𝗜𝗺𝗽𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗖𝗼𝗱𝗲 𝘃𝘀. 𝗗𝗲𝗰𝗹𝗮𝗿𝗮𝘁𝗶𝘃𝗲 𝗠𝗮𝗽𝗽𝗶𝗻𝗴𝘀 𝐀 𝐰𝐢𝐥𝐝 𝐠𝐮𝐞𝐬𝐬: most integration problems do not need custom Python or Java pipelines. Imperative pipelines offer 𝐜𝐨𝐧𝐭𝐫𝐨𝐥 - but they mix transformation details with semantics. Over time, they become brittle, hard to review, and painful to adapt when the ontology changes. 𝐃𝐞𝐜𝐥𝐚𝐫𝐚𝐭𝐢𝐯𝐞 𝐦𝐚𝐩𝐩𝐢𝐧𝐠 𝐥𝐚𝐧𝐠𝐮𝐚𝐠𝐞𝐬 flip the perspective. We describe 𝐰𝐡𝐚𝐭 the graph should look like, 𝐧𝐨𝐭 𝐡𝐨𝐰 to generate it. The result: mappings that are easy to adapt, easy to read, and aligned with the ontology lifecycle. From a DataOps perspective, this separation alone is often a 𝐠𝐚𝐦𝐞-𝐜𝐡𝐚𝐧𝐠𝐞𝐫. Still, maybe ~10% of edge cases (another wild guess) are just too hard to cover declaratively. Either because implementing the logic as a semantically described function is not worth the effort, or because the business logic itself is deeply intertwined and complex. But for 90%, a good mapping is the easier way. We personally love using 𝐌𝐨𝐫𝐩𝐡-𝐊𝐆𝐂 - it is the ingestion engine under the hood of the neonto editor and works with a variety of sources like SQL, Cypher, Excel and more. 𝐖𝐡𝐞𝐫𝐞 𝐝𝐨 𝐲𝐨𝐮 𝐝𝐫𝐚𝐰 𝐭𝐡𝐞 𝐥𝐢𝐧𝐞 𝐛𝐞𝐭𝐰𝐞𝐞𝐧 𝐝𝐞𝐜𝐥𝐚𝐫𝐚𝐭𝐢𝐯𝐞 𝐦𝐚𝐩𝐩𝐢𝐧𝐠𝐬 𝐚𝐧𝐝 𝐢𝐦𝐩𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐬?

Python isn't the only language for AI Agents. I built a full Vulnerability Detection Agent in Java — and the ecosystem is more ready than most people think. Here's what the system does: → Takes a library name + codebase path as input → Queries the NVD (National Vulnerability Database) for CVEs → Scans your actual source code for vulnerable dependency usage → Fetches patched versions and remediation steps → Synthesizes everything into a professional security report All autonomous. No manual steps. 𝗧𝗵𝗲 𝘀𝘁𝗮𝗰𝗸 𝗜 𝘂𝘀𝗲𝗱: 🔷 Spring Ai — for ChatClient, @Tool annotation, and MCP client/server auto-config 🔷 LangGraph4j — for multi-agent orchestration with StateGraph (Java port of LangGraph) 🔷 MCP (Model Context Protocol) — the open standard that lets the LLM call external tools 🔷 Java 21 + Spring Boot 4.x — because enterprise Java is still very much alive 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗶𝗻 𝗼𝗻𝗲 𝗹𝗶𝗻𝗲: 4 AI agents → 3 MCP servers → 1 LangGraph4j StateGraph → 1 security report 𝗪𝗵𝗮𝘁 𝗺𝗮𝗱𝗲 𝘁𝗵𝗶𝘀 𝗵𝗮𝗿𝗱: There are almost zero learning resources for Java + Spring AI + agent architecture together. I had to piece it together from docs, source code, and experimentation. So I documented everything — theories, code walkthroughs, architecture diagrams, data flow traces. 𝗧𝗵𝗲 𝗿𝗲𝗽𝗼 𝗶𝗻𝗰𝗹𝘂𝗱𝗲𝘀: ✅ Full source for all 4 modules ✅ Deep documentation covering MCP, Spring AI, LangGraph4j from scratch ✅ README for each module + parent-level overview ✅ Plug-and-play LLM switching (OpenAI / Azure / Anthropic / Ollama) If you're a Java developer curious about AI agents — this is the repo to start with. 🔗 GitHub: https://lnkd.in/dSApxhsC Drop a comment if you want me to do a deeper breakdown of any specific part — Spring AI tool calling, LangGraph4j state design, or MCP server setup. #Java #SpringAI #AIAgents #LangGraph4j #MCP #SpringBoot #Cybersecurity #OpenSource #GenerativeAI #SoftwareEngineering

One week ago, I shared rlm-codelens — and the #1 question I got was: "Does it support languages other than Python?" I heard you. And I’ve been busy. Today, I’m shipping a major update that moves rlm-codelens from a Python utility to a language-agnostic architectural powerhouse. By integrating tree-sitter, the tool now provides full architecture intelligence for Go, Java, Rust, TypeScript, C/C++, and more with zero configuration. The philosophy: LLMs are great at writing snippets, but they often miss the forest for the trees. Kubernetes alone has 12,000+ files—you can't fit that into a prompt. You need a graph. To prove it works at serious scale, I ran the new engine against three massive benchmarks: 📊 Kubernetes (Go) Files: 12,235 | LOC: 3.4M Result: Detected 77,373 import edges and uncovered 182 circular dependencies hidden in the noise. 📊 gRPC (C, C++, Python, Ruby + 5 more) Files: 7,163 across 9 languages | LOC: 1.2M Result: Proved seamless multi-language parsing in a complex, polyglot environment. 📊 vLLM (Python, C++, C) Files: 2,594 | LOC: 804K Result: Identified 24 circular dependency chains and 341 anti-patterns (god modules and layer violations). What rlm-codelens does now: ✅ Multi-Language: Auto-installs grammars for major languages via tree-sitter. ✅ Graph-First Analysis: Uses NetworkX to find cycles and "spaghetti" hotspots. ✅ Interactive Maps: Generates a D3.js visualization you can explore in your browser. ✅ Hybrid AI: Optionally use OpenAI, Anthropic, or Ollama (local) for deep semantic refactoring advice. Understanding a codebase shouldn't be a manual scavenger hunt. It should be a map. Whether you're dealing with a legacy monolith or a high-growth repo, rlm-codelens helps you see the architecture you actually have. Try it on your own repo: pip install rlm-codelens && rlmc analyze-architecture --repo . GitHub: https://lnkd.in/gJKQMfx5 I’d love for you to run this on your Go, Rust, or C++ repos and tell me what "architectural debt" it uncovers! GitHub Cloud Native Computing Foundation (CNCF) Thanks @https://lnkd.in/euKWfXBb #RecursiveLanguageModels #LLM #Anthropic #OpenAI #Ollama #OpenSource #SoftwareArchitecture #DevTools #SystemDesign #BuildingInPublic #GoLang #Rust #Cpp #Python

Inference4j 0.9.0 released, Summarization, Translation, Text2SQL support Summarization, translation, grammar correction, text-to-SQL — all running locally on the JVM with no Python, no microservices, no GPU required.   try (var summarizer = BartSummarizer.distilBartCnn().build()) {       String summary = summarizer.summarize(longArticle);       System.out.println(summary);   }   What's new in 0.9.0:   - Seq2seq inference engine with KV cache — the foundation for all encoder-decoder models   - 5 new task wrappers: FlanT5, BART summarizer, MarianMT translator, CoEdit grammar corrector, T5 text-to-SQL   - Unigram (SentencePiece) tokenizer with Viterbi decoding   - ByteBuffer pooling for lower GC pressure   - Download progress tracking Text2SQL models allows developers to use natural language and translate into real SQL queries to the SQL DB. This one I think may be a popular one for java devs.   That brings inference4j to 19 task wrappers supporting 30+ models across vision, audio, NLP, and multimodal — all through type-safe, builder-pattern APIs that feel like writing normal Java.   The goal hasn't changed: make on-device AI inference a first-class citizen in the Java ecosystem. No ONNX tensors, no JNI juggling — just pick a model, call a domain method.   What's coming next:   - 0.10.0 — Named Entity Recognition + more embedding models   - 0.11.0 — TikToken tokenizer, unlocking Llama 3.2 and other modern LLMs   - 0.12.0 — Text-to-speech pipeline via Piper — local voice synthesis on the JVM Docs: https://lnkd.in/e63MXwQN   GitHub: https://lnkd.in/eEUESRkq   #Java #AI #MachineLearning #ONNX #OpenSource

I had a simple question about a Django codebase the other day: "which functions call this utility, and are any of them tested?" Cursor's built-in search found the function. But it couldn't tell me who calls it, or whether those callers have test coverage. That's the problem with text-based code search. It finds matches, not relationships. CodeScan takes a different approach. It parses Python source code using AST analysis, extracts every class, function, and call relationship, and stores the whole thing in a Neo4j graph database. The codebase becomes a queryable structure, not a pile of text files. Once it's in the graph, the questions get interesting. "Show me all functions that transitively call database_connect." "Which functions have zero test coverage?" "Find recursive call chains." These are Cypher queries that run in milliseconds, not grep commands that return noise. The MCP server exposes 20+ tools, so Cursor can answer these questions directly. You ask in natural language, the AI translates to a graph query, and you get back actual structural insight. One design choice I'm happy with: test detection is pattern-based and configurable. It recognizes pytest, unittest, nose conventions, and custom naming patterns. It creates TESTS relationships in the graph, so you can instantly see which production code has coverage and which doesn't. 5,000+ lines of Python. Works on any Python project. The hard part wasn't the AST parsing - it was modeling the relationships correctly in the graph so that queries stay fast as the codebase grows. #CodeAnalysis #Neo4j #MCP #Python #DeveloperTools #GraphDatabase

Built a persistent memory layer for Claude Code with a knowledge graph and agentic retrieval. 🧠 **Memory Haiku** is a local Python agent that runs in the background as a macOS service. It stores everything in SQLite and integrates through Claude Code's skills system. Three layers work together: 🔍 **Vector search** — Memories are embedded with all-MiniLM-L6-v2 (384-dim) and stored in sqlite-vec. Semantic similarity finds relevant context even when the wording doesn't match. 🕸️ **Knowledge graph** — During ingest, Claude extracts `(subject, predicate, object)` triples and stores them as entities and relationships. Ask "what does Project X use?" and it traverses: `Project X → uses → React`, `Project X → created_by → Alice`. No Neo4j — just SQLite with indexed relationship tables. 🔄 **Agentic retrieval** — A deep query mode runs an evaluation loop instead of single-pass search. An intermediary assesses the initial results, refines the search query or requests graph traversal for specific entities, and iterates up to 3 times before synthesizing a final answer. Day-to-day it's just four skills: - `/remember` — store something - `/recall` — ask a question (`--deep` for iterative retrieval) - `/graph` — explore entity relationships - `/memories` — manage what's stored A PostToolUse hook auto-captures git commits. A Streamlit dashboard lets you browse memories and the entity graph visually. 📊 Stack: Python, aiohttp, sqlite-vec, Claude Agent SDK, sentence-transformers. ✅ Check it out on github: https://lnkd.in/emRetY5v

Developers who build parsing systems, ETL workflows, and automation pipelines know one thing: Python is everywhere. From data ingestion scripts to AI preprocessing layers, Python sits at the heart of modern parsing stacks. That’s why Moderne’s expansion of OpenRewrite to support Python is more significant than it might first appear. OpenRewrite’s Lossless Semantic Tree (LST) doesn’t just parse syntax — it resolves symbols, tracks relationships, and preserves developer intent. Now that semantic refactoring extends into Python, organizations can coordinate modernization efforts across: • Backend services (Java) • Frontend tooling (JS/TS) • Automation and data pipelines (Python) For parse developers, this means: ✔ Automated dependency upgrades across repos ✔ Safe remediation of vulnerabilities ✔ API migrations that don’t break downstream scripts ✔ Consistent refactoring applied through CI/CD Parsing systems are rarely isolated. A Java service might expose an API consumed by a Python transformation layer. A shared dependency might ripple through multiple runtimes. Coordinated, semantic-level modernization across languages reduces fragile pipelines and production risk. The bigger takeaway? Code parsing is evolving from syntax-level manipulation to semantic-aware transformation. And for developers building parsing and transformation systems, that’s a major step forward. #ParseDevelopers #Python #CodeRefactoring #OpenRewrite #DataPipelines #DevTools

There is a massive misconception right now that you have to write Python to work in AI. Python is great for the lab and training models. But as I’m wrapping up my Core Java roadmap and looking ahead to Spring Boot, the reality of production tech looks a lot different. Python trains the models, but Java is quietly becoming the engine that actually keeps them from crashing in the real world. The shift in 2026 is actually pretty wild when you look at the architecture: 1. The Hardware Barrier is Gone The old joke was that Java was terrible at talking to GPUs. Not anymore. With Project Panama and Babylon, Java is bypassing the old, slow C++ middlemen and talking directly to the hardware. You can write GPU kernels in pure Java now. 2. Spring AI is a Cheat Code Spring AI is treating LLMs like just another database. You can swap between Gemini, OpenAI, or a local model just by changing a single line in a properties file. Better yet, Java’s strict typing forces unpredictable, unstructured AI responses directly into clean Java Objects (POJOs). No more guessing what the API returned. 3. Scaling AI Agents When you start building systems where multiple AI bots have to talk to each other, Python struggles with the concurrency. Java’s Virtual Threads let you handle millions of simultaneous AI conversations on a single server without nuking your RAM. Combine that with the 40% memory reduction in Java 25, and it just makes sense for cloud costs. Java isn't just "legacy bank software" anymore. It’s the infrastructure for enterprise AI. If you're jumping into Spring Boot right now, the timing honestly couldn't be better. Is anyone else skipping the Python route and building their AI integrations straight in Java?

When scaling Python APIs, the flexibility of dynamic typing quickly becomes a liability. If you are building production-grade microservices, relying on manual if/else blocks for payload validation and authentication is a recipe for messy code and silent runtime failures. Here are the core architectural patterns essential for securing and validating modern Python APIs: 🛑 Strict Data Validation with Pydantic Instead of writing custom logic to verify if an incoming payload contains the correct data types, Pydantic enforces strict schemas right at the API entry point. By creating classes that inherit from BaseModel, you can enforce exact data types, min/max length constraints, and even complex Regex patterns. The Concept in Action: If your API expects a phone number formatted via Regex as +91-XXXXX and the client sends a plain integer, Pydantic intercepts the bad payload. It automatically returns a standardized 422 Unprocessable Entity error before your core business logic is ever touched. 🔐 Authentication via Dependency Injection Protecting sensitive routes (like PATCH or POST endpoints) shouldn't clutter your core functions. Using Dependency Injection (like FastAPI's Depends()), you can mandate that certain checks happen before the endpoint is allowed to execute. The Concept in Action: You write a standalone verify_token function that extracts a Bearer Token from the HTTP header. By injecting this dependency directly into your route decorator, any request with a missing or invalid token is instantly bounced with a 401 Unauthorized error. This keeps the actual endpoint logic completely clean and completely isolated from security checks. 📜 Auto-Generating Swagger Documentation One of the massive secondary benefits of tightly coupling your API framework with Pydantic is the automatic generation of interactive OpenAPI (Swagger) documentation. The exact schemas, constraints, and authentication requirements you define in your code are instantly translated into a visual interface. This allows frontend developers to test endpoints against automatically pre-filled, perfectly formatted JSON examples without needing separate API docs. Building enterprise APIs means treating every external payload as hostile until proven valid. What is your go-to pattern for handling payload validation? 👇 #Python #FastAPI #BackendArchitecture #SoftwareEngineering #DataValidation #Pydantic #Microservices #APIs

I talk to a lot of open source AI companies building dev tools. One interesting mistake a lot of them are making is focusing on exclusivley on Python and JavaScript/TypeScript tooling and neglecticing the langauge that most large enterprises are actually using for their AI workloads today. It's Java. The majority of AI workloads in the enterprise are running on JVM, and our data shows this trend is speeding up. When you're making strategic product decisions: market data > vibes. https://lnkd.in/gX95gPdZ