| #!/usr/bin/env python3 | |
| """ | |
| rocky_say — Text-to-speech using Rocky's cloned voice (Project Hail Mary) | |
| Transforms input text into Rocky's speech patterns ("text GAN"), then | |
| synthesizes audio using XTTS v2 voice cloning. Rocky is the Eridian alien | |
| from Andy Weir's Project Hail Mary, voiced by James Ortiz in the 2026 film. | |
| His speech patterns are distinctive: dropped articles, simplified grammar, | |
| word tripling for emphasis ("good good good", "bad bad bad"), and the |
This script does most of the widget creating work for you if you've found the manual tutorials too hard to follow.
How to use:
Console tab
| // | |
| // BlobView.swift | |
| // Prototypes | |
| // | |
| // Created by Shubham on 07/07/26. | |
| // | |
| import SwiftUI | |
| struct BlobView: View { |
| private func setupCollectionView() { | |
| let layout = UICollectionViewCompositionalLayout { [unowned self] section, environment in | |
| let spacing: CGFloat = 8 | |
| let numberOfColumns: CGFloat = 5 | |
| let numberOfSpacers: CGFloat = numberOfColumns - 1 | |
| let contentWidth = collectionView.layoutMarginsGuide.layoutFrame.width | |
| let itemWidth = (contentWidth - numberOfSpacers * spacing) / numberOfColumns | |
| let layoutItemSize = NSCollectionLayoutSize(widthDimension: .absolute(itemWidth), heightDimension: .fractionalHeight(1)) |
A Practical Guide for Building Agent Capabilities
"Skills orchestrate the how. MCP Tools execute the what."
This guide consolidates learnings from building AI agent capabilities for Azure, focusing on the relationship between Skills (workflow orchestration) and MCP Tools (discrete operations). Whether you're creating a new Copilot Skill, building an MCP server, or integrating both, this document provides the architecture patterns, development best practices, and real-world case studies you need to build effective, non-conflicting agent capabilities. The core principle is simple: Skills act as the "brain" that orchestrates complex workflows, while MCP Tools serve as the "hands" that execute individual operations—and Skills should invoke MCP Tools, not duplicate them.
| // Lista | |
| AC, Acre | |
| AL, Alagoas | |
| AP, Amapá | |
| AM, Amazonas | |
| BA, Bahia | |
| CE, Ceará | |
| DF, Distrito Federal | |
| ES, Espirito Santo |
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.
