Everything You Need to Build Faster

Harper runs live, personalized apps 4–14x faster than Vercel by unifying data, logic, cache, and messaging in one runtime.

AI coding works in a sandbox because the environment is trivially narrow. Real systems have history, constraints, and blast radius. Coding agents make sound decisions only when the architecture is explicit and shared. Opinion isn't a constraint on agentic engineering, it's what makes it possible at scale.

A nature-restoration game with six biomes, 150 animals, and a real food web — built with a single Harper component as the entire backend. One YAML file wires the database, API, content seeder, and static host. The same binary ships offline on itch.io.

The web spent a decade optimizing for browsers. JavaScript-heavy rendering, dynamic CMS templates, and client-side hydration made pages beautiful and machines blind. AI answer engines retrieve, parse, and cite content directly. If your best content is trapped behind a render cycle, a cleaner source wins.

Harper 5.1 ships three layered AI capabilities: a provider-agnostic Models API supporting OpenAI, Anthropic, Bedrock, and Ollama; a built-in agentic loop via toolMode: 'auto' with hard budget controls; and an opt-in Harper Agent component. Switching providers is a config change, not a code change.

Harper 5.1 introduces native MCP support, auto-generating secure tools from your application schema so agents can access data, invoke operations, and connect through standard clients without adapter code or infrastructure.

Harper was named a 2026 Gartner Hype Cycle for Digital Commerce Sample Vendor, recognized in Front-End Cloud for its unified runtime combining data, application logic, messaging, and agent development together.

Rails proved that opinionated frameworks make developers faster. Now AI-native, distributed applications need the same kind of defaults for data, logic, caching, and messaging.

Discover how Harper successfully transitioned to a sustainable commercial open source platform. Ethan Arrowood details their split core licensing strategy, engineering velocity, and practical framework for building true community trust.

End-to-end latency in real-time pipelines comes from coordination across systems, not from any single component. Four common workloads, tested two ways, show where multi-hop architectures compound delays and where collapsing storage, messaging, and compute into one runtime changes the math.

Most teams treat a cache as a black box: URL-keyed blobs with a TTL, useful for speed and nothing else. In Harper, cached data lands in a real table inside the same query engine. That means filtering, joining, real-time subscriptions, and vector search all work against it.

Learn how to keep AI-assisted side projects on track using a repo-native coordination layer that preserves context across session gaps.

Deserializing entire records to read one field is a bottleneck at scale. Structon stores objects in a binary format where any field is reachable by byte offset, with lazy getters that never allocate until you access a property. It's the encoding Harper has used internally for years, now a standalone package.

Production agents spend 56–74% of their time waiting on tool calls and infrastructure, not the model. Microservices were the right answer for the web era; agentic workloads need the stack pulled back into a single unified runtime.

Watch Austin rebuild his personal website live using Claude AI and Harper, including a custom Markdown CMS, GraphQL schema design, React scaffolding, and full deployment. A real-time pair coding session from design to launch.

AI has made it dramatically cheaper to get software to a working version, but most companies still plan like building is the expensive part. The new bottleneck is the product loop: forming sharp hypotheses, living inside the user experience, fixing friction as it appears, and feeding evidence back into the roadmap faster than ticket-based planning allows.

A non-developer's live stream walkthrough of building Flow State, a Colorado river-flow app for rafters, in two hours using ChatGPT dictation, Claude Code, Claude Design, and Harper. Scaffold with npm create harper@latest and deploy to Harper Fabric. No coding background required.

Harper enables production-grade agentic engineering by collapsing database, cache, runtime, and messaging into one process, reducing agent complexity and review burden. A multi-model dispatch workflow lets specialized agents plan, code, QA, and review in parallel while humans retain control over critical decisions.

Learn how Harper turns CDC streams into real-time workflows that refresh cached pages, update search indexes, and keep AI agent context current. See why landing changes in an application runtime beats warehouses, queues, and traditional CDNs.

Production agents bleed tokens and latency on repeated queries. Pair a managed model layer with a vector-indexed data layer at the edge, and an 80% cache hit rate cuts LLM spend by 80% while delivering sub-100ms responses on semantically similar requests.

Harper bridges the gap between business agility and IT security. Utilizing a unified runtime, Harper Fabric guarantees data sovereignty across any environment, from public clouds to air-gapped facilities. Empower users with secure, compliant AI application development and robust governance.

Harper offers a unified application platform alternative to Vercel + Supabase, combining database, cache, app logic, messaging, vectors, and real-time capabilities in one globally distributed runtime to reduce latency, operational complexity, and total cost of ownership.

A practical guide to building AI agents on Harper. Learn how Harper’s unified runtime supports agentic workloads with local data, durable state, native protocols, deterministic logic, and full-stack hosting, plus how it compares to LangChain, vector databases, memory layers, and hosted agent platforms.

Tech lead Matan Kushner shares his journey building Starship, contributing to AniList, and leading an engineering team at Vercel. In this podcast, we explore open source, developer tools, career growth, and lessons from a decade of building software.

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.