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Track, visualize, and compete on AI coding assistant token usage across Claude Code, Cursor, OpenCode, Codex, Gemini, Kimi, and Qwen. The Kardashev Scale for AI Devs.
翻譯準備中
個人 GitHub 帳號下公開儲存庫的擁有者可以登入並驗證所有權，自行管理介紹，無需管理員核准。
登入並產生驗證檔案，在 30 分鐘內將其提交至此儲存庫的預設分支。其他分支或分叉儲存庫中的檔案無效。然後在下方檢查檔案，即可無需管理員核准直接管理專案。請保留檔案以維持管理權限。
如需登入，將前往登入頁面。登入後請回到此處，再次點擊所選的驗證按鈕。
在專案資料夾中執行 /nomorevibe verify，即可驗證所有權並自行管理介紹。
一分鐘完成驗證製作者的介紹僅有上面的一句話。 更多詳情請閱讀儲存庫 README。 儲存庫 README ↗
正在載入完整 README。
檢視完整 README ↗A UMAP map that grows as data arrives, in the browser on WebGPU. New items (Bluesky posts, Wikipedia edits, Nostr notes, Hacker News items) are embedded in the browser and placed beside their nearest neighbours on the existing map as they arrive; static datasets (aviation safety reports, wine reviews, exam questions, papers, dog photos, tables) are fitted as one map, can be rewound on their timeline and played forward.
Open index.html (GitHub Pages). Needs a browser with WebGPU (Chrome or Edge on desktop).
?source= picks a dataset: asrs, wine-reviews, vispubdata, medmcqa, SuperGPQA, ScienceQA, imagewoof, movies,
census-income, california-housing, or a live feed (wikipedia, nostr, hackernews); none = live Bluesky.static-data/ holds the ASRS reports (public NASA ASRS synopses, labelled as in the aviation safety dashboard) and
precomputed vectors (MiniLM, BGE and GTE text, DINOv2 and CLIP images, float16) and each model's finished map for the
hosted datasets; the tables themselves are read
from Hugging Face and the other hosts the Embedding Atlas examples use.This repository is a build output of the research code (streaming-umap, browser_jetstream/build_site.py); edit there.