π Full Stack MERN Developer Available For Projects
I build:
β CMS Platforms
β Admin Dashboards
β Business Websites
β MERN Web Apps
Portfolio: https://t.co/06WMUvIG82
DM me if you need a reliable developer.
The interesting part for me?
Something that feels as simple as having a conversation involves a huge amount of computation happening behind the scenes.
The more I learn, the more fascinating it gets. π€―
Day 05 done. π
#AI#LLM#NamasteAI
π§ Day 05 β The Computational Brain of Machines
From the outside, using an LLM looks simple:
Ask a question β Get an answer.
But what's actually happening in between?
Today I started understanding that process. π§΅
Our text gets converted into tokens, represented numerically, and processed through multiple layers of the model.
The model uses what it learned during training to process the input and generate the response step by step.
What I found interesting today:
We use language so naturally that we rarely think about how difficult it is for a machine to work with meaning.
Embeddings are one piece of that puzzle.
Day 04 done. π
#AI#AIEngineering#NamasteAI
π§ Day 04 β How Machines Represent Meaning
Today I got stuck on a simple question:
How does a machine understand what a word means?
For humans, it feels automatic.
For machines, itβs much more complicated.
Today I learned about embeddings. π§΅
Embeddings basically turn words, sentences, or other information into numbers (vectors).
These representations help AI work with relationships between concepts.
So βcatβ isn't just a sequence of letters anymore its representation can capture how it relates to other concepts.
This gave me a new perspective:
LLMs don't read language exactly like humans do.
Thereβs a whole pipeline between the sentence we type and the response we receive.
And I've only started scratching the surface. π
Day 03 done. π
#AI#LLM#NamasteAI
π€― Day 03 β The Secret Language of LLMs
We type words.
LLMs process tokens.
Today I learned about tokenization β the process of breaking text into smaller pieces that an AI model can process.
But what exactly is a token? π§΅
A token isn't always a complete word.
It can be a word, part of a word, punctuation, or other pieces of text depending on the tokenizer.
Those tokens are then represented as numbers so the model can work with them.
And that's an important lesson for AI Engineering:
A confident answer doesn't necessarily mean a correct answer.
Understanding this is going to be important as I learn how to build more reliable AI applications.
Day 02 done. π
#AI#AIEngineering#NamasteAI
π§ Day 02 β Does ChatGPT know or does it guess?
Today I learned that an LLM doesn't βknowβ things the same way humans do.
So what actually happens when we ask ChatGPT a question? π
At a high level, the model processes our input and predicts what tokens are likely to come next based on patterns learned during training.
That's why it can produce incredibly useful answers β but also confidently generate something that's completely wrong.
π Day 01 β Starting my AI Engineering journey.
Started the Namaste AI course today. I don't just want to use AI β I want to understand what happens behind the scenes and eventually build AI systems myself.
Learning in public. π₯
#AI#NamasteAI