Polling, Webhooks, WebSockets, SSE: How to Pick the Right Real-Time API Pattern

·1 views

Modern applications often need data delivered instantly, and four main architectural patterns have emerged to handle this: short polling, long polling, webhooks, WebSockets, and Server-Sent Events (SSE). Short and long polling have the client repeatedly request updates, while webhooks flip the model by having the server push an HTTP POST to a registered URL the moment an event occurs. WebSockets, standardized in 2011, enable persistent two-way communication between client and server over a single TCP connection, making them suited for chat apps and live collaboration tools. SSE, by contrast, uses a plain HTTP connection to stream one-directional updates from server to browser, with built-in reconnection support via the native EventSource API. Each pattern carries distinct trade-offs around idle cost, scalability, infrastructure complexity, and directionality, making the right choice highly dependent on the specific use case.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

Developer Shares Beginner Python Calculator Project to Practice Loops

A developer on DEV Community has shared a Python-based calculator project aimed at beginners looking to apply foundational programming skills. The project uses loops and conditional statements to handle a range of operations, including basic arithmetic, power, modulus, square root, logarithm, and trigonometric functions. The author noted that many projects labeled 'beginner-friendly' online can still be overwhelming for newcomers, and designed this as a more accessible starting point. The calculator accepts user input via the terminal and returns results using Python's built-in math library. The project is intended to bridge the gap between learning Python theory and applying it through hands-on practice.

0 comments Read more at DEV Community

ProgrammingHacker News ·

Anthropic Publishes Research on Claude's Mathematical Reasoning Capabilities

Anthropic has released new research examining the mathematical capabilities of its AI model, Claude, with a focus on complex topics such as the Riemann zeta function. The study appears to explore how well Claude can handle advanced mathematical reasoning and problem-solving. The research was shared on Anthropic's official website and drew attention from the Hacker News community. While the post received modest engagement, it signals Anthropic's ongoing interest in benchmarking and understanding the technical limits of its AI systems.

0 comments Read more at Hacker News

ProgrammingDEV Community ·

Why AI Security Training for Defense Contractors Demands a Specialized Approach

Defense industrial base companies are increasingly deploying AI tools such as copilots and retrieval-augmented generation pipelines, but many of these systems interact with controlled unclassified information in ways existing regulations like DFARS 252.204-7012 and CMMC Level 2 did not anticipate. The most common security failure is not a sophisticated cyberattack but an employee pasting sensitive defense information into a general-purpose AI chatbot, which constitutes a data spillage event. Standard network security reviews cannot detect these CUI flow violations because AI prompts, retrieval contexts, and inference logs do not resemble traditional file transfers. Training delivery poses its own challenge, as courses for personnel in classified or air-gapped facilities must run entirely offline, requiring all model weights and dependencies to be pre-packaged without relying on external APIs or package managers. Additionally, downloaded AI model checkpoints carry software supply chain risks, since PyTorch's default pickle-based serialization can execute arbitrary code on load, making weight verification and secure formats like safetensors an essential part of any DIB AI security program.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

LangGraph, CrewAI, and Google ADK: How to Pick the Right AI Agent Framework

As AI agents move from experiments into production systems, developers face a critical architectural choice among three leading frameworks: LangGraph, CrewAI, and Google ADK. LangGraph centers on explicit graph-based orchestration with stateful execution, giving developers fine-grained control over nodes, transitions, retries, and human approval steps. CrewAI organizes work around specialized collaborative agents grouped into Crews, while also offering event-driven Flows for structured, deterministic orchestration. Google ADK focuses on agents, tools, and workflow mechanisms tightly integrated with the Google ecosystem, emphasizing deployment and operations. The key question for teams is not which framework is universally best, but which orchestration model best matches the specific production system being built.

0 comments Read more at DEV Community