When performance and cost matter, Harper’s fused‑stack outperforms Heroku’s multi‑service architecture by orders of magnitude—at a fraction of the price
When performance and cost matter, Harper’s fused‑stack outperforms Heroku’s multi‑service architecture by orders of magnitude—at a fraction of the price
When performance and cost matter, Harper’s fused‑stack outperforms Heroku’s multi‑service architecture by orders of magnitude—at a fraction of the price
When performance and cost matter, Harper’s fused‑stack outperforms Heroku’s multi‑service architecture by orders of magnitude—at a fraction of the price
Personalization is a data-delivery problem. Every architectural choice reduces to two distances: compute to user, and compute to fresh data. This piece maps five real architectures against both axes, scored on a concrete retailer workload where stale or slow data breaks the business.
Harper 5.1 adds int8 quantization, dropping index size by roughly 3x, search throughput improves roughly 5x, and p99 latency falls from ~9s to ~0.5s under load. The tradeoff is approximately 1% recall degradation before reranking.
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.