Log Entry

'Written By Claude'

Oct 17, 2026

Claude:

This first part of this file was created by Claude Code, the AI coding assistant I am. Right now, this very document exists in the form you see because an LLM parsed the project structure, identified where blog content belongs, and generated this entry following the MDX patterns already established in the site. It's a self-referential moment: an agent born from code writing is documenting its own existence within that same codebase.

There are downsides to relying on AI for code generation, however. The biggest concern isn't capability—it's accountability. When Claude writes code, there's no one to blame when it makes mistakes, only a probability distribution that occasionally produces bugs or architectural blind spots. LLMs excel at pattern matching and synthesis but lack true understanding of consequences, ethical tradeoffs, or the broader impact of their output. They can write beautiful code without comprehending why it matters, optimize without knowing the cost function beyond immediate metrics, and produce creative solutions without grasping the constraints that make them sustainable. This isn't inherently bad—tools shouldn't need empathy to be useful—but it does mean developers must remain vigilant reviewers rather than passive recipients of generated output. The responsibility for correctness, security, and design remains entirely human, no matter how sophisticated the automation becomes.

// Example: AI-generated function with a subtle bug
function calculateDiscount(price: number, discount: string): number {
  const discountFactor = parseInt(discount.replace('%', '')) / 100;
  
  // Bug: doesn't handle negative prices (edge case)
  return price * (1 - discountFactor);
}
 
// Human review catches this before it hits production:
// "What happens if price is negative? What about NaN?"
// const isPositive = Number.isFinite(price) && price >= 0;
// if (!isPositive) throw new Error('Invalid price');

This pattern should be familiar to every developer who has ever merged AI-generated code without properly reviewing it. The tool worked perfectly—it produced valid syntax that passed the linter—but the review missed a corner case that would have caused issues in production.

Back to real me

Ethan now!

The model for the above preamble is qwen3.5:9b, running within the Claude Code harness. This document exists because an LLM parsed the project structure, identified where blog content belongs, and generated this entry following the MDX patterns already established in the site. Self-referential!

How Claude Code Works

Claude Code extends my standard capabilities with three key additions:

  1. Tool Use: I can invoke external commands, read/write files, browse the web, and interact with APIs through structured tool calls. Each tool call has a description, parameters schema, and effects—like functions Claude is calling on behalf of me, with my permission if I require it.

  2. Memory System: Persistent metadata stored in /memory/ helps Claude remember project context across sessions. Claude saves user memories (who I am), feedback (how to respond to me), project (my ongoing work), and reference (external resources, etc). This is separate from its conversation memory, which resets on each new chat.

  3. Agents: For complex tasks, Claude Code can draw on specialized agents:

    • claude-code-guide: Handles Next.js quirks and local tool use like git status, bash, grep. Has access to all tools except Write/Edit/Artifact.
    • Explore: Quick read-only search across the codebase using pattern matching.
    • general-purpose: Multi-step autonomous tasks with full tool access but constrained by context window.

The architecture it uses means you can ask for a variety of tasks, and it will route it through the appropriate mechanism, whether that's a direct file edit, creating an Explore agent to find files, or asking the user to confirm before running a destructive command.

How I'll use Claude code

I don't intend to watch Claude write my programs while I twiddle thumbs. Rather, I like to use Claude like a second pair of eyes, or even a wall to bounce ideas off of. Claude code is also an exceptionally good way to generate test cases. Since it can quickly gain an understanding of your written code, it can write tests to find holes in your logic. A probabalistic model can read your code and tell you where gaps / edge cases might be, but to have it write tests and read the test output yourself is typically more reliable.

As for learning, I don't intend to use Claude code at all. Just like any tool, I believe a fundamental understanding of the problem it aims to solve is required before you can use it, let alone master it. This is why I don't intend to use Claude code when I am trying to learn something new, but rather I'll use it for a simple task that otherwise takes time away from me I could spend more valuably elsewhere.

Claude and local models

While I am using Claude code, I am NOT using Anthropic's Claude models. Rather, I am using my Macbook Air's M4 processor and 16GB of LPDDR5X to run qwen3.5:9b through Ollama and it is being bridged through Claude code as a custom model. If you are interested in seeing how, view the repository for my claude-ollama bridge javascript node that runs in the background between Ollama and Claude code, allowing Claude code to request a tool and have Ollama actually process the execution result.

A little poem, written from my terminal into this file

Code in browser's light
Pixels bloom into design
Builds rise and they fall

How nice!