Oracle Free Tier Limitations: Regional Resource Exhaustion and Deployment Dilemmas
Analyzing the practical issues of using Oracle Cloud Free Tier in Korea—especially the challenge of regional resource shortages that block new VM deployments.
Recent Posts
Is Using AI to Write Code Helping or Hurting My Long-Term Growth?
Reflecting on whether relying on AI tools like ChatGPT or Claude for coding helps deepen understanding or hinders the development of true programming skills.
Why Feature Engineering and Domain Knowledge Outperform Fancy Models
This post highlights why feature selection and domain knowledge matter more than complex models, especially when building real-world ML solutions.
What is LLM Fine-Tuning? Making the Model Speak Your Language
This post explains the concept of fine-tuning large language models (LLMs) from a practical perspective, focusing on shaping model outputs through diverse an...
What is an LLM, and Why Are Transformer Models So Dominant?
This post discusses the fundamental concepts behind LLMs and transformer architecture, and explains why transformers have become the backbone of modern AI mo...
Deploying LLM Services on a Minimal Server: Navigating the Constraints
This post explores the challenges and solutions for deploying LLM services on small, cost-constrained servers like Oracle Cloud Free Tier — a crucial skill f...
Exploring Trading Strategies: From API-Based Speed to Randomized ‘Monkey’ Methods
This post explores my iterative thinking on how to build a better stock trading strategy — from fast API-based entries to random selection strategies inspire...
Using Kiwoom API to Fetch Stock Volume Data for ML Forecasting
In this post, I explore how I used Kiwoom REST API to fetch real-time and historical stock trading volume data, and my efforts toward building an ML-based pr...
Why Data Matters: Lessons from Chasing Quality Datasets
This post reflects on the crucial role of data in training ML models, highlighting challenges in acquiring quality data and lessons from my experience buildi...
Why I Chose vLLM for High-Performance LLM Serving
This post explores why vLLM became the backbone of my LLM service infrastructure, focusing on speed, scalability, and real-world deployment challenges.
Understanding the Role of RAG in LLM-Based Services
This post explores why Retrieval-Augmented Generation (RAG) is critical when deploying large language models (LLMs), and how it helps mitigate hallucination ...
Choosing FAISS over Weaviate for Vector Search in Resource-Limited Environments
This post documents my decision-making process between FAISS and Weaviate for implementing vector similarity search in a resource-constrained AI project.
Real-Time Data Synchronization: Solving Market Data Freshness and UI Consistency
Building a reliable real-time stock ranking system required solving complex data synchronization challenges between market API constraints and user interface...
Bridging Legacy and Modern: Integrating Kiwoom API with Modern Web Architecture
I faced the challenge of integrating a Windows-only desktop API into a modern web application, requiring innovative solutions to bridge completely different ...
Building an AI Platform for Seniors: Facing Real-World Challenges
When I started building my AI platform for seniors, I quickly discovered that good intentions weren’t enough - I faced serious challenges in design, technolo...
Building an AI Platform for Seniors: Setting Goals and Understanding the Challenge
I set out to create an AI platform that would make artificial intelligence accessible to people in their 40s, 50s, and 60s - but first I had to understand wh...
Mastering Python Fundamentals: Slicing, Sequences, and Conditional Logic
I explored three core Python concepts that deepened my understanding of list manipulation, sequence generation, and conditional programming.
Building a Logic Gate Simulator and Expression Parser
I created two programs that helped me understand boolean logic and string parsing: a logic gate simulator and a simple expression evaluator.
Solving Two Algorithmic Problems: Bitwise Operations and Range Updates
I tackled two interesting algorithmic problems that taught me about bitwise operations and efficient range processing techniques.
Understanding Basic Sorting Algorithms: Bubble, Selection, and Insertion Sort
I explored three fundamental sorting algorithms to understand how lists work and gained my first real introduction to algorithmic thinking.
Django vs FastAPI: Choosing the Right Python Web Framework
I compared Django and FastAPI to understand which framework is better suited for building full web applications versus RESTful APIs.
SHAP and Feature Importance: A Shift Toward Biometric Signals
SHAP values revealed that biometric signals were more important than effort-based metrics. Here’s how that insight changed my feature engineering.
Hyperparameter Tuning with Optuna: What I Learned
I used Optuna to tune hyperparameters across XGBoost, LightGBM, and CatBoost — and found out why tuning matters more than the model itself.
Overfitting or Just Lucky? What I Learned Debugging a Too-Perfect ML Model
Sometimes your model is too accurate — suspiciously accurate. Here’s how I figured out whether I had a breakthrough or a bug.
Why Is My Logistic Regression So Bad? Troubleshooting Low Accuracy
I expected logistic regression to do decently — but it gave me 58% accuracy. Here’s how I questioned everything, and what finally made it better.
Learning SQL One Question at a Time: What I Asked and Understood
SQL learning doesn’t come from watching tutorials — it comes from asking real questions. This post is a reflection of the questions I asked on May 20 and how...
Further Learning Into SQL
Learning through .sql files instead of tutorials helped me understand how SQL behaves in actual scenarios — including filtering, grouping, and debugging comp...
Deeper SQL Practice: Subqueries, Functions, Joins, and Mistakes I Learned From
Today I practiced more advanced SQL concepts — from EXISTS and string functions to creating tables from subqueries — and made a lot of mistakes that taught m...
Project Reflection: Why I Paused My Data Project
I started a personal project with a big idea: ‘Why is machine learning hard for beginners?’ But I quickly hit domain, technical, and time-related limitations...
SQL Practice Log: Movie Data, Date Filtering, and Real-World Queries
Working through realistic SQL queries with movie data and date filters helped me move from syntax understanding to meaningful analysis.
From Confusion to Clarity: My Journey Into Machine Learning and Feature Engineering
This post traces my path through the foundational struggles of machine learning and data preprocessing, and how a Russian number plate dataset helped me unde...
Learning SQL Through Questions: How I Built My Understanding One Query at a Time
Today I worked through practical SQL problems — from basic filtering to ordering, joins, and searching by string length — and learned more by asking specific...
Why Regex and Virtual Environments Taught Me More Than Syntax
This week, I explored regular expressions and virtual environments — and discovered not just new tools, but new ways to think about structure and precision i...
Mastering Methods in Python OOP: Class, Static, and Decorators
Grouped insights on class methods, static methods, and decorators — key components of clean Python object-oriented design.
Applying Design Patterns and Structure in Python: Singleton, Modules, Packages
How I used singletons and modular structure to better organize larger Python projects.
When Code Gets Messy: Lessons from Building a User Registration System
I tried building a user registration system in Python with ChatGPT’s help. It was messy. But the lessons I learned were worth it.
Exploring Advanced Python Concepts: class methods, static methods, decorators, and singletons
I studied four key Python concepts — class methods, static methods, decorators, and the singleton pattern — to better understand how to write cleaner and mor...
Why Planning Matters: My Thought Process Behind a Data Project
A look into how asking ‘why’ guided my thinking while designing a data analysis project, before writing a single line of code.
What Confused Me While Building My Vending Machine in Python
This post documents the real struggles I faced while trying to build a vending machine using Python and object-oriented design.
Building a Vending Machine in Python Using OOP
This post explains how I built a basic vending machine system in Python using object-oriented programming, and what challenges I faced while applying class-b...
How to Parse and Analyze CSV Data in Python
CSV files are everywhere. I learned how to extract, clean, and analyze data from them using simple Python code.
How to Handle Basic File I/O in Python
A beginner-friendly summary of how to write and read text files using basic Python I/O syntax.
Understanding OOP in Python: Abstraction, Encapsulation, and Polymorphism
Through a simple student score management program, I explored three core concepts of object-oriented programming: abstraction, encapsulation, and polymorphism.
Discussion of Object-Oriented Programming (OOP) as a Beginner
This is a summary post about certain aspects that that needs to be discussed while learning Object-Oriented Programming (OOP)
Struggles of Object-Oriented Programming (OOP) While Making Rock Paper Scissor Game
Few real-life struggles when OOP is used to make a simple program
What I Learned from a Simple Number Game in Python
There a number of mistakes I made while I tried to build a rock paper scissor games. I will be going over key mistakes that I made
Building a Rock-Paper-Scissors Game in Python
I built a rock-paper-scissors game using Python. It runs for 10 rounds, stores each result, and calculates win rates. I also learned to simplify my logic aft...
Creating a Number Baseball Game in Python
I built a number baseball game where the player guesses a 3-digit number. The game tells you how many strikes, balls, and outs you got each round.
Working with Zip, Search, and Variable Arguments in Python
In this post, I practiced using zip to combine lists, understood how sequential search works, and wrote functions with variable arguments using *args.
Practicing Lambda Functions in Python
I practiced lambda functions in Python, learning how to use them for short anonymous functions, pass them as arguments, and filter lists.
Working with Functions and Data Structures in Python
I practiced creating Python functions that work with dictionaries and lists. I also explored the difference between value and reference in function arguments.
Using While and Nested For Loops in Python
In this post, I practiced using while loops and nested for loops to print number patterns, multiplication tables, and shapes like triangles and diamonds.
Processing Input with Lists and Dictionaries in Python
I practiced using lists and dictionaries to handle user input, calculate averages and wages, and compare different ways to store structured data.
Learning How to Define and Use Functions in Python
In this post, I practiced how to define and use functions in Python, including parameters, return values, and simple examples.
Practicing Python If Statements with Grading Logic
I practiced how to use if, elif, and else in Python to build a simple grading program based on total score.
Practicing Conditions and Understanding List Copy in Python
In this post, I practiced using if statements and learned the difference between shallow and deep copy using list comprehension.
Practicing Python For Loops and Accumulation
I practiced using Python for loops to print ranges, compute sums, and handle user input. Here’s what I learned.
Learning Python Data Structures: List, Tuple, Dictionary
I practiced how to use three major Python data structures: lists, tuples, and dictionaries. Here’s how each one works and how they are different.
Practicing Python String Basics
In this post, I practiced how to work with strings in Python, including indexing, slicing, escape characters, and string functions.
Python Lists and Strings Practice
I practiced how to use Python lists and strings. Here’s what I learned through some simple examples.