Skip to main content

money works

money makes the world go round: that's half true. money and people makes the world go round. sad to say, people are letting money make the world go round when it should be the other way around. people are working for money instead of letting money work for the people.

let's take a look at the three kinds of people-money relationship, which i have in mind.

employment: when one is employed, one is working for money. he works to receive money for him to use for daily activity. once depleted, the cycle is repeated. it's advantage: it's basic, no complications and no stress (depending on whose looking at it). drawback: unstable and routinary. once you're laid off, the pay stops and you need to look for another job.

business: when one owns a business, one is working with his money. to clarify, he is helping his money grow by making the business stay afloat. then the owner would hire managers to take charge of the operations so he can sit back. then, from time to time he takes part on the operations. that's better than being an employee, in so many ways. one disadvantage though. it consumes time. not like if you are going be an investor

investor:  when one invests, he uses his money to generate more money. sample would be time deposit, real estate, stocks, bonds, etc. simplest form would be saving up in a bank with compounding interest. that may return the smallest but adding it up to, let's say, 15 years: that's big. 1,500 monthly with 0.5%interest rate for 15 years would have a future value of 281,939.13, regardless of the currency(i think).

final note, make your money work for you. do not just work for money.
Labels:

Popular

Retrieval-Augmented Generation (RAG) Using First-Principles Thinking

Instead of just learning how Retrieval-Augmented Generation (RAG) works, let's break it down using First-Principles Thinking (FPT) —understanding the fundamental problem it solves and how we can optimize it. Step 1: What Problem Does RAG Solve? Traditional AI Limitations (Before RAG) Large Language Models (LLMs) like GPT struggle with: ❌ Knowledge Cutoff → They can’t access new information after training. ❌ Fact Inaccuracy (Hallucination) → They generate plausible but false responses. ❌ Context Limits → They can only process a limited amount of information at a time. The RAG Solution Retrieval-Augmented Generation (RAG) improves LLMs by: ✅ Retrieving relevant information from external sources (e.g., databases, search engines). ✅ Feeding this retrieved data into the LLM before generating an answer. ✅ Reducing hallucinations and improving response accuracy. Core Idea: Instead of making the model remember everything, let it look up relevant knowledge when needed....
Read more

Using AI to Reinvent My Résumé and Try to Land an Interview

Creating a résumé is a tedious job to most. It's hard, time consuming and might even be the cause for rejection-if you don't know what you're doing. Fortunately, there are AI tools out there that created to assist, us humans, in generating résumé. It save's time, effort and you get higher chance of being hired.  But what if you're transitioning to an entirely different role? You don't have experience, no educational background to back it up. and no portfolio to show. What do you do? You come up with something creative. You come up with some that has never been done before. And, just wow them... or at least try. I was messaged in LinkedIn for a position that I was eyeing for in years. The HR guy reached out and we scheduled a call interview. We talked for more than half an hour and I was enlightened that my résumé is lack-luster. I was highly considered but the résumé is not at par because I have no job experience on AI, the certifications we're not included,...
Read more

Understanding Prompt Engineering Using First-Principles Thinking

Instead of memorizing prompt techniques, let’s break Prompt Engineering down to its fundamentals using First-Principles Thinking (FPT) . Step 1: What is Communication? At its core, communication is the process of: Encoding thoughts into words (speaker). Transmitting words to a receiver. Decoding the words into meaning (listener). Now, let’s apply this to AI. Step 2: How Do Machines Process Language? A Large Language Model (LLM) doesn’t "understand" words the way humans do. Instead, it: Converts words into tokens (mathematical representations). Predicts the next word based on probability. Generates responses that appear coherent based on patterns it has learned. Thus, prompt engineering is not just about writing sentences—it’s about giving instructions that optimize LLM prediction behavior . Step 3: What is a Prompt? A prompt is just an input instruction that guides an LLM’s response. But at the most basic level, a prompt must contain three thin...
Read more