Java and Python Together for Scalability and Agility

💡 Hot take: Java isn't legacy — it's leading the AI backend race. While everyone talks Python for AI, the JVM quietly shipped 👇 ☕ Java 26 with Vector API, Structured Concurrency & AOT caching — built for AI workloads 🌱 Spring AI hitting production maturity 🔗 LangChain4j + Google ADK for Java 1.0 now GA 🤖 Keycloak adding MCP (Model Context Protocol) support The pattern is clear: enterprises aren't rewriting 20 years of Java systems in Python. They're bringing AI to the JVM. If you're a Java dev, you're not behind the curve — you're exactly where the next wave is landing. Which side are you on — "Python for AI" or "JVM all the way"? 👇 #Java #SpringAI #JVM #AI #BackendEngineering

Write native. Preview human.  🚀 I just published my first VS Code extension - bin2prev Lets stop writing code in natural language, Lets just preview if needed.  Machines don't speak Java, Python, or JavaScript. They speak native binary.  High-level languages were invented for developers - not for machines. Every line of code you write gets compiled down to the binary that the CPU  actually executes. The native code is the real code. Everything else is a translation.  With AI agents, we can now write native binary directly - the way machines actually think. No compiler, no runtime, no abstraction layers.  bin2prev bridges the gap: write native with your AI agent, then preview it in your preferred language - Java, JavaScript, Python, Ruby, or Go —  so you can read what the machine already understands.  ✅ Opens binary files directly in VS Code ✅ Raw hex dump with ASCII view ✅ Equivalent source code in 5 languages ✅ Supports Mach-O & ELF  formats   ✅ ARM64 instruction decoding  Write native. Preview human.  🔗 Install it now — search "bin2prev" in VS Code Extensions or visit: https://lnkd.in/dytMK2fm Open for all to contribute: https://lnkd.in/dpsSkVen #VSCode #Extension #Binary #NativeCode #AI #OpenSource #Developer #Hackathon  💻 Source code: https://lnkd.in/dpsSkVen

Most AI agents have no guardrails. This Java one has four. All of them were tested by breaking it. AgentOrchestrator.java is done. ReAct loop — Thought → Action → Observation → repeat — running in production Java. Not a notebook. Not a demo. A Spring Boot endpoint that returns { answer, toolCallsUsed, iterationsUsed, costTokens }. Here’s what I baked in before anything else: 10 iterations default. 20 hard cap. Full stop. No config flag that lets some engineer bump it to 1000 at 2am “just to test.” Two tools only. DBLookupTool hits PostgreSQL. HttpCallTool hits external APIs. ToolRegistry.java manages registration. Clean boundary. Easy to audit. The guardrail work was where I stopped feeling clever and started feeling scared. I intentionally broke everything: — Infinite loops. The cap caught it. — Hallucinated tool arguments. The type validation caught it. — Context window overflow. The ceiling caught it. None of these edge cases are in the Python tutorials. Because Python tutorials assume you’re demoing, not deploying. The asymmetry I’m playing is simple: ten thousand “build an AI agent in 50 lines of Python” posts exist. “Here’s what happens when your Java agent hits production load week one” posts — almost zero. That gap is the whole bet. The DLQ fills up so you don’t have to explain to your CTO why the agent called the billing API 847 times. #Kafka #Java #LLM #SpringAI #SpringBoot #AIEngineering #BackendDevelopment

𝗝𝗮𝘃𝗮 𝘁𝗲𝗮𝗺𝘀 𝘀𝗵𝗼𝘂𝗹𝗱𝗻’𝘁 𝗻𝗲𝗲𝗱 𝗣𝘆𝘁𝗵𝗼𝗻 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝗔𝗜. So for the last 18 months, Java teams have been duct-taping LLM SDKs into their stack. I decided to build what Java teams actually need. Meet 𝗡𝗮𝘂𝘁𝗶𝗹𝘂𝘀 — a Java-native AI Gateway, now open source on GitHub. One API across Claude, OpenAI, Llama, Mistral, and more. Built with Spring Boot 3 and Java 21. No Python sidecars. No reverse-proxy hacks. No “let’s switch stacks for this one service.” 𝗪𝗵𝗮𝘁’𝘀 𝗶𝗻𝘀𝗶𝗱𝗲: → Smart routing across providers (priority, round-robin, random, cost-aware) → Automatic fallback on rate limits, timeouts, and 5xx errors → Semantic caching (pgvector + Redis hot tier) — same prompt, no second bill → Per-key 𝗿𝗮𝘁𝗲 𝗹𝗶𝗺𝗶𝘁𝗶𝗻𝗴 + 𝗰𝗼𝘀𝘁 𝘁𝗿𝗮𝗰𝗸𝗶𝗻𝗴 in tokens and USD → Cross-cutting concerns done right: log redaction, audit trails, validation → Observability you already use — Micrometer, Prometheus, OpenTelemetry We’re one week away from v0.1. The core foundation is ready: provider SPI, routing, fallback, and OpenAI-compatible API. 𝗡𝗲𝘅𝘁 𝘂𝗽: → Native Claude + OpenAI adapters → Streaming support → Spring Boot starter If you're building on the JVM and waiting for first-class AI infrastructure in Java — this is for you. ⭐ Stars and early feedback will help us shape what ships first. Repo link in the first comment ↓ #Java #SpringBoot #AI #LLM #OpenSource #DeveloperTools #Github

I've been building RAG pipelines in Java for 12 months. Here's everything I wish I had on Day 1 — condensed into a free 10-page PDF guide. 📄 What's inside: ✅ RAG Architecture explained (Ingestion + Query phases) ✅ Spring Boot + LangChain4j full project setup ✅ Document chunking strategy (the #1 thing most guides skip) ✅ pgvector integration with HNSW index ✅ LLM augmentation with Claude — grounded, citation-aware answers ✅ Production patterns: Hybrid Search, Re-ranking, Metadata Filtering ✅ Complete REST API — curl examples included --- The hardest part of RAG isn't the LLM call. It's the retrieval layer. Most developers spend 90% of their time on prompt engineering and 10% on retrieval quality — and then wonder why their RAG chatbot hallucinates. The truth: garbage in, garbage out. Poor chunking → irrelevant chunks → the LLM makes stuff up. What actually moves the needle: → Recursive chunking (paragraph → sentence → word fallback) → 10-15% overlap between chunks to avoid cutting mid-thought → minScore threshold (≥ 0.70) to discard low-relevance matches → Metadata filtering for multi-tenant safety → Hybrid Search: vector + BM25 + RRF fusion Once you fix retrieval, the LLM answer quality jumps dramatically. --- Stack used in this guide: → Java 21 + Spring Boot 3.2 → LangChain4j 0.31 → Claude Sonnet (Anthropic) — 200K context window → pgvector on PostgreSQL — zero extra infra → OpenAI text-embedding-3-small --- 12 years of Java backend engineering + 12 months of GenAI production work. This guide is the bridge between the two. Grab the PDF above. Build something real. --- ♻️ Repost if this helps your team. 🔔 Follow @chandantechie for weekly Java + AI deep-dives. #Java #SpringBoot #RAG #GenAI #LLM #LangChain4j #AIEngineering #SoftwareEngineering #BackendDevelopment #VectorDatabase

Everyone's watching Python agents dance. Rod Johnson [Spring Framework] just moved the sand. The man who gave us Spring — who quietly ended the EJB era without a single press release — just did it again. Embabel. No hype tour. No VC circus. Just a framework that treats the JVM as what it always was: the load-bearing wall of global enterprise software. While the AI world argued over LangChain vs CrewAI, Embabel slipped GOAP — Goal-Oriented Action Planning, borrowed from game AI — into the Spring ecosystem. Your agent doesn't prompt its way to a plan. A deterministic algorithm computes the path. Then replans after every step. Explainable. Type-safe. Auditable. Python gave AI a playground. Rod Johnson just gave it a factory floor. Java devs — the agentic era didn't leave you behind. It was always going to be built on your stack. #Java #SpringBoot #Embabel #AgenticAI #EnterpriseAI #RodJohnson #JVM #AIEngineering

A lot of people ask me why I choose java(spring boot) over python for building AI-powered Systems ? Python dominates the AI ecosystem — no debate. But when it comes to production-grade AI applications, especially in real-world systems, I found Java + Spring Boot to be a more strategic choice. Here’s the reasoning 👇 ⚙️ 1. Production-First Mindset AI models are only part of the system — the real challenge is serving them reliably at scale. Java is built for high-performance, multi-threaded environments Spring Boot provides robust REST APIs, dependency injection, and microservices architecture Better suited for low-latency, high-concurrency workloads 🔐 2. Enterprise-Level Stability Most real-world AI systems are integrated into enterprise ecosystems. Strong type safety reduces runtime errors Mature ecosystem for security (Spring Security, JWT) Seamless integration with databases, message queues, and distributed systems 🧠 3. AI as a Service, Not Just a Model Instead of building models from scratch, modern systems often consume AI via APIs. Easy integration with external AI providers (OpenAI, Groq, etc.) Focus shifts from model training → system design & orchestration Cleaner abstraction for AI pipelines inside backend services 📈 4. Scalability & Maintainability Structured architecture makes large codebases easier to manage Ideal for teams working on long-term, evolving AI products JVM performance tuning gives better control over scaling ⚡ 5. Python Still Wins — But Not Everywhere Python is still unmatched for: Model training Research & experimentation Rapid prototyping But for deploying AI in real-world systems, Java brings: 👉 Stability 👉 Scalability 👉 Maintainability 💡 Final Thought The question isn’t Java vs Python. It’s about using the right tool at the right layer: Python → Build intelligence Java (Spring Boot) → Deliver intelligence at scale #AI #Java #SpringBoot #BackendEngineering #SystemDesign #Scalability #SoftwareEngineering

The multi-agent landscape in early 2025 looked like this: LangChain. CrewAI. AutoGen. OpenAI Swarm. All Python. All single-user. All missing what enterprises actually need to ship agents to production. Meanwhile, Java runs ~90% of enterprise backends. Spring Boot is the de-facto standard. But if you wanted to orchestrate AI agents in the JVM ecosystem, your options were essentially "write it yourself." We built SwarmAI to close that gap. Today we're open-sourcing it. → Built on Spring Boot 3.4 + Spring AI 1.0.4 GA → 8 orchestration patterns (Sequential, Parallel, Hierarchical, Iterative, Self-Improving, Swarm, Distributed, Composite) → Sealed Build → Compile → Execute lifecycle — catches errors before tokens get spent → Multi-tenancy, governance gates, budget enforcement, audit trails — architectural, not bolted on → Three RL policy engines (LinUCB contextual bandits, DQN with experience replay) for skill generation and stopping decisions, 4–12x better than Monte Carlo baselines → 38 built-in tools, MCP adapter, RAFT consensus for multi-node coordination → 1,400+ tests passing. Apache 2.0 for core. Full write-up with code, architecture, and benchmark methodology: https://lnkd.in/eWmmY2qq We'd love hard questions, issues, and PRs. #Java #SpringBoot #AIAgents #MultiAgent #OpenSource

New blog 🎉 Why Apache Fluss Chose Rust for Its Multi-Language SDK Why build one core instead of three divergent clients? 🤔 If you've ever seen non-JVM teams forced to reach for sidecars, wrappers, or half-finished clients just to talk to a data system, this one's for you. A deep dive into how Apache Fluss (Incubating) built a shared Rust core for Rust, Python, and C++. 🦀 We unpack the architecture behind fluss-rust: one implementation of protocol, batching, retries, Arrow, and idempotence, with thin bindings on top. The result is less drift, fewer edge-case bugs, and a much cleaner path for multi-language access to real-time storage. ⚙️ 📦 ⚡ We also go into why Rust beat C for this job, how async complexity stays inside the core, and why Arrow makes zero-copy interop across languages actually practical. Plus: where this goes next - DataFusion, Go, AI agents, and a multi-protocol gateway. 🚀 📕 Read: https://lnkd.in/e9uGKKBy 💬 Comments welcome #ApacheFluss #Rust #Python #Cpp #Arrow #DataEngineering #Streaming #RealTimeAnalytics #AI #OpenSource

To make your first post truly engaging, you want to hook the reader with a "problem" they didn’t know they had, and then present your page as the "solution." Here is a short, punchy, and high-energy post description for your LinkedIn launch: The Ghost in the Machine 👻 Ever wonder why your Java code behaves differently under heavy load? Or why your Python backend hits a performance wall despite "clean" logic? The truth is: The code you write isn't always the code that runs. Between your syntax and the hardware lies a world of bytecode, memory management, and invisible execution layers. That is where the real bugs—and the real optimizations—hide. Welcome to The Bytecode Phantom. We’re here to deconstruct the "tricky" side of backend engineering: * 🔍 Java Internals: JVM secrets and bytecode-level logic. * 🐍 Python Mastery: Beyond the basics into the runtime core. * 🏗️ System Design: Architectural patterns that actually scale. Stop following the documentation. Start mastering the mechanics. [Follow to Decrypt the Complex] #TheBytecodePhantom #Java #Python #BackendEngineering #SystemDesign #SoftwareArchitecture #CodingSecrets