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SAN FRANCISCO — Test automation platform provider Sauce Labs today announced the general availability of Sauce AI for Test Authoring, an AI Agent that translates business intent into framework-agnostic executable test suites that can be run instantly on Sauce Labs’ virtual and real-device clouds. This launch introduces a new paradigm for DevOps: Intent-Driven Testing, designed … continue reading

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Vibe coding changed how I build software. I describe what I want, AI writes the code. But there’s a problem nobody talks about.

The 60-80 problem

AI tools generate 60-80% of boilerplate correctly. The remaining 20-40% requires debugging, refactoring, and domain knowledge.

This is where most vibe coders get stuck. The AI builds something that looks right, but under the hood it’s fragile and inconsistent.

My solution: give AI better context

I built KitRocket — a SaaS boilerplate designed specifically for AI coding tools.

The key insight: AI works better when it understands the codebase.

Here’s what that means in practice:

Small files

Every file is under 100 lines. AI tools have context windows — smaller files mean better comprehension.

Consistent patterns

Every module follows the same structure: lib/ → api/ → components/ → hooks/ → types/. Once AI learns one module, it can build another.

Types as documentation

All shared types live in src/types/. AI reads these to understand the data contracts between modules.

Prompt Recipes

This is the killer feature. Each recipe is a markdown file that describes a complete feature:

# Recipe: Add Dashboard

## What this builds
Analytics dashboard with stat cards and charts.

## Files created
- src/app/(dashboard)/dashboard/analytics/page.tsx
- src/components/dashboard/stat-card.tsx
- src/components/dashboard/signup-chart.tsx

## Prompt
You are working on KitRocket, a Next.js SaaS boilerplate.
Create an analytics dashboard following existing patterns...

You paste this into Claude Code or Cursor. The AI reads the architecture context, follows the file paths, and builds the feature correctly.

CLAUDE.md

Claude Code reads this file automatically at the start of every session. It’s under 80 lines and includes: project structure, commands, patterns, and rules.

The tech stack

I chose each tool deliberately:

• Next.js 15 (App Router) — Server Components, API routes, SSG
• Supabase — free PostgreSQL, real-time capable
• Drizzle ORM — lightweight, type-safe, AI-friendly
• Better Auth — flexible, no lock-in
• DodoPayments — Merchant of Record (taxes handled globally)
• Vercel AI SDK — streaming, multi-provider
• Resend + React Email — beautiful transactional emails
• Tailwind v4 + shadcn/ui — utility-first, accessible

What I’d do differently

1. Start with one tier. I initially had Starter ($99) and Pro ($199). Two tiers create decision friction. One price ($77) converts better.

2. Build the marketing site separately. The product repo and the sales site are different codebases. Mixing them creates complexity.

3. Invest in Prompt Recipes earlier. They’re the differentiator. Every hour spent on recipes saves buyers 10+ hours.

Results

Just launched. The site is live at kitrocket.dev. Goal is 65 sales in 3 months.

If you’re building a SaaS in 2026 and using AI coding tools — give it a look. The boring part is done.

At the JavaOne conference today, Oracle made a series of announcements related to a new Java Verified Portfolio (JVP) and new JDK Enhancement Proposals (JEPs). The  JVP is designed to give developers a trusted set of tools and services to support today’s speed of AI-driven application development, and also includes commercial support for the JavaFX … continue reading

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The rapid adoption of AI coding assistants has introduced a new and pressing challenge for the software industry: ensuring the security of AI-generated code. Harness, a software delivery platform provider, is tackling this today with two significant product announcements aimed at securing the entire Software Development Life Cycle (SDLC), from the moment code is written … continue reading

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