Posts

A new kind of practitioner has emerged in the age of generative AI. They prompt, copy, paste, and publish. No verification. No critical thinking. No human layer added anywhere in the process. They function almost like conduits, not contributors, not value-adders.

If you have been following the AI agent space, you have probably come across OpenClaw. It’s an open-source, self-hosted AI assistant that connects to your messaging platforms and runs as a persistent daemon on your machine. It can execute shell commands, manage files, and automate tasks.

Recently, there was a news article about a Singaporean firm using robots to do painting. This shatters two commonly made assumptions. One is that many people assume that remote work would make their jobs easy to replace, easy to outsource.

How much do you spend on subscriptions every month? Do you even know it offhand or do you have to check it? I know I can only recall 60-70% of them. But if you think about how much you spent on your last laptop, your last mobile phone, you typically can remember if it cost almost $2,000.

Many companies are very creative and do great work, bringing value to that of their customers, their employees, and of course, their shareholders.

In today’s streaming world, we can actually access millions of TV shows, movies, and even music albums and songs at the touch of a button (and sometimes not even a button).

It used to be that when we caught TV shows, we would all watch them at the same time, some prime time soap episode that would become the talk of the week if it was awesome.

In machine learning, there is a concept of explore vs exploit. How often do you explore versus exploit? For example, when the algorithm is doing its supervised learning, you need to know how often to actually utilize the data that you have to get the right result. Let’s say you are trying to classify animals.

In life and at work, 95% of the time, even if you are working on research, or operations, or software engineering, are common things you do to keep at your work. Build this widget, solve that business requirement, make this odd unclear and uncertain idea a reality.

On the internet, or more specifically, the world wide web, Yahoo search used to be the de facto search engine for many people. Launched in 1994, I personally started to use it on a 56kbps modem connection in 1998.

I used to work with a colleague who constantly lamented how overwhelmed he was by the sheer number of platforms and tools he had to support. It didn’t make sense to me at first, none of these tools were custom-built, yet they were all self-hosted on virtual machines, requiring constant maintenance.

Imagine that you’re at a social event and you walk up to shake someone’s hand. Instead of receiving an outstretched hand, you get a hand in multiple layers of gloves and the person is inside what seems like a plastic hamster ball. That’s what it’s like to be represented by layers of generative AI.

This is part 4 of the 4-part guide. In this final part, we upload what we have created onto GitHub Pages to publish it.

This is part 3 of the 4-part guide. In this part, we look at how we can create the d3 map visualization.

This is part 2 of the 4-part guide. In this part, we process the data that we have collected in part 1.

This is a practical step-by-step guide to creating a map visualization in d3. In this 4-part guide, we set off to create a visualization to display whether the area where the resale prices of HDB flats have changed throughout the years.

In this post, I want to share about the Stacking Plan visualization commonly used in the real estate domain.