Parses, generates, and filters training data from noisy sources like PDFs and weak QA, then feeds it into LLM pre-training, SFT, RL, or RAG cleaning. Ships 100+ operators and ready-made pipelines for text, reasoning, Text2SQL, and agentic data.
Python web scraping framework that automatically relocates elements when a site's HTML changes, so selectors survive redesigns. Bundles Cloudflare Turnstile bypass, TLS fingerprint impersonation, and a Scrapy-like async spider for full crawls.
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.
Runs text-to-speech, speech-to-text, and speech-to-speech models natively on Apple Silicon via MLX — no CUDA or cloud. Supports 20+ TTS and 15+ STT models (Kokoro, Whisper, Qwen3), low-bit quantization, an OpenAI-compatible API, and a Swift package.
A library of specialized AI agents that automate data science steps: loading, cleaning, wrangling, feature engineering, SQL queries, EDA, and ML modeling via H2O and MLflow. Higher-level analyst workflows chain these under a supervisor agent.
Elixir-native autonomous agent framework that models state changes as pure cmd/2 operations and describes side effects with typed directives; integrates with OTP supervision and optional LLM plugins for AI-driven agents.
Simulates a trading firm using LLM agents in specialized roles — fundamentals, sentiment, news and technical analysts feed bull/bear researcher debates, then a trader and risk team decide. Works across US, global and crypto markets and 10+ LLM providers.
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.
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.
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.
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.