Systems | Development | Analytics | API | Testing

You will encounter debugging while developing backend systems, web applications, or automation scripts – it’s an inevitable step in the development life cycle. Debugging is a necessary step preceding any delivery of a software product. The Python ecosystem academic is different in that you may not see your errors until you are running the code. Having a stable way to debug your Python code is very important for continuing your development journey.

As enterprises increasingly embed AI and Large Language Models (LLMs) into their digital experiences, enforcing robust AI guardrails becomes paramount to safeguard users, protect data, manage operational costs, and comply with regulatory and ethical standards. Think of AI guardrails as essential controls: policy, technical, and operational layers carefully placed around your AI services to detect, prevent, and mitigate any unsafe, abusive, or unintended behaviors.

Based in Delhi, India, Vineet Singh has worked as a Senior Software Engineer at Confluent for the past three years, and he has contributed to various parts of Confluent’s core data streaming engine. Now he’s part of the team that makes Apache Kafka cloud-native, serverless, and able to power robust and scalable solutions for Confluent Cloud customers. Learn more about Vineet’s experience and growth at Confluent and how his team and environment have set him up for success.

How does your Magento 2 store’s PHP backend performance compare to other operators of Magento in general? To answer this question, we have aggregated and anonymized performance data from over 70 Magento 2 stores over the last quarter and computed benchmark numbers to compare to for the most important page types: Product details, Category Page, Search, and Homepage.

If you’re a current customer of Informatica, it may be time to consider alternatives.

If you work in QA or have experience in software engineering, it’s likely that you’ve heard the terms “test plan” and “test strategy” used interchangeably. In actuality, they’re quite different, and understanding those differences will help you write clearer documentation, run a tighter team, and ship higher-quality software. This guide breaks down test plan vs test strategy in practical terms, then shows how they fit together on real projects. Ready?

Zero-trust security ensures you verify every interaction, whether it’s a user, system, or API, before granting access. For large language models (LLMs), this approach is vital to prevent data breaches and maintain control over sensitive information. Here’s how zero-trust principles apply to LLMs: Identity Verification: Use multi-factor authentication (MFA) for users and secure API keys for systems. Regularly review and update permissions.

Agentic AI systems don’t behave the same way twice, so traditional test cases with fixed inputs and expected outputs no longer work. But unpredictability doesn’t mean untestability. Instead of checking for exact answers, testers must probe for unsafe, misaligned, or unintended behavior. Techniques like scenario replay, adversarial prompting, constraint injection, and behavioral thresholds help uncover risk, drift, and reasoning errors.

Agentic AI systems don’t follow scripts — they make decisions. That means your tests can all “pass” while the AI still hallucinates, misfires, or behaves unpredictably. Traditional QA, built for deterministic workflows, simply isn’t enough. Testing these systems is less like checking a vending machine and more like evaluating a junior employee: you’re judging reasoning, not just output.

AWS MSK has become popular because it deploys Kafka easily and bills alongside other AWS services. Over the past few years, AWS announced Express Brokers, a new cluster type that offers unlimited storage and separates brokers from storage resources. This simplifies scaling and reduces the time needed to rebalance topics when adding or removing brokers.