System Design Under Real Load: Scaling from One to a Million Users

A system that works for one hundred users is not automatically a system that works for one million.

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System Design Under Real Load: Scaling from One to a Million Users

A system that works for one hundred users is not automatically a system that works for one million. Real scale exposes architectural assumptions through traffic spikes, database contention, overloaded services, queue backlogs, hot keys, cascading failures, and infrastructure limits that rarely appear during a controlled demo. Our latest LUNARTECH handbook explores load balancing, horizontal scaling, stateless services, caching, asynchronous processing, database scalability, capacity planning, and load testing to show how engineers can design systems that continue to perform as real usage grows.

System Design Under Real Load: Scaling from One to a Million Users has been written by Bevan To. A practical deep dive for software engineers, backend developers, platform teams, and technical leaders looking to understand how production architectures evolve under genuine load and how to scale systems deliberately without introducing unnecessary complexity too early.

Database Performance Engineering: Indexing, Query Plans, and Caching That Actually Helps

Database performance problems are rarely solved by adding an index to every column or placing Redis in front of every query. Real performance engineering starts with understanding how the database executes work, where latency is actually coming from, and whether a proposed optimization reduces cost or simply moves the bottleneck somewhere else. Our latest LUNARTECH handbook explores indexing strategies, query plans, EXPLAIN and EXPLAIN ANALYZE, composite indexes, covering indexes, connection pooling, slow-query investigation, and caching patterns that deliver measurable performance improvements.

Database Performance Engineering: Indexing, Query Plans, and Caching That Actually Helps has been written by Bevan To. A practical engineering handbook for backend developers, database engineers, platform teams, and technical leaders looking to diagnose slow systems systematically, understand how databases execute real workloads, and build a performance strategy based on measurement rather than guesswork.

Building an Internal Developer Platform

Building an Internal Developer Platform is not simply about adding another layer of tooling. It is about reducing developer cognitive load, replacing infrastructure ticket queues with self-service systems, and creating clear golden paths that allow engineering teams to move faster without sacrificing security or operational consistency. Our latest LUNARTECH handbook explores how Backstage, developer portals, self-service infrastructure, policy-as-code, and a product-oriented platform model can come together to build an IDP that engineers actually use.

Building an Internal Developer Platform: Backstage, Golden Paths, and Self-Service Infrastructure has been written by Steven Crickman. A practical deep dive for platform engineers, DevOps teams, SREs, and engineering leaders thinking seriously about developer experience and infrastructure at scale.

Cutting Your LLM Bill 10x - Caching, Model Routing, Quantization, and Local Inference with vLLM

LLM costs rarely explode because of a single expensive API call. They grow through longer prompts, unnecessary context, repeated requests, oversized outputs, agent loops, retries, and premium models being used for work that could be handled far more efficiently. Our latest LUNARTECH handbook explores caching, model routing, prompt and token reduction, quantization, and local inference with vLLM to show how engineering teams can systematically reduce AI infrastructure costs without blindly sacrificing product quality.

Cutting Your LLM Bill 10x - Caching, Model Routing, Quantization, and Local Inference with vLLM has been written by Jim Amuto. A practical deep dive for AI engineers, technical founders, and product teams looking to understand where LLM spend actually comes from and how to build a smarter, measurable cost optimization strategy.

Evaluating LLM Apps When There’s No Ground Truth

Evaluating an LLM application becomes much harder when there is no single correct answer to compare against. Summaries, RAG assistants, agents, recommendation systems, and AI support tools can produce different outputs that are all technically acceptable, making traditional exact-match testing insufficient. Our latest LUNARTECH handbook explores evaluation-driven development, golden datasets, LLM-as-judge, product-specific rubrics, human calibration, RAG evaluation, and regression testing to show how teams can replace subjective “vibe testing” with a repeatable engineering workflow.

Evaluating LLM Apps When There’s No Ground Truth has been written by Vahe Aslanyan. A practical deep dive for AI engineers, software engineers, product builders, and technical teams looking to define what “good” actually means, measure open-ended AI systems more reliably, catch silent regressions, and build evaluation infrastructure that can support real production LLM applications.

Modern Full-Stack with Next.js: Server Components, Streaming, and the Vercel AI SDK

Modern full-stack development is moving beyond the traditional model of pushing everything into the browser. React Server Components, the Next.js App Router, streaming interfaces, Server Actions, and AI-native application patterns are reshaping how developers think about rendering, data mutations, performance, and intelligent user experiences. Our latest LUNARTECH handbook explores how these technologies come together to build scalable, high-performance, AI-powered applications using a server-first architecture.

Modern Full-Stack with Next.js: Server Components, Streaming, and the Vercel AI SDK has been written by Julius Adelekun. A comprehensive guide for React and Next.js developers looking to deepen their understanding of modern full-stack architecture, streaming AI responses, the Vercel AI SDK, Retrieval-Augmented Generation, and production-ready application development.

Creating Production-Ready MCP Servers: A Practical Engineering Handbook

Building an MCP server that responds to tool calls is one thing. Building an MCP server that can safely interact with real databases, files, SaaS products, infrastructure, and business systems is a completely different engineering problem. Our latest LUNARTECH handbook explores authentication, authorization, tenant isolation, safe tool design, prompt injection boundaries, rate limiting, audit trails, monitoring, backups, incident response, and the operational controls required to move MCP systems beyond a demo.

Creating Production-Ready MCP Servers: A Practical Engineering Handbook has been written by Jim Amuto. A practical engineering deep dive for AI developers, platform teams, and organizations building MCP integrations that need to be secure, observable, recoverable, and genuinely ready to interact with production systems.

Policy-as-Code in Practice: OPA, Kyverno, and Guardrails That Don’t Slow Teams Down

Security and compliance guardrails should not require an engineer to wait in another review queue every time infrastructure changes. Policy-as-code moves these controls directly into CI/CD pipelines, Terraform plans, GitOps workflows, and Kubernetes admission systems, allowing compliant changes to move quickly while violations are caught at the cheapest possible point. Our latest LUNARTECH handbook explores OPA, Rego, Kyverno, shift-left enforcement, and the engineering patterns required to build guardrails that protect systems without becoming organizational bottlenecks.

Policy-as-Code in Practice: OPA, Kyverno, and Guardrails That Don’t Slow Teams Down has been written by Steven Crickman. A practical deep dive for platform engineers, DevOps teams, SREs, and security engineers looking to replace inconsistent manual reviews with automated, transparent, and developer-friendly policy enforcement.

OpenTofu vs. Terraform: Migrating Off the BUSL License Safely

The Terraform and OpenTofu conversation is no longer purely about licensing. As the two projects continue to evolve independently, platform teams must think seriously about governance, technical divergence, state compatibility, provider ecosystems, migration risk, and the long-term direction of their infrastructure-as-code stack. Our latest LUNARTECH handbook examines what the BUSL license actually changed, how OpenTofu and Terraform differ, and how engineering teams can evaluate or execute a migration without turning critical infrastructure into an unnecessary experiment.

OpenTofu vs. Terraform: Migrating Off the BUSL License Safely has been written by Steven Crickman. A practical guide for infrastructure engineers, DevOps teams, SREs, and platform leaders evaluating OpenTofu and looking for a controlled, low-drama approach to migrating real infrastructure away from Terraform.