I'm halfway through the 21 day Javascript challenge I set myself. How are things going? I'd like to complete at least the first 12 chapters of Eloquent Javascript. Today I completed Chapter 5. With another 10 days to go and 7 chapters left, I might have
Today I covered more higher order functions. Map and reduce. Yesterday I covered filter. One of the benefits of using these functions is composability. This is the idea of using relatively small blocks of code chained together for more complex behaviour. Here's a quick example. If we have
I have to say, I'm really enjoying using ChatGPT as an additional tutor alongside the key book I'm leaning on for this journey. For example once I got to a section that introduced JS filters, I worked with GPT to produce clear summary notes about filter
I'm now on Chapter 5 of Eloquent Javascript. I'm yet to finish the chapter but a key takeaway from today is the power of abstraction. I've blogged about this before, but came to appreciate it more today. The basic takeaway is this: Abstractions help
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
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
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.
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_
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 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 // -
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
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.
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.
Tech
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
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,
Tech
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
ai agents
AI agents have quickly evolved from obscure technical experiments to mainstream buzz. In the last 18 months, new automation capabilities have captured the imagination of entrepreneurs and established organizations alike. While it’s possible that we’re in another AI bubble, the early adopters of AI agents are reporting impressive
Tech
Last week I attended the Algorithmic Innovation & Entrepreneurship Summit on Open Problems for AI. That’s a bit of a mouthful but in essence, it was a 2-day conference covering the challenges and opportunities for AI over the next 5 years. Day 1 was focussed on algorithms and AI
Essays
An undeniable killer use case for large language models is code generation. It’s already helping software developers write code up to twice as fast and with more delight. As LLM models improve with each release, they can write better software and handle more complex architectures at a fraction of
Tech
AI agents have been getting a lot of attention but they can be expensive to run. This is because they use a lot of tokens and make multiple calls to LLMs. Some AI developers have beat performance benchmarks by using exceptionally expensive agentic runs. However, this is unrealistic because in
Tech
I switched one of my AI agents projects from GPT-4o to the new Claude 3.5 Sonnet model. The agents now run with a cost that’s roughly 85% cheaper and the final results arrive about 15% faster. To upgrade my AI agents project, all I had to do was
Tech
On the way home today I was listening this podcast — “Lessons from a Year of Building with LLMs“. The biggest takeaway? While everyone has been building lots of experimental projects with LLMs and often shipping to production scrappy builds of seemingly impressive demos (I’m guilty of this!), few people
Tech
AI Agents are a mixed bag. I tried this morning to get a group of CrewAI agents to complete some Youtube summaries and they kept failing or going off the rails. My deterministic version which I hard coded works significantly better than leaving things to a group of autonomous agents.
Tech
There’s an article in the Wall Street Journal I came across today with the headline below. The article ends with a quote from a Google Cloud Chief Evangelist, Richard Seroter: “If you don’t have your data house in order, AI is going to be less valuable than it