AI Dataset2026
JSONL dataset of Claude Opus 4.6 chain-of-thought traces paired with high-difficulty math and logic problems for supervised fine-tuning and distillation; exposes step-by-step reasoning to teach process-oriented problem solving and improve math/logic accuracy in smaller LLMs.
Hands-on, phase-based curriculum for building end-to-end AI systems from first principles — implement algorithms, run tests, and ship reusable artifacts (prompts, skills, agents, MCP servers) across Python, TypeScript, Rust, and Julia under an MIT license.
AI Agent2026
Turns a domain description into a Claude Code agent team and the skills they use — auto-generates agent definitions and skill files from six pre-defined team-architecture patterns. Best for teams building structured multi-agent workflows on Claude Code.
AI Dataset2026
Provides multi-turn agent trajectories with real tool executions and explicit <think> reasoning blocks for training and evaluating tool-calling agents. Contains two model-sourced configs (Kimi-K2.5, GLM-5.1) totaling ~14.7K samples — useful for SFT, agent-skill research, and tool-integration experiments.
AI Agent2026
Provides a curated collection of DESIGN.md files extracted from real websites so AI coding and design agents can generate visually consistent UIs from a single markdown file. Includes previews, extracted tokens, and ready prompts for quick agent integration.
AI Dataset2026
Aggregates and deduplicates public Claude distillation datasets into a unified 'messages' format with source attribution; focused on instruction-tuning and reasoning samples for SFT and LLM training, while requiring users to follow original sources' licenses.
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 Model2026
An open text-to-image generation model built on an 8B Diffusion Transformer that focuses on layout-sensitive, text-heavy, and instruction-following image synthesis. Notable for accurate text rendering, structured/compositional generation (posters, comics), and ability to run on consumer 24GB GPUs when paired with prompt enhancement.
AI Agent2026
An AI-agent value-investing research framework for Claude Code/Codex that encodes Buffett/Munger/Duan Yongping/Lilu methodologies into multi-agent skills — enforces decisive buy/sell outputs, multi-source financial rigor, and reproducible research workflows for investment decision-making.
AI Dataset2026
Provides 1,003,589 full chain-of-thought reasoning traces and final answers generated by GLM-5.1, split into main/Math/PHD-Science/Multilingual-STEM subsets. Useful for instruction-tuning, supervised fine-tuning, and reasoning experiments; released under Apache-2.0.
AI Agent2026
Turns books, long videos, and podcasts into executable, testable AI agent skills using a structured RIA‑TV++ pipeline. Produces multi-file skill packs (BOOK_OVERVIEW.md, SKILL.md, INDEX.md, DIGEST.md), applies triple verification and pressure tests, and can install skills into Claude Code/Cursor for agent use.
AI Dataset2026
Contains 8,124 reasoning conversations (extended-thinking + final responses) generated by Anthropic Claude Opus 4.7 for distillation into open-source LLMs. Each row stores the prompt, thinking trace, final answer and usage metadata; packaged under Apache‑2.0.