lucastavarex

Proposta: Configuração de mapas direto no R

Contexto

Atualmente, o fluxo do Vinicius é:

1. Importar shapes e fazer limpeza no R
2. Exportar para geojson/mbtiles
3. Upload manual no Mapbox Studio
4. Estilização manual no Mapbox Studio (cores, breaks, etc.)

qiqiandfei

🎬 免费影视资源汇总v1.0

📅 最后更新：2025年7月22日
🔗 收录优质免费影视资源站点，持续更新中...

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🌟 观影(无名小站)

• 推荐指数： ⭐⭐⭐⭐⭐
• 特色： 海量资源全部免费，更新快

dctanner

name	demo-video
description	Generate a narrated demo video from browser screenshots and TTS audio. Captures scenes via agent-browser, generates voice narration via Inworld TTS API, and stitches into an MP4 with ffmpeg.

Demo Video Generation

Generate a narrated demo video from browser screenshots and TTS audio. The output is an MP4 with still-image scenes synced to voice narration.

Prerequisites

tsvtt

• Открыть страницу с видео, видеоплеер должен быть виден на странице
• Открыть средства разработчика в браузере (F12), открыть вкладку с консолью
• Выполнить:
  document.querySelector('vk-video-player').store.actions.internal.downloadVideo()

RaphaelWimmer

#!/usr/bin/env python3

# Simple tool to switch the Creative BT-W5 Bluetooth Audio dongle between AptX Adaptive **Low Latency** or **High Quality** mode.
# Of course, only works with Bluetooth headphones that support AptX Adaptive, such as the Tranya X3
# Reverse engineered based on communication between Creative's desktop app for Windows and the BT-W5.
# Might also accidentally overwrite other settings as a whole config data array is sent without taking into account the existing config.
#
# Usage: sudo ./btw5-switch.py ll  (for low-latency mode)
#        sudo ./btw5-switch.py hq  (for high-quality mode)
#

janert

Running GUI Applications in a Docker Container

Containers are not usually associated with GUI applications, but there may be times when one might still want to run such a program inside a container, for example to isolate the application's dependencies. Installing a GUI application in a container means that not only the application, but also all its specific dependencies are encapsulated inside the container (respectively, the container image), and can therefore reliably be removed from the system in a single step.

The primary challenge is to let a container communicate with the host's display system, so that it can create GUI windows on the host. A GUI application will likely also need to share files with the host system, which in turn requires the appropriate user permissions.

In this example, I will use the pinta paint program, which requires the Mono runtime. I do not use any other programs that depend on Mono, and as I like to keep my system installation re

win3zz

GitHub Search Syntax for Finding API Keys/Secrets/Tokens

As a security professional, it is important to conduct a thorough reconnaissance. With the increasing use of APIs nowadays, it has become paramount to keep access tokens and other API-related secrets secure in order to prevent leaks. However, despite technological advances, human error remains a factor, and many developers still unknowingly hardcode their API secrets into source code and commit them to public repositories. GitHub, being a widely popular platform for public code repositories, may inadvertently host such leaked secrets. To help identify these vulnerabilities, I have created a comprehensive search list using powerful search syntax that enables the search of thousands of leaked keys and secrets in a single search.

Search Syntax:

(path:*.{File_extension1} OR path:*.{File_extension-N}) AND ({Keyname1} OR {Keyname-N}) AND (({Signature/pattern1} OR {Signature/pattern-N}) AND ({PlatformTag1} OR {PlatformTag-N}))

Examples:

**1.

karpathy

LLM Wiki

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.

The core idea

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.

RichardHightower

Migrating AI Agents: A Systematic Approach to Cross-Platform Architecture

How systematic methodology and architectural understanding enabled the successful migration of 12 specialized AI agents between platforms, creating reusable patterns for future migrations

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The Migration Challenge

Migrating AI agents between platforms isn't just a technical exercise—it's an architectural transformation. When you port agents from one system to another, you're not simply copying code; you're translating between different philosophies of agent interaction, tool management, and capability expression.