Skill UI

This comprehensive checklist guides the deployment of Perplexity Sonar API integrations to a robust production environment. It covers critical aspects including API key management, robust code quality practices (retries, rate limiting), performance optimization (caching, streaming), detailed monitoring setup, and cost control strategies. Use this checklist when preparing for a major Perplexity feature launch or going live to production.

This skill provides comprehensive patterns for building highly reliable applications that interact with external Large Language Model (LLM) APIs, such as Perplexity Sonar. It implements critical reliability techniques including Circuit Breakers, Model Fallback, Streaming Timeout handling, and Caching layers. Use this to ensure your application maintains high availability and graceful degradation even when external services experience intermittent failures or variable performance.

This skill enables the development of robust, event-driven pipelines interacting with the Perplexity Sonar API. Since Perplexity lacks native webhooks, this system simulates event patterns using advanced techniques: Streaming Server-Sent Events (SSE) for real-time data, job queues (like BullMQ/SQS) for asynchronous batch research, and cron-triggered monitoring for scheduled news and trend alerts. It provides patterns for building complex, reliable, and scalable AI applications.

Highlights React 19 idioms such as the new use() hook, server components, server actions, optimistic updates, suspense, error handling, and compound hooks to structure Next.js apps with streaming data, interactivity, and reusable hooks.

Explains how to create route.ts endpoints in the Next.js App Router, covering HTTP methods, request/response handling, streaming, cookies, redirects, CORS, and caching so you can build REST APIs.

This comprehensive guide details the expertise needed for a Senior Data Engineer. It covers building, optimizing, and governing scalable data pipelines and robust ETL/ELT systems using modern tools like Spark, Kafka, Airflow, and dbt. Includes decision frameworks for architecting data solutions (Batch vs Streaming, Lakehouse vs Warehouse) and implementing advanced data modeling patterns.

Guides you through React Server Components in Next.js App Router, covering when to use server versus client pieces, interop boundaries, data fetching patterns, streaming with Suspense, and composition techniques such as client wrappers or slot-based shells for mixed trees.

Comprehensive expertise in designing and implementing low-latency, production-ready voice AI applications and conversational agents. Covers the full stack from real-time audio streaming (WebRTC, LiveKit) to advanced NLU and speech synthesis (OpenAI, ElevenLabs, Deepgram). Ideal for building next-generation voice assistants, virtual call centers, and voice-enabled mobile experiences while optimizing for perceived responsiveness and latency budgets.

MolyKit is a comprehensive toolkit designed for building robust, cross-platform AI chat applications. It provides core utilities for integrating large language models (LLMs) like OpenAI, handling real-time SSE streaming responses, and managing complex cross-platform asynchronous patterns (PlatformSend, spawn()). The kit includes ready-to-use widgets for messages, prompt inputs, and avatars, making it ideal for developers creating sophisticated, multilingual chat experiences.

This skill provides best practices for structuring complex, production-ready applications built with Makepad. It details robust asynchronous communication patterns, showing how to integrate the Tokio runtime, manage state synchronization, and handle data flow between the main UI thread and background worker tasks using MPSC channels and SignalToUI. Essential for developing advanced chat clients or data streaming services.

An expert guide for developing production-grade AI applications, chatbots, and generative UIs using the Vercel AI SDK, Next.js, and React. Covers core concepts including streaming text (`streamText`), managing chat state (`useChat`), implementing complex tool/function calling, and ensuring robust structured data generation (JSON schemas) across multiple LLM providers.

polars-bio accelerates genomic interval math and bioinformatics I/O by combining Polars, Arrow, and DataFusion to handle BED/VCF/BAM/GFF/FASTA streams, SQL queries, and cloud storage with overlap, coverage, and nearest APIs plus streaming fallbacks.