Build Any Mac App With Claude Code
Learn how to build any native Mac App using BAMA, a free set of Claude Code skills. This process guides AI agents through planning, design, and shipping, creating optimized Swift/SwiftUI applications.
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Digests from Dave Ebbelaar — Dave Ebbelaar videos on AI automation, agents, and practical AI development.
Learn how to build any native Mac App using BAMA, a free set of Claude Code skills. This process guides AI agents through planning, design, and shipping, creating optimized Swift/SwiftUI applications.
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AI is drastically changing software development. The focus is shifting from writing code to mastering system architecture, process governance, and the role of the forward-deployed engineer.
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Learn the optimal freelance model: combining long-term contracts, project work, and maintenance retainers. Focus on AI automation for SMBs and building proprietary IP.
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Learn how to run powerful coding agents like Claude Code and Codex in a persistent cloud environment using a VPS and Herdr. This guide covers everything from secure SSH setup to advanced workflow integration.
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Learn how to build a scalable company knowledge base using Fumadocs and Sanity. This tutorial covers integrating semantic search and adding a powerful AI chat widget for enterprise-level documentation.
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Learn about TypeSafe's Jev model, a new paradigm for structured AI classification. We cover Choice, Score, and Null, demonstrating how to build reliable, cost-effective, and fast decision-making workflows using the Python SDK.
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Master multi-agent coding with Herder. Learn how to set up persistent, customizable workspaces, orchestrate AI agents (Grock, Codeex, Claude), and integrate tools like zoxide and Lazygit into a single, powerful CLI environment.
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Build an end-to-end document review app using Python, React, and Azure. Learn to implement a robust pipeline covering classification, data extraction (Document Intelligence), validation, and GL account suggestion.
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This roadmap guides transitioning from Data Science to AI Engineering, emphasizing the shift from notebooks to structured Python projects using FastAPI, Pydantic, Docker, and PostgreSQL for production-ready systems.
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Learn the definitive five-layer stack for AI automation: Backend (Python/FastAPI), Database (Postgres/Supabase), Frontend (React/Vite), AI Models, and Infrastructure (Docker). Build production-ready systems.
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Build a full-stack GenAI Document Copilot from scratch using FastAPI, React, and Supabase Postgres with pgvector. Learn the entire RAG workflow: data ingestion, chunking, embeddings, and retrieval.
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