Implementing Decoupled Architecture

Esteemed colleagues, Legacy E/E architectures hard-bind a function to “its” 𝗘𝗖𝗨. Change the function → change the hardware. Scale or redeploy it → impossible. That coupling is exactly what 𝘇𝗼𝗻𝗮𝗹 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀 are breaking. 𝘍𝘶𝘯𝘤𝘵𝘪𝘰𝘯 𝘥𝘦𝘤𝘰𝘶𝘱𝘭𝘪𝘯𝘨 = 𝘵𝘩𝘦 𝘢𝘣𝘪𝘭𝘪𝘵𝘺 𝘵𝘰 𝘳𝘶𝘯 𝘢 𝘷𝘦𝘩𝘪𝘤𝘭𝘦 𝘧𝘶𝘯𝘤𝘵𝘪𝘰𝘯 (𝘦𝘹: 𝘥𝘰𝘰𝘳 𝘭𝘰𝘤𝘬𝘪𝘯𝘨, 𝘓𝘒𝘈, 𝘵𝘩𝘦𝘳𝘮𝘢𝘭 𝘮𝘨𝘮𝘵) 𝘰𝘯 𝘢𝘯𝘺 𝘤𝘰𝘮𝘱𝘭𝘪𝘢𝘯𝘵 𝘤𝘰𝘮𝘱𝘶𝘵𝘦 𝘯𝘰𝘥𝘦, 𝘪𝘯𝘥𝘦𝘱𝘦𝘯𝘥𝘦𝘯𝘵𝘭𝘺 𝘰𝘧 𝘵𝘩𝘦 𝘰𝘳𝘪𝘨𝘪𝘯𝘢𝘭 𝘌𝘊𝘜. 𝘏𝘢𝘳𝘥𝘸𝘢𝘳𝘦 𝘣𝘦𝘤𝘰𝘮𝘦𝘴 𝘢 𝘱𝘰𝘰𝘭 𝘰𝘧 𝘳𝘦𝘴𝘰𝘶𝘳𝘤𝘦𝘴; 𝘴𝘰𝘧𝘵𝘸𝘢𝘳𝘦 𝘣𝘦𝘤𝘰𝘮𝘦𝘴 𝘥𝘦𝘱𝘭𝘰𝘺𝘢𝘣𝘭𝘦, 𝘮𝘰𝘷𝘢𝘣𝘭𝘦, 𝘢𝘯𝘥 𝘶𝘱𝘨𝘳𝘢𝘥𝘢𝘣𝘭𝘦. 𝗪𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀: 𝗘𝗖𝗨 𝗲𝘅𝗽𝗹𝗼𝘀𝗶𝗼𝗻 → consolidation: from ~100–150 ECUs to ~20–40 nodes (zonal + a few HPCs). 𝗧𝗶𝗺𝗲-𝘁𝗼-𝗳𝗲𝗮𝘁𝘂𝗿𝗲: from 36–60 months program cycles to sub-12-month software feature drops. 𝗪𝗶𝗿𝗶𝗻𝗴 & 𝘄𝗲𝗶𝗴𝗵𝘁: zonal + decoupled functions enable double-digit % reductions in harness length/weight. 𝗥𝗲𝗰𝗮𝗹𝗹𝘀 → OTA: many software defects no longer imply hardware recalls; they become patchable services. 𝗦𝗮𝗳𝗲𝘁𝘆 & 𝗮𝘃𝗮𝗶𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆: failover becomes a software decision (reallocate the service to another zone) rather than a hardware redesign. 𝗛𝗼𝘄 𝗶𝘁’𝘀 𝘁𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹𝗹𝘆 𝗲𝗻𝗮𝗯𝗹𝗲𝗱: 𝙎𝙚𝙧𝙫𝙞𝙘𝙚-𝙊𝙧𝙞𝙚𝙣𝙩𝙚𝙙 𝘼𝙧𝙘𝙝𝙞𝙩𝙚𝙘𝙩𝙪𝙧𝙚 (𝙎𝙊𝘼): functions exposed as discoverable, versioned services. 𝙈𝙞𝙙𝙙𝙡𝙚𝙬𝙖𝙧𝙚 / 𝙃𝘼𝙇: AUTOSAR Adaptive, DDS, POSIX layers abstract I/O, scheduling, and communication. 𝙄𝙨𝙤𝙡𝙖𝙩𝙞𝙤𝙣 & 𝙥𝙖𝙧𝙩𝙞𝙩𝙞𝙤𝙣𝙞𝙣𝙜: hypervisors, time & memory partitioning for mixed-criticality (ASIL-D next to QM). 𝘿𝙮𝙣𝙖𝙢𝙞𝙘 𝙤𝙧𝙘𝙝𝙚𝙨𝙩𝙧𝙖𝙩𝙞𝙤𝙣: runtime deployment / re-deployment of services based on load, failure, or updates. 𝙫𝙀𝘾𝙐𝙨 & 𝙨𝙞𝙢𝙪𝙡𝙖𝙩𝙞𝙤𝙣-𝙛𝙞𝙧𝙨𝙩: develop, validate, and integrate functions before they ever touch silicon. 𝗪𝗵𝗮𝘁 𝗰𝗵𝗮𝗻𝗴𝗲𝘀 𝗳𝗼𝗿 𝗢𝗘𝗠𝘀 & 𝗧𝗶𝗲𝗿-1𝘀: The integration bottleneck shifts from hardware to software orchestration. KPIs move from “ECU cost & count” to compute density, latency budgets, service SLAs, and OTA cadence. The sourcing model evolves: fewer black-box ECUs, more platform + service ecosystems. Micro-example: A body control “door lock” service originally running in the Front-Left zone can be reallocated to the Central HPC (or another zone) during a controller fault ; no harness redesign, no ECU swap, no vehicle immobilization. This is the quiet foundation of everything we sell as “𝗦𝗗𝗩”, “𝘇𝗼𝗻𝗮𝗹”, and “𝗵𝘆𝗽𝗲𝗿𝗰𝗼𝗻𝘀𝗼𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻”. #ZonalArchitecture #SoftwareDefinedVehicle #FunctionDecoupling #AUTOSARAdaptive #SOA #Middleware #EEArchitecture #AutomotiveSoftware #SDV #SystemsEngineering

Building Strong and adaptable Microservices with Java and Spring While building robust and scalable microservices can seem complex, understanding essential concepts empowers you for success. This post explores crucial elements for designing reliable distributed systems using Java and Spring frameworks. 𝗨𝗻𝗶𝘃𝗲𝗿𝘀𝗮𝗹 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲𝘀 𝗳𝗼𝗿 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱 𝗦𝘆𝘀𝘁𝗲𝗺𝘀: The core principles of planning for failure, instrumentation, and automation are crucial across different technologies. While this specific implementation focuses on Java, these learnings are generally applicable when architecting distributed systems with other languages and frameworks as well. 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 𝗳𝗼𝗿 𝗠𝗶𝗰𝗿𝗼𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲: A typical microservices architecture involves: Multiple Microservices (MS) communicating via APIs: Services interact through well-defined Application Programming Interfaces (APIs). API Gateway for routing and security: An API Gateway acts as a single entry point, managing traffic routing and security for the microservices. Load Balancer for traffic management: A Load Balancer distributes incoming traffic efficiently across various service instances. Service Discovery for finding MS instances: Service Discovery helps locate and connect to specific microservices within the distributed system. Fault Tolerance with retries, circuit breakers etc.: Strategies like retries and circuit breakers ensure system resilience by handling failures gracefully. Distributed Tracing to monitor requests: Distributed tracing allows tracking requests across different microservices for better monitoring and debugging. Message Queues for asynchronous tasks: Message queues enable asynchronous communication, decoupling tasks and improving performance. Centralized Logging for debugging: Centralized logging simplifies troubleshooting by aggregating logs from all services in one place. Database per service (optional): Each microservice can have its own database for data ownership and isolation. CI/CD pipelines for rapid delivery: Continuous Integration (CI) and Continuous Delivery (CD) pipelines automate building, testing, and deploying microservices efficiently. 𝗟𝗲𝘃𝗲𝗿𝗮𝗴𝗶𝗻𝗴 𝗦𝗽𝗿𝗶𝗻𝗴 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 𝗳𝗼𝗿 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻: Frameworks like Spring Boot, Spring Cloud, and Resilience4j streamline the implementation of: Service Registration with Eureka Declarative REST APIs Client-Side Load Balancing with Ribbon Circuit Breakers with Hystrix Distributed Tracing with Sleuth + Zipkin 𝗞𝗲𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀 𝗳𝗼𝗿 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗥𝗼𝗯𝘂𝘀𝘁 𝗠𝗶𝗰𝗿𝗼𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀: Adopt a services-first approach Plan for failure Instrument everything Automate deployment

Designing #AI applications and integrations requires careful architectural consideration. Similar to building robust and scalable distributed systems, where principles like abstraction and decoupling are important to manage dependencies on external services or microservices, integrating AI capabilities demands a similar approach. If you're building features powered by a single LLM or orchestrating complex AI agents, a critical design principle is key: Abstract your AI implementation! ⚠️ The problem: Coupling your core application logic directly to a specific AI model endpoint, a particular agent framework or a sequence of AI calls can create significant difficulties down the line, similar to the challenges of tightly coupled distributed systems: ✴️ Complexity: Your application logic gets coupled with the specifics of how the AI task is performed. ✴️ Performance: Swapping for a faster model or optimizing an agentic workflow becomes difficult. ✴️ Governance: Adapting to new data handling rules or model requirements involves widespread code changes across tightly coupled components. ✴️ Innovation: Integrating newer, better models or more sophisticated agentic techniques requires costly refactoring, limiting your ability to leverage advancements. 💠 The Solution? Design an AI Abstraction Layer. Build an interface (or a proxy) between your core application and the specific AI capability it needs. This layer exposes abstract functions and handles the underlying implementation details – whether that's calling a specific LLM API, running a multi-step agent, or interacting with a fine-tuned model. This "abstract the AI" approach provides crucial flexibility, much like abstracting external services in a distributed system: ✳️ Swap underlying models or agent architectures easily without impacting core logic. ✳️ Integrate performance optimizations within the AI layer. ✳️ Adapt quickly to evolving policy and compliance needs. ✳️ Accelerate innovation by plugging in new AI advancements seamlessly behind the stable interface. Designing for abstraction ensures your AI applications are not just functional today, but also resilient, adaptable and easier to evolve in the face of rapidly changing AI technology and requirements. Are you incorporating these distributed systems design principles into your AI architecture❓ #AI #GenAI #AIAgents #SoftwareArchitecture #TechStrategy #AIDevelopment #MachineLearning #DistributedSystems #Innovation #AbstractionLayer AI Accelerator Institute AI Realized AI Makerspace

Sometimes I have to step away from the business and turn to look at the product. In our latest iteration, we didn't just build a tool; we engineered a new interaction paradigm for high-end BIM workflows. The result is a Conversational AI Chatbot fully integrated into Autodesk Revit, capable of understanding and executing complex design commands—like a genuine, on-demand co-pilot. The real achievement here isn't the underlying LLM, but the decoupled system architecture we designed. This is the key to ensuring ultra-low latency performance and model integrity despite the rigorous single-thread constraints of the Revit engine. The Engineering Behind Conversational BIM: My architectural approach was driven by solving the fundamental clash between the high-performance, asynchronous nature of cloud AI and the strict single-thread model governing the Revit project database. 1. Architectural Decoupling (The External Brain): The core AI logic operates entirely outside the Revit process. This was a non-negotiable architectural decision to guarantee scalability and unburdened performance for the user's desktop application. AI Service Function: Its primary duty is to take natural language input (e.g., "Create a 4-meter wall") and transform it into a Structured Command Object—a precise, machine-readable data structure containing the exact parameters and coordinates required for execution. Net Benefit: Revit remains focused on modeling, while a scalable cloud service handles the intensive NLP processing. 2. Persistent Asynchronous Channel: To achieve a true real-time feel, we abandoned slow, traditional HTTP polling. Protocol Choice: We established a persistent, bidirectional WebSocket connection between the Revit Add-in (the client) and the AI Microservice (the server). Impact: This high-speed channel ensures that the AI's structured command is instantly delivered back to the client. It allows for real-time contextual querying—the AI can request a snapshot of relevant model data (e.g., "Get coordinates of nearest grid") to refine its geometric output. 3. The Transaction Guardian (Safe Execution): This is the most critical and sophisticated layer, ensuring zero corruption and maximum stability. Command Queuing: Upon receiving the AI's structured command, the Revit Add-in does not execute it immediately. It securely places the command into a Dedicated Event Queue. Synchronization: Only the primary Revit Application Thread interacts with this queue. A dedicated Handler takes the command, opens an official Transaction on the Revit Document, executes the parametric code, and safely commits the Transaction. This rigorous architecture of strict separation, continuous communication, and synchronized transactional execution is how we successfully merge intelligent conversation with robust BIM modeling. We're delivering stable, automated design functionality at scale. More information at ixaia.com #AEC #BIM #AI #Architecture #SoftwareEngineering

MES-ERP integration creates tech debt. There's a better way. Most manufacturers know they need MES and ERP talking to each other. The value is obvious — real-time plant visibility, accurate inventory, production actuals vs. plan, root cause data across the enterprise. So why do so many integrations fail or stall? Point-to-point integrations. Every time you connect two systems directly, you create a dependency. Add a few more and you have a web of brittle connections — each one a liability when a system upgrades, a vendor changes an API, or you add a new plant. We've seen manufacturers with 5-15 point-to-point integrations grinding to a halt. The data syncs but isn't accurate. As a result no one knows which system is the source of truth. Lastly, IT is struggling to get out from under this massive tech debt and instead get to driving value. There's a better architectural approach — Event-Driven Architecture (EDA) with pub/sub and message queuing. Instead of connecting systems directly to each other, every system publishes and subscribes to a central data broker. MES publishes production events. ERP subscribes to what it needs. Add a new system — connect it once to the data hub and not to every other system. The result: • No point-to-point debt — systems are decoupled; one change doesn't break everything • Real-time data flow — events publish the moment they happen on the floor • Scale without chaos — add plants, systems, or consumers without rewiring integrations We're starting a MES-to-ERP integration project using exactly this approach. First phase: real-time visibility from a Level 2/3 plant system up to Level 4 corporate ERP — WIP value, utilization, production actuals. Future projects will include, among others, enterprise-wide root cause analysis across multiple plants that are vertically integrated. Why will it succeed where others have failed? Leadership defined the business outcomes first, built an internal transformation team (and in IT no less), and that team is using good strategy and principles we're bringing to the plate to chose an architecture designed to scale — not just solve today's problem. Are you stacking up point-to-point integrations and wondering why your data still isn't trustworthy? There's a better way to build this. Let's talk.

Zep AI (YC W24) launched metered billing in ten minutes. Then iterated later on to launch a credit-based model. Plotly shipped two AI products two quarters faster than expected. Neither had a better billing tool. They had a better pricing architecture. Most companies treat pricing changes like software releases. There's a queue, a sprint, a deploy. Change a limit? Engineering ticket. Add an add-on? Two-quarter roadmap. Test a new tier? "Maybe next year." Meanwhile, Shar Dara's billing team at Vercel averages five to six pricing changes per month. New SKUs, packaging tweaks, add-on experiments. That pace isn't possible when your pricing logic is scattered throughout your application code. My CTO Benjamin the butterfly wrote this: "Entitlements start as a pebble in your shoe and become cement around your feet. You can't move." The same four blockers show up everywhere: 1. Multiple sources of truth — PLG stack says one thing, CPQ says another, billing says a third. 2. SKU sprawl nobody can actually track. 3. Hard-coded plan logic scattered across the codebase. 4. Every product team inventing its own checkout and upgrade flow. The fix isn't a better billing tool. It's a decoupled architecture: - A unified product catalog — one schema for every plan, feature, and price. - Decoupled entitlements — access rules stored centrally, queried at runtime, not hard-coded per plan. - Real-time metering — so customers and finance see the same usage truth. - A control plane for GTM — so people (and increasingly agents) can change packaging without filing engineering tickets. The before and after is dramatic. Kurt Smith, CEO of Fexa, said something that always sticks with me: "Think about all the creative pricing conversations in SaaS and AI companies that have simply stopped happening, because people have accepted that their systems would make testing those ideas impossible." That's the real cost. Not the engineering hours. The missed opportunity cost, the lost iteration speed, the sacrificed competitiveness. If you're an engineering or product leader and the left side of that table looks familiar, I'd love to hear how you're dealing with it.

One of the most interesting and useful ideas in this report is the "Agentic AI mesh". Here is the essence of the idea and how to architect it. There are three key challenges to scale agents: ➡️ New risks including uncontrolled autonomy, fragmented system access, and lack of traceability ➡️ Blending oss-the-shelf with custom-built agents for high-impact processes ➡️ Staying agile while tech rapidly evolves. There are five mutually reinforcing design principles to Agentic AI mesh: 🧩 Composability. Any agent, tool, or LLM can be plugged into the mesh without system rework. 🌐 Distributed intelligence. Tasks can be decomposed and resolved by networks of cooperating agents. 🏗️ Layered decoupling. Logic, memory, orchestration, and interface functions are decoupled to maximize modularity. ⚙️ Vendor neutrality. All components can be independently updated or replaced. 🛡️ Governed autonomy. Agent behavior is proactively controlled via embedded structure for safe, transparent operation. There are seven interconnected capabilities for the required architecture: 🧭 Agent and workflow discovery. Enable reuse and policy enforcement by maintaining a dynamic catalog of agents and workflows. 📚 AI asset registry. Centralize governance of prompts, tools, and models with controlled access and versioning. 👀 Observability. Provide full tracing across systems through standardized metrics, audit logs, and diagnostics. 🔐 Authentication and authorization. Enforce fine-grained access to protect systems and contain potential breaches. 🧪 Evaluations. Ensure reliability by testing agent pipelines for accuracy, performance, and compliance over time. 🔄 Feedback management. Drive improvement through automated loops that evolve agent behavior using real performance data. ⚖️ Compliance and risk management. Embed policies and guardrails to meet regulatory, ethical, and institutional standards. There is a lot more in the report. But however you choose to describe it, establishing a robust architecture for agentic AI is a necessary foundation for success. This is a very solid framing.

Printing a receipt is easy. Reprinting it? Not so much. Let me explain 👇 Our app handles store returns. Customer comes in → Returns item → Gets refund → Gets receipt. Easy, right? Now imagine this: Customer comes back the next day and asks: “Can I get a copy of my receipt?” That’s where things get tricky. The original transaction is done. The upstream service that gave us the data is silent now. We can’t reprocess the transaction or we risk double refunding. So what do we do? Answer: Event-driven architecture. Here’s how we solved it: ➞ The upstream service emits an event when the return is completed. ➞ That event is published to Kafka. ➞ Our service consumes it and stores what’s needed for a reprint. ➞ Now, whenever we need a receipt later, we’re covered without re-triggering the business logic. You might ask: “Why not just store all this in the upstream DB and call it when needed?” Because then: ➞ Every consumer needs an API key. ➞ Every new consumer adds load. ➞ The upstream owns the DB but now they’re also managing access and logic for everyone. With events, none of that’s needed. It’s decoupled. It’s scalable. It works. Anyone else dealt with tricky “edge cases” that changed the way you thought about system design? #SoftwareEngineering #EventDrivenArchitecture #Kafka #SystemDesign #CareerInTech #BackendEngineering #Microservices

Clean Architecture in .NET – Scalable & Maintainable Project Structure Over the past few months, I’ve been architecting enterprise-grade applications using Clean Architecture principles in .NET — and the impact has been incredible! 💥 ✅ Scalability improved ✅ Code became modular & testable ✅ Development speed increased Here's the structure I follow to keep things clean, decoupled, and easy to maintain: 📂 API / Presentation Layer 🎯 Entry point for HTTP requests via Controllers 🧭 Sends Commands/Queries to the Application layer 🧩 Configures Dependency Injection in Program.cs / Startup.cs 📂 Application Layer ⚙️ Pure Application Logic – no infrastructure dependencies 📬 Implements CQRS using MediatR 🔁 Handles DTOs, Mapping, Events & Custom Exceptions 📂 Domain / Core Layer 🏛️ Contains core business rules and domain models 💼 Includes Entities, Interfaces, Domain Services 🚫 No EF Core, No HTTP, No UI logic 📂 Infrastructure Layer 🗄️ Handles persistence, file system, email, external APIs 🧱 Implements interfaces defined in the Domain Layer 🔌 Injected into the Application layer via DI 🎯 Why it works: This structure enables clean, scalable, and testable applications – perfect for microservices and enterprise systems. #DotNetCore #CleanArchitecture #Microservices #ScalableCode #SoftwareEngineering #CSharp #CodeStructure #DevArchitecture #DomainDrivenDesign #MediatR #CQRS #FullStackDeveloper #MaintainableCode #EnterpriseApps #CleanCode #SOLIDPrinciples

We were preparing a major feature release for an application, and the pressure was on. A single deployment meant touching multiple areas of the codebase, and one small issue could cause unexpected failures everywhere. After a rollback that took hours to stabilize, we knew we needed a better way: Moving to microservices. Here’s a quick overview of the basic architecture and key principles behind: Microservices architecture structures applications as small, independent services. Each service operates autonomously and is designed to: → Run in its own process without dependency on others. → Communicate via protocols such as HTTP/REST, gRPC or message queues like AMQP. → Be deployed, scaled, and updated independently. 𝗔𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲𝘀 𝗼𝗳 𝗠𝗶𝗰𝗿𝗼𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀 • Scalability: Individual services can scale based on their specific demands. • Flexibility: Each service can use the most suitable technology stack for its needs. • Resilience: Failures in one service do not disrupt the entire system. 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 𝘁𝗼 𝗖𝗼𝗻𝘀𝗶𝗱𝗲𝗿 • Increased Complexity: Managing numerous services adds operational overhead. • Data Consistency Issues: Keeping data synchronized across distributed services can be difficult. • Monitoring Requirements: Effective real-time monitoring helps ensure service health by providing timely insights into potential issues. 𝗕𝗲𝘀𝘁 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀 𝗳𝗼𝗿 𝗔𝗱𝗼𝗽𝘁𝗶𝗻𝗴 𝗠𝗶𝗰𝗿𝗼𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀 1/ Define Services Clearly: Break down your application’s functions into distinct, manageable services. 2/ Design Communication Wisely: Choose efficient inter-service communication protocols, such as REST or gRPC. 3/ Decouple Databases: Use independent databases per service to avoid tight coupling. 4/ Automate Deployments: Set up CI/CD pipelines to streamline integration and deployment processes. Switching to microservices didn’t solve everything overnight, but it made deployments manageable and gave us the confidence to scale with greater ease and stability. 📷 Visualizing Software Engineering, AI and ML concepts through easy-to-understand Sketech. I'm Nina, software engineer & project manager. Sketech Newsletter now has a LinkedIn Page. Join me! ❤️ #api #microservices #devops #technology