Coding Notes

I found this chapter to be insightful and can relate it to my experience as an engineer, both good and bad. The chapter discusses good design, DRY, tracer code, prototyping, and estimating. I’ll share what I learned along with my thoughts. Easy To Change (ETC) The chapter begins with a discussion on what makes good design. They emphasize the benefits of good design, including that it must be changeable without changing any adjacent, upstream, or downstream code. In essence, use ETC or make it easier to change by using techniques such as modularization and interfaces to keep responsibilities separate. ETC is more …

Continue reading “Chapter 2: A Pragmatic Approach”

Continuing with chapter one, section seven (7) on communication. Thomas and Hunt offer advice on how to be a better engineer by communicating more effectively. The section begins by advising that it’s “not just what you’ve got, but also how you package it”. They caution that even the best code or ideas are useless unless other people are aware of them. Developers create various forms of communication, including in meetings, through written code, proposals, and reports. When preparing non-code communication, treat your native language like a programming language by honoring the DRY principle and leveraging automation. Automation in this case …

Continue reading “Pragmatic Programmer Notes #2”

I finally got the chance to start reading The Pragmatic Programmer, 20th Anniversary Edition by David Thomas and Andrew Hunt. These are my initial notes from this first reading. These are my rough notes and are intended for me to reflect on what I read. I capture the key points that I feel are important to pay attention to. Attitude and Style Team Trust Own It Software Entropy Handling Change Software Quality Keep Skills Fresh

Codelab #2 – Use Google Search In Generation This is the first assigned codelab on day four of the intensive. Download it here from Github to run locally or run in this Kaggle notebook.

Codelab #1 – Tune A Gemini Model This is the first assigned codelab on day four of the intensive. Download it here from Github to run locally or run in this Kaggle notebook.

Codelab #1 How to build an agent using LangGraph. Try code locally by downloading from Github or run it in a Kaggle notebook.

Google Gen AI 5-Day Intensive – Capstone Project This post describes my goals and learnings from completing the capstone project for Googles Gen AI 5-Day Intensive Course, hosted by Kaggle. Resources to Know Here are some important resources to use while reading this post: Introduction When learning a new language, as I’m currently pursuing with French, once you get past the basics of conjugation, grammar and pronunciation, becoming fluent requires a deeper understanding of the language. It is necessary to not only know how to speak the language but also know the nuances of a langauge including common usage patterns, …

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Codelab 2/2 from day one. The code lab is here in Kaggle. Download the code on Github to run it locally. Recent Posts

Codelab 1/2 from day one. The code lab is here in Kaggle and you can download it to run locally on Github.

In late March 2025, Google held a generative AI 5-day intensive for developers. It was hosted by Kaggle and included daily codelabs for the first four days. The course included daily live video sessions on YouTube with insights from current Google employees. The course featured: The course is targeted towards developers who already know how to write code. If you’re new to coding, the code will be more challenging because the codelabs require you to read the provided code. It taught us how to use Vertex AI and Gemini APIs to implement generative AI for a range of use cases. …

Continue reading “Google Gen AI 5-Day Intensive Course Overview”