Provides high-throughput, low-latency GPU communication kernels for Mixture-of-Experts (MoE) and expert-parallel workloads, with NVLink↔RDMA-aware forwarding, FP8/BF16 support, and low-latency RDMA hooks for inference decoding.
Performs fast static type checking and provides a language server with code navigation, semantic highlighting, and completions for Python. Processes ~1.85M lines/sec and completes IDE rechecks typically under 10ms — intended for responsive editor workflows and large codebases.
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.
Optimized MLA (Multi-head Latent Attention) decoding kernels powering DeepSeek-V3/V3.2 inference on Hopper and Blackwell GPUs. Dense decoding reaches ~3000 GB/s and 660 TFLOPS on H800; the sparse path stores the KV cache in FP8.
Distributes one post across 14+ platforms (Douyin, Xiaohongshu, TikTok, X), automates likes and replies via a browser plugin, and matches creators to paid brand tasks settled by sales, views, or engagement. Drivable from Claude/Cursor via MCP.
Trains LLM reasoning and agentic models with fully asynchronous reinforcement learning, decoupling rollout generation from policy updates for a 2.77x speedup over synchronous RL. Covers GRPO, PPO and DAPO across Megatron, FSDP, vLLM and SGLang backends.
Provides curated ComfyUI workflow templates and subgraph blueprints that package reusable node graphs, preview assets, and publishing pipelines for image/video generation. Includes a browsable Astro site with i18n, CI-driven sync/publish scripts, and PyPI packaging for easy distribution.
Keeps codebases, PDFs, Slack, and docs continuously indexed for RAG and knowledge graphs by recomputing only what changed, not the whole dataset. You declare target state in Python; a Rust engine maintains it with per-row lineage back to the source.
Coordinates role-playing agents to automate real-world tasks — web search and browsing, code execution, document parsing, and multimodal handling. Built on the CAMEL-AI framework; scored 69.09% on the GAIA benchmark, topping open-source frameworks.
Lets AI agents drive GitHub in natural language via MCP: browse repos, triage issues, review pull requests, and trigger Actions runs. Runs as a GitHub-hosted remote OAuth server or a local Go binary, with per-toolset scoping and a read-only mode.
Simulates adversarial attacks against LLMs and AI agents to surface vulnerabilities (e.g., jailbreaks, prompt injection, PII leakage) and ships guardrails to block risky inputs/outputs; runs locally and can be driven from CLI or Python.