LLVM

scriptc is an experimental TypeScript-to-native compiler from Vercel Labs. It uses the TypeScript compiler for parsing and type checking, then emits typed IR, readable C, textual LLVM IR, native assembly and object files, native executables, or WebAssembly modules. Static builds include a small native runtime and do not require Node.js or a JavaScript engine when run; code that cannot compile statically is reported through diagnostics, and coverage reporting identifies the statically compilable portions and blockers. For npm packages and other dynamically typed code, an optional dynamic mode embeds quickjs-ng. The compiler itself requires Node.js 24 or newer and targets macOS, Linux, Windows, and WebAssembly through WASI Preview 1.

Clang is a C language family compiler front end and tooling infrastructure for LLVM, supporting C, C++, Objective-C, OpenCL, and CUDA. It provides GCC-compatible and MSVC-compatible compiler drivers, along with libraries for clients such as refactoring, static analysis, code generation, and IDE integration. In the yk build process described by the video, Clang compiles the C interpreter with yk-specific flags. The project is distributed under the Apache 2 license.

ykllvm is a fork of LLVM used by YK to compile C-based interpreters. It inserts trace-recording functions into the compiled interpreter and serializes a simplified representation of the interpreter's LLVM IR into the executable, allowing YK to construct and compile execution traces at runtime.

TypeScript is a programming language developed by Microsoft as a superset of JavaScript. It adds optional static types for application-scale JavaScript and compiles to readable, standards-based JavaScript for browsers, hosts, and operating systems. The compiler is distributed through npm, with stable and nightly builds, and the project provides a browser-based playground, documentation, roadmap, and community resources.

llama.cpp is a C/C++ implementation for running large language and vision-language model inference locally or in the cloud, including on CPUs, GPUs, laptops, Apple silicon devices, and small computers such as Raspberry Pis. The project is built on the ggml library and uses integer quantization from 1.5-bit through 8-bit to reduce memory use and accelerate inference, with CPU, CUDA, HIP, Metal, Vulkan, SYCL, OpenCL, and other hardware backends. It supports hybrid CPU-GPU inference for models larger than available VRAM and includes command-line tools, a built-in web interface, and an OpenAI-compatible REST API server. The project is distributed as source code, Docker images, and pre-built binaries, with installation instructions at llama.app.