Day 7 of 21 Days of Javascript

Using LLMs to generate test cases. For a long while I was stuck on a bonus exercise from Chapter 4 of my guide book. I ended up with a very verbose solution and the only way I could fully test it was to ask ChaptGPT to create several test cases

Day 6 of 21 Days of Javascript

Today I started looking at how objects work in JS. The really cool thing about using AI for learning (and not for answers) is that you can use it to help you create cheat sheets to help you remember key ideas. Here's one I asked Chat GPT-5 to

Day 5 of 21 Days of Javascript

I'm starting to see why some people said this book isn't for beginners. The exercises are hard and it takes wider reading outside of the book and re-reading chapters for ideas to really stick. For example here's a concept one exercise requires: Linked Lists.

Day 4 of 21 Days of Javascript

Javascript still seems strange to me compared to Python. This is something I'll get over with some muscle memory. Here's a quirk I found today. Accessing Items in an Array by their Position: In python we simply use square brackets and this works just fine: my_

Day 3 of 21 Days of Javascript

Today I worked through Chapter 4 of Eloquent Javascript and completed the first exercise. I'll do the rest tomorrow. Key areas covered were: * Arrays and how to traverse them. * Methods and properties of objects and how to access them. * A short bit on JSON. I'm starting

Day 2 of 21 Days of Javascript

Day 2 Learnings Today I firmed up three main ways of writing functions * Function Expressions (Need semicolon at the end, not hoisted.) * Function Declarations (No semicolon, hoisted.) * Arrow Functions (Short syntax, , not hoisted.) Here's some example code: // 1. Function Expression // - Assigned to a variable named min // -

Day 1 of 21 Days of Javascript

I've long avoided trying to learn Javascript. It's virtually everywhere (including this web browser) and it's the number 1 programming language in the world (see chart below.) I much prefer Python, which I've used for a few years now. I got to

Why I Prioritise Questions Over Predictive Claims About AI Capabilities

We’re in an AI frenzy, and amidst the noise, I’ve occasionally fallen into a predictive trap. It’s easy to look at model capabilities today and given previous trends, you extrapolate enthusiastically. Likewise, when existing AI techniques appear to plateau, pessimism takes hold and we undershoot our expectations.

3 Learnings from Building a Weekend App with AI

This past long weekend (thanks, Easter break) I dug into an idea I had knocking around. I read Hacker News religiously and the lengthy comments sections can be as insightful as the articles they reference. However, the most interesting stories on Hacker News often have too many comments to digest.

Learn Slow So You Can Move Fast

I learned to code the old-school way: I bought a Python textbook and went through examples and exercises, page by page, writing all the code from scratch. Today, we have AI agents writing code for us. I often use Cursor and LLMs to rapidly generate snippets or whole sections of

The Gen AI Frenzy: What’s Hype, What’s Real, and Where’s the Productivity?

Today, I read two contrasting articles. One posited that we are near the peak of investor hype in Gen AI. It argued that productivity gains from this new technology will be incremental rather than transformative. Another article suggested the opposite. It made the distinction between good bubbles and bad bubbles,

Prompt Engineering: A Surprising Switching Cost of Large Language Models

I've been working on some exceptionally long LLM prompts for a couple of projects at work. I've noticed a fascinating phenomenon: A prompt that works well with one model can diverge in performance when applied to another. This presents switching costs for developers and businesses. You