Esta ruta está diseñada para aprender desde cero hasta alcanzar un nivel de maestría en Bash y Shell Scripting. Se divide en 4 niveles, cada uno con objetivos, recursos didácticos, ejercicios prácticos y recomendaciones para no dejar huecos en el aprendizaje.
Large language models are increasingly delivered as network-dependent services, yet their underlying runtimes (llama.cpp, vLLM, and derivatives) are trivially portable. This work addresses the gap between portable and deployable: constructing a fully offline, zero-install, GPU-accelerated LLM environment that boots from a removable USB drive on an Apple M4 Mac mini with 16 GB of unified memory. We analyze the llamafile distribution model (Cosmopolitan Libc fat binaries, mmap'd weight loading, runtime GPU compilation), the GGUF quantization ecosystem, and the memory/performance physics of Apple Silicon unified memory, and derive a set of falsifiable hypotheses (H1–H5) that an empirical benchmark phase will confirm or refute. We further identify and correct seven defects in a prior community guide — most critically a Windows-only binary selection, an overly conservative context window, and a missing first-run compiler dependency that silent
If you are a React Native, NativeScript, Flutter or Ionic developer maybe you don't want to install the entire Android Studio just to have your environment ready. If this is your case, this guide will help you to setup your minimal Android SDK environment in Windows.
List of freely available resources to study computer graphics programming.
flowchart TD
A([Start]) --> B{Did you fix a bug?}
B -->|Yes| C([ **fix** ])
B -->|No| D{Did you change functionality or affect the UI?}
D -->|Yes| E([**feat**])
D -->|No| F{Did you add or change tests?}
F -->|Yes| G([**test**])
| -- Add these settings to ~/.config/hypr/input.lua on Omarchy. | |
| hl.config({ | |
| input = { | |
| touchpad = { | |
| natural_scroll = true, | |
| tap_to_click = true, | |
| disable_while_typing = true, | |
| tap_and_drag = false, | |
| drag_lock = false, | |
| clickfinger_behavior = true, |
Используйте только Stable-версии приложений!
Установите приложение для вашего устройства, импортируйте ключ (или отсканируйте QR-код) и включите подключение. Ключ или ссылку на подписку вы получаете от администратора — сами приложения лишь подключаются к серверу по этому ключу.
Для кого эта страница: шаги рассчитаны на человека без опыта настройки сетей. Непонятные слова по ходу текста поясняются.
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.