「streaming」の検索結果

Data structures and serialization formats for agent-to-agent communication. Covers message envelopes, structured output schemas, capability declarations, task handoff payloads, error/retry signaling, and context windows as data structures. Deep comparison of A2A protocol, MCP, OpenAI function calling, and LangChain message types. Teaches when to use rigid schemas vs free-form with validation, typed vs untyped, streaming vs batch. Activate on: "agent message format", "agent communication schema", "agent-to-agent protocol", "A2A protocol", "MCP message format", "structured output for agents", "agent interop", "interchange format", "agent serialization", "task handoff format", "capability declaration". NOT for: what agents say to each other (use agent-conversation-protocols), orchestration topology (use multi-agent-coordination), building agent infrastructure (use agentic-infrastructure-2026).

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Build audio transcription pipelines with Whisper, Deepgram, and AssemblyAI including speaker diarization and real-time streaming. Activate on: transcription, speech-to-text, diarization, audio processing, meeting transcripts. NOT for: text-to-speech synthesis (voice-audio-engineer), music generation (ai-engineer).

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Audit social media accounts (TikTok, IG, etc.): scrape profiles, calculate engagement metrics, diagnose performance drops, interpret analytics screenshots.

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Writes Next.js App Router code — Server Components, Server Actions, RSC boundaries, route handlers, caching, and streaming — matching framework conventions and project architecture.

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Extraia dados estruturados de respostas de LLM com validação Pydantic, tente novamente automaticamente extrações que falharem, analise JSON complexo com segurança de tipo e transmita resultados parciais com Instructor - biblioteca de saída estruturada testada em batalha

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Construa pipelines de dados escaláveis, data warehouses modernos e arquiteturas de streaming em tempo real. Implementa Apache Spark, dbt, Airflow e plataformas de dados nativas em nuvem.

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Processamento escalável de dados para workloads de ML. Execução em streaming em CPU/GPU, suporta Parquet/CSV/JSON/imagens. Integra-se com Ray Train, PyTorch, TensorFlow. Escala de uma única máquina para centenas de nós. Use para inferência em lote, pré-processamento de dados, carregamento de dados multi-modal ou pipelines distribuídos de ETL.

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Lark/Feishu real-time event listening / subscribing / consuming: stream events as NDJSON via `lark-cli event consume <EventKey>` (covers IM message receive, reactions, chat member changes, etc.). Use for Lark bots, real-time message processing, long-running subscribers, streaming webhook/push handlers. Supports `--max-events` / `--timeout` bounded runs and a stderr ready-marker contract — designed for AI agents running as subprocesses.

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Azure AI VoiceLive SDK for Java. Real-time bidirectional voice conversations with AI assistants using WebSocket. Triggers: "VoiceLiveClient java", "voice assistant java", "real-time voice java", "audio streaming java", "voice activity detection java".

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Azure AI Agents Persistent SDK for Java. Low-level SDK for creating and managing AI agents with threads, messages, runs, and tools. Triggers: "PersistentAgentsClient", "persistent agents java", "agent threads java", "agent runs java", "streaming agents java".

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Azure AI Agents Persistent SDK for .NET. Low-level SDK for creating and managing AI agents with threads, messages, runs, and tools. Use for agent CRUD, conversation threads, streaming responses, function calling, file search, and code interpreter. Triggers: "PersistentAgentsClient", "persistent a...

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Batteries-included agent component for React/Next.js from ui.inference.sh. One component with runtime, tools, streaming, approvals, and widgets built in. Capabilities: drop-in agent, human-in-the-loop, client-side tools, form filling. Use for: building AI chat interfaces, agentic UIs, SaaS...

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Educational single-threaded TypeScript vanity address generator for Solana using @solana/web3.js with async iteration, event-loop yielding, streaming generator API, and best-effort memory zeroization.

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Integrating TanStack Router with TanStack Query: queryClient in router context, ensureQueryData/prefetchQuery in loaders, useSuspenseQuery in components, defaultPreloadStaleTime: 0, setupRouterSsrQueryIntegration for SSR dehydration/hydration and streaming, per-request QueryClient isolation.

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Use when diagnosing CopilotKit issues -- runtime connectivity failures, agent not responding, streaming errors, tool execution problems, transcription failures, version mismatches, and AG-UI event tracing.

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Use when building custom agent backends, implementing the AG-UI protocol, debugging streaming issues, or understanding how agents communicate with frontends. Covers event types, SSE transport, AbstractAgent/HttpAgent patterns, state synchronization, tool calls, and human-in-the-loop flows.

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Search and analyze Estuary task logs with jq filtering. Use when investigating task failures, debugging connector errors, analyzing behavior patterns, or streaming live logs. Use when user says "check logs", "show me logs", "task failing", "why is my capture failing", "why is my materialization failing", "error messages", "why did it fail", "search task logs", "shard failed", "follow logs", "increase log level", "debug logging", "is this log message normal", "what does this message mean", "connection timeout", or "i/o timeout".

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Create a MongoDB CDC capture using flowctl. Use when setting up real-time streaming from MongoDB Atlas, DocumentDB, or self-hosted MongoDB. Use when user says "capture MongoDB", "stream from Mongo", "MongoDB CDC", or "connect MongoDB to Estuary".

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Create a MySQL CDC capture using flowctl with binlog replication. Use when setting up streaming from MySQL, Amazon RDS MySQL, or Aurora MySQL. Use when user says "capture MySQL", "stream from MySQL", "MySQL CDC", "binlog replication", or "connect MySQL to Estuary".

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Create a SQL Server CDC capture using flowctl. Use when setting up real-time streaming from SQL Server, Azure SQL, Amazon RDS SQL Server, or Google Cloud SQL. Use when user says "capture SQL Server", "stream from MSSQL", "SQL Server CDC", or "connect SQL Server to Estuary".

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Create an Estuary derivation that continuously aggregates metrics (daily totals, running counts, min/max, lifetime revenue) using reduction annotations. Use for real-time dashboards, continuous materialised views, and replacing batch ETL aggregations with streaming. Derivations add complexity and cost — confirm the user wants a derivation before reaching for this. Use when user says "aggregation derivation", "derivation to sum daily totals", "running-count derivation", "transformation for min/max per group", "derive lifetime metrics", "aggregate-by-date derivation", or "real-time-dashboard derivation".

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Inspect, debug, and inject custom code into a running Steam client — desktop (macOS, Linux, Windows) or Steam Deck over the network — through its Chromium Embedded Framework (CEF) Chrome DevTools Protocol endpoint. Use when developing Steam UI plugins, mods, themes, or integrations — injecting CSS/JS to try a change, locating webpack modules and obfuscated class names, reading React or SteamUIStore state, inspecting Big Picture / Quick Access Menu windows, screenshotting what actually rendered, streaming console logs, or diagnosing runtime errors in Steam's UI. The same commands run against either device, or both at once. Injection is per-session and for development only. Not for building a plugin loader or making changes persist across restarts, Steamworks game SDK work, game development, or scraping the public Steam web store.

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Build blob storage applications with Azure Storage Blob SDK for Java. Use when uploading, downloading, or managing files in Azure Blob Storage, working with containers, or implementing streaming da...

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Azure Event Hubs SDK for Python streaming. Use for high-throughput event ingestion, producers, consumers, and checkpointing.

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