# AI Knowledge Base — articles, guides, and lessons learned about AI tools and workflows > Source: > Published: 2026-07-16 17:17:23+00:00 A living collection of articles, guides, and lessons learned about AI tools, coding agents, and personal AI workflows. [ A Hitchhiker's Guide to AI](https://vhyrro.neorg.org/posts/a-hitchhikers-guide-to-ai/) — Vhyrro, Jul 2026 Comprehensive primer on programming with LLMs. Key takeaways: **How LLMs work:** They predict the next token based on training data. Temperature controls creativity vs. accuracy. Context is everything.**Agents** loop until a*task*is done (not just a sentence). They use tool calling to bridge the LLM and the outside world — reading files, searching the web, running commands.**MCPs**(Model Context Protocol servers) are plugins for your coding agent — they add tools and context. Top picks:[Context7](https://context7.com)(library docs),[Serena](https://oraios.github.io/serena/)(semantic code editing + memory),[Ponytail](https://github.com/DietrichGebert/ponytail)(prevents overengineering).**Skills** are mini-prompts that teach the agent best practices for a specific task. DOs and DON'Ts: | Do | Don't | |---|---| | Constrain agents (stricter environment = fewer mistakes) | Obsess over prompt engineering — just say what you want | | Use strongly typed languages with good compiler errors | Let agents write your tests (they confabulate passing tests) | | Write the API surface yourself, let LLMs fill in details | Start brand-new ambitious projects with an agent | | Use agents for big refactors (their best use case) | Thrash the context window with too much data | | Write your own tests so agents catch their own mistakes | Force agents to re-learn your codebase every session — use memory | | Get good at code review — it's now a core skill | [ 5 Agent Skills I Use Every Day](https://www.aihero.dev/5-agent-skills-i-use-every-day) — Matt Pocock, Mar 2026 A practical skill-based workflow for getting high-quality code out of AI agents. Core idea: agents are engineers with no memory, so you need strict, repeatable processes. The five skills: | Skill | What it does | |---|---| `/grill-me` | Forces the agent to interview you exhaustively before coding — walk every branch of the "design tree" until you reach shared understanding | `/to-prd` | Converts the conversation into a Product Requirements Document with user stories, submitted as a GitHub issue | `/to-issues` | Breaks the PRD into a Kanban board of vertical-slice issues (thin cuts through all layers, not horizontal slabs) | `/tdd` | Red-green-refactor loop — write one test, implement, refactor. Most consistent way to improve agent output quality | `/improve-codebase-architecture` | Weekly audit: find shallow modules, tightly coupled code, and unclear test boundaries. Better structure → better AI output | Key insight: **"If you have a garbage codebase, the AI will produce garbage within that codebase."** Invest in deep modules with thin interfaces so agents can navigate easily. Full skill set + install: [aihero.dev/skills](https://www.aihero.dev/skills) · [GitHub](https://github.com/mattpocock/skills) [ Zo-topia: My Computer In The Cloud](https://www.jplhomer.org/posts/zo-topia-my-zo-computer-experience/) — Josh Larson, Jan 2026 Developer's deep dive into Zo as a personal cloud computer. Highlights: **What makes it click:** A remote Linux box + AI-native OS + text/email interface. You can text or email your computer from anywhere — even via Siri while driving.**Sites** are Zo's pre-packaged dev templates (Bun-powered) with preview → production workflow. Good for vibe-coding.**Security mindset:** Treat Zo as a separate computer. Be intentional about what data you give it. Ask: "How damaging would it be if this leaked?" [ Zo Computer as a No-Code Build/Host All-in-One Tool](https://www.reddit.com/r/nocode/comments/1qqkkww/zo_computer_as_a_nocode_buildhost_allinone_tool/) — r/nocode, Jan 2026 Community discussion on Zo for non-technical users: - Collapses setup, deployment, hosting, and automation into one conversational flow — the real friction for beginners is *after*code is generated, and Zo handles that. | Term | What it means | |---|---| LLM | Large Language Model — the AI "brain" that predicts text | Token | Smallest unit of text an LLM processes (a few characters) | Context window | How much text the LLM can "see" at once (bigger = more expensive) | Temperature | Controls randomness: low = focused, high = creative | Agent | An LLM that loops + uses tools until a task is complete | MCP | Model Context Protocol — plugin system for AI agents | Skill | A packaged set of instructions an agent can call for a specific task | Tool calling | How agents interact with the outside world (read files, search, run code) | Vibe coding | Building software by describing what you want in natural language | *Last updated: Jul 16, 2026*