against scams and the sieve for finding signal in an ocean.

8. July 13, 202617 min read

   Iceberg Variant Type for AI JSON Data

   An in-depth exploration of iceberg variant type for ai json data

9. July 6, 202616 min read

   Mapping the Variant Type in Iceberg v3: Standardizing Semi-Structured AI JSON Payloads

   AI applications are messy data producers. They create prompts, completions, tool calls, retrieval traces, ranking signals, evaluation scores, safet...

10. June 1, 202620 min read

    What AI Is and Isnt: A Laypersons Guide to How LLMs Actually Work

    AI is not magic or sentient. Learn what large language models actually do, how vectors and embeddings work, and why todays AI is pattern matching not thinking.

11. June 1, 202620 min read

    Getting Started with AI for Free: Every Tool Google Gives You at No Cost

    You already have access to powerful AI tools through your Google account and no subscription needed.

12. June 1, 202620 min read

    ChatGPT and Claude: Which AI Service Should You Pay For

    ChatGPT and Claude are the two leading AI assistants. Here is what each offers at every price tier, how to use their desktop apps and advanced features.

13. June 1, 202620 min read

    A Tour of Specialized AI Tools: Music, Video, Images, and More

    Beyond chatbots lie specialized AI tools for creating music, generating videos, and editing images.

14. June 1, 202620 min read

    Going Advanced: Open Source Models, Hermes Agent, and Local AI

    Tired of subscriptions and privacy concerns? Open source models and agent frameworks let you run AI on your own hardware.

15. April 14, 20255 min read

    A Journey from AI to LLMs and MCP - 10 - Sampling and Prompts in MCP – Making Agent Workflows Smarter and Safer

    Sampling and Prompts in MCP – Making Agent Workflows Smarter and Safer

16. April 13, 20255 min read

    A Journey from AI to LLMs and MCP - 9 - Tools in MCP – Giving LLMs the Power to Act

    Tools in MCP – Giving LLMs the Power to Act

17. April 12, 20255 min read

    A Journey from AI to LLMs and MCP - 8 - Resources in MCP – Serving Relevant Data Securely to LLMs

    Resources in MCP – Serving Relevant Data Securely to LLMs

18. April 11, 20255 min read

    A Journey from AI to LLMs and MCP - 7 - Under the Hood – The Architecture of MCP and Its Core Components

    Under the Hood – The Architecture of MCP and Its Core Components

19. April 10, 20255 min read

    Journey from AI to LLMs and MCP - 6 - Enter the Model Context Protocol (MCP) – The Interoperability Layer for AI Agents

    Enter the Model Context Protocol (MCP) – The Interoperability Layer for AI Agents

20. April 9, 20258 min read

    A Journey from AI to LLMs and MCP - 5 - AI Agent Frameworks – Benefits and Limitations

    AI Agent Frameworks – Benefits and Limitations

21. April 8, 20255 min read

    A Journey from AI to LLMs and MCP - 4 - What Are AI Agents – And Why They're the Future of LLM Applications

    What Are AI Agents – And Why They're the Future of LLM Applications

22. April 7, 20255 min read

    A Journey from AI to LLMs and MCP - 3 - Boosting LLM Performance – Fine-Tuning, Prompt Engineering, and RAG

    Boosting LLM Performance – Fine-Tuning, Prompt Engineering, and RAG

23. April 6, 20255 min read

    A Journey from AI to LLMs and MCP - 2 - How LLMs Work – Embeddings, Vectors, and Context Windows

    How LLMs Work – Embeddings, Vectors, and Context Windows

24. April 5, 20254 min read

    A Journey from AI to LLMs and MCP - 1 - What Is AI and How It Evolved Into LLMs

    What Is AI and How It Evolved Into LLMs

25. April 4, 202515 min read

    Building a Basic MCP Server with Python

    The Basics of Building a Basic MCP Server

26. February 1, 202512 min read

    Crash Course on Developing AI Applications with LangChain

    A guide on building AI applications with LangChain, a framework for developing AI applications powered by Large Language Models (LLMs).

Browse all posts