✨ By Ben "MDX" Holmes ✨
Background: I worked on an ecommerce site with a medium-sized enterprise team. This team needed a lot of cross-communication copy writers, designers, product managers, and developers.
Purpose: To thoroughly understand the content management problem space, and analyze existing solutions.
React server components (initial proposal) was a so-so DX that solved an important problem: I want to render a React component on the server only, with APIs to easily fetch data for rendering.
| import plugin from "tailwindcss/plugin"; | |
| /** @type {import('tailwindcss').Config} */ | |
| export default { | |
| // ... existing tailwind config here | |
| theme: { | |
| backgroundImage: {}, | |
| }, | |
| plugins: [ | |
| plugin(function spicyGradients({ addUtilities }) { |
| name = "warp internal" | |
| [[panes]] | |
| id = "root" | |
| split = "horizontal" | |
| children = ["claude", "build"] | |
| [[panes]] | |
| id = "claude" | |
| type = "terminal" |
| name = "Worktree: MY REPO" | |
| [[panes]] | |
| commands = [ | |
| "git worktree add -b {{autogenerated_branch_name}} ~/.warp/worktrees/MY_REPO/{{autogenerated_branch_name}}", | |
| "cd ~/.warp/worktrees/MY_REPO/{{autogenerated_branch_name}}", | |
| "find ~/Projects/MY_REPO -type f -name '.env.*' -exec sh -c 'for f do rel=\"${f#~/Projects/MY_REPO/}\"; [ -e \"$rel\" ] || [ -L \"$rel\" ] && continue; mkdir -p \"$(dirname \"$rel\")\"; ln -s \"$f\" \"$rel\"; done' sh {} +", | |
| "pnpm i" | |
| ] | |
| directory = "~/Projects/MY_REPO" |
| <!doctype html> | |
| <html lang="en"> | |
| <head> | |
| <meta charset="utf-8"> | |
| <meta name="viewport" content="width=device-width, initial-scale=1"> | |
| <title>Fix tag placeholder visibility</title> | |
| <style>:root { | |
| --warp-bg: #121212; | |
| --warp-panel: #1e1e1d; | |
| --warp-panel-2: #292929; |
Delegate substantive coding tasks when a focused agent can investigate, implement, or review with clear context. Do not delegate trivial edits or merely expedient work.
Rankings, higher = better. Cost reflects what I actually pay (OpenAI has really generous limits), not list price. Intelligence is how hard a problem you can hand the model unsupervised. Taste covers UI/UX, code quality, API design, and copy.
| model | cost | intelligence | taste |
|---|---|---|---|
| gpt-5.6 | 9 | 8 | 5 |
| sonnet-5 | 5 | 5 | 7 |
claude CLI installed for this)
Rubric
This scorer applies only to implementation-agent runs, including a later revision re-entry. It grades the final delivered test diff — the tests and cases that landed on the PR after self-review — not intermediate tests the agent wrote and then deleted, and not whether a human later asked to remove them (that event is unnecessary-tests-human-correction). The question is surplus in the artifact: redundant, tautological, or low-signal tests that would not fail on a real defect any other added or existing test misses.
This is a within-run ordinal so the across-run frequency is the sampled fail rate. samplingRate is 100. passingScore is 0.5, which means any surplus fails the run: mostly_surplus_tests (0.2) and some_surplus_tests (0.4) both fail, with the two ordinals keeping a material share of surplus distinguishable from a minority of low-signal cases. no_surplus_tests (1) is a clean pass. insufficient_evidence (0.5) is a pass at the threshold, not a clean pass: it keeps missing co