Systems | Development | Analytics | API | Testing

If you are a developer that is running Cursor as your IDE, you have probably had the ability to experiment with different AI agents in pursuit of productivity. One of the most exciting new offerings is DeepSeek V3 is open-source LLM, with added capabilities for code generation, reasoning, and multi-turn conversations.

It’s been almost a year since we released our Konnect Terraform provider. In that time we’ve seen over 300,000 installs, have 1.7 times as many resources available, and have expanded the provider to include data sources to enable federated management of your Konnect organization. There have been many changes in the last year, but there are some changes that we’ve been holding off on as they would break your CI/CD pipelines.

Real-time AI is transforming how businesses process and use data, demanding faster, more reliable, and scalable infrastructure. Unlike older batch processing systems, real-time AI provides instant insights for applications like fraud detection, personalized recommendations, supply chain adjustments, and predictive maintenance. However, scaling these systems introduces challenges like managing massive data streams, ensuring low latency, and maintaining security.

Growing business demands require enterprises to manage a vast number of APIs. The volume and complexity of these integrations make maintaining quality a challenge. To address this, governing the API lifecycle—from design to retirement—has become essential. API management vendors are therefore developing tools and frameworks to assist.

You have probably seen pages where fresh information loads in without a page reload, or some forms that submit without an apparent refresh. What is happening here is that API calls are being made to send and receive information in background. API calls generate seamless and responsive application experiences. In this introductory tutorial, you will learn to examine an API call in the Chrome DevTools; replay it in Python, and sanitise the Flask REST API data for safe and organised input into the database.

In an increasingly connected digital ecosystem, with applications allowing for smooth communication across platforms and services, it is important to understand what is API testing in order to maintain the delivery of reliable software solutions. API testing is foundational to modern software assurance, effectively auditing the invisible bridges lending themselves to data us and functionality between different software components to ensure they are working optimally numerous conditions and scenarios.

Tired of wasting hours triaging errors and digging through logs? With BugSnag from SmartBear and GitHub Copilot, you can now debug faster : AI surfaces the root cause, explains the issue, and helps you fix it – all from your IDE. Powered by SmartBear’s integration with the Model Context Protocol (MCP), and bringing runtime context into your development tools, errors from BugSnag are automatically enriched with the technical metadata your LLM needs to deliver accurate, relevant suggestions.

Modern innovation demands fast, secure, and flexible access to data. But when organizations deal with scattered databases and strict security policies, manual API development slows everything down. The solution? Automate how APIs are built, secured, and managed—using AI and open-source tools like DreamFactory.

If you build APIs, you’ve probably been in two kinds of conversations. One is the war room, where the team is chasing a latency spike, an outage, or a sudden drop in traffic. The other is the boardroom, where you need to show how the platform is performing, which APIs are growing, and whether your investments are paying off. Dashboards are the tool people turn to in both cases.

Service level agreements (SLAs) are the contractual backbone between customers and technology vendors, outlining expected service availability, performance metrics, and remedies like service credits when service providers fail to meet agreed-upon service levels. This service agreement assures both the technical quality as well as the service quality of the services provided, and underpins the value perspective of the client.