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一分鐘完成驗證Which AI model should you actually use? An independent, sourced decision tool
翻譯準備中
取自儲存庫 README
正在載入完整 README。 以下為已儲存的摘錄。
Which AI model should you actually use?
Everyone has access to more models than they can keep track of — and no time to work out which
one fits the task and the budget in front of them. Modelproof answers that in two ways:檢視完整 README ↗Always-current AI model facts, and a setup that installs into the tools you already use.
Everyone has access to more models than they can keep track of. Modelproof keeps the facts straight and puts them where you work:
AGENTS.md. It sets which model each helper agent runs on — your choice first, then the
tool's or lab's own published default for that job, otherwise the same model as your main one —
plus a few lines on using them well. Every package includes a checker helper that re-checks
finished work in a fresh context with real evidence and never edits files. Two opt-ins stay off
unless you ask: big-job mode (your main model plans and reviews, the helpers do every piece) and
an example setup someone shared, labeled as one person's own choice. It adds files next to yours
instead of overwriting them, shows you the full plan before it writes anything, and undoes byte
for byte.Live at lucascashwell3-ai.github.io/modelproof.
assets/install-prompt.txt (also
on the home page) goes into Claude Code, Codex or Cursor. It reads your setup, asks a few quick
questions, shows every file before it writes, installs on your yes, and prints one undo line.skills/modelproof-advisor/ runs the same flow. The
prompt runs it once without installing it; to keep it, copy the folder into ~/.claude/skills/
(or your tool's skills folder).board.html builds the same package from a board you lay out, under
Install package.mcp/ serves the same facts (prices, releases, sourced quotes) over the
Model Context Protocol.table.html) — every tracked model with its sources and sourced
quotes, filterable by lab.board.html) — lay out the teams and routines in a company, or your
own set of models, and see the monthly bill and the cost per person and per year.how-we-pick.html) — where every number and quote
comes from.data/models.json is the single source: models across the major labs and
open-weight vendors, refreshed twice a week by an automated Collect → Judge → Verify pipeline
(GitHub Actions + a research pass with citations; see
automation/jobs/auto-refresh/). Anything the pipeline can't
source cleanly is held in a review issue instead of published. Each model also carries an
availability object — where it can actually be reached (direct API, OpenRouter, AWS Bedrock,
open weights) — derived automatically every Collect run by
scripts/derive-availability.mjs; a field is true only when
sourced, null otherwise, never a guessed negative. data/plans.json holds
seat/subscription pricing for the major chat apps and coding tools (Claude, ChatGPT, Gemini,
Grok, Cursor, GitHub Copilot), refreshed manually (see
scripts/refresh-plans.md) straight from each vendor's own pricing
page — a null price means the page didn't show that number in static HTML, never an invented one.
Honesty rules:
scripts/check-sources.mjs) — a claim that can't
be found on the page it cites is rejected, not softened.scripts/validate-data.mjs) blocks any publish that breaks
schema or sourcing rules.Retired pieces (the old ranking code and its answer key) are kept for history in
archive/.
Pure static site — no build step, no dependencies.
python3 -m http.server 8475 --directory .
# → http://localhost:8475
Vanilla HTML + CSS + JS, one models.json. Type: Fraunces (wordmark) + Cabinet Grotesk /
Switzer / JetBrains Mono. Restrained, GPU-friendly motion; no build tooling anywhere.
usage.openrouter on every
model, collected from OpenRouter's own rankings feed (scripts/derive-usage.mjs).Independent project · not affiliated with any model vendor · built by Lucas Cashwell. Sibling project: DATproof.