Remy Articles
Browse 64 articles about Remy.
The AI App Builder That Fits How PMs Actually Work
The best AI app builder for PMs maps to your workflow: describe the app, review a readable spec, get a roadmap and pitch deck. Here's how five tools compare.
Where the AI App Builder Category Is Headed in 2027
Seven predictions for the AI app builder in 2027 — why every tool ships a backend, the spec becomes the differentiator, and apps start composing each other.
The Real Cost of AI-Generated Code Drift, and How to Stop It
AI-generated codebases rot as engineers hand-edit and models change. Here is why that drift compounds, what it costs, and how a spec resets it.
Best AI App Builders With a Real Backend, Database, and Auth
Most AI builders generate a frontend. Far fewer ship a real backend, a persistent database, and working auth. Here are the ones that pass the test.
The Best AI Tools for Building Internal Tools in 2026
A field guide to the strongest AI tools for internal tools — coding agents, product agents, and AI low-code — matched to the apps ops teams build.
Best Bolt.new Alternatives for Production Full-Stack Apps
Bolt is fast for prototypes, but iteration burns credits and the backend leans on third-party infra. Here are the strongest Bolt alternatives for production.
Best Lovable Alternatives in 2026: Past the Prototype
Seven Lovable alternatives ranked on backend depth, auth, database persistence, deployment, and lock-in — for builders who need apps that survive production.
Best Replit Agent Alternatives in 2026: Five That Ship Real Apps
Replit Agent builds full-stack apps from a prompt in the browser. These five alternatives go from natural language to a deployed app a different way.
The Death of the $50K Internal Tool Build (and What Survives It)
AI app builders now compile production-grade internal tools for the cost of dinner. Here's what that does to the custom-software dev-shop economy.
The Compiler Comparison: Is the LLM Actually a Compiler?
An LLM is non-deterministic where gcc is not — but reproducibility is a workflow property, not an engine one. Here is why that distinction matters.
What Lovable's Backend Push Reveals About the Spec-Layer Race
As Lovable, Bolt, and v0 all ship backends, feature parity stops being a differentiator. The real race is over who owns the spec layer.
The mindstudio.json Manifest: The One File a Remy Project Requires
A field-by-field walkthrough of the mindstudio.json manifest — appId, roles, tables, methods, interfaces, and scenarios — and what each one declares.
The 'Build It For Me' Shift: Why No-Code Gave Way to AI App Builders
No-code asks you to assemble the app by hand. AI app builders generate it from a description. Here is what that shift in interaction model actually changes.
The One Layer of Your AI-Built App You Actually Own
"Open source AI app builder" hides four different things. Here's a taxonomy of what's open across Remy, Bolt, Lovable, and Replit — and what you keep.
Scenarios: How Remy's Agent-Authored Test Cases Work
Remy scenarios are seed scripts the agent writes to put your dev database into a known state. Here's the execution model and the headless protocol.
Manifest, Methods, Tables, Roles, Interfaces, Scenarios: The Remy Vocabulary
A plain-language Remy glossary covering the six core primitives every builder meets: the manifest, methods, tables, roles, interfaces, and scenarios.
The Unit Economics of $30 Full-Stack Apps (Yes, Really)
AI-compiled apps cost $30-40 in inference to build. Here's the cost breakdown—and what it means for traditional dev-shop pricing.
The Vertical Internal-Tool Market Is About to Restructure
As AI compiles custom internal apps for the cost of a lunch, the build-vs-buy line moves — and enterprise software spend reorganizes around owned apps.
What Does 'Full-Stack' Actually Mean in an AI App Builder?
Most AI app builders claim full-stack. Few meet the bar. Here are the five criteria that separate a real backend from a polished demo.
How AI Compiles a Spec Into a Full-Stack App: The Real Pipeline
From markdown spec to deployed app: the parse, generate, compile, migrate, and deploy pipeline that turns annotated prose into production code.