ethanniser/nextfaster

A highly performant e-commerce template using Next.js

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Summary Information

Updated 6 minutes ago
Added to GitGenius on September 14th, 2026
Created on October 18th, 2024
Open Issues & Pull Requests: 13 (+0)
GitHub issues: Enabled
Number of forks: 655
Total Stargazers: 4,883 (+0)
Total Subscribers: 34 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 5.6 days
Mean response time: 75.6 days
90th percentile: 366.3 days
Tracked items: 7

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Open issues: 11
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 534 days
Stale 30+ days: 11
Stale 90+ days: 11

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Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

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Detailed Description

NextFaster is a highly performant e-commerce template built with Next.js that demonstrates modern web performance techniques for product-heavy applications.

The template addresses the challenge of building fast e-commerce sites that serve millions of product pages without excessive server load. It uses Next.js 15 with Partial Prerendering to precompute page shells that are served statically from the edge, while dynamic data like cart information streams in separately. Server Actions handle all mutations. The backend uses Drizzle ORM with Neon Postgres for the database, Vercel Blob for image storage, and AI-generated content created via OpenAI's batch API and GetImg.ai for product data and images.

The template suits teams building large-scale e-commerce sites on the Vercel platform who want a reference implementation of modern Next.js patterns. It includes a complete schema and sample dataset of over one million products, though the full dataset exceeds free-tier storage limits. The project demonstrates practical cost management, having served over one million page views across forty-five thousand unique users with transparent infrastructure expenses. Developers should expect to use Vercel's ecosystem including Postgres, Blob Storage, and their AI SDK for the intended deployment model.

The project shows active development with detailed design documentation explaining architectural choices and performance decisions. The team used AI-assisted UI generation via v0 for initial component creation and documented specific examples of this workflow. The codebase is written in TypeScript and includes comprehensive local development setup instructions with database seeding guidance for working with the large product dataset.