Systems | Development | Analytics | API | Testing

Here's what I've learned after working with hundreds of QA teams: the bugs that hurt most aren't the obvious ones. They're the edge cases we once deemed "unlikely." When it comes to test coverage, these overlooked scenarios often become your biggest headaches. I see it constantly: teams with impressive coverage metrics still miss critical scenarios. Like when a major retailer's checkout system failed because nobody tested what happens when a discount code expires mid-transaction during Black Friday!

Today, we’re excited to introduce Message Reactions in Ably Chat - a quintessential part of any modern chat experience, now available as a native feature. How message reactions work Each reaction in Ably Chat is defined by a simple string, often a UTF-8 emoji like , but it could also be a tag (:like:) or text (+1). Reactions are aggregated in realtime based on their name.

APIs are the digital lifelines powering modern applications, microservices, IoT devices, and everything in between. They act as the universal translators of data, ferrying information between diverse software platforms. API security encompasses the technologies, practices, and protocols dedicated to protecting these invisible workhorses from unauthorized access, data breaches, and malicious misuse.

The evolution of artificial intelligence (AI) in the enterprise has reached an inflection point. While the early days of generative AI focused on chatbots responding to human prompts, today's enterprise AI agents are fundamentally different—they're event-driven, autonomous systems that continuously process streams of business data, make real-time decisions, and take actions at scale.

Large Language Models (LLMs) like GPT-4, Claude, and LLaMA have reshaped the way businesses think about intelligence, automation, and human-computer interaction. But the performance of an LLM hinges entirely on what powers it: data. And that data must be systematically collected, cleaned, enriched, and delivered—a task owned by the ETL (Extract, Transform, Load) pipeline.

In a modern data stack, reliability isn't optional, it's a requirement. Data teams are tasked with building pipelines that extract from dozens (sometimes hundreds) of disparate sources, transform data under strict business logic, and load it into analytics-ready destinations. But even the most well-architected ETL workflows can fail silently without rigorous testing.