Systems | Development | Analytics | API | Testing

Amazon S3 became the de facto standard for storing objects due to its cheap price, and it’s designed for high durability, with a 99.999999999% durability guarantee. We can talk a lot about Amazon S3, but today in this blog, let’s see how to upload a file to S3 using the REST API. I hope most of you have tried using the SDK approach with boto3, but today let’s see the different ways to upload a file to S3 using the REST API and guess what, we’ll see a demo as well.

If you’re in QA or a developer who’s been looped into test automation, chances are you’ve heard the term UFT tossed around. Maybe you’ve worked with it back in the day when it was called QTP (QuickTest Professional), or maybe you’re hearing it for the first time amidst all the buzz around Selenium and Cypress. But here’s the thing: UFT (Unified Functional Testing) isn’t some relic of the past.

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.

Traditional test coverage focuses on code paths and user flows but agentic AI doesn’t follow flows. It reasons, adapts, and improvises. That means your 95% coverage report might look solid while the system still makes unsafe, biased, or unexpected decisions. To test these systems, coverage must evolve: you now measure things like goal alignment, reasoning paths, tool usage patterns, memory accuracy, and failure behavior.

ATLANTA, GA – August 21, 2025 - Katalon, the AI-native testing company redefining how software teams deliver quality at scale, has been named a Leader in G2’s newly launched AI Software testing category. The recognition affirms Katalon’s position as the strategic partner for global enterprises under pressure to release faster, reduce risk, and deliver reliable digital experiences in the AI era.

Think about launching a new AI chatbot for the company. After a short period, it is providing customers with inaccurate information about your return policy. Within hours, you receive customer complaints, and the customers are annoyed. Your support team is trying to address the technology-induced chaos caused by the AI chatbot. This is happening far more often than you might think, simply because a large number of businesses skip a proper chatbot evaluation platform before deploying their bot.

Choosing the right tool for quality assurance is not easy. There are so many options that promise to handle everything from planning to reporting. That is why we put together this guide to the 20 end-to-end test management software for 2025. These tools are built to manage the full testing lifecycle in one place, from test case creation to execution, analytics, and reporting.

Agentic AI systems don’t fail like traditional software - they hallucinate facts, pursue the wrong goals, overuse tools, and forget context. These failures look “correct” to traditional test cases, but feel dangerously wrong to users. One team tested an AI support bot - it passed every check, but in production, it gave refund advice that violated company policy. Not a code error. A reasoning failure.

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.

Software is now shipped faster than ever and testing evolved beyond rigid scripts and predefined steps. One approach that has always embraced adaptability, critical thinking, and curiosity is exploratory testing: the process of learning, designing, and executing tests simultaneously — often uncovering issues that traditional testing might miss. As Artificial Intelligence (AI) becomes more embedded in the software development lifecycle, many wonder: will AI replace exploratory testing?