AI Train2025
Bundles a dataset, an interaction harness, and rubric-based reward functions into one RL environment for training and evaluating LLMs — also usable as an eval, synthetic-data pipeline, or agent harness for any OpenAI-compatible endpoint.
AI Agent2025
Runs stateful AI agents as Cloudflare Durable Objects — each keeps its own storage and lifecycle, hibernating when idle and waking on demand. Adds WebSocket state sync, type-safe RPC, resumable LLM streaming, MCP roles, and durable workflows.
AI Agent2025
Hands-on studio to design, test and deploy declaratively configured multi-agent systems built on the Neuro SAN framework. Ships ready examples, an Agent Network Designer UI (nsflow), CLI tooling, and integrations with major LLMs and external tools for rapid prototyping.
AI Infra2025
High-performance CUDA tensor-core GEMM kernel library for LLM workloads: supports FP8/FP4/BF16, fused Mega MoE and MQA scoring, and runtime JIT-compiled kernels. Targets NVIDIA SM90/SM100 and PyTorch—for teams working on low-level GPU kernel optimization.
AI Infra2025
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.
AI Infra2025
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.
AI Agent2025
Framework-agnostic library for connecting and optimizing teams of AI agents built in LangChain, LlamaIndex, CrewAI, Semantic Kernel, or Google ADK. Profiles them down to individual tokens, traces execution, and runs built-in evaluation.
AI Infra2025
Runs and fine-tunes LLMs locally on Apple silicon via the MLX framework, pulling thousands of Hugging Face models with one command. Adds 4- and 8-bit quantization, LoRA and full fine-tuning, prompt caching, and distributed inference across Macs.
AI Agent2025
Builds, evaluates, and deploys multi-agent systems in Python, code-first. A graph-based runtime handles routing, fan-out/fan-in, loops, retries, and human-in-the-loop; a Task API covers agent-to-agent delegation, plus a CLI and web UI.
AI Train2025
Provides a modular full-stack reinforcement learning stack to train and evaluate long-horizon, multi-turn tool-use LLM agents, including a performant trainer, a Tinker-compatible backend, agent orchestration, and Gymnasium-style environments for task design.