Generates and deploys full-stack React apps from natural-language prompts on Cloudflare’s platform, combining AI code generation, previews, Workers, Durable Objects, and containers.
Runs AI models on user devices with native SDKs, optimized model management, hardware acceleration, and OpenAI-compatible APIs for apps that need offline, private inference.
Runs autonomous AI-agent workforces where each agent, skill, and company process lives as version-controlled code you own. Agents act in isolated sandboxes and submit deliverables for human review, with 3,000+ connectors plus MCP support.
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.
Self-hostable alternative to Google NotebookLM: organize PDFs, videos, audio, web pages, and Office docs, then chat over them, take AI-assisted notes, and search via full-text and vector. Routes to 18+ model providers and generates 1-4 speaker podcasts.
Reference architectures and microservices for building GPU-accelerated vision agents that enable natural-language video search, long-video summarization, visual Q&A, and alert verification. Integrates NVIDIA NIM models, embeddings, VLMs/LLMs, and agent workflows for deployable video-analytics stacks.
VideoCaptioner is an AI-powered video subtitling assistant that combines ASR (local or cloud) with LLM-based subtitle segmentation, correction and translation. It supports offline GPU transcription, concurrent chunk transcription, VAD, speaker-aware processing, batch subtitling and one-click subtitle-to-video synthesis, with both GUI and CLI options.
Open-source HybridFlow implementation for RL post-training of LLMs. Decouples control flow from compute so PPO, GRPO, GSPO and DAPO share one dataflow; pairs FSDP/Megatron with vLLM/SGLang rollout and reports 1.5-20x throughput over prior RLHF stacks.
A GitHub repository of learning notes and code dedicated to ML + SYS (machine learning systems). It collects tutorials, code walkthroughs and engineering notes on RLHF, distributed training (FSDP, Megatron), inference and scheduling (SGLang, vllm), quantization, CUDA/GPU optimization, system design, and practical engineering.
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.
Translates scientific PDFs while keeping the original layout intact: parses text, tables, and figures, then re-renders bilingual or monolingual output via any OpenAI-compatible LLM. Tuned for English-to-Chinese papers, with CSV glossary support.