May 2026 Summaries

Austin Akers documents the process of revamping his personal website, which had been neglected since 2024, by collaborating with AI to improve the site's content management system (CMS). Emphasizing a chat-first approach, Akers partners with Claude, an AI tool, to plan and execute the rebuild while integrating a custom Markdown CMS. This approach contrasts with his coworker Aleks's preference for a more direct command-line interface (CLI) method. Akers highlights the benefits of using AI to streamline coding tasks, particularly in backend setup through Harper, which simplifies API creation via schema-first design. He also experiences firsthand the efficiency gains and pitfalls of relying on AI, such as overcoming the reflex to solve coding issues manually. The project underscores the evolving role of AI in software development, particularly for developers who understand the underlying systems and can leverage AI to enhance productivity. Akers plans to finalize and launch the updated site at austinakers.com, inviting discussions about the workflow on Harper's Discord platform.

In a live-streamed session, the author, who lacks formal coding expertise, successfully built a web application called Flow State to aid in planning Colorado raft trips by aggregating and simplifying access to river flow data. The app was developed using Harper's integrated platform, which consolidates various functionalities like database management, API handling, and real-time messaging, thereby reducing the complexity typically encountered in traditional microservices setups. Utilizing AI tools like ChatGPT for voice dictation and Claude Code for backend processes, the author swiftly scaffolded the app, while Claude Design handled the UI elements. The development emphasized the importance of clarity in conceptualizing app requirements, as well as the ease of integration offered by Harper's unified system, which facilitated a seamless deployment process. Although the app reached 95% completion within two hours, the author acknowledged that the remaining refinements would require more effort, highlighting the iterative nature of development in agentic coding.

In the context of building efficient agent systems using Large Language Models (LLMs), the combination of Harper and Vertex AI offers a robust solution for optimizing performance and reducing costs through semantic caching. Traditional approaches treat each query as an independent transaction, leading to increased latency and costs at scale. Semantic caching, as implemented with Harper—a distributed data platform with native vector indexing—and Vertex AI—a managed machine learning platform from Google Cloud—addresses this by storing responses to frequently asked queries, reducing unnecessary model calls. Harper's HNSW vector index facilitates fast in-memory searches, while Vertex AI provides optimized text embeddings and model inference. This architecture is particularly advantageous for applications with high semantic overlap, such as customer support, where cache hit rates can significantly cut LLM expenses. By prioritizing efficient data handling and caching strategies over brute model usage, this approach allows for substantial improvements in both latency and cost-effectiveness, making it ideal for large-scale, user-facing applications.

In the evolution from traditional coding to the use of coding agents, a new paradigm emerges where production-quality code can be achieved at the speed of prototype development, thanks to a streamlined infrastructure approach. By collapsing the stack, the coding process is simplified, allowing agents to focus on producing application features rather than managing infrastructure. This shift, exemplified in the Harper platform, integrates various components such as the database, cache, and API layer into a single Node.js process, enhancing the quality and speed of code production. The workflow involves multiple specialized roles, each driven by different AI models, which engage in a collaborative process to generate, review, and refine code. This system surpasses traditional single-agent models in efficiency and reliability, as it leverages adversarial review and a structured constitution to ensure quality and consistency across projects. The approach not only optimizes the coding process by reducing redundant infrastructure tasks but also scales effectively across multiple projects, allowing human oversight to focus on strategic decisions rather than routine supervision.

Harper emerges as a versatile runtime solution capable of handling both payload and trigger-based change data capture (CDC) events, offering a distinct advantage over traditional data destinations like data warehouses, message queues, and CDN caches, which typically struggle with trigger events. Unlike these systems, Harper allows data to be queried and updated in real-time, efficiently supporting complex workflows such as re-rendering pages or updating AI agent contexts with minimal latency. This unique capability is illustrated by a top US department store's implementation, where Harper processes product change events, enabling rapid page updates directly tied to these changes. Harper's architecture allows it to serve as both a destination and a potential future source of truth, with its native replication feature ensuring data consistency across regions. This positions Harper as a robust solution for modern applications requiring immediate and dynamic data handling, particularly in environments with large catalogs and diverse consumer demands.

Harper offers a comprehensive platform designed to address the limitations faced by citizen developers in enterprise environments, where existing platforms fail to balance ease of use with stringent security and compliance requirements. By integrating a unified database, API, and runtime platform, Harper eliminates the need for multiple security measures by consolidating authentication, authorization, and access control, ensuring that applications are secure by default. This platform also addresses data sovereignty concerns by allowing organizations to deploy their applications anywhere, guaranteeing data processing remains within specified jurisdictions, crucial for compliance with regulations like GDPR. Furthermore, Harper provides IT departments with the control they need over deployment, ensuring that citizen-built applications can be managed, audited, and scaled without compromising operational governance. This approach allows citizen developers to create applications that meet enterprise standards without sacrificing the agility and innovation that make citizen development appealing, making Harper a unique solution in the realm of enterprise application development.

Harper offers a comprehensive application platform that serves as an alternative to Vercel and Supabase by integrating database, cache, application logic, messaging, vectors, and real-time capabilities into a single globally distributed runtime, thereby minimizing latency, operational complexity, and total cost of ownership. Recognized as a Sample Vendor in the 2026 Gartner Hype Cycle for Digital Commerce and awarded two 2026 Data Breakthrough Awards, Harper's platform excels in unifying various components to support agentic engineering effectively. Its innovations include int8 quantization in Harper 5.1, which enhances HNSW vector search by improving throughput and reducing latency while maintaining high recall accuracy. Harper's architecture enables faster live personalized applications than Vercel, and its sandbox game development and AI-oriented web optimization illustrate its versatile applications.

Harper offers a unified runtime environment for building AI agents by integrating data, application logic, messaging, and agent development into a cohesive system that enhances the performance and scalability of agentic workloads. It supports local data, durable state, native protocols, and deterministic logic while providing full-stack hosting capabilities, distinguishing itself from other platforms such as LangChain and vector databases. Harper's innovation has been recognized in the 2026 Gartner Hype Cycle for Digital Commerce and with two Data Breakthrough Awards, highlighting its efficient runtime capable of running personalized applications significantly faster than competitors like Vercel. Additionally, Harper 5.1 introduces int8 quantization to optimize vector search performance, and its platform supports diverse applications, including a nature-restoration game and personalized web architectures, addressing challenges in data delivery and AI integration for enhanced user experiences.

Austin Akers and Matan Kushner engage in a compelling conversation, exploring Matan's career evolution from maintaining AniList to spearheading the development of Starship, a widely adopted cross-platform shell prompt written in Rust. They examine the complexities of sustaining open-source initiatives, the intricacies of learning Rust, and the rapid expansion of Starship. Matan also discusses his current role at Vercel, where he is focused on creating AI-driven customer support solutions that improve user experience and operational efficiency. The dialogue underscores the significance of community involvement, strategic architectural choices, and future plans for Starship, highlighting the need to balance innovation with quality maintenance in software development.