People
 :bowtie: |
😄 :smile: |
😆 :laughing: |
|---|---|---|
😊 :blush: |
😃 :smiley: |
:relaxed: |
😏 :smirk: |
😍 :heart_eyes: |
😘 :kissing_heart: |
😚 :kissing_closed_eyes: |
😳 :flushed: |
😌 :relieved: |
😆 :satisfied: |
😁 :grin: |
😉 :wink: |
😜 :stuck_out_tongue_winking_eye: |
😝 :stuck_out_tongue_closed_eyes: |
😀 :grinning: |
😗 :kissing: |
😙 :kissing_smiling_eyes: |
😛 :stuck_out_tongue: |
| # ============================================================================= | |
| # CONFIGURATION - Customize for your project | |
| # ============================================================================= | |
| PROJECT_NAME ?= my-project | |
| BUILD_DIR ?= build | |
| VERSION ?= $(shell cat VERSION 2>/dev/null || echo "0.1.0") | |
| # Tool versions (adjust or remove as needed) | |
| # NODE_VERSION ?= 20 | |
| # PYTHON_VERSION ?= 3.12 |
People
:bowtie: |
😄 :smile: |
😆 :laughing: |
|---|---|---|
😊 :blush: |
😃 :smiley: |
:relaxed: |
😏 :smirk: |
😍 :heart_eyes: |
😘 :kissing_heart: |
😚 :kissing_closed_eyes: |
😳 :flushed: |
😌 :relieved: |
😆 :satisfied: |
😁 :grin: |
😉 :wink: |
😜 :stuck_out_tongue_winking_eye: |
😝 :stuck_out_tongue_closed_eyes: |
😀 :grinning: |
😗 :kissing: |
😙 :kissing_smiling_eyes: |
😛 :stuck_out_tongue: |
使用JAV金鸡儿奖官网附带的工具JAV SQL 查询器，可查询各种类别的JavDB TOP250影片：
及分年数据（存在部分重复影片，原始数据的问题）：
A pattern for building personal knowledge bases using LLMs. Extended with lessons from building agentmemory 20K+ Stars ⭐️, a persistent memory engine for AI coding agents.
This builds on Andrej Karpathy's original LLM Wiki idea file. Everything in the original still applies. This document adds what we learned running the pattern in production: what breaks at scale, what's missing, and what separates a wiki that stays useful from one that rots.
The core insight is correct: stop re-deriving, start compiling. RAG retrieves and forgets. A wiki accumulates and compounds. The three-layer architecture (raw sources, wiki, schema) works. The operations (ingest, query, lint) cover the basics. If you haven't read the original, start there.
| # SKILL — Construire un foyer connecté, digne et fiable pour un proche en perte d'autonomie | |
| > **Version 1 (les piliers).** Inspiré d'un système réel construit pour une maman atteinte d'Alzheimer : une grande télé qui lui montre la date, des photos de famille, un mot doux, et qui sonne quand un proche l'appelle en visio — le tout 100 % à la maison, sans abonnement cloud, pensé pour la **dignité** avant la « surveillance ». | |
| --- | |
| ## 👋 À LIRE EN PREMIER (toi, l'aidant·e) — 2 minutes | |
| Ce document est un **assistant d'installation**. Tu le **copies-colles en entier** dans une IA, et elle t'**accompagne** pour construire le même genre de système pour ton proche — **à ton rythme** : plus directe si tu as déjà une petite base technique, et elle ralentit + explique tout dès qu'un terme te bloque. Pas besoin d'être expert·e, juste un peu curieux·se. |
Цель: Автоматическая выдача и обновление Let’s Encrypt сертификатов через DNS-01 challenge для доменов, размещённых на Beget, в связке с Traefik.
Этот контейнер — HTTP-хук для Traefik, который:
Most rooms are already full of radio waves. Your WiFi router is constantly transmitting, and your phone, laptop, and smart-home gear are constantly listening. Every human body in the room subtly disturbs those waves — moving, breathing, even just sitting still. RuView is open-source software that turns those disturbances into useful data: where people are, what they're doing, whether they're okay.
Just shipped v0.6.5 of the ESP32 firmware. This is the bit that runs on the $9 sensor board.
Links:
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.
grep for .icnsSimilarly you could run this command, but it may take several minutes to complete: