How to modernize legacy software with AI and GraphDB

*🔧 How to Build a Custom MCP Server to Power AI Agents with Smarter Memory & Control* Building your own *Modular Control Plane (MCP) Server* allows you to orchestrate multi-agent workflows, manage context-rich memory, and unlock advanced agentic capabilities in GenAI applications. 🚀 *Steps to Build Your Custom MCP Server:* 1. *Set Up Backend Infrastructure* Use *Node.js*, *FastAPI*, or *Express* for a lightweight server. 2. *Define Agent Interfaces* Set up endpoints for interaction between agents, tools, and LLMs. 3. *Implement Memory Storage* Integrate *Redis*, *Weaviate*, or *Pinecone* for vector-based semantic memory. 4. *Session & Task Routing* Use a router to manage conversations, multi-turn tasks, and workflows. 5. *Integrate Toolchains* Connect with APIs, databases, and custom tools via modular plugins. 6. *Secure & Scale* Add auth (JWT/OAuth2), logging, and containerization (Docker/K8s) for deployment. 🎯 *Boost Memory & Autonomy With:* - *LangGraph* for stateful graph-based agent flow - *AutoGen* for custom agent behaviors - *AgentOps* for logging, feedback, and monitoring #GenAI #AIInfrastructure #MCPServer #AgenticAI #LangGraph #AutoGen #AIagents #LLMOps #MemoryOrchestration

Databento's Rust Real-Time Market Gateway The Challenge: Databento required a key piece of infrastructure to provide normalized market data to trading customers with thousands of concurrent connections with minimal latency and maximum security. The Solution: Wrote their entire Live Subscription Gateway in Rust, which now supports production market data distribution with a median latency of only 6.1 microseconds from infrastructure ingress to egress. The gateway also clocks 19 million events per second for historic order book replay—same as their C++ client performance with better memory safety. How It Solves Latency: No garbage collection: Rust's zero-cost ownership model removes unplanned-for pause events introduced by C++ garbage collectors Memory safety with performance: Unsafe code contained in small, auditable units for hardware-level optimizations (kernel bypass, memory-mapped files) Tokio async runtime: Supports thousands of concurrent connections without context-switching overhead Lock-free data structures: Rust's ownership guarantees allow safe concurrent operations without legacy mutex locks Engine Architecture: Over 50% of their foundation infrastructure is Rust-based today, and all API calls pass through high-performance Rust codepaths. The gateway leverages custom FFI bindings to legacy C primitives so they can take advantage of existing optimizations without having to leave safe Rust. #databento #Rust

Explore the key trends shaping full-stack development in 2025! From AI-powered interfaces to cloud-native architectures and no-code tools, this infographic distills what developers and businesses need to know now. 📘 Check it out and stay ahead: https://lnkd.in/dzQK4JfY #fullstack #fullstackdevelopment #trends

Large #Enterprise #Schemas = Large Confusion Even #GraphRAG needs boundaries to keep #LLMs focused. Here’s how to fix it. Use #Memgraph’s Fine-Grained #AccessControls. They make GraphRAG more accurate and explainable at enterprise scale. Josip Mrđen explains how this is so 👉 https://lnkd.in/gsiGAQr6 #ContextEngineering

We Cut 70% Bundle Size: TanStack Query + Zustand at GLINR The Problem: Complexity Without Benefits Three months ago at GLINR, our AI-powered developer platform was shipping 50kb of Apollo Client just to fetch billing quotas and user data. As the lead architect (GDSKS here), I kept asking: "Are we actually using these features?" The answer was uncomfortable: we were using about 10% of what Apollo offered. Metric Value Status Apollo Bundle 50kb gzipped Too large Cache Complexity High Unused Time to Interactive 2.8 seconds Slow Developer Confidence 6.2/10 Low Features Used ~10% Wasteful The breakthrough came from a simple realization: server state and client state are fundamentally different. State Type Characteristics Best Tool Example Server State Asynchronous, cached, needs sync TanStack Query API data, user info, billing Client State Synchronous, local, ephemeral Zustand Modals, themes, form wizards Pain Points: Mixed responsibilities (server + client state) Unclear patterns for UI state Complex cache inva https://lnkd.in/g6eQiaQg

Hello, happy Monday! We often imagine “adding AI” means reinventing our entire stack. But what if you could bring intelligence into your backend, without rebuilding it? This week in my weekly tech series on Medium about how I engineer impacts, I explored a pragmatic approach for modern engineers: - Why AI Middleware is the missing layer between APIs and systems. - How to design a “reasoning layer” that learns context and makes smarter decisions. If you’re building enterprise APIs, integrations, or just experimenting with AI; this one's for you. Have a quick read, here: https://lnkd.in/gJ-eUjuh Always here to hear you if you have worked on similar challenges or for a simple chat over a cup of coffee ;) Hashtags: #AIEngineering #BackendDevelopment #SystemDesign #SpringBoot #EnterpriseAI #Middleware #SoftwareArchitecture #FullStackDevelopment #APIs

Sometimes, improving performance isn’t about adding more—it’s about simplifying what’s already there. One of my APIs was running slow, and initial tweaks didn’t help. Then I started looking deeper — logs, traces, and distributed tracing with New Relic — and found the real issue: hidden nested calls consuming massive time. After restructuring the logic, merging multiple Stored Procedures into a few, and adding parallel execution, the performance jumped by nearly 90%. “When in doubt, measure. Don’t guess. Every millisecond has something to say.” #PerformanceOptimization #BackendEngineering #Measure #CodeRefactoring #DistributedTracing #APIDevelopment

Chaitra K. I remember an interesting post of yours a couple of years back on Low Code. Its interesting to see visual interface of low-code platforms accelerate the whole process of building AI applications, cutting deployment down from months to weeks, Especially when Low-code platforms are built to integrate easily with various software systems, databases, and APIs, essential for AI solutions that rely on real-time data

Ever had your pipeline refuse to run until you “set expectations”? Same—Databricks just taught my DAG boundaries and manners. 😅 What’s hot right now (and why your pipeline suddenly feels opinionated):Declarative pipelines with Lakeflow/DLT: define tables, dependencies, and data quality; the platform handles orchestration, scaling, and recovery so you write intent, not glue code. Unity Catalog everywhere: enforce row- and column-level security, masking, and tags across workspaces, plus multi-catalog writes and consistent governance end-to-end. Lineage as a first-class feature: visual impact analysis across tables, jobs, and notebooks directly in Catalog Explorer and APIs for faster audits and RCA. Medallion with Delta superpowers: Bronze→Silver→Gold flows powered by Change Data Feed, deletion vectors, and Liquid Clustering for snappy, incremental performance at scale. Ops that think ahead: DLT adds better observability, expectations, and cost controls so you tune compute, compact files, and alert on data quality before users notice. Pipelines are becoming “secure by default” and “smart by design,” leaving humans to focus on contracts, governance, and performance strategy—not babysitting cron. #Databricks #Lakeflow #DeltaLiveTables #UnityCatalog #MedallionArchitecture #DataEngineering #GenAI

Barna Ibrahim from Rivos Inc. will give the opening keynote at the RISC-V Summit Developer Workshops on 22 October, Santa Clara In this keynote, Barna will dive into how the RISE Project is tackling key challenges head-on. From foundational gaps to high-performance optimization, RISE is investing deeply in the core building blocks of the RISC-V software stack while driving broad collaboration across the open-source community. What You’ll Learn: Key gaps and recent wins in the RISC-V software ecosystem How RISE is driving AI/ML readiness through real-world projects How to get involved Whether you're a system architect, open-source contributor, or ML developer, this session will give you a clear view of where RISC-V is headed and how you can help shape its future.