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Every day, thousands of new apps make their way into the mobile app world. Remember 2018? That’s when app downloads shot up by 9%, and smartphone users spent a jaw-dropping $100 billion on apps. Crazy, right? Now, let’s talk about custom app development and cross-platform apps. By 2018, their market had already crossed $7.5 billion. But why are we going on about 2018?

This post was originally published on Medium on Nov. 20, 2024. The rise of agentic AI has fueled excitement around agents that autonomously perform tasks, make recommendations, and execute complex workflows blending AI with traditional computing. But creating such agents in real-world, product-driven environments presents challenges that go beyond the AI itself.

Regular expressions, commonly known as regex, are a tool for text processing and pattern matching. In Python, the re module offers a robust implementation of regex, allowing developers to handle complex text manipulation efficiently. In this article, we'll get to grips with regular expressions and provide practical code examples — from the basic to more advanced — so you can understand how to use regex in Python.

We’re on a mission to simplify application deployment for developers and businesses worldwide, whether they're AI-driven models, full stack applications, APIs, or databases. Our next-generation serverless platform significantly accelerates your deployments and improves efficiency, enabling you to build more with less spend. 2024 was a major year for us, packed with crucial serverless milestones.

With the ever-growing focus on GenAI, many legacy BI tools have failed to invest in the analyst. By focusing solely on AI experiences for business teams, they’ve alienated data teams, relegating analysts to disjointed tools and data silos. When in reality, businesses still need people who can help decision-makers assess messy data to diagnose and evaluate business problems. In fact, in this age of AI, we need analysts more now than ever.

Is shifting right trendy, is the center just moving, or are teams just carrying on because nothing breaks?

Explore the evolving challenges of data security and how modern strategies, like Fivetran’s Hybrid Deployment, enable businesses to scale securely and efficiently.

You’ve put a lot of work into getting traffic to your site and conversions are low. The good news is you’re not alone—many businesses have the same problem. The even better news is there are proven ways to increase your conversion rate and get more from your website. Conversion Rate Optimization (CRO) testing is the foundation of all of these strategies. Through trial and error of the tools and techniques, the best practice is found and the audience is reached.

Over the last three geospatial-centric blog posts, we’ve covered the basics of what geospatial data is, how it works in the broader world of data and how it specifically works in Snowflake based on our native support for GEOGRAPHY, GEOMETRY and H3. Those articles are great for dipping your toe in, getting a feel for the water and maybe even wading into the shallow end of the pool. But there is so much more you can do with geospatial data in your Snowflake account!

NYC, often referred to as Istanbul’s command-line interface (CLI), is a powerful code coverage tool designed specifically for JavaScript testing. It works seamlessly with testing frameworks like Mocha, Jest, and Ava, making it an invaluable resource for developers looking to measure and improve the coverage of their tests. NYC not only tracks how much of your code is covered by your existing test suite but also provides detailed reports that help identify untested parts of your codebase.