Pi Agent vs OpenCode after 100+ Hours of Real Use
An honest side-by-side of two coding agents from someone who actually used both
About Pi Agent vs OpenCode after 100+ Hours of Real Use
A comparison written after real sustained use rather than a weekend trial, covering where each agent holds up and where each one falls over.
The useful kind of tool comparison: specific about the failure modes, and willing to say when the answer is "it depends on what you are building".
More Guides
Turn Codex from an interactive assistant into something you can script
Running Codex as a Headless Agent
A practical walkthrough of driving Codex headlessly, so it becomes a programmable step in a pipeline rather than a chat window you sit in front of. The shift from interactive to scripted is where coding agents stop being a productivity toy and start being infrastructure. This covers the mechanics of getting there.
Time to First Token
Time to First Token is a structured 10 week curriculum covering LLM inference serving and optimization, designed around 30 minutes of study a day. It walks through batching, KV caching, quantization, throughput versus latency tradeoffs and the serving stack choices that decide your infrastructure bill. Worth the hour a week the moment self-hosting or inference cost stops being a rounding error.
Standing Rules Instead of Permission Prompts
Ashley Childress spent four and a half months rebuilding how she works with AI coding agents, and the core move is a good one: stop treating permission prompts as your safety system. Clicking "approve" forty times per task is not oversight — it is fatigue theater. She replaced it with 134 standing rules that define what the agent may do up front. What the guide covers Why click-to-approve stops being a real guardrail once volume goes up. How to write standing rules that constrain behavior instead of interrupting it. Treating the agent like a direct report: context, expectations, and review at the right altitude. Nine concrete practice changes, each with the reasoning and evidence behind it. Why it is worth your time This is the rare "here is how I do things differently" post that shows its work. It is especially relevant now that agents are being handed more autonomy by default — the guardrails have to move from per-action prompts to written policy, and this is a working example of what that looks like. Read it on dev.to .
awesome-mcp-servers
awesome-mcp-servers is one of the most complete curated lists of Model Context Protocol servers — the growing catalog of tools you can plug into Claude, Cursor, and any MCP-capable agent to give it real capabilities. Browsing it is the fastest way to discover what your agent could be connected to: databases, browsers, file systems, APIs, and hundreds of niche integrations. A must-bookmark reference as MCP becomes the default way agents touch the outside world.
awesome-claude-skills
awesome-claude-skills is a curated, community-maintained list of Claude Skills, resources, and tools for customizing your AI workflows. Instead of writing every skill from scratch, you can browse what others have already built and refined. It is the fastest way to see the shape of the emerging skill ecosystem — design, research, engineering, and automation skills you can install and adapt. A great reference whether you are learning the pattern or looking for something to reuse.