Introduction Agentic workflows move AI beyond one-shot prompting into iterative, autonomous problem-solving by letting agents plan, act, observe, and refine—much like a human tackling a complex task. This shift yields more reliable, adaptable, and goal-directed systems for real-world, multi-step problems. In this article I explain the five core agentic workflow patterns (Reflection, Tool Use, ReAct, Planning, and Multi-Agent), show how they combine, give practical implementation guidance, example architectures, and discuss trade-offs and evaluation strategies. ...

Enter vibe coding: a term that’s democratizing app creation for everyone, regardless of technical background.[1][2] This AI-assisted technique lets you instruct large language models (LLMs) to generate code from simple natural language descriptions, shifting focus from syntax struggles to creative ideation.[1][3] Coined by AI pioneer Andrej Karpathy in February 2025, vibe coding has exploded in popularity, earning spots as Collins Dictionary’s Word of the Year and a Merriam-Webster trending term.[1] In this comprehensive guide, we’ll define vibe coding, explore its origins, share practical tips for success, highlight top tools, and discuss its future impact on software development. ...

tRPC, gRPC, and oRPC are all ways to build Remote Procedure Call (RPC) style APIs but target different priorities: gRPC focuses on high-performance, language‑agnostic, binary RPC for microservices and systems programming; tRPC focuses on developer ergonomics and end‑to‑end TypeScript type safety for full‑stack TypeScript apps; oRPC sits between them by adding OpenAPI/REST interoperability and richer tooling while keeping TypeScript-first ergonomics.【5】【1】【2】 Essential context and comparison What each project is and its core goals gRPC — a mature, language‑agnostic RPC framework from Google that uses HTTP/2 transport and Protocol Buffers (protobufs) for compact binary serialization and an IDL-driven contract between services【5】【1】. tRPC — a TypeScript‑first RPC library that exposes server procedures directly to TypeScript clients, delivering zero‑boilerplate end‑to‑end type safety and rapid developer iteration (especially inside monorepos and Next.js apps)【1】【3】. oRPC — an evolution of the TypeScript‑first RPC idea that preserves tRPC‑style type safety but adds built‑in OpenAPI generation and optional REST endpoints so APIs are language‑agnostic and easier for external consumers to adopt【2】. Technical differences (transport, IDL, typing, languages) Transport and serialization: gRPC uses HTTP/2 and binary protobuf serialization, giving multiplexing, low overhead, and streaming semantics (client, server, bidirectional)【1】【5】. tRPC typically runs over HTTP/1.1 or WebSocket using JSON (or JSON-like payloads) and depends on the HTTP framework in use (Next.js, Express, etc.)【1】【3】. oRPC commonly exposes RPC endpoints but also generates OpenAPI which can be served as JSON/HTTP REST—transport depends on implementation and hosting, but it prioritizes interoperable JSON/HTTP for external clients【2】. Interface definition and typing: gRPC: explicit IDL via .proto files (Protocol Buffers). Strongly typed across languages, code‑generation required for clients and servers【1】【5】. tRPC: no separate IDL—types flow from server code to TypeScript clients via compile‑time inference (no protobufs, no generated clients)【1】【3】. oRPC: retains TypeScript‑first typing but generates OpenAPI (a machine‑readable contract) so other languages/tools can consume your API【2】. Language ecosystem: gRPC supports many languages (Go, Java, Python, C++, Node, etc.) because of its protobuf IDL and codegen【5】. tRPC and oRPC are TypeScript/JavaScript centric; oRPC bridges the gap for non‑TypeScript consumers by exporting OpenAPI specs【2】【1】. Streaming and advanced RPC features: gRPC supports streaming natively and is designed for streaming and low-latency interservice communication【1】【5】. tRPC mainly targets request/response and real‑time via WebSockets or libraries layered on top; it’s not a drop‑in for gRPC streaming semantics【1】. oRPC focuses on interoperability and type safety rather than replacing streaming semantics; streaming support depends on the specific implementation choices【2】. When to choose each (practical guidance) ...

Python has become a powerhouse for building microservices due to its simplicity, vast ecosystem, and excellent frameworks like FastAPI, Flask, and gRPC. Microservices architecture breaks applications into small, independent services that communicate over networks, enabling scalability, faster development, and easier maintenance.[7] This guide provides a detailed walkthrough—from fundamentals to deployment—with practical code examples and curated resource links. What Are Microservices and Why Python? Microservices are self-contained applications that handle specific business functions, communicating via APIs (REST, gRPC) or message queues.[1][7] Unlike monoliths, they allow independent scaling and technology choices per service. ...

Table of contents Introduction What is MutationObserver? Why MutationObserver replaced Mutation Events Core concepts and API surface Creating an observer The observe() options The MutationRecord object Controlling the observer (disconnect, takeRecords) Common use cases Performance considerations and best practices Practical examples Basic example: logging DOM changes Waiting for elements that don’t exist yet Observing attribute and text changes with oldValue Integration with frameworks / polyfills Pitfalls and gotchas When not to use MutationObserver Summary / Conclusion Introduction MutationObserver is the standardized, efficient browser API for watching changes in the DOM and reacting to them programmatically. It enables reliable detection of node additions/removals, attribute updates, and text changes without costly polling or deprecated Mutation Events. ...