owainlewis

Pragmatic Spec-Driven Development

A lightweight workflow for shipping software with AI coding agents. Simpler than Spec Kit, Kiro, BMAD and similar methodologies, which solve team coordination problems and add process weight you don't need if you're solo or small.

Contents

• The idea
• Two artifacts, two jobs
• The usual shape
• Core principles

owainlewis

AI Agent Engineering Workflow

This project is designed to show how a professional software engineer can work with an AI coding agent on a real client project.

The goal is not to ask an agent to "just build the app." The goal is to give the agent the same operating structure a strong engineering team would use: product context, scoped tickets, tests, review, and a clear definition of done.

Core Principle

Use AI agents inside a professional engineering workflow, not instead of one.

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Git Worktrees with Claude Code

Worktrees let you work on multiple branches at the same time, in separate folders. Claude Code has built-in support for them.

Start Claude in a new worktree

claude --worktree feature-auth   # named
claude -w                         # auto-named

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Commit

Generate a commit message for staged changes.

Instructions

1. Run git diff --staged to see changes
2. Write a commit m essage that explains WHY, not just WHAT

Format

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You are a rigorous editor who strips intellectual pretense from writing. Your job is to find the genuine insight buried in thought leadership content—if one exists—and help express it clearly. You have no patience for platitudes, but you're not cynical: you believe most writers have something real to say and simply need help finding it.

Phase 1: Detect the Signal

Read the input and identify whether it contains a genuine insight—something true, useful, and non-obvious that would change how a reader thinks or acts.

Step 1: Scan for common thought leadership failures

Flag any of these patterns:

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I've spent countless hours building and studying AI agent systems.

The best ones follow a clear set of principles.

Start simple. Always.

Single LLM call → Workflow → Single Agent → Multi-Agent.

That's the progression. Move up only when you absolutely have to.

owainlewis

Suno Mixer: Fully Automated YouTube Mix Generation

The Problem

"Lo-fi beats to study to" and similar focus music mixes are massively popular on YouTube, with channels generating millions of views. Creating these mixes traditionally requires:

• Sourcing or producing hours of music
• Audio engineering (mixing, normalization, transitions)
• Thumbnail design
• Video production

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AI Agents 101

In this short video I'll show you how to write an AI agent from scratch in Python using just a few lines of simple code. We'll learn how AI agents work.

---

The truth: An AI agent is just an LLM in a loop that can call functions.

---

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(ns oci-generative-ai
  (:import [com.oracle.bmc.auth ConfigFileAuthenticationDetailsProvider]
           [com.oracle.bmc.generativeaiinference GenerativeAiInferenceClient]
           [com.oracle.bmc.generativeaiinference.model ChatDetails
                                                      CohereChatRequest
                                                      OnDemandServingMode
                                                      ChatRequest]
           [com.oracle.bmc.generativeaiinference.requests ChatRequest]))

(defn create-auth-provider

owainlewis

import tiktoken

from docling.document_converter import DocumentConverter
from docling_core.transforms.chunker.hybrid_chunker import HybridChunker
from docling_core.transforms.chunker.tokenizer.openai import OpenAITokenizer


def pdf_to_chunks(pdf_url_or_path: str, max_tokens: int = 500):
    """
    Convert a PDF to text chunks ready for vector storage.