AI Infra2026
Measures multiline text layout and block height without triggering browser reflow: it measures text segments once via Canvas+Intl.Segmenter and caches widths, then computes line breaks with pure arithmetic. Useful for streaming AI text, virtualization, and custom per-line rendering.
AI Video2026
Author HTML-based video compositions and render deterministic, frame-accurate MP4s with agent-friendly tooling — preview in the browser, drive generation via AI agent skills, and use adapter runtimes (GSAP, Lottie, Three.js).
AI Agent2026
Provides modular “skills” that help designers and engineers audit, find, and improve UI animations and interaction decisions — optimized for use by AI agents or human-in-the-loop workflows. Distills domain-expert rules into actionable SKILL.md modules (review, improve, find opportunities, vocabulary).
AI Client2026
Compresses LLM/agent replies into a terse “caveman” style to cut output tokens (~65–75%) while preserving technical accuracy. Offers per-agent skills, intensity modes, memory-compression and middleware to lower token cost and extend usable context.
AI Agent2026
Turns plain-English system or process descriptions into polished, themeable architecture, workflow, sequence, data-flow and lifecycle diagrams as a self-contained HTML file, with one-click theme toggle, copy-to-clipboard and export to PNG/JPEG/WebP/SVG (native up-to-4× rasterization).
AI Client2026
Runs multiple AI agents in parallel inside a single macOS browser, giving each agent an isolated Space that can use your real logins without touching your tabs; controllable via an ego-browser JavaScript skill to perform web automation with fewer tokens and faster task completion.
AI Dataset2026
Early-preview (≈1.2k rows) dataset of agentic coding prompts and unedited model responses generated by DeepSeek‑V4‑Pro, covering real-world programming tasks across many languages. Intended for research, filtering, and model evaluation rather than production training without review.
AI Dataset2026
Agentic coding evaluation dataset containing real-world, multi-step developer tasks and raw model responses across 20+ programming languages. Emphasizes challenging, persona-driven prompts for benchmarking and fine-tuning; users should filter and audit outputs before training.
MCP Server2026
Lets AI agents produce expressive, polished charts from compact, human-editable semantic specs; the compiler infers layout, scales, and labels and emits Vega-Lite, ECharts, or Chart.js outputs, with an MCP server for agent-driven chart creation and rendering.
AI Dataset2026
Contains a sanitized Claude Code (Fable 5) JSONL transcript of a session that procedurally built a Boeing 747 in Three.js, including assistant messages, tool calls, and base64 screenshots — useful for studying agent trace, tool use, and vision self‑verification workflows.