Bend 2

Lean 4 is a programming language and theorem prover used to formalize mathematical proofs, including proofs generated by AI systems. Its project repository provides the language implementation along with theorem-proving and functional-programming tutorials, reference documentation, examples, installation guidance, and instructions for building from source.

TileLang is a Pythonic domain-specific language and compiler for developing high-performance GPU, CPU, and NPU kernels such as GEMM, quantized matrix multiplication, FlashAttention, and linear attention. Built on TVM, it uses tiled programming primitives for memory transfers, pipelining, matrix operations, parallel elementwise work, layouts, and target-specific code generation; its JIT specializes kernels for input shapes and compile-time arguments on first use. The project supports CUDA, ROCm/HIP, Apple Metal, Huawei Ascend, and experimental LLVM, CuTe DSL, and WebGPU backends, with additional ecosystem adapters for other accelerator platforms. It is distributed as a Python package through PyPI, with source builds and nightly wheels available.

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.

Procedura is an open-source agentic 3D-modeling tool from SpatiaOS that converts text prompts into editable procedural assemblies rather than point clouds or triangle meshes. It generates OpenSCAD source with named parts and typed mates, plans and builds parts incrementally, and can use reference images and optional Blender render feedback during refinement. Optional passes assign per-part PBR materials and plan articulation, exporting motion to OpenUSD and URDF with Isaac-based validation. It runs locally as a Bun/TypeScript pipeline using a configurable OpenAI-compatible, Gemini, or local model endpoint; it does not provide hosted inference or API keys. The pipeline uses a Manifold-capable OpenSCAD build to compile the generated programs and Blender for renders, and includes a web Studio for composing runs and inspecting intermediate artifacts. The repository is MIT-licensed.

LLM Space v4 is a local-first desktop workbench for prototyping and developing AI agents. It lets users version prompts, system messages, tools, and model settings; trace each model call and tool run in an agent loop; replay historical runs to debug failures; and evaluate agent performance across runs. It can generate prompts and tools with AI and turn a thread into a runnable LangGraph agent. Threads, project files, and API keys remain on the user's computer, while the project describes the app as cloud-ready for managed agents. The desktop application uses Electrobun with a React, Tailwind CSS, and shadcn/ui interface, and is built as a Bun monorepo around Pi Agent Core.