There are many ways and tools to use proxy in Linux. But, let's use what I already used.
- This is the easiest way. If you have a GUI settings, just search for
Network > Network Proxy > Manual. - Fill in the input with your own proxy settings.
| Skills: https://github.com/vltansky/foundry-skills | |
| GDrive: https://drive.google.com/drive/folders/1ajHwhzXWOHwMTu1yljxsSBD_Oqa0Ord9?usp=sharing | |
| Recommendations: | |
| https://impeccable.style/ | |
| https://github.com/garrytan/gstack | |
| https://openspec.dev/ | |
| https://docs.bmad-method.org/ | |
| https://jakub.kr/skills/make-interfaces-feel-better | |
| https://www.agentation.com/ |
| """ | |
| The most atomic way to train and run inference for a GPT in pure, dependency-free Python. | |
| This file is the complete algorithm. | |
| Everything else is just efficiency. | |
| @karpathy | |
| """ | |
| import os # os.path.exists | |
| import math # math.log, math.exp |
Michael operates on out-of-favor industries to find best-of-breed companies trading at steep discounts.
Michael focuses on free cash flow and private market valuation as he scours out-of-favor industries and overlooked securities.
| #EXTM3U | |
| #PLAYLISTV: pltv-logo="http://megafilmeshdplus.org/wp-content/uploads/2017/08/Mega-Filmes-HD-Plus-.Org_.png" pltv-name="CINEMA" pltv-description="Marcus" pltv-cover="XXX CAPA DA LISTA XXX" pltv-author="XXX AUTOR DA LISTA XXX" pltv-site="XXX SITE CASO HOUVER XXX" pltv-email="XXX E-MAIL XXX" | |
| #EXTINF:-1 tvg-logo="https://goo.gl/dMrVaZ" group-title="Seriado" pltv-subgroup="2 Garotas em Apuros",2GEA T1|EP01 | |
| https://cdn2.canais.ink/RCServer09/ondemand/2BRKGLT01EP01.mp4?expires=1581552100&token=f1093bb907fb240d5b2721638c8116cf | |
| #EXTINF:-1 tvg-logo="https://goo.gl/dMrVaZ" group-title="Seriado" pltv-subgroup="2 Garotas em Apuros",2GEA T1|EP02 | |
| https://cdn2.canais.ink/RCServer09/ondemand/2BRKGLT01EP02.mp4?expires=1581546741&token=0e2d8a7b3fd27dc9319ca18abe47e75c | |
| #EXTINF:-1 tvg-logo="https://goo.gl/dMrVaZ" group-title="Seriado" pltv-subgroup="2 Garotas em Apuros",2GEA T1|EP03 | |
| https://cdn2.canais.ink/RCServer09/ondemand/2BRKGLT01EP03.mp4?expires=1581546741&token=0e2d8a7b3fd27dc9319ca18abe47e75c |
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.
| import { GoogleGenAI, Modality } from "https://esm.sh/@google/genai"; | |
| import { key } from './_key.js' | |
| const API_KEY = key; | |
| let audioContext; | |
| let nextStartTime = 0; | |
| const audioSources = new Set(); | |
| let audioInputContext; |