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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.
Memory engine that lets AI apps remember users across conversations: it extracts facts, tracks updates, resolves contradictions, and auto-forgets stale info, returning context in ~50ms. Tops the LongMemEval, LoCoMo and ConvoMem memory benchmarks.
Automates browser workflows using LLMs and computer vision instead of XPath selectors, so it works on unseen sites and survives layout changes. Drive tasks with natural-language prompts: act, extract, validate. Handles 2FA and multi-step flows.
Generates HD short videos from a single topic/keyword — auto-creates script, finds/assembles footage, generates subtitles, TTS and background music. Offers web UI + API, batch mode, multiple LLM/TTS providers and common short-video aspect ratios.
Open-weights 314B-parameter Mixture-of-Experts language model (8 experts, 2 active per token, 8,192-token context) released under Apache 2.0. Ships a raw JAX checkpoint plus reference inference code; needs heavy multi-GPU memory to load.
Orchestrates low-code multi-agent teams that plan, research, code and deliver results to Telegram, Discord, and WhatsApp. Includes handoffs, guardrails, memory and RAG, and integrates 100+ LLM providers via MCP for production-ready agent workflows.
Runs coding agents and automations from a self-hosted developer control center, with local, remote, cloud, and ACP-compatible backends for managed engineering workflows.
End-to-end framework for running and reproducing foundation-model research workflows — from data curation and tokenization to training and evaluation. Emphasizes reproducibility by recording every step (including failed runs) and expressing experiments as dependency-ordered steps.
Provides 300k annotated multilingual text examples for identifying and masking personally identifiable information (PII) across multiple domains and languages (EN, FR, DE, IT, ES, NL). Intended for training and evaluating token-level PII detection and masking models; includes a DOI for citation.
Framework for building offensive and defensive security agents that run real pentests autonomously. Uses a ReACT loop over 300+ models (OpenAI, Anthropic, DeepSeek, local Ollama) with built-in recon, exploitation, and privilege-escalation tools.
Nine-chapter course teaching prompt engineering for Claude: from basic prompt structure through roles, output formatting, and hallucination control to complete prompts for chatbot, legal, finance, and coding tasks. Runs as editable Jupyter notebooks.