How SpringApplication.run() Bootstraps a Spring Boot App: A Technical Deep Dive

·5 views

Every Spring Boot application starts when SpringApplication.run() is called from the main() method, triggering a multi-step startup sequence. Internally, the static run() method first creates a SpringApplication instance — where all configuration occurs — before invoking the instance-level run() for execution. During construction, Spring Boot deduces the web application type (SERVLET, REACTIVE, or NONE) by scanning the classpath for relevant classes. It also loads ApplicationContextInitializers and ApplicationListeners from META-INF/spring.factories, assigning each a distinct role in configuring the context or reacting to application events. For advanced use cases, developers can manually instantiate SpringApplication or use SpringApplicationBuilder to customize settings or build parent-child context hierarchies before startup.

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 builds comment section for GitHub profiles using Discussions and SVG

A developer has created a tool that adds a functional comment section to GitHub profile pages, a feature the platform has never natively offered. The system uses GitHub Discussions on a user's profile repository to store messages, which appear on the profile within about 30 seconds of posting. Because GitHub's profile README only supports SVG-based dynamic content, all user avatars must be pre-downloaded and embedded as data URIs at build time, with no external fetching allowed. The project requires no database or dedicated server, relying entirely on GitHub's own infrastructure. It is now available on the GitHub Marketplace, with source code published under the repository LISK819129/GitComment.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Developer Guide: Building a Multi-Agent RAG Legal Assistant Using LangGraph and FastAPI

A new beginner-oriented tutorial published on DEV Community walks developers through building an AI-powered legal assistant tailored to UAE Federal Law documents. The system uses Retrieval-Augmented Generation (RAG) to answer questions based solely on uploaded legal PDFs rather than relying on a language model's training data, reducing the risk of hallucinated legal clauses. Unlike basic RAG setups, the architecture employs a cyclic multi-agent pipeline built with LangGraph, where a fact-checker node verifies each drafted answer against the source text before it reaches the user. The full stack includes FastAPI for the backend, Streamlit for the frontend, and Pinecone as the vector database for storing and retrieving document embeddings. Although the tutorial uses UAE labor law as its example dataset, the guide notes the same architecture can be adapted to medical guidelines, company policies, or any custom document collection.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Beginner's Guide to HTML: Comments, Tags, and Your First Web Page

A Phase 1 HTML tutorial aimed at beginners introduces core concepts including how to set up VSCode with a live server extension. The guide explains HTML comments — notes written in code that browsers read but do not display on the rendered page. It warns developers that comments remain visible in the page source, making it unsafe to store passwords or API keys within them. The tutorial also covers HTML tags as the fundamental building blocks that instruct browsers on how to structure and display content. Learners are guided through hands-on exercises, including creating their first HTML element and building a basic web page.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Jev AI Tool Cuts Agent Search Decision Time Fourfold, Tests Show

Jev is a lightweight AI model designed to return decisions, scores, or true-or-false outputs rather than generating long-form text, making it significantly faster and cheaper than general-purpose models. A team working on open-source search projects integrated Jev into three of their systems to evaluate its performance quantitatively. In one key test using 100 multi-hop questions from the DeepSearcher project, Jev matched a DeepSeek model's retrieval quality — both achieving 93.25% Recall@5 — while cutting decision response time from 2.23 seconds to 0.55 seconds. The estimated cost per decision also dropped to roughly one-seventh of the original. Further experiments explored whether Jev could replace dedicated rerankers in more specialized tasks such as agent memory retrieval for coding assistants.

0 comments Read more at DEV Community