TIL: Auto-Reload Files in Vim When Working With AI Agents
TL;DR A 3-line vimscript snippet that polls checktime every second keeps vim
buffers in sync when AI agents modify files on disk.
Blog#
TIL: How to Not Let Coding Agents Commit Code Automatically
TL;DR
Disable coding agents from committing code automatically. Use git permission
denies in .claude/settings.local.json to prevent auto-commits and force a
review-before-commit workflow. This keeps you in control of your codebase and
maintains your understanding of each change.
Markdown Viewers on macOS: Terminal and Browser Options
TL;DR: Render markdown from terminal using glow (for terminal viewing),
mdcat (for rich formatting), grip (for GitHub-accurate previews with
images), or pandoc (for document conversion and flexibility).
Rendering Markdown Files From Your Terminal with open_md.py
TL;DR: open_md.py is a flexible CLI tool that renders markdown files to HTML
using pandoc or grip, and can open them directly on GitHub or in your
browser, with support for embedded diagrams and both local and dockerized execution
hllm: A Unified Helper for LLM API Calls
TLDR A Python helper that wraps OpenAI and OpenRouter APIs with built-in caching, cost tracking, structured outputs, and batch processing for DataFrame workflows.
How to Use OpenRouter with Simonw's LLM CLI
TLDR: A practical guide on using OpenRouter through Simon Willison's LLM CLI to access dozens of LLM models from a single terminal, including free tier options.
How to Use Claude Code with OpenRouter
TL;DR Learn how to configure Claude Code to route LLM calls through OpenRouter, enabling access to models from multiple providers through a single API.
What Is LLM Reasoning Effort and How Does It Affect Cost?
TL;DR: LLM reasoning effort settings (low, medium, high) affect how much compute a model allocates to internal reasoning, increasing total token consumption and cost, even when per-token pricing stays the same.
How to Count LLM Usage Costs
TL;DR Track your LLM API spending with open-source tools like ccusage and
OpenUsage to gain visibility into daily costs, model-level breakdowns, and usage
patterns.
How to Compare and Choose LLM Models
TL;DR Comparing LLMs across real benchmarks and use an efficiency metric that combines quality, cost, and speed to pick the right model for each task