A living collection of articles, guides, and lessons learned about AI tools, coding agents, and personal AI workflows.
A Hitchhiker's 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 ataskis 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(library docs),Serena(semantic code editing + memory),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 — 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 — 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 aftercode 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