Systems | Development | Analytics | API | Testing

How Medical Practice Consulting Improves Both Compliance and Patient Experience

How closely are compliance and patient experience actually connected in a medical practice? While compliance often focuses on regulations, documentation, privacy, and operational standards, it can directly influence how smoothly patients move through their care. Disorganised processes, inconsistent communication, or poorly managed administrative responsibilities can create compliance risks while also frustrating patients.

ThoughtSpot + ClickHouse Delivers Agentic Analytics at Scale

Agentic analytics, embedded customer-facing reporting, and everyday business metrics now demand the same thing: performance at massive scale, and answers fast enough that a business user never notices the wait. Most generic databases were never designed for that combination. They assume a small population of analysts writing SQL and query latency measured in seconds, not AI agents and business users asking questions around the clock.

Five Habits That Cut IT Energy Waste, Without Spending More

Sustainability often gets treated like a policy or a pledge. But real progress comes from concrete operational habits that IT teams can adopt directly and measure over time. The urgency is growing. The International Energy Agency projects that global data center electricity consumption will more than double to around 945 TWh by 2030, with AI the most important driver of that growth. That makes efficiency not only a sustainability goal, but an operating requirement.

How to Find and Fix JavaScript Memory Leaks

Memory leaks are the bugs that really creep up on you. One minute your app is running perfectly, then suddenly it slows to a crawl or crashes. We don’t get a warning with memory leaks, so we need to prepare proactively and be ready when they hit. This means we need to: This guide is here for all of that.

How Developers Organize Web Resources During Software Testing and Development

Modern software development depends on far more than a code editor and source repository. Developers regularly move between documentation, API references, testing tools, staging environments, issue trackers, monitoring dashboards, browser tools, and deployment services. When these resources are scattered across open tabs, messages, bookmarks, and project notes, valuable time is lost simply finding the right page again. A small amount of organization can make both development and testing more efficient.

Use AI and traffic replay to test AI-generated code

When I ask an AI agent to change code, I also want it to run the application and test what it changed. Asking it to write some tests is a start. But if it invents the expected responses from the same assumptions it used to write the code, those tests can miss the same mistake. Traffic replay gives the agent something concrete to test against: requests and responses captured from a working application.

Which AI Analyst Holds Up Best for Your Hard Questions?

Analytics vendors claim their AI answers questions accurately, but almost none of them will show you how they checked. The standard move is a percentage with no denominator: "90%+ accuracy on internal benchmarks." No dataset you can download. No scoring method you can inspect. No competitor runs under the same conditions. You're asked to trust the grade without ever seeing the exam1 We ran the exam in public terms instead.

Your analytics platform is part of your attack surface

Analytics platforms are built to help organizations understand what their users are doing. Increasingly, they do much more than that: they connect behavioral data with customer context, power personalization, inform automated decisions, and provide intelligence to teams and AI systems. To do this well, analytics needs access to valuable data. That makes analytics more than a measurement layer. It makes analytics part of your security perimeter.

Start your AI agent testing with deterministic tooling

By now everyone is aware of the limitations inherent in generative AI and the AI agents that use it to complete their tasks, and the challenges involved in getting them enterprise quality. If you are planning to incorporate AI agents into your enterprise IT architecture, how are you planning to validate their quality and accuracy?