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Why Most ML Projects Break After the Model is Built

A machine learning model may perform brilliantly on test data, but it can also turn problematic as soon as it is pushed to production. The issue is rarely the algorithm itself. While prototyping and during testing, a model usually runs in an ideal environment where the data is prepared, the infrastructure is known, the inputs are relatively predictable, and someone is usually closely watching the experiment.

Boosting Customer Experience With Seamless Payment Flows

The final step in any transaction, payment, is often the most overlooked part of the customer experience. Businesses pour resources into attracting customers and guiding them to a purchase, only to lose them at the last minute because of a clunky, confusing, or untrustworthy payment process. A smooth payment flow isn't just a technical detail; it's important for customer satisfaction and brand loyalty. When a customer decides to buy, giving you their money should be the easiest part of their entire interaction with your brand.

The StatusGator mobile app is here

We’re excited to launch the StatusGator mobile app for iOS and Android, designed to put outage alerts right in the palm of your hand. Your StatusGator status page is the central hub for your entire organization to view and understand the status of all your services. These pages have always been accessible on mobile. Now, with the dedicated app, end users of your status page can take the services they depend on with them and receive updates directly on their phones.

Building an AI-Powered Time Series Dashboard for Data Center Ops

AI demand is pushing data centers to get bigger, denser, and more distributed, while the data that runs them still often sits in different systems, slowing down operations and hampering efficiency. By leveraging a time series database like InfluxDB to create a single, unified telemetry layer, you can simplify your data stack, comfortably handle the high volume of data, and solve problems faster and more efficiently, helping to minimize waste and maximize efficiency, saving time, electricity, equipment, and money.

Android development shouldn't start with a physical device

How on-demand Android environments lay the foundation for Android engineering Software engineering has evolved dramatically over the last decade. Development environments that once depended on dedicated hardware have become resources that can be provisioned, configured, and removed on demand. Infrastructure is now expected to be reproducible, automated, and integrated into continuous development workflows. However, Android has largely remained an exception.

What Is an Agentic Development Environment? Kepler Is GitKraken's Answer.

Every new AI coding agent comes with the same pitch: write code faster. For most devs, that part already checks out. Codex writes a function in seconds. Claude Code refactors a file mid-meeting. Copilot fills in a test before you finish describing it. None of that touches the problem that shows up an hour later: five agents running across three repositories, each with its own diff, and no single place to see what’s stuck, what’s done, and what’s actually safe to ship.