Glossary OS

Leitura rápida

Comece pela explicação mais curta e útil antes de aprofundar.

Anthropic's terminal-based agentic coding tool launched in early 2025 alongside Claude 3.7 Sonnet. It accepts natural-language commands in the shell and autonomously performs multi-step coding tasks including file editing, git operations, test execution, and large-scale refactoring using a 200K token context window. Claude Code can be extended with hooks, MCP servers, and custom slash commands for project-specific workflows.

Modelo mental

Use primeiro a analogia curta para raciocinar melhor sobre o termo quando ele aparecer em código, docs ou prompts.

Pense nisso como uma peça da pilha de contexto ou inferência usada em produtos com agentes ou LLMs.

Contexto técnico

Coloque o termo dentro da camada de Solana em que ele vive para raciocinar melhor sobre ele.

LLMs, RAG, embeddings, inferência e primitivas voltadas a agentes.

Por que builders ligam para isso

Transforme o termo de vocabulário em algo operacional para produto e engenharia.

Este termo destrava conceitos adjacentes rapidamente, então funciona melhor quando você o trata como um ponto de conexão, não como definição isolada.

Handoff para IA

Use este bloco compacto quando quiser dar contexto aterrado para um agente ou assistente sem despejar a página inteira.

Claude Code (claude-code)
Categoria: IA / ML
Definição: Anthropic's terminal-based agentic coding tool launched in early 2025 alongside Claude 3.7 Sonnet. It accepts natural-language commands in the shell and autonomously performs multi-step coding tasks including file editing, git operations, test execution, and large-scale refactoring using a 200K token context window. Claude Code can be extended with hooks, MCP servers, and custom slash commands for project-specific workflows.
Aliases: Anthropic CLI
Relacionados: AI Coding Assistant, Agente de IA, Model Context Protocol (MCP)

Glossary Copilot

Faça perguntas de Solana com contexto aterrado sem sair do glossário.

Use contexto do glossário, relações entre termos, modelos mentais e builder paths para receber respostas estruturadas em vez de output genérico.

Abrir workspace completa do Copilot

Pergunta

Explicar este código

Opcional: cole código Anchor, Solana ou Rust para o Copilot mapear primitivas de volta para termos do glossário.

Faça uma pergunta aterrada no glossário

O Copilot vai responder usando o termo atual, conceitos relacionados, modelos mentais e o grafo ao redor do glossário.

Grafo conceitual

Veja o termo como parte de uma rede, não como uma definição sem saída.

Esses ramos mostram quais conceitos esse termo toca diretamente e o que existe uma camada além deles.

Ramo

AI Coding Assistant

An AI tool that helps developers write, debug, review, and explain code. Examples: GitHub Copilot (inline suggestions), Claude Code (agentic CLI), Cursor (AI-native editor), Cody (Sourcegraph). These tools use LLMs to understand codebases, generate implementations, fix bugs, and write tests. Particularly valuable for Solana development where boilerplate is significant.

LLM (Modelo de Linguagem Grande)

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Ramo

Agente de IA

An autonomous AI system that can plan, use tools, and take actions to accomplish goals. Agents use LLMs as the reasoning core and have access to tools (APIs, code execution, web browsing, database queries). In blockchain: agents can analyze smart contracts, execute transactions, monitor DeFi positions, and automate trading strategies. Frameworks: LangChain, CrewAI, Claude Agent SDK.

Tool Use (Function Calling)

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Ramo

Model Context Protocol (MCP)

An open standard introduced by Anthropic in November 2024 for connecting AI applications to external data sources, tools, and workflows via a unified protocol. Often described as 'USB-C for AI,' MCP eliminates the need for custom integrations per data source. Adopted by OpenAI in March 2025 and donated to the Linux Foundation's Agentic AI Foundation. MCP handles standardized tool/data connections while agent frameworks handle orchestration.

LangChain / LangGraph

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Próximos conceitos para explorar

Continue a cadeia de aprendizado em vez de parar em uma única definição.

Estes são os próximos conceitos que valem abrir se você quiser que este termo faça mais sentido dentro de um workflow real de Solana.

IA / ML

AI Coding Assistant

An AI tool that helps developers write, debug, review, and explain code. Examples: GitHub Copilot (inline suggestions), Claude Code (agentic CLI), Cursor (AI-native editor), Cody (Sourcegraph). These tools use LLMs to understand codebases, generate implementations, fix bugs, and write tests. Particularly valuable for Solana development where boilerplate is significant.

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IA / ML

Agente de IA

An autonomous AI system that can plan, use tools, and take actions to accomplish goals. Agents use LLMs as the reasoning core and have access to tools (APIs, code execution, web browsing, database queries). In blockchain: agents can analyze smart contracts, execute transactions, monitor DeFi positions, and automate trading strategies. Frameworks: LangChain, CrewAI, Claude Agent SDK.

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IA / ML

Model Context Protocol (MCP)

An open standard introduced by Anthropic in November 2024 for connecting AI applications to external data sources, tools, and workflows via a unified protocol. Often described as 'USB-C for AI,' MCP eliminates the need for custom integrations per data source. Adopted by OpenAI in March 2025 and donated to the Linux Foundation's Agentic AI Foundation. MCP handles standardized tool/data connections while agent frameworks handle orchestration.

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IA / ML

Constitutional AI

An alignment technique developed by Anthropic where an AI model is guided by a 'constitution'—a set of explicit principles defining allowed and disallowed behavior—rather than relying solely on human feedback. The model critiques and revises its own outputs against these principles. Constitutional Classifiers extend this by training input/output classifiers that detect policy violations at low compute cost.

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Termos relacionados

Siga os conceitos que realmente dão contexto a este termo.

Entradas de glossário só ficam úteis quando estão conectadas. Esses links são o caminho mais curto para ideias adjacentes.

IA / MLai-coding-assistant

AI Coding Assistant

An AI tool that helps developers write, debug, review, and explain code. Examples: GitHub Copilot (inline suggestions), Claude Code (agentic CLI), Cursor (AI-native editor), Cody (Sourcegraph). These tools use LLMs to understand codebases, generate implementations, fix bugs, and write tests. Particularly valuable for Solana development where boilerplate is significant.

Abrir termo

IA / MLagent-ai

Agente de IA

An autonomous AI system that can plan, use tools, and take actions to accomplish goals. Agents use LLMs as the reasoning core and have access to tools (APIs, code execution, web browsing, database queries). In blockchain: agents can analyze smart contracts, execute transactions, monitor DeFi positions, and automate trading strategies. Frameworks: LangChain, CrewAI, Claude Agent SDK.

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IA / MLmcp

Model Context Protocol (MCP)

An open standard introduced by Anthropic in November 2024 for connecting AI applications to external data sources, tools, and workflows via a unified protocol. Often described as 'USB-C for AI,' MCP eliminates the need for custom integrations per data source. Adopted by OpenAI in March 2025 and donated to the Linux Foundation's Agentic AI Foundation. MCP handles standardized tool/data connections while agent frameworks handle orchestration.

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Mais na categoria

Permaneça na mesma camada e continue construindo contexto.

Essas entradas vivem ao lado do termo atual e ajudam a página a parecer parte de um grafo maior, não um beco sem saída.

IA / ML

LLM (Modelo de Linguagem Grande)

A neural network trained on vast text corpora to understand and generate human language. LLMs (GPT-4, Claude, Llama, Gemini) use transformer architectures with billions of parameters. They power chatbots, code generation, summarization, and reasoning tasks. In blockchain development, LLMs assist with smart contract writing, audit review, documentation, and code explanation.

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IA / ML

Transformer

The neural network architecture underlying modern LLMs, introduced in 'Attention Is All You Need' (2017). Transformers use self-attention mechanisms to process input sequences in parallel (unlike recurrent networks). Key components: multi-head attention, positional encoding, feedforward layers, and layer normalization. Variants include encoder-only (BERT), decoder-only (GPT), and encoder-decoder (T5).

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IA / ML

Attention Mechanism

A neural network component that allows models to weigh the relevance of different parts of the input when producing output. Self-attention computes query-key-value dot products across all positions, enabling each token to 'attend' to every other token. Multi-head attention runs multiple attention functions in parallel. Attention is O(n²) in sequence length, driving context window research.

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IA / ML

Foundation Model

A large AI model trained on broad data that can be adapted for many downstream tasks. Foundation models (GPT-4, Claude, Llama 3, Gemini) are pre-trained on internet-scale text/code and can be fine-tuned, prompted, or used via APIs for specific applications. The term emphasizes that one base model serves as the foundation for diverse use cases rather than training task-specific models.

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