December 2025 Summaries

A benchmark comparison of two e-commerce architectures, a Unified Harper Runtime and a Standard Microservices Stack, reveals significant performance differences when tested on localhost using k6. Harper exhibits superior performance, achieving sub-millisecond latencies and 100% success rates across all tests, even at high concurrency levels, due to its unified architecture that combines application logic and data in a single runtime, eliminating network hops and serialization overhead. Conversely, the microservices stack experiences catastrophic failures at moderate concurrency levels (200+ virtual users) and demonstrates 3-10 times higher latency even at low loads, due to the "fragmentation tax" of network serialization, connection overhead, and coordination latency inherent in its distributed architecture. The study highlights the architectural resilience of Harper in maintaining low-latency performance under load, while the microservices stack faces saturation due to its coordination demands, suggesting that unified architectures may offer clear advantages in performance, efficiency, and simplicity, particularly in production environments where network infrastructure would exacerbate the observed performance gaps.

Harper offers a streamlined solution for real-time data management by integrating messaging, fan-out, and persistence into a single runtime, serving as a comprehensive backbone rather than just a message broker. It addresses the fragmentation seen in traditional systems where different protocols like MQTT, WebSockets, and Server-Sent Events operate separately, often necessitating complex integrations and multiple services. By allowing MQTT, WebSocket, and Server-Sent Events clients to publish and subscribe to the same resources without translation layers, Harper simplifies the process by ensuring messages are immediately usable across all protocols. This architecture benefits various real-world applications, such as IoT telemetry, operational dashboards, and event-driven applications, by reducing the complexity and infrastructure wiring, ultimately enabling developers to focus more on application logic. Harper's setup requires minimal configuration, with REST and MQTT support enabled by default, and allows for data persistence and replayability through structured tables defined in schema.graphql, making real-time systems easier to build and manage.

Austin and Ivan explore the integration of Edge AI into the Harper platform, emphasizing its role in enhancing real-time inferencing capabilities, which can improve customer experiences through quicker and more pertinent recommendations. They discuss the platform's performance metrics and the importance of data privacy while acknowledging the potential applications of Edge AI beyond e-commerce. The conversation highlights the significance of community involvement in AI development and addresses ethical considerations related to data privacy.

MQTT is a widely adopted protocol for real-time messaging in IoT and distributed systems due to its lightweight footprint and resilience in unreliable network environments, employing a simple publish-subscribe pattern. While setting up and managing MQTT can be complex, Harper simplifies this process by integrating MQTT natively into its unified application platform, eliminating the need for configuration or third-party services. This platform enables developers to easily publish and subscribe using various MQTT clients or libraries and even supports authentication and WebSocket capabilities. Harper's standout feature is its ability to persist retained messages directly into tables, allowing seamless integration of real-time streams with long-term storage and analysis. This capability enables developers to build IoT, AI-at-the-edge, and event-driven systems more efficiently by providing a unified runtime that combines database, caching, messaging, and application logic, significantly reducing the complexity of managing multiple services.

Harper's unified application platform offers a streamlined approach for real-time systems by integrating MQTT, storage, and application logic into a single binary, contrasting with the more complex, service-heavy AWS IoT Core. Harper eliminates the need for multiple services by combining the database, broker, and application engine, allowing data to be moved, stored, and acted upon without integrating separate services. This model reduces operational overhead, providing an adaptable, high-performance platform that simplifies the developer workflow to just a few steps. AWS IoT Core, while robust as an MQTT broker, requires extensive integration with services like AWS Lambda, DynamoDB, and IAM to achieve similar functionalities, leading to a fragmented architecture with high operational complexity. Harper's approach offers superior adaptability and simplicity, enabling developers to focus on their data and users without being burdened by the intricacies of managing multiple services, positioning it as a modern alternative for teams building distributed systems.

Austin's interview with Charlie Gerard, a senior research engineer in cybersecurity, explores her groundbreaking project that leverages gesture recognition and AI to control smart lights, highlighting the creative intersection of technology and innovation. Charlie shares insights into her inspiration, the technical intricacies of her work, and her vision for future developments, underscoring the transformative potential of AI in smart home technology. The discussion reflects on the significance of creativity in programming and positions AI as a catalyst for advancing smart home capabilities.

Ivan Judson, a Solution Architect at Harper, explores the developer experience by guiding users on setting up and deploying applications with Harper's platform. He details the initial steps for using Docker, demonstrates deploying a single Harper instance with Grafana for monitoring, and explains how to scale up to a three-node cluster. The session wraps up with thanks to the engineering team and highlights the resources available for developers, including Docker for Desktop and a getting started repository on GitHub.

Trent Ballard elaborates on his project, Convo IQ, which utilizes AI to analyze conversations, detailing the inspiration, his technical background, and the development process including the tech stack. He also discusses the user experience and interface design, provides a demonstration of its features, and considers potential future enhancements for the platform.