# Sources

One synthesized summary per video, with provenance to the immutable [raw transcript](../raw/index.md). Grouped by theme.

## Agentic coding workflows
- [I Turned Claude Code Into a Complete Video Generation System (with Archon)](i-turned-claude-code-into-a-complete-video-generation-system-with-archon.md) - Archon's agentic harness pattern isn't limited to AI coding; the same Ralph-loop orchestration can fan out parallel agents to generate an entire product catalog of marketing videos using the Higgsfield CLI.
- [The Best AI Coding Setup Isn't the Most Autonomous One (Here's Why)](the-best-ai-coding-setup-isnt-the-most-autonomous-one-heres-why.md) - The most autonomous AI coding setup isn't the best one to reach for now; level three (delegating all coding while staying in the loop with planning and validation) is the reliability sweet spot, and higher autonomy like the dark factory should only come after you've built and evolved a trusted system.
- [The Creators of Claude Code and OpenClaw don't Prompt Their Agents Anymore?!](the-creators-of-claude-code-and-openclaw-dont-prompt-their-agents-anymore.md) - \"Loop engineering\" is a useful but over-hyped idea best folded into harness engineering: don't let a single looping agent session drive everything, wrap loops in durable, deterministic, cost-controlled harnesses that orchestrate many isolated coding-agent sessions.
- [Claude Plans, Gemini Designs: The Workflow to Build BEAUTIFUL Frontends](claude-plans-gemini-designs-the-workflow-to-build-beautiful-frontends.md) - By splitting a full-stack build into single-task coding-agent sessions that pass markdown handoff documents, you can mix providers, Gemini 3.5 Flash designing the UI and Opus 4.8 handling planning, copy, and integrations, to one-shot beautiful, non-trivial web apps cheaper and better than any single model.
- [FULL Guide to Becoming a Principled Agentic Engineer (Build Anything with AI)](full-guide-to-becoming-a-principled-agentic-engineer-build-anything-with-ai.md) - Reliable, repeatable AI coding comes not from fancy harnesses but from a simple, ownable three-phase foundation: ideate to a structured PRD, run the PIV loop per ticket, and continuously evolve your AI layer whenever the agent makes a mistake.
- [Parallel Claude Code + Git Worktrees: This Setup Will Change How You Ship](parallel-claude-code-git-worktrees-this-setup-will-change-how-you-ship.md) - To 10x AI coding output you must run many coding agents in parallel, and the only way that scales reliably is a system built on git worktrees, issue-driven work, isolated validation, and a self-healing AI layer.
- [Coding Agent Reliability EXPLODES When They Argue (New Adversarial Dev Technique)](coding-agent-reliability-explodes-when-they-argue-new-adversarial-dev-technique.md) - Pairing a coding agent with a separate, adversarial evaluator agent (a GAN-inspired harness) dramatically improves reliability by defeating the sycophancy that ruins single-agent self-review.
- [Stripe's Coding Agents Ship 1,300 PRs EVERY Week - Here's How They Do It](stripes-coding-agents-ship-1300-prs-every-week-heres-how-they-do-it.md) - Stripe ships 1,300+ fully AI-written PRs weekly by building an internal agent harness (Minions) that interweaves non-deterministic agent nodes with deterministic code steps, and you can extrapolate the same pattern into a simple plan-implement-validate loop yourself.
- [Is Software Engineering Finally Dead?](is-software-engineering-finally-dead.md) - Coding agents are powerful enough to replace most coding, but software engineering is not dying, the role is evolving toward context and intent engineering, and enterprise adoption is far slower than tech-leader hype claims.
- [Build BEAUTIFUL Diagrams with Claude Code (Full Workflow)](build-beautiful-diagrams-with-claude-code-full-workflow.md) - Packaging a diagramming workflow into a reusable skill lets any coding agent produce beautiful, educational Excalidraw diagrams that argue visually and self-validate by rendering and critiquing their own output.
- [This One Command Makes Coding Agents Find All Their Mistakes (Use it Now)](this-one-command-makes-coding-agents-find-all-their-mistakes-use-it-now.md) - Packaging a comprehensive, browser-driven end-to-end validation process into a single reusable slash command lets a coding agent test its own work as a real user would, fixing the big blockers and surfacing everything else so far less review burden falls back on you.
- [My COMPLETE Agentic Coding Workflow to Build Anything (No Fluff or Overengineering)](my-complete-agentic-coding-workflow-to-build-anything-no-fluff-or-overengineering.md) - A dead-simple, evolvable greenfield workflow beats overengineered multi-agent frameworks: build an AI layer (PRD + rules + commands), split the PRD into phases, and knock each out with a plan-implement-validate loop while obeying four golden rules (protect context, commandify everything, git as memory, system evolution).
- [How to Properly Use Claude Code Agent Teams (FULL LIVE BUILD)](how-to-properly-use-claude-code-agent-teams-full-live-build.md) - Claude Code's new agent teams let multiple agents work in parallel AND communicate with each other, and the way to use them reliably today is a contract-first plan produced through heavy clarifying questions plus end-to-end browser validation.
- [Claude Code's Agent Teams Are Insane - Multiple AI Agents Coding Together in Real Time](claude-codes-agent-teams-are-insane-multiple-ai-agents-coding-together-in-real-time.md) - Claude Code's new experimental Agent Teams feature lets multiple collaborating agents share one task list and coordinate peer-to-peer, making it far better than isolated sub-agents for implementation work if you guide it with the right instructions.
- [Turn Claude Code into Your Full Engineering Team with Subagents](turn-claude-code-into-your-full-engineering-team-with-subagents.md) - By wrapping an Anthropic-style agent harness with service-connected sub-agents (Linear, GitHub, Slack via Arcade), you can turn Claude Code into a full autonomous AI engineer that plans, codes, tracks tasks, and reports progress across many fresh-context sessions.
- [I Was Wrong About Ralph Wiggum](i-was-wrong-about-ralph-wiggum.md) - The real value of the Ralph Wiggum agent loop is not autonomous production coding but rapidly building fully-validated proof of concepts to de-risk your architecture and tech stack before applying a rigorous workflow.
- [Your AI Coding Workflow NEEDS This New Agent Browser CLI](your-ai-coding-workflow-needs-this-new-agent-browser-cli.md) - Giving coding agents a reliable, token-efficient browser-automation tool lets them validate their own front-end work autonomously, and Vercel's Agent Browser CLI beats Playwright MCP because it condenses the site into interactable references instead of relying on non-deterministic element searches.
- [Ralph Wiggum is the Final Evolution of Vibe Coding (Here's What Comes Next)](ralph-wiggum-is-the-final-evolution-of-vibe-coding-heres-what-comes-next.md) - The Ralph Wiggum bash-loop is the ceiling of vibe coding (a persistent hook that reruns a coding agent until it says done), useful for unambiguous grunt work but broken for judgment-heavy tasks, so the real next step is building proper agent harnesses.
- [AI Exploded in 2025 - Here's Everything That Happened](ai-exploded-in-2025-heres-everything-that-happened.md) - A fast chronological recap of the ~50 biggest AI events of 2025 (models, coding agents, mega-fundings, and acquisitions) read as signals for where the industry is heading in 2026.
- [I Forced Claude to Code for 24 Hours NONSTOP, Here's What Happened](i-forced-claude-to-code-for-24-hours-nonstop-heres-what-happened.md) - Anthropic's open-sourced long-running-agent harness lets a coding agent build a complex app over 24 hours by splitting work across fresh context windows, coordinated through a few shared artifact files and test-driven development.
- [AI Fixes My Code Better than Me Now?! (Here's How)](ai-fixes-my-code-better-than-me-now-heres-how.md) - A single reusable meta command can make any coding agent build a living, end-to-end validation command that tests your whole app like a real user would, catching bugs you'd never find yourself.
- [GitHub is the Future of AI Coding (Here's Why)](github-is-the-future-of-ai-coding-heres-why.md) - GitHub (via Actions) is the durable orchestration layer for AI coding: you invoke any coding assistant with a comment trigger on an issue or PR, and choose a hybrid, deterministic, or fully-autonomous workflow structure to fit how much control you want.
- [The OFFICIAL Archon Guide - 10x Your AI Coding Workflow](the-official-archon-guide-10x-your-ai-coding-workflow.md) - Archon is a command center for AI coding that gives humans back control over the knowledge (RAG) and tasks that coding assistants otherwise handle opaquely under the hood, so you can collaborate with your agent instead of being kicked out of the driver's seat.
- [Claude Sonnet 4.5 - The New Coding King? (Sonnet 4.5 vs. GPT 5 Codex)](claude-sonnet-45-the-new-coding-king-sonnet-45-vs-gpt-5-codex.md) - Claude Sonnet 4.5 (in Claude Code 2.0) beats GPT-5 Codex on a live, identical real-world coding task, finishing a Stripe integration in 15 minutes versus Codex's 1h20m with better code quality.
- [The True Power of AI Coding - Build Your OWN Workflows (Full Guide)](the-true-power-of-ai-coding-build-your-own-workflows-full-guide.md) - Rather than adopting an off-the-shelf framework like PRP or BMAD, you should understand the plan-implement-validate philosophy behind them all and build your own reusable AI coding workflow out of global rules, slash commands, and sub-agents.
- [I Built My Claude Code Subagents DREAM TEAM to Create Any AI Agent](i-built-my-claude-code-subagents-dream-team-to-create-any-ai-agent.md) - Claude Code subagents plus Archon can be composed into a reusable agent factory where a primary agent plans, splits, and validates work across specialized subagents that communicate through markdown files.
- [Archon Beta Launch Livestream - What You Missed!](archon-beta-launch-livestream-what-you-missed.md) - Archon is an operating system for AI coding that layers cleanly on top of any existing context-engineering strategy, demonstrated live by driving the BMAD method (then PRP) through Archon's MCP, RAG knowledge base, and project/task management.
- [Build ANY AI Agent with this Context Engineering Blueprint](build-any-ai-agent-with-this-context-engineering-blueprint.md) - Context engineering via the PRP framework (describe, generate a product requirements prompt, execute) gives an AI coding assistant enough robust, repeatable context to reliably build production AI agents, demonstrated with a Pydantic AI-tuned template.
- [Context Engineering 101 - The Simple Strategy to 100x AI Coding](context-engineering-101-the-simple-strategy-to-100x-ai-coding.md) - Context engineering, providing extensive information, examples, and constraints to AI coding assistants up front and best structured through Rasmus's PRP framework, is what separates production-ready builds from vibe coding, demonstrated by one-shotting a non-trivial MCP server.
- [Context Engineering is the New Vibe Coding (Learn this Now)](context-engineering-is-the-new-vibe-coding-learn-this-now.md) - Vibe coding fails in production because AI coding assistants lack context. Context engineering, architecting all the rules, docs, examples, and plans an LLM needs, is the durable skill that replaces it, demonstrated by a Claude Code template that plans and implements a full project via PRPs and slash commands.
- [BRAND NEW \"Stealth\" LLM Released on OpenRouter (It's a BEAST)](brand-new-stealth-llm-released-on-openrouter-its-a-beast.md) - OpenRouter's first stealth Foundation model, Quasar Alpha, is a fast, coding-optimized, million-token LLM that matches Claude 3.7 Sonnet's quality at roughly 4x the speed, hinting that the usual speed-vs-power tradeoff may be closing.
- [Code 100x Faster with AI, Here's How (No Hype, FULL Process)](code-100x-faster-with-ai-heres-how-no-hype-full-process.md) - A repeatable, tool-agnostic process (golden rules, planning/task markdown docs, global rules, MCP servers, and disciplined one-feature-at-a-time prompting) is what turns AI coding assistants from erratic to consistently high-output.
- [Let's Settle the o3-mini Vs. R1 Debate for Coding ONCE and for ALL](lets-settle-the-o3-mini-vs-r1-debate-for-coding-once-and-for-all.md) - By building the same apps in bolt.diy with both OpenAI's o3-mini and DeepSeek R1, Cole finds no single winner: o3-mini is faster and more literally correct while R1 produces nicer UIs and reasons better about unstated needs, so the two are best used together.
- [Pydantic AI + DeepSeek V3 - The BEST AI Agent Combo](pydantic-ai-deepseek-v3-the-best-ai-agent-combo.md) - You can turn a no-code n8n agent prototype into a production-grade, model-agnostic Python agent using Pydantic AI and DeepSeek V3 through OpenRouter, keeping it powerful, flexible, and cheap.
- [V0 + Claude: A GAME CHANGER for Building AI Apps](v0-claude-a-game-changer-for-building-ai-apps.md) - Pairing Vercel's v0 (for generating clean shadcn/ui React components) with Claude 3.5 Sonnet (for debugging and detail work) lets you build a full, authenticated Next.js front end for a RAG AI agent in minutes so you can spend your time on the backend agent instead.

## AI coding tools & harnesses
- [This Completely Changes the Way We Build Production AI Agents (Vercel Eve)](this-completely-changes-the-way-we-build-production-ai-agents-vercel-eve.md) - Vercel's open-source Eve framework makes file-system-first AI agents (an agent that is just a folder of markdown and TypeScript) production-grade by handling compilation, durable sessions, sandboxing, human-in-the-loop, and evals under the hood.
- [Pydantic AI 2.0: The New Best Way to Build AI Agents is Composing Capabilities](pydantic-ai-20-the-new-best-way-to-build-ai-agents-is-composing-capabilities.md) - Pydantic AI 2.0 reframes agent-building around a single composable primitive, the capability, which bundles instructions, tools, hooks, guardrails, and model settings into reusable units that snap together like Lego blocks.
- [Google Just Dropped a Masterclass on Agentic Engineering (It's SO Good)](google-just-dropped-a-masterclass-on-agentic-engineering-its-so-good.md) - Google's agentic-engineering masterclass confirms the industry consensus: AI coding is a spectrum whose highest form is building a reliable harness (the AI layer), where the model is only 10% of the system and everything you engineer around it is the other 90%.
- [Omnigent: The New Meta-Harness for EVERY Coding Agent - Claude Code, Codex, Pi, More](omnigent-the-new-meta-harness-for-every-coding-agent-claude-code-codex-pi-more.md) - Omnigent is an open-source meta-harness from Databricks that orchestrates multiple AI coding agents (Claude Code, Codex, Pi) from a single layer, making it trivial to run mixed-model workflows like Claude-implements/Codex-reviews.
- [Google's Agents CLI: The CLI + Skills Combination to Ship AI Agents EASILY](googles-agents-cli-the-cli-skills-combination-to-ship-ai-agents-easily.md) - By pairing Google's Agents CLI (capabilities) with skills (instructions), a coding assistant can drive an AI agent from idea to a scalable production deployment entirely through natural language, without the developer touching the docs or the terminal.
- [Harness Engineering: What Separates Top Agentic Engineers Right Now](harness-engineering-what-separates-top-agentic-engineers-right-now.md) - Harness engineering, building and continually evolving the AI layer around a coding agent, and orchestrating multiple focused agent sessions into automated workflows, is the 2026 evolution of context engineering and what separates top agentic engineers.
- [Anthropic Just Dropped a Masterclass on Building Agent Harnesses (for Large Codebases)](anthropic-just-dropped-a-masterclass-on-building-agent-harnesses-for-large-codebases.md) - To make coding agents work in large codebases, invest in the AI layer (the harness) around the model, lean, layered global rules, scoped skills, self-improving hooks, symbol-level LSP search, and exploration subagents, because the harness matters as much as the model.
- [AI YouTube Is Only Claude Hype Now](ai-youtube-is-only-claude-hype-now.md) - Cole is shifting his channel toward frequent live-building streams focused on durable AI-engineering principles rather than chasing the interchangeable 'Claude hype' news videos that dominate AI YouTube.
- [Pi Coding Agent + Archon: Build ANY AI Coding Workflow (No Claude Code Bloat)](pi-coding-agent-archon-build-any-ai-coding-workflow-no-claude-code-bloat.md) - Pi is a minimalistic, self-extending coding agent that fights coding-agent bloat by shipping a tiny foundational harness you extend yourself, and pairing it with Archon lets you build provider-mixing PIV-loop workflows for any AI coding task.
- [I'm Building an AI Dark Factory That Ships Its Own Code (Public Experiment)](im-building-an-ai-dark-factory-that-ships-its-own-code-public-experiment.md) - A 'dark factory' hands a codebase entirely over to AI to triage, plan, implement, validate and merge to production with no human review, and its reliability depends almost entirely on the governance and validation system you build around it.
- [The Next Evolution of AI Coding Is Harnesses - Here's How to Build Them](the-next-evolution-of-ai-coding-is-harnesses-heres-how-to-build-them.md) - AI coding has evolved from prompt engineering to context engineering to harness engineering, and the new Archon lets anyone build custom harnesses that orchestrate coding-agent sessions into deterministic, repeatable, reusable workflows.
- [You're Hardly Using What Claude Code Has to Offer, it's Insane](youre-hardly-using-what-claude-code-has-to-offer-its-insane.md) - In the last couple of months Claude Code has added roughly a dozen features (1M context, agent teams, git worktrees, new slash commands, remote control, automemory, and scheduling) that turn it into a multi-agent orchestration platform, and knowing when NOT to lean on each one matters as much as knowing they exist.
- [Claude Skills Aren't Just for Claude - Here's How to Build Them for ANY Agent](claude-skills-arent-just-for-claude-heres-how-to-build-them-for-any-agent.md) - Anthropic's skills are a universal, framework-agnostic pattern built on progressive disclosure that you can reimplement in any agent (here Pydantic AI) with a dynamic system prompt plus a couple of file-reading tools, then verify with evals and observability.
- [I Built My Second Brain with Claude Code + Obsidian + Skills (Here's How)](i-built-my-second-brain-with-claude-code-obsidian-skills-heres-how.md) - Claude Code paired with Obsidian and Skills is a powerful second-brain system for research, ideation, and organization, not just coding.
- [The Way We Use AI Will Completely Change in 2026 (Hot Takes)](the-way-we-use-ai-will-completely-change-in-2026-hot-takes.md) - In 2026 the way we build with AI will fundamentally shift: the code-centric IDE dies in favor of parallel agent-manager interfaces, providers specialize rather than one model winning everything, code execution replaces tool calling, and engineers become system architects who ship validated code they never read.
- [Is Gemini 3 Really the Best AI Ever?](is-gemini-3-really-the-best-ai-ever.md) - Gemini 3 is a genuinely powerful model, but LLM benchmarks are unreliable marketing-like signals, and the real gains developers see come as much from the tools and systems built on top of models as from the models themselves, so the industry needs real-task evals like Cline's Kleinbench.
- [Claude Code Web JUST Dropped but I Already Built Something BETTER (with Codex!)](claude-code-web-just-dropped-but-i-already-built-something-better-with-codex.md) - Instead of using locked-down cloud coding tools like Claude Code for the web, you can use the Codex SDK to build your own remote agentic coding assistant that carries your full AI layer and integrates with any app like Telegram.
- [Claude Code's Real Purpose (It's Bigger Than You Think)](claude-codes-real-purpose-its-bigger-than-you-think.md) - Claude Code is really a lightweight, powerful wrapper on top of Claude, and via the Claude Agent SDK you can embed that same agent harness into your own code, applications, and workflows to build any custom agent, not just a coding assistant.
- [This New Protocol Will Change AI Coding Forever (ACP)](this-new-protocol-will-change-ai-coding-forever-acp.md) - Zed's new Agent Client Protocol (ACP) is an MCP-like open standard that lets any coding agent connect to any code editor, making it trivial to switch between agents, build custom ones, and orchestrate them in parallel.
- [A Complete Guide to Claude Code - Here are ALL the Best Strategies](a-complete-guide-to-claude-code-here-are-all-the-best-strategies.md) - A start-to-finish tour of Claude Code's core features (global rules, permissions, slash commands, MCP, sub-agents, hooks, GitHub CLI, YOLO dev containers, and parallel git worktrees) and how to combine them into reusable agentic coding workflows.
- [I've Been Holding Back on YouTube... But Not Anymore](ive-been-holding-back-on-youtube-but-not-anymore.md) - Cole is shifting his channel from two videos a week to one high-value video a week to lean harder into his core teaching philosophy: build durable capabilities, not tool-specific skills.
- [Introducing Archon - an AI Agent that BUILDS AI Agents](introducing-archon-an-ai-agent-that-builds-ai-agents.md) - Archon is an open-source AI agent that builds, iterates on, and (eventually) runs other AI agents, serving both as a specialized agent-generation engine and as a public teaching tool for building agents with Pydantic AI and LangGraph.
- [How to DOMINATE with AI in 2025](how-to-dominate-with-ai-in-2025.md) - To stay ahead as AI accelerates into 2025, invest in a small set of high-leverage, tool-agnostic capabilities (agents, reasoning LLMs, local LLMs, tech-stack selection, working with LLMs) rather than mastering specific tools that get replaced every week.
- [bolt.diy: The Future of Open Source AI Coding - What's Next?](boltdiy-the-future-of-open-source-ai-coding-whats-next.md) - Fresh off making bolt.diy the official open-source bolt.new, Cole walks through the community roadmap, self-hosting resources, and a wave of new maturity features aimed at building the best open-source AI coding assistant.
- [I Created the BEST AI Agent Platform in Just 2 MONTHS!](i-created-the-best-ai-agent-platform-in-just-2-months.md) - Cole announces the pre-release of the Live Agent Studio, a community-driven, open-source platform where anyone can try, contribute, and eventually monetize AI agents built on any framework, unified by a standard payload and built-in chat history.
- [BREAKING NEWS: oTToDev is Now the OFFICIAL Open Source Bolt.new](breaking-news-ottodev-is-now-the-official-open-source-boltnew.md) - oTToDev, the community-built fork of bolt.new, is becoming the official open-source version of bolt.new (renamed bolt.DIY) through a partnership between the automator community and StackBlitz.
- [Our Bolt.new Fork's Most Requested Feature is FINALLY Here](our-boltnew-forks-most-requested-feature-is-finally-here.md) - oTToDev, the community-built open-source fork of Bolt.new, ships its most-requested feature (importing existing local projects) alongside a wave of contributor-driven improvements that push it toward a mature, best-in-class open-source AI coding assistant.
- [oTToDev is Becoming the BEST Open Source AI Coding Assistant](ottodev-is-becoming-the-best-open-source-ai-coding-assistant.md) - oTToDev, a community-driven open-source fork of bolt.new, is rapidly adding features (code streaming, an integrated terminal, provider auto-fetching, local-model support) and a clear roadmap to become the best open-source AI coding assistant.
- [How to Use Bolt.new for FREE with Local LLMs (And NO Rate Limits)](how-to-use-boltnew-for-free-with-local-llms-and-no-rate-limits.md) - You can run bolt.new-style AI app-building entirely on free local LLMs via the oTToDev fork and Ollama, provided you fix Ollama's tiny default context window and prompt incrementally from simple to complex.
- [We're Taking this AI Coding Assistant to the MOON (Plus Installation Instructions!)](were-taking-this-ai-coding-assistant-to-the-moon-plus-installation-instructions.md) - Cole's rapidly-growing fork of bolt.new (being renamed 'autoDev') is becoming a community-driven, LLM-agnostic open-source AI coding assistant, and this video announces project updates while giving a step-by-step Docker installation guide.
- [This Bolt.new Fork is EXPLODING - Don't Miss out on What's Happening!](this-boltnew-fork-is-exploding-dont-miss-out-on-whats-happening.md) - Cole's open-source fork of bolt.new is rapidly becoming a community-driven project whose core mission is to let any LLM (especially local ones) power an in-browser AI coding assistant, with backend agentic workflows as the most exciting next frontier.
- [I Forked Bolt.new and Made it WAY Better](i-forked-boltnew-and-made-it-way-better.md) - Because Bolt.new is open source, you can fork it and run it locally to break its two biggest limits, usage caps and being locked to a single model, by adding a model dropdown that supports any provider or local LLM.
- [You NEED These 5 Tools For Coding!! (Better Than ChatGPT)](you-need-these-5-tools-for-coding-better-than-chatgpt.md) - An early-2023 roundup of five AI coding assistants (GitHub Copilot, Tabnine, Mutable.ai, Sapphire AI, 10Web) that offload the tedious, low-leverage parts of software development so developers can focus on what matters.

## Building AI agents
- [Build + Render ENTIRE Videos with Claude Code (Full Workflow)](build-render-entire-videos-with-claude-code-full-workflow.md) - By stitching HyperFrames (HTML-based rendering), a TTS engine, and an Archon-orchestrated Claude Code skill into a template-driven workflow, you can now generate full short videos with synced animation and audio end-to-end from a single idea or URL.
- [AI Agents Can Now Build Their Own UI in Real Time (Personalized to You)](ai-agents-can-now-build-their-own-ui-in-real-time-personalized-to-you.md) - Generative UI lets an agent choose the components and layout of a front end on the fly, and a Gen-UI-agnostic stack (Pydantic AI + A2UI + AG-UI + CopilotKit) makes it practical to build ultra-personalized, self-adapting interfaces.
- [Build a RAG AI Agent with REAL-TIME Source Validation (CopilotKit + Pydantic AI)](build-a-rag-ai-agent-with-real-time-source-validation-copilotkit-pydantic-ai.md) - A RAG agent becomes far more reliable when you put a human in the loop for source validation, letting users approve or reject retrieved chunks in real time before the agent synthesizes an answer, made simple by CopilotKit + Pydantic AI wired together through the AG-UI protocol and the useAgent hook.
- [Learn 90% of Building AI Agents in 30 Minutes](learn-90-of-building-ai-agents-in-30-minutes.md) - You can build 90% of any AI agent by mastering four simple core components (LLM, tools, system prompt, memory) and deliberately deferring the production-grade complexity until you actually hit its limits.
- [Build Your First Voice AI Agent in 20 Minutes with LiveKit (Open Source)](build-your-first-voice-ai-agent-in-20-minutes-with-livekit-open-source.md) - LiveKit is an open-source Python framework that makes building fully customizable, self-hostable voice AI agents nearly as easy as no-code platforms like Vapi, but without the black-box trade-offs.
- [The Future of AI and SaaS is Agentic Experiences (Here's How to Build Them)](the-future-of-ai-and-saas-is-agentic-experiences-heres-how-to-build-them.md) - The future of SaaS isn't standalone agents but 'agentic experiences,' AI agents deeply embedded into products via the AG-UI protocol, which standardizes front-end-to-agent communication so state syncs both ways in a hundred lines of code.
- [Why is Everyone Missing This with AI Agents?! (Memory + Tools that Scale)](why-is-everyone-missing-this-with-ai-agents-memory-tools-that-scale.md) - To scale AI agents to thousands or millions of users, each user needs personalized, per-user tools and memory, which requires just-in-time agent authorization (via Arcade) and per-user long-term memory (via LangGraph).
- [How I'd Learn AI Agents FAST if I Had to Start Over (Full Roadmap)](how-id-learn-ai-agents-fast-if-i-had-to-start-over-full-roadmap.md) - A 10-phase roadmap for learning to build AI agents from scratch in a couple of months, anchored by the meta-principle of prioritizing transferable capabilities over specific tools and always learning by doing.
- [AG-UI Just Released: The NEW WAVE of AI Agent Apps](ag-ui-just-released-the-new-wave-of-ai-agent-apps.md) - AG-UI is the missing protocol that standardizes the connection between AI agents and any front end, completing the trio alongside MCP (tools) and A2A (agents) to turn agents into full user-facing applications.
- [Google, Anthropic, and OpenAI's Guides to AI Agents ALL in 18 Minutes](google-anthropic-and-openais-guides-to-ai-agents-all-in-18-minutes.md) - Google, Anthropic, and OpenAI's three agent guides converge on the same fundamentals, and mastering that shared core (what an agent is, when to build one, its four components, its patterns, and its guardrails) puts you ahead of 99% of builders.
- [Give Me 28 Minutes and I'll Completely Change the Way You Build AI Agents](give-me-28-minutes-and-ill-completely-change-the-way-you-build-ai-agents.md) - Any AI agent, no matter how complex, can be understood as a graph and decomposed into seven reusable node types (LLM, tool, control, memory, guardrail, fallback, user input) that you build one at a time and combine like Lego bricks.
- [If You're Serious About Building AI Agents, This is Your Secret Weapon](if-youre-serious-about-building-ai-agents-this-is-your-secret-weapon.md) - Agent observability via Langfuse is the most underrated, must-have capability for anyone building production AI agents, and it can be wired into any framework (especially Pydantic AI) in minutes.
- [Build a ChatGPT Style App for Your n8n AI Agents in MINUTES](build-a-chatgpt-style-app-for-your-n8n-ai-agents-in-minutes.md) - You can wrap your n8n AI agents in a polished, ChatGPT-style interface in minutes by connecting the open-source Open Web UI to an n8n webhook via a custom function, giving your agents conversation history, formatted output, and metadata without writing a custom app.
- [Build an ARMY of AI Agents on Autopilot with Archon, Here's How](build-an-army-of-ai-agents-on-autopilot-with-archon-heres-how.md) - Archon, an open-source AI agent that builds other agents in pure code, can generate an 'AI agent army' of specialized MCP-backed sub-agents coordinated by one routing primary agent, so you can connect to many services without overwhelming any single LLM.
- [10x Your AI Agents with this ONE Agent Architecture](10x-your-ai-agents-with-this-one-agent-architecture.md) - Fragmenting a single overloaded AI agent into a team of narrowly-scoped specialized sub-agents that run in parallel and are combined by a synthesizer agent produces far more powerful, focused agentic systems.
- [OpenAI's BRAND NEW Agents SDK (Crash Course)](openais-brand-new-agents-sdk-crash-course.md) - OpenAI's new Agents SDK makes it remarkably simple to build multi-agent systems with tools, handoffs, guardrails, and tracing, but its higher-level abstraction sacrifices the control, human-in-the-loop, and testing maturity that Pydantic AI and LangGraph still offer.
- [Are You Building REAL AI Agents or Just Using LLMs?](are-you-building-real-ai-agents-or-just-using-llms.md) - A real AI agent is a non-deterministic program where the LLM decides what tools to invoke and how many times to interact with its environment to reach a goal, which is fundamentally different from a workflow that merely chains LLM calls in a fixed sequence.
- [Coding Subagents - The Next Evolution of AI IDEs](coding-subagents-the-next-evolution-of-ai-ides.md) - The next evolution of AI IDEs is specialized sub-agents exposed as MCP tools, so a general coder like Windsurf or Cursor can call a framework-expert agent (Archon) instead of hallucinating unfamiliar code.
- [This is Hands Down the BEST Way to Build AI Agents](this-is-hands-down-the-best-way-to-build-ai-agents.md) - Combining Pydantic AI (to build individual agents) with LangGraph (to orchestrate them into a non-deterministic workflow) is the most powerful, controllable way to build robust multi-agent systems, demonstrated by building Archon, an agent that builds other agents.
- [I'm Hosting a HUGE AI Hackathon Competition](im-hosting-a-huge-ai-hackathon-competition.md) - Cole announces a $5,000 AI-agent hackathon for the oTTomator Live Agent Studio, laying out sponsors, prizes, timeline, and the judging criteria for building a real-world AI agent.
- [Revealing my COMPLETE AI Agent Blueprint](revealing-my-complete-ai-agent-blueprint.md) - Building a production-ready AI agent follows a repeatable end-to-end blueprint: plan, prototype in no/low-code, add a database, optionally move to Python, build a UI, test, deploy, monitor, and evaluate.
- [We've Been Building AI Agents WRONG Until Now](weve-been-building-ai-agents-wrong-until-now.md) - PydanticAI matters less as a specific framework than as a demonstration of the production-grade features (validation, dependency injection, retries, testing, logging) every agent framework must implement.
- [Flowise + n8n - The BEST No Code + Local AI Agent Combo](flowise-n8n-the-best-no-code-local-ai-agent-combo.md) - Pairing Flowise (fast no-code agent prototyping) with n8n (workflows exposed as agent tools), all inside a fully local AI stack, is the fastest way to build powerful AI agents that can actually do anything.
- [OpenAI's Swarm - a GAME CHANGER for AI Agents](openais-swarm-a-game-changer-for-ai-agents.md) - OpenAI's open-source Swarm framework teaches bulletproof multi-agent orchestration through handoff and routine patterns, demonstrated by building a router-plus-specialists swarm that manages a SQL database from natural language in under 100 lines of Python.
- [Build a FULL Stack App for your n8n AI Agent in 20 Minutes](build-a-full-stack-app-for-your-n8n-ai-agent-in-20-minutes.md) - You can wrap an n8n AI agent in a clean, secure, production-leaning front end in an afternoon by having Claude write a Streamlit app, exposing the agent over a webhook, and layering Supabase authentication across both the front end and the n8n workflow.
- [Llama 3.2 is INSANE - But Does it Beat GPT as an AI Agent?](llama-32-is-insane-but-does-it-beat-gpt-as-an-ai-agent.md) - Llama 3.2 90B is a real step forward for local LLMs as AI agents and can do function calling, but it still falls short of GPT-4o mini's reliability, especially on complex tool calls.
- [Don't Sleep on the ULTIMATE AI Agent Combo (n8n, LangChain, Python)](dont-sleep-on-the-ultimate-ai-agent-combo-n8n-langchain-python.md) - The best way to build AI agents combines no-code and code: use n8n workflows exposed as webhook API endpoints for the tools, and Python plus LangChain for the agent itself, getting fast development plus full control.
- [OpenAI's o1 is Barely Usable, but I Fixed it...](openais-o1-is-barely-usable-but-i-fixed-it.md) - OpenAI's o1 launched without native function calling, so Cole hand-codes a custom tool-calling layer in LangChain that turns o1 (or any LLM) into a capable, self-correcting AI agent.
- [I Found the Limits of the Most Popular LLMs](i-found-the-limits-of-the-most-popular-llms.md) - By escalating the complexity of requests to an AI agent wired to RAG, Google Drive, and Asana until each model breaks, you can empirically find which LLM is actually best for agentic workflows, and in 2024 that winner is Claude 3.5 Sonnet.
- [THE BEST Tool for AI Agent Workflows - LangGraph FULL Guide](the-best-tool-for-ai-agent-workflows-langgraph-full-guide.md) - LangGraph gives production-grade AI agent applications a clear, stateful graph structure (state, nodes, edges) that turns spaghetti multi-agent code into clean, scalable, extensible agentic workflows.
- [AI is TOO EXPENSIVE if You Don't Do This](ai-is-too-expensive-if-you-dont-do-this.md) - Instead of paying for one expensive LLM to handle every request, put a cheap router model in front that classifies each request's complexity and dispatches simple requests to a cheap model and only the rare complex ones to the expensive model, cutting AI costs by 90% or more.
- [AI Agents: The Future of AI or a Passing Fad?](ai-agents-the-future-of-ai-or-a-passing-fad.md) - AI agents are not hype but a practical necessity, because they solve the five biggest structural problems of raw LLMs: isolation, prompt bloat, hallucination, cost, and lack of state persistence.
- [New Llama 3 Model BEATS GPT and Claude with Function Calling!?](new-llama-3-model-beats-gpt-and-claude-with-function-calling.md) - Groq's fine-tuned Llama 3 models top the Berkeley Function Calling Leaderboard, so an open-source model you can run locally is now competitive with GPT and Claude for driving AI agents.
- [Build a Hilarious AI Game from SCRATCH in 10 Minutes](build-a-hilarious-ai-game-from-scratch-in-10-minutes.md) - You can build a genuinely fun, useful AI app fast by pairing a single well-structured LLM prompt with schema validation and a simple state machine in a Streamlit chat UI.
- [Function Calling with ANY LLM for Local AI Agents (Feat. LangChain, HuggingFace, and Llama 3)](function-calling-with-any-llm-for-local-ai-agents-feat-langchain-huggingface-and-llama-3.md) - You can add function/tool calling to any local LLM that lacks it natively by injecting tool descriptions into the system prompt and forcing the model to return a validated JSON schema of tool calls.
- [AI Agents Just Got Sexy - AI Agents Masterclass #3](ai-agents-just-got-sexy-ai-agents-masterclass-3.md) - You can wrap an existing tool-calling AI agent in a beautiful, deployable chat UI in about five minutes using Streamlit, then add typewriter-style streaming with Python generators.
- [I Made a Custom AI Chatbot Site in 47 Lines of Code](i-made-a-custom-ai-chatbot-site-in-47-lines-of-code.md) - You can build a fully-functional GPT-powered custom chatbot with a real web UI in just 47 lines of Python using Streamlit and LangChain, which is the foundation for building AI agents that go beyond what ChatGPT can do.
- [The Secret Sauce for SIMPLE + POWERFUL AI Apps - AI Agents Masterclass #2](the-secret-sauce-for-simple-powerful-ai-apps-ai-agents-masterclass-2.md) - LangChain dramatically simplifies building tool-using AI agents by replacing the raw GPT SDK's verbose function-calling boilerplate with docstring-driven tool decorators and message abstractions, and makes agents model-agnostic so you can swap between GPT and Claude in a single line.
- [The ONLY AI Developer Roadmap You Need in 2024](the-only-ai-developer-roadmap-you-need-in-2024.md) - A 10-step roadmap to go from zero to hero as an AI developer: master fundamentals fast, do not over-invest in prompt engineering or custom model training, and spend most of your time on LLM app-building, RAG, and AI agents.
- [Developers, THIS is How you Stay Relevant with AI - AI Agents Masterclass #1](developers-this-is-how-you-stay-relevant-with-ai-ai-agents-masterclass-1.md) - AI agents, LLMs given the ability to interact with the outside world through tool calls, are the real way to unleash AI's power, and you can build a functional one from scratch in a few minutes.
- [Introducing Trainer's Ally - Nvidia AI Agents Competition Submission](introducing-trainers-ally-nvidia-ai-agents-competition-submission.md) - Trainer's Ally is an AI agent app (built on Nvidia + LangChain) that lets personal trainers co-create fully customized multi-day client workouts in minutes through an iterative human-in-the-loop generate-revise-continue flow.
- [I Will Win the Nvidia AI Agents Developer Contest](i-will-win-the-nvidia-ai-agents-developer-contest.md) - Cole announces he will win the Nvidia/LangChain AI agents developer contest by building an agentic, human-in-the-loop application that helps personal trainers generate personalized weekly workouts for their clients.
- [Use ChatGPT in Two Clicks on ANY website with a Custom Chrome Extension](use-chatgpt-in-two-clicks-on-any-website-with-a-custom-chrome-extension.md) - You can build a simple Chrome extension backed by a small Express API that lets you invoke ChatGPT on any website via a right-click, replacing highlighted or in-box text with the model's response.
- [ChatGPT is the GIFT that keeps on GIVING - Introducing Auto-GPT](chatgpt-is-the-gift-that-keeps-on-giving-introducing-auto-gpt.md) - Auto-GPT wraps GPT-4 in an autonomous loop that decomposes a goal into self-generated steps, executes them with tool use and memory, and self-criticizes along the way, an early glimpse of agentic AI.

## RAG, knowledge & memory
- [I Love the Karpathy LLM Wiki but it Doesn't Scale. Here's What Does.](i-love-the-karpathy-llm-wiki-but-it-doesnt-scale-heres-what-does.md) - Markdown-driven second-brain / LLM-wiki agents are ideal for personal use but don't scale to production; shipping an agent to many users requires a database with a context-retrieval layer and managed agent memory instead of markdown files.
- [Finally, an Open Standard for the Karpathy LLM Wiki is HERE](finally-an-open-standard-for-the-karpathy-llm-wiki-is-here.md) - Google's Open Knowledge Format (OKF) is a minimal open standard on top of Karpathy's LLM wiki that lets everyone build and share LLM knowledge bases in the same producible/consumable structure.
- [I Built Self-Evolving Claude Code Memory w/ Karpathy's LLM Knowledge Bases](i-built-self-evolving-claude-code-memory-w-karpathys-llm-knowledge-bases.md) - Karpathy's LLM-wiki pattern for organizing external knowledge can be inverted to give Claude Code a self-evolving, per-codebase memory built entirely from Claude Code hooks and the Claude Agent SDK.
- [Full Guide - Build Your Own AI Second Brain with Claude Code](full-guide-build-your-own-ai-second-brain-with-claude-code.md) - Rather than running an out-of-the-box agent like OpenClaw, you should build your own AI second brain on top of Claude Code and Obsidian so you control the permissions, understand the codebase, and minimize exposure to the lethal trifecta.
- [Everything You Thought About Building AI Agents is Wrong](everything-you-thought-about-building-ai-agents-is-wrong.md) - Whether to build an AI agent on a batteries-included coding-agent SDK or a from-scratch framework isn't a fashion choice but a use-case decision driven by who uses it and your tolerance for speed, cost, and scale.
- [Why the Best AI Coding Tools Abandoned RAG (And What They Use Instead)](why-the-best-ai-coding-tools-abandoned-rag-and-what-they-use-instead.md) - Traditional RAG (semantic search over a vector database) is dead only for coding because code is highly structured; for the majority of unstructured-data use cases it remains essential, and the real frontier is letting agents choose which retrieval strategy to use per query.
- [I Built a Safer OpenClaw Alternative Using Claude Code](i-built-a-safer-openclaw-alternative-using-claude-code.md) - Rather than run the insecure but magical OpenClaw personal assistant, clone its MIT-licensed repo, point a coding agent at it, and rebuild its four core components (memory, heartbeat, channel adapters, skills) into your own secure, customized second brain.
- [The Simplest RAG Stack That Actually Works (Complete Guide)](the-simplest-rag-stack-that-actually-works-complete-guide.md) - A simple, robust RAG stack combines keyword and semantic search on every query (hybrid search) merged via reciprocal rank fusion, built on MongoDB, Pydantic AI, and Docling.
- [The ONLY AI Tech Stack You Need in 2026](the-only-ai-tech-stack-you-need-in-2026.md) - Cole walks through his stable, AI-first, mostly open-source tech stack for 2026, organized by category, arguing you should pick capabilities-focused tools that work for you and stick with them rather than obsessing over the tools themselves.
- [Every RAG Strategy Explained in 13 Minutes (No Fluff)](every-rag-strategy-explained-in-13-minutes-no-fluff.md) - There are ~11 practical RAG strategies spanning data preparation and retrieval, and the optimal system combines three to five of them for your specific use case.
- [Super Special Livestream this Weekend! (LIVE AI Agent Build)](super-special-livestream-this-weekend-live-ai-agent-build.md) - Cole announces his first live AI agent build stream where he'll construct a full-stack personal AI coach powered by advanced agentic RAG over his YouTube content.
- [Turn ANY File into LLM Knowledge in SECONDS](turn-any-file-into-llm-knowledge-in-seconds.md) - Docling, a free open-source Python tool, turns any complex file type (PDF, Word, audio, and more) into clean markdown and RAG-ready chunks, solving the hardest and most important part of a RAG pipeline: data curation.
- [Knowledge Graphs in n8n are FINALLY Here!](knowledge-graphs-in-n8n-are-finally-here.md) - You can layer a knowledge graph onto an existing n8n vector-database RAG template using the Graphiti MCP server and Neo4j, giving your agent a second, relationship-aware way to search its knowledge base.
- [Your ULTIMATE n8n RAG AI Agent Template just got a Massive Upgrade](your-ultimate-n8n-rag-ai-agent-template-just-got-a-massive-upgrade.md) - A basic RAG implementation is not enough; combining agentic chunking, agentic RAG, and reranking into a single n8n template solves both the knowledge-curation and knowledge-search halves of RAG.
- [Introducing RAG 2.0: Agentic RAG + Knowledge Graphs (FREE Template)](introducing-rag-20-agentic-rag-knowledge-graphs-free-template.md) - Combining agentic RAG with knowledge graphs gives an AI agent the flexibility to reason about how it retrieves knowledge, choosing a vector database for single-entity lookups and a knowledge graph for relational questions.
- [AI Code That Fixes Itself (An MCP You Can Try Now)](ai-code-that-fixes-itself-an-mcp-you-can-try-now.md) - Turning an open-source library's entire GitHub repo into a knowledge graph lets an MCP server deterministically detect and self-correct the function/parameter hallucinations that plague AI coding assistants.
- [All the BEST RAG Strategies in ONE MCP for AI Coding Assistants](all-the-best-rag-strategies-in-one-mcp-for-ai-coding-assistants.md) - Combining four advanced RAG strategies (contextual embeddings, hybrid search, agentic RAG, and reranking) into a single open-source Crawl4AI RAG MCP server gives AI coding assistants an accurate, up-to-date documentation knowledge base and forms the knowledge backbone of the next evolution of Archon.
- [Make RAG 100x Better with Real-Time Knowledge Graphs](make-rag-100x-better-with-real-time-knowledge-graphs.md) - Temporal-aware knowledge graphs (via Graphiti + Neo4j) layer on top of RAG to continuously ingest changing data and preserve historical context, giving AI agents far richer, time-aware retrieval than static traditional RAG.
- [The EASIEST Possible Strategy for Accurate RAG (Step by Step Guide)](the-easiest-possible-strategy-for-accurate-rag-step-by-step-guide.md) - Contextual retrieval (contextual embedding) is the single most impactful, easy-to-implement upgrade to basic RAG, prepending an LLM-generated situating sentence to each chunk before embedding to dramatically cut retrieval failure rates.
- [I Built the Ultimate RAG MCP Server for AI Coding (Better than Context7)](i-built-the-ultimate-rag-mcp-server-for-ai-coding-better-than-context7.md) - Cole builds an open-source Crawl4AI RAG MCP server that gives AI coding assistants a private, self-hostable documentation knowledge base, solving the privacy and scoping limitations of Context7.
- [Turn ANY Website into LLM Knowledge in Seconds - EVOLVED](turn-any-website-into-llm-knowledge-in-seconds-evolved.md) - Crawl4AI lets you turn any website into clean, LLM-ready markdown knowledge for RAG using one of three strategies (sitemap, recursive navigation crawl, or llms.txt), automatically chunked and loaded into a vector database.
- [This MCP Server for AI Coding Assistants Will 10x Your Productivity](this-mcp-server-for-ai-coding-assistants-will-10x-your-productivity.md) - Context7, a free MCP server, gives AI coding assistants instant, up-to-date, example-rich documentation via RAG so they stop hallucinating APIs for the tools and frameworks you build with.
- [The PROVEN Solution for Unbelievable RAG Performance (LightRAG Guide)](the-proven-solution-for-unbelievable-rag-performance-lightrag-guide.md) - Enhancing traditional vector RAG with a knowledge graph via the open-source LightRAG framework dramatically improves retrieval accuracy, especially as knowledge bases scale.
- [Build AI Agents that EVOLVE Over Time](build-ai-agents-that-evolve-over-time.md) - AI agents become far more human-like and personalized when you give them long-term memory that learns from conversations, which the open-source Mem0 library makes simple by extracting, storing, and retrieving per-user memories from a vector database.
- [The ULTIMATE n8n RAG AI Agent Template - Local AI Edition](the-ultimate-n8n-rag-ai-agent-template-local-ai-edition.md) - Agentic RAG, giving an agent tools to explore a knowledge base in multiple ways instead of a single one-shot vector lookup, can run 100% locally and offline in n8n using Ollama and self-hosted Postgres/Supabase.
- [I Built the ULTIMATE n8n RAG AI Agent Template](i-built-the-ultimate-n8n-rag-ai-agent-template.md) - Traditional RAG fails because it can't zoom out to whole documents or do real data analysis, so give the agent multiple tools (RAG, list-documents, get-file-contents, SQL-over-tables) and let it reason about how to explore the knowledge base, which is agentic RAG in n8n.
- [Run Supabase 100% LOCALLY for Your AI Agents](run-supabase-100-locally-for-your-ai-agents.md) - Supabase can be self-hosted alongside the rest of a local AI stack via Docker so you can run a fully local RAG AI agent (database, vector store, and chat memory) with no cloud dependency.
- [You HAVE to Try Agentic RAG with DeepSeek R1 (Insane Results)](you-have-to-try-agentic-rag-with-deepseek-r1-insane-results.md) - Reasoning models like DeepSeek R1 are powerful but slow, so wrapping R1 as a RAG tool called by a faster, lightweight primary LLM unlocks a simple yet powerful local agentic RAG workflow.
- [n8n + Crawl4AI - Scrape ANY Website in Minutes with NO Code](n8n-crawl4ai-scrape-any-website-in-minutes-with-no-code.md) - You can deploy Crawl4AI as a Dockerized API endpoint and drive it entirely from an n8n workflow to scrape any website and build a Supabase RAG knowledge base with no code.
- [The Future of RAG is Agentic - Learn this Strategy NOW](the-future-of-rag-is-agentic-learn-this-strategy-now.md) - Turning standard one-shot RAG into agentic RAG, where retrieval becomes a set of tools an agent can reason about and choose between, solves RAG's most common failures and delivers far more accurate, consistent results.
- [Turn ANY Website into LLM Knowledge in SECONDS](turn-any-website-into-llm-knowledge-in-seconds.md) - Crawl4AI is an open-source, fast, memory-efficient web crawler that converts any website into clean markdown ready to ingest into an LLM knowledge base for RAG.
- [Claude Computer Use + Bolt.new - The ULTIMATE AI Coding Combo?!](claude-computer-use-boltnew-the-ultimate-ai-coding-combo.md) - AI coding assistants fail on new or niche libraries because they lack external knowledge, and Claude's computer use API can bridge that gap by autonomously researching documentation and injecting it into a tool like Bolt.new before the build.
- [Use Open WebUI with Your N8N AI Agents - Voice Chat Included!](use-open-webui-with-your-n8n-ai-agents-voice-chat-included.md) - You can extend the self-hosted local-AI starter kit by adding Open WebUI and a custom function so its ChatGPT-like interface (including voice) talks directly to your n8n agent workflows via a webhook.
- [Effortless RAG in n8n - Use ALL Your Files (PDFs, Excel, and More)](effortless-rag-in-n8n-use-all-your-files-pdfs-excel-and-more.md) - The hardest part of building a RAG agent in n8n is not the vector store but reliably extracting text from varied file types, which you solve by branching on a file's MIME type to a matching extract node.
- [This Slack AI Personal Assistant is Better than a Real Person](this-slack-ai-personal-assistant-is-better-than-a-real-person.md) - Building an AI personal assistant inside a chat platform like Slack is a classic, high-value use case that no longer needs to be built from scratch. A no-code platform like RunBear delivers RAG, web search, tool calling, and conversation summarization out of the box.
- [I Built a FULL RAG AI Agent with No Code in 4 Minutes with VectorShift](i-built-a-full-rag-ai-agent-with-no-code-in-4-minutes-with-vectorshift.md) - For simple use cases or fast proofs of concept, a no-code drag-and-drop platform like VectorShift can build a full, working RAG AI agent in minutes instead of coding one from scratch.
- [Run ALL Your AI Locally in Minutes (LLMs, RAG, and more)](run-all-your-ai-locally-in-minutes-llms-rag-and-more.md) - The n8n self-hosted AI starter kit bundles Ollama, Qdrant, Postgres, and n8n into one Docker package that lets you stand up a fully local RAG AI agent in minutes, with a few key customizations to fill gaps in the official setup.
- [This RAG AI Agent with n8n + Supabase is the Real Deal](this-rag-ai-agent-with-n8n-supabase-is-the-real-deal.md) - You can build a production-ready, cost-effective RAG AI agent with no code by combining n8n for workflow automation and Supabase for both the vector store (pgvector) and Postgres-backed chat memory.
- [No Code RAG Agents? You HAVE to Check out n8n + LangChain](no-code-rag-agents-you-have-to-check-out-n8n-langchain.md) - You can build a full RAG AI agent with tool calling and a Google Drive knowledge base entirely with no code by combining n8n's workflow automation with its built-in LangChain nodes.
- [Build a Chatbot for Your Website in 4 Minutes (No Code)](build-a-chatbot-for-your-website-in-4-minutes-no-code.md) - Building a custom RAG-powered website chatbot trained on your own data is a use case worth handling with a no-code product like Folder rather than coding it yourself.
- [This AI Agent with RAG Manages MY LIFE](this-ai-agent-with-rag-manages-my-life.md) - An AI agent becomes genuinely useful for daily life when it can intelligently decide whether to search documents via RAG and then act on what it finds through external tools like Asana.
- [The Most Value Packed RAG Guide on YouTube (Feat. Llama 3.1 405B!)](the-most-value-packed-rag-guide-on-youtube-feat-llama-31-405b.md) - RAG works by chunking documents into embeddings, matching a user's question to the most similar chunks via vector math, and feeding those chunks to an LLM as context, and you can build a working local-document chatbot on Llama 3.1 405B from scratch in minutes.

## Context & prompt engineering
- [The Subagent Era Is Officially Here - Learn this Now](the-subagent-era-is-officially-here-learn-this-now.md) - As models get cheaper, faster, and smarter, subagents are becoming the default tool for context isolation, so use them everywhere for research, codebase analysis, and code review, but never for implementation.
- [I've Used Claude Code for 2,000+ Hours - Here's How I Build Anything With It](ive-used-claude-code-for-2000-hours-heres-how-i-build-anything-with-it.md) - After 2,000+ hours in Claude Code, the single highest-leverage skill is context engineering, captured in the WHISK framework (Write, Isolate, Select, Compress) that keeps the context window as lean as possible while still giving the agent everything it needs.
- [The 5 Techniques Separating Top Agentic Engineers Right Now](the-5-techniques-separating-top-agentic-engineers-right-now.md) - The top agentic engineers separate themselves not with new tools but with a disciplined system: a PRD north star, modular rules, commandified workflows, context resets between plan and execution, and treating every bug as a chance to evolve the system.
- [The Kiro AI Coding Hackathon has Officially Started! Build ANYTHING, Win Big Prizes.](the-kiro-ai-coding-hackathon-has-officially-started-build-anything-win-big-prizes.md) - Cole announces the Dynamous x Kiro AI coding hackathon ($17K prize pool), explaining how to register, submit, and get started building any application with the Kiro coding agent.
- [Are Agent Harnesses Bringing Back Vibe Coding?](are-agent-harnesses-bringing-back-vibe-coding.md) - Agent harnesses are the natural evolution of prompt and context engineering that connect many agent sessions to reliably run long tasks, and they can make a heavily-engineered, human-in-the-loop form of vibe coding viable once two problems (bounded attention and compounding reliability) are solved.
- [My Top 20 Lessons from Building 100s of AI Agents (Super Actionable)](my-top-20-lessons-from-building-100s-of-ai-agents-super-actionable.md) - The hardest problem in building AI agents is non-determinism and hallucination, so nearly every practical lesson is really a strategy for reducing or catching hallucinations.
- [Introducing Archon - The Revolutionary Operating System for AI Coding](introducing-archon-the-revolutionary-operating-system-for-ai-coding.md) - Archon is being reborn as an operating system and command center for AI coding assistants, giving them the two things they lack: RAG over your own knowledge and real-time project/task management, through a human-native UI and an LLM-native MCP server.
- [The 3 MUST Have MCP Servers for Any AI Coding (and How to Use Them)](the-3-must-have-mcp-servers-for-any-ai-coding-and-how-to-use-them.md) - Every AI coding workflow should run on three core MCP servers, a RAG documentation knowledge base, a database manager, and a web search, used together from the very start of a build so the assistant has real knowledge before it writes any code.
- [INSANE New Strategy for Building AI Agent Apps](insane-new-strategy-for-building-ai-agent-apps.md) - The fastest, cheapest way to build a custom frontend for an AI agent is to chain three kinds of AI coding tools together: a single-LLM builder to lay a strong foundation, an open-source local editor to iterate for free, and an AI IDE for final directed changes.
- [I'm Addicted to Windsurf, Send Help](im-addicted-to-windsurf-send-help.md) - Windsurf's flow-state IDE fuses co-pilot and agent into one experience, and a handful of disciplined habits (resetting long conversations, one request at a time, scoping context with @-references, and switching models when stuck) turn it into a massive time-saver.
- [\"Prompt Engineering\" is a Bunch of BS - Here's the 5% of it to Learn](prompt-engineering-is-a-bunch-of-bs-heres-the-5-of-it-to-learn.md) - Most prompt engineering is fluff; learn a handful of core techniques in a few hours, then use a simple four-part prompt template and go build.
- [I FINALLY Built an AI Agent to Organize my Life - AI Agents Masterclass #4](i-finally-built-an-ai-agent-to-organize-my-life-ai-agents-masterclass-4.md) - By giving an LLM a set of well-documented Python functions as tools, you can build a conversational agent that fully manages your Asana workspace (projects and tasks) from natural language.
- [ChatGPT Teaches the Secrets to Financial Freedom!?](chatgpt-teaches-the-secrets-to-financial-freedom.md) - You can turn any popular book's author into a personalized coach by giving ChatGPT the book's philosophy plus your full life context, then asking for a concrete tailored plan.
- [3 MOST Important Prompt Engineering Techniques you NEED to know!](3-most-important-prompt-engineering-techniques-you-need-to-know.md) - Effective prompt engineering comes down to three simple, high-leverage techniques (self-critique, role personification, and few-shot examples) rather than the dozens of overwrought methods people obsess over.
- [How to Use GPT4 To Grow on Twitter The Right Way](how-to-use-gpt4-to-grow-on-twitter-the-right-way.md) - Good prompt engineering, setting a role, giving context and format constraints, then having the model critique and rewrite its own output, turns generic, cringey ChatGPT tweet replies into human-sounding ones.

## MCP & protocols
- [The BIG Problem with MCP Servers (and the Solution!)](the-big-problem-with-mcp-servers-and-the-solution.md) - MCP servers bloat an agent's context by loading every tool definition up front, and the fix is code execution via skills that load capabilities on demand, trading MCP's control for far greater token efficiency and flexibility.
- [Docker Just Made Using MCP Servers 100x Easier (One Click Installs!)](docker-just-made-using-mcp-servers-100x-easier-one-click-installs.md) - Docker's MCP Catalog, Toolkit, and open-source Gateway turn the painful JSON-config chore of wiring MCP servers into agents into a one-click experience, letting you connect curated tools to any client (or your own custom agents) and chain them into full agentic workflows.
- [Build and Ship Any MCP Server in MINUTES (Full Guide)](build-and-ship-any-mcp-server-in-minutes-full-guide.md) - Building production-ready remote MCP servers is now surprisingly straightforward using a TypeScript template that layers GitHub OAuth, security best practices, Sentry monitoring, and Cloudflare deployment onto the core tool-registration pattern.
- [Google is Quietly Revolutionizing AI Agents (This is HUGE)](google-is-quietly-revolutionizing-ai-agents-this-is-huge.md) - Google's A2A (Agent-to-Agent) protocol is an open standard that lets independently-built agents discover and communicate with each other at runtime, and paired with MCP it forms the backend of scalable multi-agent AI systems, though real production challenges remain.
- [The ULTIMATE Guide to Building Your Own MCP Servers (Free Template)](the-ultimate-guide-to-building-your-own-mcp-servers-free-template.md) - Building your own MCP server is the real unlock of the Model Context Protocol, and with a best-practices Python template (FastMCP + lifespan + dual transports) you can connect your agents to any service you want.
- [The MCP Integration EVERYONE is Sleeping On (MCP + Custom AI Agents)](the-mcp-integration-everyone-is-sleeping-on-mcp-custom-ai-agents.md) - MCP's real power is unlocked not by MCP-native apps like Claude Desktop or Cursor but by integrating MCP servers directly into your own custom AI agents, which is straightforward with a reusable client that converts MCP tools into your framework's tool format.
- [Claude MCP has Changed AI Forever - Here's What You NEED to Know](claude-mcp-has-changed-ai-forever-heres-what-you-need-to-know.md) - Anthropic's Model Context Protocol (MCP) is a durable, non-hype standard that packages LLM tools into reusable servers, letting you share and reuse the same tools across any agent framework or AI IDE.

## No-code agent building
- [Should I Build My AI Agents with n8n or Python?](should-i-build-my-ai-agents-with-n8n-or-python.md) - Use n8n (no-code) for integration-heavy internal tools, proofs of concept, and mixed-skill teams; move to Python (code) when you need production scale, speed, control, and flexibility, or combine both in a hybrid architecture.
- [10 n8n Tips in 10 Minutes to 10x Your AI Automations](10-n8n-tips-in-10-minutes-to-10x-your-ai-automations.md) - n8n is a powerful no-code AI automation platform, but the underutilized and trickier parts (production-grade memory/RAG stores, node referencing, looping, error handling, scheduling) are what separate toy workflows from scalable production ones.
- [Turn ANY AI Agent into a Live Person - Voice Chat Setup in Minutes](turn-any-ai-agent-into-a-live-person-voice-chat-setup-in-minutes.md) - You can build and deploy a full voice AI agent with a Google Drive knowledge base in minutes, no coding, by chaining audio input, speech-to-text, an LLM with RAG, and text-to-speech in a no-code platform like Vector Shift.
- [The KING of No Code AI Agent Builders?!](the-king-of-no-code-ai-agent-builders.md) - Voiceflow is a no-code AI agent platform that lets you build production-ready agents with fine-grained control by acting as the brain between a front end and back-end services, driving tools through intents and entities.
- [Get Qualified Leads While you Sleep with AI](get-qualified-leads-while-you-sleep-with-ai.md) - Instead of building a website lead-generation agent from scratch, use a purpose-built no-code platform like Chatling to visually assemble a chatbot that qualifies leads, answers questions from a RAG knowledge base, and collects contact info automatically.
- [Bubble is My BIGGEST Regret for No Code AI Apps - What I Use Instead](bubble-is-my-biggest-regret-for-no-code-ai-apps-what-i-use-instead.md) - No-code AI app builders with usage-based workload-unit pricing (like Bubble) can silently drain your wallet at scale, so prefer platforms with predictable infrastructure-based pricing (like Moment) that also let you build both the AI agent and the front end in one place.
- [The MOST Profitable AI Niche NO ONE is Talking About](the-most-profitable-ai-niche-no-one-is-talking-about.md) - Building AI agents on top of GoHighLevel, a feature-rich but AI-weak CRM for marketing agencies, is a lucrative, underserved niche, and lead-nurturing agents are the single biggest opportunity.
- [You're WASTING TIME Coding Your AI Agents](youre-wasting-time-coding-your-ai-agents.md) - You do not need to code every AI agent from scratch: n8n lets you build powerful, self-hosted, no-code AI agent workflows with LangChain integration so you can reserve hand-coding for only the most complex agents.

## Deployment & production
- [Deploying Billions of AI Agents is Easier than You Think](deploying-billions-of-ai-agents-is-easier-than-you-think.md) - Packaging an AI agent as a Docker container makes deploying and scaling it to the cloud trivial, which is why a future of billions of configurable, business-customized agents is realistic.
- [The GLOW-UP Every AI Agent Needs](the-glow-up-every-ai-agent-needs.md) - A terminal-bound Pydantic AI agent becomes production-usable by wrapping it in a FastAPI endpoint with Supabase-backed conversation history and instantly hooking it into a free frontend (Agent Zero / the Live Agent Studio).
- [Deploy ANY AI App to the Cloud FAST](deploy-any-ai-app-to-the-cloud-fast.md) - Any application that runs on localhost with a port can be taken to production by putting it on a cloud VPS behind an Nginx reverse proxy with a custom domain and SSL, shown step by step using the n8n self-hosted local AI starter kit.
- [COMPLETE Guide to LangServe - Make Your AI Agent Actually Useful](complete-guide-to-langserve-make-your-ai-agent-actually-useful.md) - Any agent built with LangChain/LangGraph can be turned into a scalable, always-on cloud API in minutes by wrapping it with LangServe and deploying it to a cheap DigitalOcean droplet.

## Local & self-hosted AI
- [The Ultimate Guide to Local AI and AI Agents (The Future is Here)](the-ultimate-guide-to-local-ai-and-ai-agents-the-future-is-here.md) - You can run large language models and their entire supporting infrastructure entirely on your own hardware to build 100% private, offline AI agents, and Cole's curated 'local AI package' makes standing up that full stack (and deploying it securely to the cloud) achievable step by step.
- [Create Your Own Private Local AI Cloud Stack in Under 20 Minutes](create-your-own-private-local-ai-cloud-stack-in-under-20-minutes.md) - You can deploy the bundled Local AI Package to your own private cloud instance in under 20 minutes and keep it fully self-managed (still 'local'), gaining always-on access, more hardware, and team sharing without hogging your own machine.
- [DeepSeek R1 Just Revolutionized AI Forever](deepseek-r1-just-revolutionized-ai-forever.md) - DeepSeek R1 is an open-source reasoning LLM that rivals OpenAI's o1 at a fraction of the cost, ships in distilled sizes you can run locally, and outperforms Claude 3.5 Sonnet as a coding assistant in a one-shot app build.
- [Qwen Just Casually Started the Local AI Revolution](qwen-just-casually-started-the-local-ai-revolution.md) - Qwen 2.5 Coder 32B closes the gap between free, locally-run open-source LLMs and expensive closed models like Sonnet and o1, making capable local coding and agent workloads practical for the first time.
- [Ollama + OpenAI's Swarm - EASILY Run AI Agents Locally](ollama-openais-swarm-easily-run-ai-agents-locally.md) - By overriding the OpenAI base URL to point at a local Ollama instance, you can run OpenAI's Swarm multi-agent framework entirely locally and for free using small tool-calling models like Qwen 2.5.
- [STOP Paying for GPT, Claude, & Other LLMs - Get them ALL in One Place](stop-paying-for-gpt-claude-other-llms-get-them-all-in-one-place.md) - Instead of paying for separate GPT, Claude, Gemini, and Llama subscriptions, a single unified multi-model chat platform (ChatLLM by Abacus.AI) gives access to all the top LLMs plus extra features for a fraction of the cost.
- [I'm Building the BEST Open Source AI Coding Assistant with YOUR Help](im-building-the-best-open-source-ai-coding-assistant-with-your-help.md) - Cole's community-driven fork of bolt.new adds multi-provider LLM selection (including free local models) and is being improved collaboratively by viewers, with agentic multi-LLM code generation as the headline future goal.
- [The HARD Truth About Hosting Your Own LLMs](the-hard-truth-about-hosting-your-own-llms.md) - Self-hosting powerful open LLMs is only cost-effective at scale, so start by paying per token for the same models via a cheap inference service like Groq and switch to your own cloud GPU only once napkin-math break-even makes it worthwhile.
- [Local vs. Cloud LLMs/RAG - Let's FINALLY End this Debate](local-vs-cloud-llmsrag-lets-finally-end-this-debate.md) - Choosing between local/self-hosted and managed-service AI stacks is not a dogmatic all-or-nothing choice but a nuanced decision driven by whether you're building a POC, data sensitivity, capability needs, and scale.
- [ChatGPT Users BE AWARE of this Major Weakness](chatgpt-users-be-aware-of-this-major-weakness.md) - The single biggest weakness of GPT and similar AI models is the training cutoff, which freezes their knowledge at a point in time and causes them to silently produce outdated answers, but fine-tuning, search plugins, and faster training are closing that gap.
