Search Results for "masmt runtime engine"

...The custom .base format supports affine quantization from Q2 through Q8, optional AWQ calibration, and signed model bundles. Stable C interfaces connect the engine with Python, Node.js, Rust, and Swift applications. The repository contains the open CLI, format specifications, bindings, documentation, and benchmarks, while the prebuilt inference engine uses a separate license.

Claw Code is an open-source AI agent harness project focused on building better tools for orchestrating and managing autonomous coding agents. It originated as a clean-room reimplementation inspired by the architecture of Claude Code, aiming to replicate core concepts without using proprietary code. The project provides a Python-based foundation for experimenting with agent workflows, tool integration, and task execution pipelines. It emphasizes harness engineering—how agents are structured,...

Spice is a portable runtime offering developers a unified SQL interface to materialize, accelerate, and query data from any database, data warehouse, or data lake. Spice connects, fuses, and delivers data to applications, machine-learning models, and AI backends, functioning as an application-specific, tier-optimized Database CDN. The Spice runtime, written in Rust, is built-with industry-leading technologies such as Apache DataFusion, Apache Arrow, Apache Arrow Flight, SQLite, and DuckDB....

Ante is a self-contained terminal coding agent written in Rust and designed to work with many AI models rather than one vendor. It ships as a roughly 15 MB binary with no external runtime dependencies. Users can work through an interactive terminal interface, headless commands, a server protocol, or Slack and Discord gateways. A built-in inference engine can run GGUF models entirely offline without an account, API key, or internet connection. Ante also supports more than a dozen hosted providers and can switch between commercial, open-weight, and local models. ...

Luminal is a framework designed to accelerate and simplify the development of systems-level data applications by using a typed, functional, and streaming-first approach. Instead of treating data processing as a series of ad-hoc scripts, Luminal models transformations as strongly typed building blocks that can be composed into reliable, scalable pipelines. The project emphasizes correctness and performance by requiring explicit types for the data flowing through transformations, reducing...