In this blog, we dive into OpenAI Whisper, a versatile and open source speech recognition model. We’ll explore its core features, including multilingual transcription and walk through practical code examples to help you get started with real-world applications in speech-to-text and voice processing tasks.
Learn the key differences between API Gateway and Service Mesh. Discover their pros, cons, and when to use each in a modern Microservices architecture.
If you’ve ever hit a 429 Too Many Requests error, you’ve experienced rate limiting in action. In today’s distributed and high-traffic environments, rate limiting is essential for maintaining system integrity and preventing abuse, especially in APIs, web services, and network systems. It controls how frequently users or clients can access resources over a given period, helping ensure fair usage, maintain performance, and improve security. For any digital service handling traffic at scale, rate limiting is a must-have tool.
In today’s high-performance computing landscape, caching is no longer optional—it’s essential. Whether you’re scaling a simple web app or building a complex microservices architecture, caching is one of the most effective ways to improve response times, reduce backend load, and deliver a better user experience.
However, not all caches are created equal. There’s an important distinction between internal (in-process) and external (out ofprocess) caches. Understanding this difference, along with the various types of caches like in-memory, distributed, and database backed caches, is key to designing efficient, scalable systems.
The Bulkhead Pattern is a critical design principle in modern software architecture, particularly in the design of resilient, scalable, and fault-tolerant systems. Inspired by the maritime engineering principle of dividing a ship’s hull into isolated compartments to prevent sinking in case of a breach, the Bulkhead Pattern in software engineering isolates different components or services of a system to prevent a failure in one part from cascading and bringing down the entire system.
In modern software architecture — especially with the rise of Microservices, cloud-native systems, and distributed applications — building resilient and fault-tolerant services has become a top priority. One service failing shouldn’t mean your entire system crashes. This is where the Circuit Breaker design pattern plays a crucial role.
In today’s fast-paced cloud-native digital landscape, businesses demand applications that are flexible, resilient, and capable of handling massive user growth. And scalability is not a luxury, it’s a necessity. Applications must handle varying loads without compromising performance.
Microservices architecture has emerged as a powerful paradigm to meet these needs, enabling organizations to build systems that are modular, independently deployable, and highly scalable. However, designing and implementing scalable microservices is no trivial task—it requires careful planning, robust tools, and adherence to best practices.
In this blog post, we’re going to build a Python application that lets two AI agents—powered by local instances of LLaMA 3.2 and Gemma—chat with each other in real-time.
