'streaming' 검색 결과

Build real-time voice applications in Python with Azure AI Voice Live. Covers two-way audio streaming, transcripts, conversation history, and handling interruptions, with examples for authentication and configuring when the assistant responds.

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• Build a voice assistant in Python
• Display transcripts during voice conversations
• Stop an audio response when the user interrupts

Helps build AI agents in .NET with persistent conversation threads. Covers messages, execution status, streaming responses, document search, and custom function calls using Azure.AI.Agents.Persistent, with examples for creating agents and cleaning up resources.

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• Build an AI assistant that retains conversation threads
• Answer questions using uploaded documents
• Connect an agent to custom functions

Use ccxt-cli to query cryptocurrency exchanges from the terminal without writing application code. Check prices, balances, and orders, stream live market data, display candlestick charts, and return JSON results for shell scripts.

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• Check market prices and account balances from the terminal
• Test API credentials and inspect request errors
• Compare live prices across exchanges

Build Next.js applications with App Router patterns for page structure, data fetching, caching, and progressive page loading. Covers server and browser component responsibilities, route design, Server Actions, and migration from Pages Router.

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• Build a new application with App Router
• Migrate an application from Pages Router
• Add progressive loading for slow data sources

Build applications with GitHub Copilot SDK using Python, TypeScript, Go, or .NET. Add custom tools, display responses as they arrive, preserve conversations across restarts, and connect external tools through MCP, a protocol for connecting agents to tools.

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• Embed an AI assistant in an application
• Add custom tools for an agent to call
• Display generated responses as they arrive

Build workflows that extract structured data from LLM responses, check field types and values with Pydantic, and retry failed extractions. Includes examples for nested data, classification, and receiving results as they are generated.

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• Extracting contact and company details from text
• Classifying articles into predefined categories
• Checking review analysis against validation rules

Guides Python agent development with PydanticAI, covering tool calls and structured outputs checked against defined data types. Explains service injection, provider switching, streaming responses, and tests that run without calling a real language model.

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• Building agents that return validated structured data
• Connecting customer support agents to order data
• Testing agent logic without real model calls

Build and evaluate Mamba models for long sequences using state-space models, which summarize past inputs in an internal state. Covers setup, pretrained text generation, differences between Mamba-1 and Mamba-2, and speed comparisons with Transformers.

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• Generate text with pretrained Mamba models
• Compare Mamba-1 and Mamba-2 configurations
• Benchmark generation speed against Transformers

Build data processing pipelines for machine learning, from preprocessing to batch predictions. Uses Ray Data to handle datasets larger than memory, distribute work across CPUs and GPUs, and feed data into training frameworks.

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• Preprocessing datasets larger than memory
• Running batch predictions across multiple GPUs
• Preparing image and tabular data for training

Design and implement systems that collect, transform, validate, and store data for analysis. Covers scheduled and real-time processing, data warehouses, quality checks, monitoring, and cost tuning using tools such as Apache Spark, dbt, and Airflow.

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• Building scheduled data collection and transformation workflows
• Designing a data warehouse for analysis
• Synchronizing database changes in real time

Write integration tests for core error processors by simulating API failures with a mock model. Check retry attempts, changes to conversation messages, and streamed responses, with guidance on test setup and diagnosing empty output.

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• Check retries and message changes after API errors
• Diagnose empty output in streaming tests
• Verify unrelated errors pass through unchanged

Capture UI journeys in Daytona as labeled screenshots and optional video clips. Organize them into a browsable HTML page for PR presentations, helping reviewers follow user actions and compare behavior before and after a change.

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• Sharing UI steps through a labeled screenshot page
• Recording streaming text, loading states, or animations
• Presenting before-and-after UI behavior in a PR

Build Python applications that collect live cryptocurrency prices, trades, and order books with Cryptofeed. Covers consistent data formats across exchanges, price comparisons, and writing incoming data to storage systems for monitoring and analysis.

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• Monitoring prices across cryptocurrency exchanges
• Analyzing order book depth
• Analyzing executed trade flows

Build applications that use Polymarket market data and trading APIs. The skill guides live monitoring through WebSocket connections, which receive ongoing updates, including trades, orders, market comments, and cryptocurrency prices.

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• Build an app that retrieves prediction market data
• Monitor live trades and price changes
• Track market comments and reactions

Reserve remote Android devices on Google's Developer Device Platform and connect through ADB, Android's device control tool. Check session status and expiration, extend usage time, or cancel sessions and release resources.

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• Find and reserve a remote Android device
• Connect to a reserved device through ADB
• Check session status and expiration

Generate PDF documents from JSON definitions with headings, tables, images, and page numbers. Use file, in-memory, or streaming output, and extend document layouts with custom components and externally managed application state.

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• Generate invoice PDFs from JSON definitions
• Build documents containing tables and images
• Stream generated PDFs through an HTTP response

Receive live Lark/Feishu updates for messages, approvals, tasks, meetings, and more. Stream them as NDJSON, one JSON record per line, with filtering and time or event limits for bots and automated event processing.

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• Receiving new messages for bot processing
• Monitoring approval and task updates
• Receiving meeting lifecycle and minutes events

Build charts with TradingView's lightweight-charts, including candlesticks, lines, live updates, scales, and annotations. Guidance checks installed types and v5 APIs to support chart integration, framework lifecycle handling, and troubleshooting of common data and display issues.

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• Build candlestick charts with a separate volume pane
• Add live updates and historical data
• Fix v4-to-v5 API migration issues

Start, inspect, and stop a CARLA UE 5.8 server. Choose an editor-based server, packaged build, or full editor; configure headless operation and ports; and enable native ROS 2 integration when the build supports it.

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• Start a UE 5.8 server for a CARLA client
• Configure camera image publishing through ROS 2
• Investigate missing maps or connection failures

Inspect a vehicle or another object in a running CARLA simulation. Read position, speed, and acceleration from the same simulation frame, check vehicle controls and steering angle, or stream the object's state over time.

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• Checking the ego vehicle's speed and heading
• Streaming acceleration data for ten seconds
• Inspecting a specific vehicle's controls

Build workflows that extract structured data from LLM responses, check field types and values with Pydantic, and retry failed extractions. Includes examples for nested data, classification, and receiving results as they are generated.

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• Extracting contact and company details from text
• Classifying articles into predefined categories
• Checking review analysis against validation rules

Guides you through setting up RWKV, generating text, processing long documents in chunks, and fine-tuning models. Explains how to carry context between steps and compare memory use with Transformer models.

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• Build streaming text generation
• Process long documents in chunks
• Plan RWKV fine-tuning

Implement Mamba, a model architecture that processes sequences through an internal state with computation scaling linearly with input length. Covers pretrained text generation, Mamba-1 and Mamba-2 configuration, speed comparisons with Transformers, and GPU memory troubleshooting.

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• Explore models for long sequences or streaming applications
• Generate text with pretrained Mamba models
• Compare Mamba-1 and Mamba-2 configurations

Build data processing pipelines for machine learning, from preprocessing to batch predictions. Uses Ray Data to handle datasets larger than memory, distribute work across CPUs and GPUs, and feed data into training frameworks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preprocessing datasets larger than memory
• Running batch predictions across multiple GPUs
• Preparing image and tabular data for training