TECHNICAL RESOURCES

The Engineer's Guidebook

Battle-tested mental models, configuration snippets, and architectural blueprints for shipping production-grade AI systems.

AI Security Enforcement

Protect your vibe apps from prompt injection, data leaks, and insecure API handling. A guide to building "AI-Safe" production systems.

Engineering →

Deploying Your Vibe App

Move from "local vibe" to production. A guide to deploying your AI-generated applications on Vercel, Supabase, and AWS.

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MCP for Vibe Coders

Master the Model Context Protocol (MCP) to connect your AI co-developer to your local tools, databases, and APIs.

Engineering →

Mastering the .md Architecture

How to use Markdown documentation to give your AI a permanent memory and prevent context drift.

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Reasoning Optimization

Move beyond simple "magic prompts." Learn how to use Chain-of-Thought, few-shot prompting, and recursive debugging to solve hard engineering problems.

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Advanced State Management in Vibe Apps

Transition from simple variables to complex system-wide state. Learn how to architect predictable, AI-maintained data flows.

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Vibe DevOps & Scaling

Transition from a single-founder "vibe" to a multi-million user platform. Learn how to automate your AI-driven engineering at scale.

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Setting Up Your Vibe Coding Environment

A comprehensive guide to choosing and configuring the best tools for natural language software development.

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The Vibe Retro Loop

A disciplined protocol for ending every AI building session to ensure zero technical debt and clean context for the next day.

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Visual Prompting Strategies

Learn how to use screenshots and visual feedback loops to debug UI and CSS 10x faster than traditional inspecting.

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SYSTEM READY

VIBE CONSOLE V1.0

PROBLEM_SOLVED:

AGENT_ACTIVITY:

> Initializing vibe engine...

Dropped pgvector latency from 4.2s to 18ms (SaaS) • Reduced OpenAI API costs by 68% (LegalTech) • Fixed ReAct loop dropping 34% of context (FinTech) • Scaled Python MVP to 5k concurrent users (AI Marketing) • Eliminated 98% of RAG hallucinations with hybrid search (HealthTech) • Automated 15,000 monthly support tickets using AI agents (E-commerce) • Slashed multi-agent execution time by 80% via parallel processing (Logistics) • Migrated undocumented legacy monolith to AI-generated Next.js (PropTech) • Cut token usage by 50% via prompt compression algorithms (EdTech) • Diagnosed and patched catastrophic memory leaks in node containers (GovTech) • Deployed zero-shot product classification system mapping 2M products (Retail) • Rescued stranded MVP by integrating resilient vector database (BioTech) • Resolved concurrent websocket latency for live AI translations (Media) • Built dynamic CI/CD test generation with local LLMs reducing QA queue (DevTools) • Dropped pgvector latency from 4.2s to 18ms (SaaS) • Reduced OpenAI API costs by 68% (LegalTech) • Fixed ReAct loop dropping 34% of context (FinTech) • Scaled Python MVP to 5k concurrent users (AI Marketing) • Eliminated 98% of RAG hallucinations with hybrid search (HealthTech) • Automated 15,000 monthly support tickets using AI agents (E-commerce) • Slashed multi-agent execution time by 80% via parallel processing (Logistics) • Migrated undocumented legacy monolith to AI-generated Next.js (PropTech) • Cut token usage by 50% via prompt compression algorithms (EdTech) • Diagnosed and patched catastrophic memory leaks in node containers (GovTech) • Deployed zero-shot product classification system mapping 2M products (Retail) • Rescued stranded MVP by integrating resilient vector database (BioTech) • Resolved concurrent websocket latency for live AI translations (Media) • Built dynamic CI/CD test generation with local LLMs reducing QA queue (DevTools) • Dropped pgvector latency from 4.2s to 18ms (SaaS) • Reduced OpenAI API costs by 68% (LegalTech) • Fixed ReAct loop dropping 34% of context (FinTech) • Scaled Python MVP to 5k concurrent users (AI Marketing) • Eliminated 98% of RAG hallucinations with hybrid search (HealthTech) • Automated 15,000 monthly support tickets using AI agents (E-commerce) • Slashed multi-agent execution time by 80% via parallel processing (Logistics) • Migrated undocumented legacy monolith to AI-generated Next.js (PropTech) • Cut token usage by 50% via prompt compression algorithms (EdTech) • Diagnosed and patched catastrophic memory leaks in node containers (GovTech) • Deployed zero-shot product classification system mapping 2M products (Retail) • Rescued stranded MVP by integrating resilient vector database (BioTech) • Resolved concurrent websocket latency for live AI translations (Media) • Built dynamic CI/CD test generation with local LLMs reducing QA queue (DevTools) •