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Dev tools · February 17, 2026

Yule

Run models locally. Prove what ran.

What it does

Every local AI runtime is C++ bindings wrapped in Python. llama.cpp has 15+ CVEs. Yule is written from scratch in pure Rust -- no llama.cpp, no CUDA, no C++. Every tensor is Merkle-verified. Every inference is Ed25519-signed. The model process is sandboxed. 12 Vulkan compute shaders, 8.5x GPU speedup, no NVIDIA lock-in.

Does the same job

all alternatives →
  • WM
    We made our own inference engine for Apple Silicon2025 · github.com · ▲186

    We wrote our inference engine on Rust, it is faster than llama cpp in all of the use cases. Your feedback is very welcomed. Written from scratch with idea that you can add support of any kernel and platform.

  • 8F
    80% faster, 50% less memory, 0% loss of accuracy Llama finetuning2023 · github.com · ▲385

    Hi HN! I'm just sharing a project I've been working on during the LLM Efficiency Challenge - you can now finetune Llama with QLoRA 5x faster than Huggingface's original implementation on your own local GPU. Some highlights: 1. Manual autograd engine - hand derived backprop steps. 2. QLoRA / LoRA 80% faster, 50% less memory. 3. All kernels written in OpenAI's Triton language. 4. 0% loss in accuracy - no approximation methods - all exact. 5. No change of hardware necessary. Supports NVIDIA GPUs since 2018+. CUDA 7.5+. 6. Flash Attention support via Xformers. 7. Supports 4bit and 16bit…

  • LA
    LocalGPT – A local-first AI assistant in Rust with persistent memoryFeb 2026 · github.com · ▲331

    I built LocalGPT over 4 nights as a Rust reimagining of the OpenClaw assistant pattern (markdown-based persistent memory, autonomous heartbeat tasks, skills system). It compiles to a single ~27MB binary — no Node.js, Docker, or Python required. Key features: - Persistent memory via markdown files (MEMORY, HEARTBEAT, SOUL markdown files) — compatible with OpenClaw's format - Full-text search (SQLite FTS5) + semantic search (local embeddings, no API key needed) - Autonomous heartbeat runner that checks tasks on a configurable interval - CLI + web interface + desktop GUI - Multi-provider:…

  • LL
    LocalScore – Local LLM Benchmark2025 · localscore.ai · ▲124

    Hey Folks! I've been building an open source benchmark for measuring local LLM performance on your own hardware. The benchmarking tool is a CLI written on top of Llamafile to allow for portability across different hardware setups and operating systems. The website is a database of results from the benchmark, allowing you to explore the performance of different models and hardware configurations. Please give it a try! Any feedback and contribution is much appreciated. I'd love for this to serve as a helpful resource for the local AI community. For more check out: - Website:…

  • LC
    Llama.cpp Tutorial 2026: Run GGUF Models Locally on CPU and GPUApr 2026 · ▲13

    Complete llama.cpp tutorial for 2026. Install, compile with CUDA/Metal, run GGUF models, tune all inference flags, use the API server, speculative decoding, and benchmark your hardware. https://vucense.com/dev-corner/llama-cpp-tutorial-run-gguf-m...

  • IB
    I built a tensor library from scratch in C++/CUDA2025 · github.com · ▲119

    Hi HN, Over the past few months, I've been building `dsc`, a tensor library from scratch in C++/CUDA. My main focus has been on getting the basics right, prioritizing a clean API, simplicity, and clear observability for running small LLMs locally. The key features are: - C++ core with CUDA support written from scratch. - A familiar, PyTorch-like Python API. - Runs real models: it's complete enough to load a model like Qwen from HuggingFace and run inference on both CUDA and CPU with a single line change[1]. - Simple, built-in observability for both Python and C++. Next on the roadmap is…

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