Munimentum’s Post

Business development perspective: This systematic approach perfectly demonstrates why companies choose Munimentum for Go performance optimisation. ⚡ Instead of expensive performance monitoring tools, we leverage Go’s built-in capabilities to deliver superior performance while reducing infrastructure costs. The formula is clear: 3-10x performance + 30-60% cost reduction + proven AI optimisation = competitive advantage through efficient Go performance engineering. See the full methodology from Alexis Morin and the team here! (Proud to be driving this strategy with Alex!) #GoPerformance #CostEfficiency #BusinessDevelopment #ROI

Optimizing Web APIs: Lessons from the Startup Trenches As we build scalable platforms, API performance becomes non-negotiable. Whether you're serving thousands of ad impressions or powering real-time assistants, here are 5 battle-tested ways to optimize your Web API: 1️⃣ Strategic Caching Use Redis or in-memory caching to avoid redundant DB hits. Even short-lived caches can cut response times dramatically. 2️⃣ Connection Pooling Reuse DB connections with proper pool sizing. This reduces handshake overhead and boosts throughput—especially in high-concurrency scenarios. 3️⃣ Payload Control Limit response size with pagination, filtering, or compression (gzip). Don’t send what the client won’t use. 4️⃣ Parallel Processing For large data sets, use parallel loops to speed up transformations. But avoid over-threading for small payloads. 5️⃣ Query Optimization Eliminate N+1 queries and consolidate DB calls. Profiling your endpoints often reveals hidden bottlenecks. 💡 Bonus: Always profile before you optimize. Premature tuning wastes time—let real usage guide your decisions. We’re applying these lessons daily as we build our AI assistant and campaign platform. Curious how others are scaling their APIs—what’s worked for you?

Optimizing code is so 2023, your team needs to orchestrate #intelligence. Anthropic just released Skills: "super hero prompts" with tools that give Claude specific capabilities. The model decides when to use it. Simple example: feed invoices, provide a skill with your commission formula, get results. The stack is fundamentally different now. Your engineers used to work with code, frameworks, databases, servers. You know classic choices: React vs Angular, Postgres vs MySQL, AWS vs Azure, etc. Now they need to be orchestrating across an entirely new dimension. Whether a task needs an #MCP server, a skill connecting to your database, or an agent that chains through multiple systems. The decision tree has exponentially more branches. This changes what "good architecture" means. As a leader, you need to train your team to think at this level, and not try to awkwardly fit AI into "old" patterns: - Know when traditional code is still the right tool - Recognize when AI augmentation amplifies your existing systems - Identify when intelligence should orchestrate entirely - Design for composition #AI, services, and human oversight that reassemble as needs evolve The question isn't whether to adopt these tools. It's whether your team knows how to think at this level. Are you designing architectures that can absorb these new capabilities? The gap between these two approaches is about to become very expensive, and very visible.

AI Engineers, Don’t Just Build — Learn to Architect. Many of us focus on building features, training models, or optimizing AI workflows. But one thing I’ve realized is that when features and models are integrated into a production-ready application, system design becomes the most critical factor. Learning system design helps you: - Boost your application’s performance. - Handle large traffic and data efficiently. - Build fault-tolerant and scalable applications. - Make AI and software solutions production-ready. I’ve shared a breakdown of System Design Fundamentals in my latest Medium post - covering everything from client-server architecture to caching, databases, APIs, and microservices. For engineers looking to grow, understanding system design can make a real difference.

Many enterprises are stuck with legacy applications that are costly to maintain and slow to evolve. Traditional modernization efforts often stall or, worse, technical debt and complexity are shifted from one outdated relational database to another. The new MongoDB Application Modernization Platform (AMP) changes that. Powered by AI, MongoDB AMP helps teams modernize the full application stack—code, data, and architecture—faster than traditional approaches. ⚙️ Analyze, test, and deliver comprehensive transformations using agentic AI. 🚀 Transform monolithic systems into scalable, production-ready services. 📉 Reduce manual effort and go live in months, not years. 🔒 Improve resilience, security, and AI readiness. Read our blog to learn how AMP is redefining modernization: https://lnkd.in/gJWSu9qJ

Many enterprises are stuck with legacy applications that are costly to maintain and slow to evolve. Traditional modernization efforts often stall or, worse, technical debt and complexity are shifted from one outdated relational database to another. The new MongoDB Application Modernization Platform (AMP) changes that. Powered by AI, MongoDB AMP helps teams modernize the full application stack—code, data, and architecture—faster than traditional approaches. ⚙️ Analyze, test, and deliver comprehensive transformations using agentic AI. 🚀 Transform monolithic systems into scalable, production-ready services. 📉 Reduce manual effort and go live in months, not years. 🔒 Improve resilience, security, and AI readiness. Read our blog to learn how AMP is redefining modernization: https://lnkd.in/dztPQFbZ

Many enterprises are stuck with legacy applications that are costly to maintain and slow to evolve. Traditional modernization efforts often stall or, worse, technical debt and complexity are shifted from one outdated relational database to another. The new MongoDB Application Modernization Platform (AMP) changes that. Powered by AI, MongoDB AMP helps teams modernize the full application stack—code, data, and architecture—faster than traditional approaches. ⚙️ Analyze, test, and deliver comprehensive transformations using agentic AI. 🚀 Transform monolithic systems into scalable, production-ready services. 📉 Reduce manual effort and go live in months, not years. 🔒 Improve resilience, security, and AI readiness. Read our blog to learn how AMP is redefining modernization: https://lnkd.in/g_WhmG48

🚀 Supercharge Your Microservices: The Protobuf Advantage for Lower Latency 🚀 In the world of microservices, every millisecond counts. As architectures become more distributed, the overhead of inter-service communication can quickly add up, leading to increased latency and a degraded user experience. This is where Protocol Buffers (protobuf) shine! So, how exactly does protobuf help reduce microservice latencies? Let's break it down: Compact Data Format: Unlike verbose formats like JSON or XML, protobuf serializes data into a highly efficient binary format. This means smaller message sizes on the wire, less data to transmit, and faster network transfer times. Think of it like sending a zip file instead of individual uncompressed documents! Faster Serialization/Deserialization: Protobuf uses highly optimized code generators to create language-specific classes for your data structures. This compiled code is significantly faster at converting data to and from its binary format compared to reflection-based parsers often used with JSON or XML. Less CPU spent on parsing equals more time for business logic. Strong Typing and Schema Enforcement: Protobuf schemas (.proto files) provide a clear, strongly typed contract between services. This eliminates ambiguity and reduces errors that can lead to retries or complex error handling, which inherently add latency. When services know exactly what to expect, communication is smoother and more reliable. No More Text Parsing Overhead: With text-based formats, services spend valuable CPU cycles parsing strings, handling encoding, and converting data types. Protobuf skips this entirely by working directly with binary data, leading to a much more efficient process. The Impact? Quicker API Responses: Faster data transfer and processing directly translate to lower end-to-end latency for your microservice calls. Reduced Network Congestion: Smaller messages mean less bandwidth consumption, which can be critical in high-traffic environments. Improved Throughput: Services can handle more requests per second when they spend less time on communication overhead. If you're looking to optimize your microservice performance and shave off those critical milliseconds, protobuf is an invaluable tool to have in your arsenal. #MetLife #Microservices #Protobuf #Latency #Performance #SoftwareArchitecture #DistributedSystems #Tech

Consistent hashing solves a tricky problem: when scaling distributed systems, adding or removing nodes usually means rehashing almost all your data, causing massive data movement and downtime. Consistent hashing minimizes this by only moving a small fraction of keys when nodes change, making scaling smooth and efficient. The internet is full of high-level explanations of consistent hashing, but finding an actual implementation of the algorithm is rare. Finding a near-real implementation with nodes acting as cache partitions is even rarer. I built a hands-on system in Golang with a hash ring, an API to store and retrieve keys, add and remove Dockerized nodes, and real-time D3 visualization. It’s a small system, but it gives a clear view of how data moves and how nodes handle load. Check it out here: https://lnkd.in/dZvYNy5F