Everything You Need to Build Faster

AI agents can generate and refine their own instructions using AGENTS.md, but effectiveness depends on clear human guidance, structured context, and iterative feedback rather than fully autonomous self-creation.

Harper v5.0 introduces a VM-based JavaScript environment that enhances application isolation, security, and developer experience. With application-specific context, module-level separation, and protections against prototype pollution, unauthorized access, and supply chain attacks, it delivers a more secure, scalable foundation for modern distributed applications.

AI agent costs are driven up by inefficient architecture. This guide breaks down five proven patterns, including deterministic workflows, parallel tool calls, and semantic caching, to reduce token usage, improve performance, and scale AI systems more efficiently.

Build a real-time, geofenced promo engine on Harper's agentic runtime. The Nearstore Agent collapses geofence lookup, customer data, campaigns, and AI decisions into a single process. Clone the reference repo and deploy in minutes.

Explore how a Shopify-based custom tie shop reveals a critical flaw in one LLM agent design strategy, and why context-first architectures with unified runtimes deliver faster, more accurate, and scalable customer support automation.

Harper Fabric simplifies cloud deployment by eliminating the need to choose data centers, automating infrastructure, scaling, and global distribution. Built for Harper’s unified runtime, it enables developers to deploy high-performance, distributed applications quickly without managing complex cloud configurations or infrastructure overhead.

Harper has been named the winner of two 2026 Data Breakthrough Awards: Open Source Data Platform of the Year and In-Memory Solution of the Year. Harper is the only company recognized in two categories this year, reflecting its unified runtime for agentic engineering that co-locates database, vector search, cache, and application logic into a single multi-threaded process.

rocksdb-js is a modern Node.js binding for RocksDB, offering full transaction support, lazy range queries, and a TypeScript API. Built for performance and scalability, it enables reliable write-heavy workloads, real-time replication, and high-concurrency applications in Harper 5.0 and beyond.

Harper is now open source, with its core platform released under Apache 2.0 and enterprise features source-available. This shift builds trust, enables community contributions, and positions Harper as a unified, transparent platform for developers and AI-driven applications.

This blog shows how to migrate a Harper-based AI agent from a local setup to Google Cloud Vertex AI with minimal changes—just three files—enabling scalable, production-ready deployment while preserving features like semantic caching, vector memory, and integrated tooling.

Harper v5's Resource API maps JavaScript class methods directly to HTTP verbs, eliminating routing and translation layers. Tables extend the same Resource class, unifying HTTP handling and data access into one interface. Key v5 additions include pre-parsed RequestTarget objects, Response-aware source caching with stale-while-revalidate support, and async context tracking via getContext().

Harper 5.0 launches with a fully open-source core under Apache 2.0, RocksDB as a native storage engine alongside LMDB, and source-available Harper Pro. This release delivers a unified runtime purpose-built for agentic engineering, from prototype to production.

In this podcast episode, Daniel Roe, lead of the Nuxt framework, shares insights on Nuxt 3, 4, and the upcoming Nuxt 5 release. We discuss open-source development, upgrading Nuxt apps, Vue-powered full-stack web apps, version maintenance, and the future of modern web development.

Semantic caching for LLMs can reduce API costs by 20–70% by reusing similar responses. Combined with deterministic routing and improved retrieval, enterprises can significantly lower LLM usage, though effectiveness varies by workload and improves over time.

Build a conversational AI agent in minutes using Harper’s unified platform. This guide shows how to create, deploy, and scale real-time AI agents with built-in database, vector search, and APIs—eliminating infrastructure complexity for faster development.

Trezy shares his journey from professional drummer and filmmaker to software engineer and open source maintainer. Learn about PixieJS, game development, AT Proto, BlueSky, data sovereignty, and how developers can confidently contribute to open source projects.

AI agents write decent application code but choke on multi-service infrastructure. This piece breaks down four architectural approaches emerging to close the gap, and why the winner will be the stack with the smallest surface area.

Learn how to implement secure web app authentication with Harper using the Resource API, including user schema design, session-based authentication, custom sign-in and sign-up endpoints, and protected routes for modern full-stack applications.

AI search bots skip JavaScript-heavy sites, leaving brands invisible in ChatGPT, Perplexity, and Gemini. Learn how pre-rendering at the infrastructure layer fixes the problem.

When experimentation logic, redirect limits, and origin failures were quietly costing a $1B+ retailer tens of millions, Harper delivered edge-deployed acceleration without re-platforming. 47x ROI. Six weeks to prove it.

A unified runtime collapses database, cache, messaging, and APIs into one system, reducing latency, improving throughput, and giving AI a safer, clearer platform for building durable applications.

Agentic AI promises autonomous software development, but enterprise security concerns block adoption. This article explains how credential sprawl creates risk—and how a unified runtime architecture like Harper eliminates infrastructure access requirements, enabling secure agentic engineering in production environments.