Browsing: Business & Startups

Image by Ideogram   # Introduction  When you hear the word data science, you probably think of two words: programming and statistics. In fact, the prerequisite of learning statistics often discourages people from pursuing a career in data. It doesn’t help that most data science job descriptions make it seem like you need a PhD in statistics to thrive in the role, when the reality is entirely different. In a majority of data science positions, especially in tech companies focused on product development, you need to know applied statistics. This involves using existing statistical frameworks to solve business problems. This is…

I’ve been using n8n for quite a while now, building all kinds of automation workflows: some simple, some pretty complex. But after OpenAI announced AgentKit, and especially their new Agent Builder tool, I couldn’t help but check it out. It’s been getting a lot of buzz lately, and honestly, it looks like it could shake up the entire automation space. In this blog, I’ll walk through what AgentKit actually is, try out Agent Builder, and see how it stacks up against n8n. What is AgentKit?  OpenAI’s AgentKit is a new suite of tools built to help developers and teams create,…

Being data-driven is usually used and understood with positive connotations but when I hear the word used I get a little anxious about the “data-driven” decisions that might be about to happen. Let me explain why.According to Wikipedia data-driven means “The adjective data-driven means that progress in an activity is compelled by data, rather than by intuition or by personal experience.” In other words – Look at the data as a primary source of information to act on. When the data gives you a reason to act, you act. It might at a glance seem like a very sound way…

August 1, 2024, marked a significant milestone as the European Union’s AI Act officially came into force. This groundbreaking legislation is set to reshape the landscape of AI development and deployment, not just within the EU, but globally. But what does this mean for you and your company? In this article, we’ll cut through the complexity of the AI Act, exploring its real-world implications for businesses of all sizes and sectors. We’ll break down what qualifies as an AI system, the risk-based classification system, who needs to comply, key obligations, implementation timelines, and potential penalties. Whether you’re developing, integrating, or…

Image by Author   # Introduction  You’ve been coding in Python for a while, absolutely love it, and can probably write decorators in your sleep. But there’s this nagging voice in your head saying you should learn TypeScript. Maybe it’s for that full-stack role, or perhaps you’re tired of explaining why Python is “totally fine” for large codebases. Here’s the thing: TypeScript isn’t just “JavaScript with types.” It’s what JavaScript should have been from the start. And if you’re coming from Python, you already understand more than you think.   # Going from Python to TypeScript  Python gives you duck typing and…

Artificial intelligence is changing quickly from simple chatbots to more capable autonomous agents that exhibit reasoning, coordination, and execution of complex tasks. Microsoft has recently made Agent Framework publicly available in public preview as an open-source SDK and runtime to ease the orchestration of multi-agent systems, an important step forward for enterprises adopting agentic AI to alleviate fragmentation in tooling while now providing a bridge between experimenting and production.-grade deployment. What is Microsoft Agent Framework? The Microsoft Agent Framework solves a key developer dilemma: choosing between cutting-edge AI research and stable, production-ready tools. It unifies two frameworks: AutoGen: Brings advanced multi-agent…

Artificial intelligence is becoming mainstream and many organizations, startups and big corporates alike, are now starting internal AI projects or are incooperating AI into other existing IT. There’s just one problem. Leading AI projects are very different from leading in traditional IT. As a result many AI projects either fail or ignite frustration with project participants, users of the AI and the management involved. They are unaware that they are now in a new paradigm and should have different expectations than usually have to IT. Even implementing off-the-shelf AI components or systems can cause problems that are not usual to the…

Today’s businesses constantly seek ways to accelerate innovation processes, reduce costs, and minimize risks. However, the traditional waterfall model is often time-consuming and expensive. So, how can companies innovate quickly and efficiently without breaking the bank? Enter design sprints, a game-changing approach revolutionizing AI product development. They provide a flexible, five-day framework for rapid prototyping and user testing. This approach, pioneered by Google Ventures, has been adopted by leading companies like Slack, Spotify, and Google to enhance their innovation capabilities. But what makes design sprints so effective? In this article, we’ll explore the key features and real-world applications of design…

Image by Editor   # Introduction  In any machine learning project, feature selection can make or break your model. Selecting the optimal subset of features reduces noise, prevents overfitting, enhances interpretability, and often improves accuracy. With too many irrelevant or redundant variables, models become bloated and harder to train. With too few, they risk missing critical signals. To tackle this challenge, we experimented with three popular feature selection techniques on a real dataset. The goal was to determine which approach would provide the best balance of performance, interpretability, and efficiency. In this article, we share our experience testing three feature selection…

Recruiters no longer ask you to recite the six normal forms. They want to hear you reason about data at 2 a.m. when the primary shard is hot and the CFO is responding to the stakeholders. The questions you’ll encounter in this article have been harvested from real interviews at Google, Amazon, Stripe, Snowflake, and a handful of YC unicorns. Each answer is long enough to sql muscle memory, short enough to fit in the conversational window before the interviewer nods or interrupts. Use these DBMS interview questions as a checklist, and a non-exhaustive one at that. Metric for Segregation…