Builds a semantic context layer for enterprise data that helps agents interpret metrics, entities, and relationships consistently. It validates generated queries against certified definitions and lineage, and returns provenance with answers.
Audits deployed AI agents for governance and operational misalignment, running up to 60 inspections that detect deception, manipulation, fabrication, unpredictability and opacity; outputs JSON/Markdown scorecards, a graded A–F score, and optional Pro reports and dashboards.
Parallel Khasi–English sentence pairs for machine translation research focused on low-resource NLP in Northeast India. Provided as a small CSV (sentence_id, english_text, khasi_text) under CC BY‑NC 4.0 for non-commercial research use.
Curated monolingual Khasi sentence corpus (CSV) with under 1,000 sentences for language modeling, tokenization, and low-resource NLP experiments. Single-column structure (khasi_sentence) and CC BY‑NC 4.0 license — suitable for research and data-augmentation workflows, not for commercial use.
Lets developers build stateful, tool-enabled Python AI agents that run on Google's Antigravity runtime. Includes built-in tools (file I/O, shell, image generation), a declarative policy/hook system, multimodal input, and MCP integration.
Early-preview (≈1.2k rows) dataset of agentic coding prompts and unedited model responses generated by DeepSeek‑V4‑Pro, covering real-world programming tasks across many languages. Intended for research, filtering, and model evaluation rather than production training without review.
Converts technical books and document collections into an on-demand agent “skill” that Claude Code, GitHub Copilot CLI, and Amp can load to answer questions from the original content. Produces a compact SKILL.md plus per-chapter files so agents load only the needed sections, cutting token use and reducing hallucination risk.
Ingests and normalizes security telemetry, runs multi-model AI agents to produce replayable investigations and automated triage/response; key features include a step-by-step Investigation Ledger, CI-gated eval harness, and self-hostable deployments.
Runs local LLMs on Apple Silicon using native MTP speculative decoding to accelerate token generation while preserving the model's output distribution. Leverages the model's own MTP heads with batched verification and exact rejection sampling; ships with a Mac app, CLI, local OpenAI/Anthropic-compatible server, auto-tune, and Forge for building/verifying MTP adapters.
Agentic coding evaluation dataset containing real-world, multi-step developer tasks and raw model responses across 20+ programming languages. Emphasizes challenging, persona-driven prompts for benchmarking and fine-tuning; users should filter and audit outputs before training.
A retrieval benchmark suite focused on “oblique queries,” where relevance depends on latent attributes rather than surface keywords. Includes five tasks with large corpora, qrels (and pooled judgments), and task-specific constraints for evaluating embedding-based retrievers and reasoning-augmented retrieval.
Converts text into natural-sounding speech locally using compact ONNX TTS assets. Optimized for CPU/edge inference (~99M params) with support for 31 languages, expression tags (e.g., <laugh>), and improved stability versus Supertonic 2 — suitable for on-device multilingual TTS.