AI Dataset2024
Provides a multilingual, deduplicated corpus of public source code in Parquet for large-scale model training and evaluation. Includes license metadata, language splits, and streaming-friendly packaging for use with Hugging Face Datasets — suited to training code-focused foundation models but requires careful license/provenance review.
AI Dataset2024
A mixed instruction dataset for SFT and RLHF research that combines chat, math, code and instruction-following samples from multiple public datasets under an Apache-2.0-compatible license; intended for instruction tuning and evaluation.
AI Agent2024
Framework for building and orchestrating multi-agent LLM systems, with agent types, tool integration, and human-in-the-loop workflows. Supports multi-agent conversation patterns, multiple LLM providers, and RAG-style tooling for research and prototyping agentic workflows.
Provides end-to-end PyTorch scripts to download/prepare data, implement a transformer from scratch, train LLMs (13M→billion-scale) and generate text. Emphasizes educational clarity and single‑GPU experiments; useful for researchers or hobbyists, but large-scale training still requires substantial compute and engineering.
AI Dataset2025
A curated dataset of ~30,000 CUDA kernels generated by an agentic pipeline, including reference PyTorch implementations, runtime metrics, NCU/Torch/Clang-Tidy profiles, error messages and correctness labels — released under CC-BY-4.0 for model fine-tuning and offline RL/optimization research.
AI Dataset2025
A benchmark dataset for evaluating MLLM-driven interactive webpage code generation: provides prototyping screenshots, action.json interaction metadata, and example generation scripts across 127 webpages and 374 interactions to test dynamic UI-to-code capabilities.
AI Dataset2025
Provides 5 million instruction–response pairs for supervised fine-tuning of code LLMs, with inputs, outputs, unit tests, and automated LLM judgments. Uses hybrid automated/synthetic generation and is released under CC BY 4.0 for large-scale SFT workflows.
AI Coding2025
Parses multi-language repositories into a Memgraph knowledge graph and enables natural-language RAG querying, grounded source retrieval, and AI-driven AST edits. Key features include Tree-sitter parsing, LLM-to-Cypher generation, structural search-and-replace, and MCP server integration.
MCP Server2025
Provides a long‑lived, in‑process file and content search library for editors and AI agents, with typo‑resistant fuzzy matching, frecency‑ranked results, background watchers, and a lightweight in‑memory content index — optimized for repeated searches in long‑running processes.
MCP Server2025
Helps developers connect IDEs and AI agents to the Figma MCP server to extract design context, generate code from frames, and write updates back to Figma. Includes client setup (VS Code, Cursor, Claude Code, Gemini CLI), tools and skills, best practices, and beta rate-limit notes.
AI Dataset2025
Benchmark dataset for evaluating agents on long-horizon software-engineering tasks (repo-level patches, test-driven fixes). Includes golden patches, related tests, and problem statements in parquet format; aimed at agent debugging and code-modification evaluation but requires full test environments.
AI Agent2026
Teaches LLMs to detect and remove “AI tells” from prose using curated phrase/structure lists, before/after examples, and a 5‑dimension scoring rubric. Delivered as a reusable skill (SKILL.md + reference files) designed to plug into Claude or any LLM workflow for automated style sanitization.