AI Knowledge Base — articles, guides, and lessons learned about AI tools and workflows

A developer known as Vhyrro published a comprehensive primer on programming with large language models, covering how LLMs work, agent loops, MCPs, and skills, along with practical do's and don'ts. Matt Pocock shared five agent skills he uses daily to improve AI code output, emphasizing strict processes and codebase quality. Josh Larson detailed his experience with Zo, a personal cloud computer with an AI-native OS, highlighting its text/email interface and security considerations.

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