Systems | Development | Analytics | API | Testing

Just like humans, AI systems are shaped by how they are brought up. In the case of Large Language Models, this upbringing happens through data collection, training, and productization. At each of these stages, bias can quietly enter the system through the data we select, the way models are trained, or the assumptions embedded into the final product. These biases, whether intentional or accidental, influence how models think, respond, and interact with users in the real world.

As AI becomes more capable, many managers assume that knowledge workers can be easily replaced by machines. Yet innovation still comes from people learning, collaborating, and sharing ideas. Rather than worrying about replacement, knowledge workers can actively demonstrate their value by developing strong public speaking skills.

Learn more at speedscale.com or read more at https://speedscale.com/blog/peeking-under-the-hood-with-claude-code/

While analyzing network traffic, we found huge amounts of telemetry including chat snippets, being sent to statsig.anthropic.com. The irony? Statsig was recently acquired by OpenAI. In this video, we use proxymock to intercept the traffic and show you exactly what’s being sent from your terminal to Anthropic (and technically, OpenAI’s infrastructure).

Everyone is talking about Claude Code, but few people understand the machinery running in the background. Today, we’re opening up the terminal to see how Anthropic’s coding agent manages state, runs tests, and fixes its own bugs. From the Model Context Protocol (MCP) to its unique React-based terminal UI, find out what makes Claude Code the most "senior" feeling AI assistant on the market.

This talk explores the challenges of testing agentic AI systems—AI that autonomously reacts to events and initiates processes. Drawing on decades of experience, Robert Sabourin emphasizes that testing begins and ends with risk. A three-dimensional model (business impact, technical risk, autonomy) guides evaluation. Testers generate ideas using a broad taxonomy, from capabilities and failure modes to creative and adversarial approaches. Continuous testing and monitoring ensure findings inform business decisions, emphasizing learning over correctness.

Katalon’s AI approach is different because it builds on tools teams already use, adds AI without forcing process changes, and introduces novel capabilities like generating tests directly from real user behavior. It also applies AI across the entire testing lifecycle, creating a more complete and unified solution than most tools offer. — Coty Rosenblath, Chief Technology Officer at Katalon Follow Katalon for more insights in our series!

As organizations rapidly adopt Large Language Models, many discover that building reliable and trustworthy AI systems is far more complex than traditional software development. LLMs are non-deterministic, context-sensitive, and prone to issues like bias, hallucinations, and prompt injection, making quality assurance a deeper challenge than simple testing.