Me in 2012 instead of learning: DSA System Design AI / ML Generative AI LLMs AI Agents RAG Vector Databases Embeddings Fine-tuning Prompt Engineering MCP AI Coding Agents Python Java C C++ Go Rust JavaScript TypeScript HTML CSS React Next.js Vue Angular Node.js Express.js NestJS Spring Boot Django FastAPI REST APIs GraphQL WebSockets SQL PostgreSQL MySQL MongoDB Redis Elasticsearch Kafka RabbitMQ Docker Kubernetes Terraform AWS Azure GCP Linux Git GitHub CI/CD DevOps Microservices Serverless Cloud Cybersecurity Data Engineering Data Science MLOps Data Analytics Blockchain Web3 Smart Contracts Solidity Edge Computing Distributed Systems Observability Prometheus Grafana OpenTelemetry Supabase Firebase Vercel Cloudflare PostgreSQL + pgvector LangChain LlamaIndex PyTorch TensorFlow Hugging Face Ollama OpenAI APIs Claude APIs Gemini APIs Model Context Protocol AI Infrastructure GPU Computing CUDA

An official, hands-on curriculum for learning MCP — the common language of agents — from the ground up 📚 And it covers 6 languages. Title: microsoft/mcp-for-beginners URL: 📚 Overview An open-source educational curriculum from Microsoft that teaches the Model Context Protocol (MCP) through practical, hands-on instruction. MCP is positioned as a "universal translator" that standardizes interactions between AI models and client applications. ❓ Challenges Solved MCP is a cutting-edge framework standardizing communication between AI systems and various tools and services. ・But as a new protocol, it lacked a systematic introductory resource ・Learners needed a foundation covering basics, security, implementation, and applications step by step 💡 Content & Structure It's organized into 11 modules across 4 phases. ・Foundation (0-2): introduction, core concepts, security ・Building (3): create your first implementations with 15 practical guides ・Growing (4-5): advanced concepts and real-world applications ・Mastery (6-11): community contribution and specialized topics, with a 13-lab capstone Code samples come in six languages — C#, Java, JavaScript, Python, TypeScript, and Rust — progressing from a basic calculator to advanced database-integrated implementations. 🌍 Use Cases / Audience It's for developers with basic programming knowledge in a supported language who understand client-server models and REST APIs; AI/ML background is optional. It's a reliable starting point for anyone adopting MCP at work. #MCP# #AIAgents#

📚 Auto-generate documentation for an entire repository, complete with diagrams. CodeWiki handles up to 1.4M lines across 8 languages and beats DeepWiki on quality (ACL 2026). Title: FSoft-AI4Code/CodeWiki URL: 📦 Overview CodeWiki is an AI-powered framework that automatically generates comprehensive documentation for whole software repositories. Beyond simple API references, it produces system-level docs with architecture diagrams, data-flow visuals, sequence diagrams, and prose. It tackles the challenge of keeping documentation current and complete across large, multi-language codebases. ❓ Challenges Solved Traditional documentation tools struggle with scale and context. CodeWiki addresses three core issues: documenting cross-module interdependencies, maintaining architectural context while scaling, and capturing system-level interactions rather than isolated components. 🛠 Methodology & Approach It runs in three stages. ・Hierarchical decomposition: algorithmic clustering inspired by dynamic programming partitions the codebase into coherent modules ・Recursive multi-agent processing: an adaptive agentic system delegates tasks dynamically for complex modules ・Multi-modal synthesis: it integrates text and visual artifacts (Mermaid diagrams), validating diagrams via Node.js ・It supports Python, Java, JavaScript, TypeScript, C, C++, C#, and Kotlin, and can run through Claude Code or Codex CLI with no per-token cost 🎯 Use Cases It fits generating living documentation for large multi-language codebases, onboarding material for new members, and interactive HTML output compatible with GitHub Pages. 📊 Experimental Results ・On its CodeWikiBench, quality scores were 79.14% for high-level languages (Python, JS) and 68.84% for managed ones (C#, Java) ・It improves on DeepWiki by +4.73 points and scored 82.45% on the 229K-line OpenHands project ・It has 1.2k GitHub stars, 193 forks, an ACL 2026 publication, and handles codebases from 86K to 1.4M lines ・The repository's own documentation was generated by CodeWiki, embodying its capability #AIAgents# #DevTools#

Java 27 is coming up fast. As with every JDK release, there are plenty of changes that'll impact developers. In this episode of the Inside Java Newscast, we "declassify" all the developer-impacting changes in Java 27.

Java 27 is almost here! 🕐 Join our team on September 15 for a celebration of our latest launch, including a breakdown of key enhancements and platform changes and the latest developments at the intersection of Java and AI. Subscribe today to stay up to date and get notified when the party starts. 🥳

Java developers do not need to abandon their stack to build on Cardano. @satran004 explains how in @JAVAPROmagazin, using the example of BloxBean, the open-source Java toolchain maintained by the Cardano Foundation and the community. Read the full article:

Is Java wasteful because it uses too much memory—or are other languages wasteful because they use too little? 🤔 In this #JavaOne# 2026 talk, Ron Pressler explores this question, as well as why the JDK’s garbage collectors work the way they do and how Java memory management affects application performance.

Modern Java in the Wild is officially live. Build a project using Java 26, share it with the Hackster community, and compete for a share of the $4,000+ prize pool. What will you build? #Java# #OracleJava# #SoftwareDevelopment# #Coding# #Hackster#