Field notes · 10 entries

Writing about building.

Practical notes on AI, data, and the tools that make ambitious work feel lighter.

01

I benchmarked all 17 GPT-6 settings; this is how I use them now

GPT-6 results suggest increasing reasoning effort pays off—but cost and latency rise sharply, and the best model family depends on your budget.

02

Does your agent need Context7?

A 600-task paired comparison shows how Context7 influences agent behavior, cost and time.

03

Does RTK really reduce token costs the advertised 60-90% percent?

A 600-task paired comparison shows how RTK changes tool output, message count, and the cost of running an LLM agent.

04

How I use OpenAI's GPT-5.6, based on 175,000+ tests

Surprising results from looking at the cost, intelligence, and latency tradeoffs between GPT-5.6 capability tiers and reasoning effort.

05

Stop using MCP, use CLI instead

MCPs are powerful, but their context bloat makes AI agents less efficient. Here’s why CLIs are a better interface for tool use.

06

Snowball: Self-Improving AI

AI automates a lot, but it quickly devolves into tedious context engineering. Snowball automates the context management engineering.

07

Slash Commands ate My Workflow

Opencode commands turn my parallel agentic worktree coding sessions into one-liners. 3-4X faster.

08

Managing Prompts in a Git Repo

How I manage all my prompts in a Git repository.

09

Parallelize Coding Agents with Git Worktrees

How I get 2-3X the productivity from parallel coding agents.

10

Welcome to My Blog

Introducing my new blog where I share thoughts on AI, engineering, data science, technology, and more.