How we ship a finished, fully-owned product in days, at a fixed price. Technical deep dives, case studies, and the real numbers behind AI-native development.
A WooCommerce store was failing under load: 0.7% of visits succeeded, 41-second loads, 281,509 503 Service Unavailable errors in a day. How we made it production-ready at a fixed price, measured across 2.75 million log lines.
Read more →A practical guide to operating WordPress from the terminal through the Model Context Protocol: reading state, editing Gutenberg and Elementor, running WP-CLI and PHP, and the gotchas that break pages if you skip them.
Read more →How we wire Convex into an Astro site for real-time data without giving up Astro's static-by-default speed. The recipe, plus the gotcha that breaks first attempts.
Read more →A practical guide to adding AI to an existing SaaS: which feature to build, the tools and APIs to connect an AI assistant, what it costs, and what to skip.
Read more →42% of startups fail building the wrong thing. Only 3% hit $1M ARR inside a year. The winners ship faster, learn faster, and re-decide weekly. Here is how.
Read more →Vibe coding produces great prototypes. Shipping them to production without review has caused data breaches, app store bans, and deleted databases.
Read more →A side-by-side comparison of MVP budgets in 2026: $5K validates demand, $12K builds the production moat, $50K adds enterprise features. Pick by stage.
Read more →Stop searching for a technical cofounder. Developer shortage, equity fights, and hiring timelines make it the wrong move for most founders. Here is what works.
Read more →What it actually costs to build a SaaS MVP in 2026. Side-by-side: hiring, freelancers, agencies, no-code, AI builders, and AI-native development. With numbers.
Read more →AI builders like Lovable and Bolt deliver ~70% of a working app. The remaining 30% costs $5K-$20K in engineering. Here is what breaks and how to plan for it.
Read more →Our opinionated SaaS MVP stack: Next.js, Convex, TypeScript, Tailwind, Mastra, Python, Vercel. Why we chose each piece, what we ruled out, what we'd revisit.
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