Browsing: Business & Startups

  # Introduction  Dictionaries in Python are useful for everything from configs, JSON data, to API responses. Most beginners only learn the basics, like creating a dictionary, accessing a key, and updating a value. That’s it. However, there’s a lot more to dictionaries than that. In this article, we’ll go through 7 tips that will make your code cleaner and more Pythonic. So, let’s get started.   # Using .get() Instead of [] for Lookups  Let’s say that you are working with a dictionary and you need to access a value. But what if the key is not there? Let’s say we…

  # Introduction  Focusing only on SELECT, WHERE, and GROUP BY is enough for basic aggregation, but many real analytical tasks require patterns that go beyond simple queries. Examples include detecting consecutive activity streaks, segmenting customers by spend tier, smoothing noisy time-series data, or tracing plan upgrade paths across rows. This article walks through 7 practical SQL patterns beyond the basics, focusing on techniques that solve real analytical problems.   # Setting Up the Dataset  We’ll use a sample customer transactions table from a fictional subscription software as a service (SaaS) company: CREATE TABLE transactions ( transaction_id SERIAL PRIMARY KEY, customer_id INT, …

  # Introduction  I know that when beginners start learning machine learning, things seem easy at first. You follow a tutorial that asks you to load a dataset, train a model, and then you see something like this: loss = “mse” or criterion = nn.CrossEntropyLoss(). And just like that, the tutorial starts talking about equations, gradients, optimization, and Greek letters. If you have ever nodded along without really understanding what a loss function does, you are not alone. Loss functions are often explained backward. Most tutorials start with the formula when they should start with the idea. This article is part…

ML system design interviews test how well you can think beyond models. In these interviews, choosing an algorithm is only one part of the answer. You also need to explain how data is collected, how features are created, how predictions are served, and how the system improves over time.  Most real ML systems are built around product decisions. A feed system decides what to show. A fraud system decides what to block. A search system decides what to rank. This article walks through 10 such problems in a practical interview style.  How to Think in an ML System Design Interview…

  # Introduction  INNER JOIN and LEFT JOIN handle most SQL queries. A smaller class of problems needs other join types: counting set-returning function results row by row, filtering rows by existence in another table, and returning rows that have no match in another table. Three less-common joins handle these cleanly. LATERAL joins let a subquery in the FROM clause reference columns from earlier in the same FROM clause. Semi joins return rows where a match exists in another table, without duplicating those rows. Anti joins return rows where no match exists. Let’s explore how to apply these patterns in practice.…

Most people used ChatGPT like a smarter search engine. Ask a question, get an answer, and move on. It works but it leaves a surprising amount of value on the table. Over the past few years, ChatGPT has evolved far beyond a simple chatbot. It can browse the web, analyze files, generate images, maintain memory, and even conduct realistic voice conversations. Yet many users continue to interact with it exactly as they did on day one. The result is a gap between what ChatGPT can do and what most people actually use it for. If you’ve ever felt that ChatGPT’s…

  # Introduction  OpenAI Codex is one of the most useful tools for building software with AI. Instead of only asking for code, you can use it to plan features, edit files, fix bugs, connect tools, and turn ideas into working projects faster. If you are new to Codex, start with this tutorial first: OpenAI Codex Full Tutorial in 2026 | Using Codex from Scratch. It shows how Codex works on a real project from start to finish. You will see how to give instructions, improve the app step by step, and use Codex like a coding partner. Once you know…

  # Introduction  It started with a Tuesday that completely got away from me. I had three client briefs to summarize, a backlog of research tabs I kept promising myself I’d get to, a few emails that needed thoughtful replies, and a half-written technical document sitting open in one tab for the better part of four days. By the time I looked up from context-switching between all of it, it was past 7 PM and I’d shipped almost nothing meaningful. That evening, instead of closing my laptop and calling it a loss, I started thinking about the problem differently. I wasn’t…

Having the right certificate can make all the difference. But with so many out there, getting the right one isn’t easy. That’s where OpenAI Academy comes in. OpenAI, the company behind the ChatGPT models, has introduced a learning platform through its OpenAI academy that offers AI courses for upskilling professionals. These courses cover topics like AI fundamentals, Applied AI, Agents, and Workflows. The best part? All of these courses are completely free and offer certificate of completion after finishing the final assessment. This article lists the best OpenAI Academy’s AI courses available and explains whom each course is best suited…

  # Introduction  Row-by-row iteration is one of the most common performance bottlenecks in pandas code. On small datasets it goes unnoticed, but for processing large datasets, this becomes impactful. pandas is built on top of NumPy, which executes operations on entire arrays at once using compiled C code. Looping through rows in Python bypasses that entirely and forces every operation back into the Python interpreter — one row at a time. This article covers 7 alternatives to loops in pandas, each suited to a different kind of transformation. By the end, you’ll have a clear mental map of which tool…