We Ditched Terraform 1.10 for CloudFormation: Reducing IaC Complexity for Our Small AWS Team We’re a 4-person engineering team managing 32 AWS resources across dev, staging, and production environments for a B2B SaaS product. For 18 months, we relied on Terraform 1.10 to manage our infrastructure as code (IaC). But by Q3 2024, the overhead of maintaining Terraform outweighed its benefits for our
Most cloud sustainability tools are built for sustainability officers. They pull three-month-old billing data, run it through a proprietary model, and produce a PDF that engineers never see. By the time you know your us-east-1 cluster emits twice as much as us-west-2 would have, it's been running for a quarter. The architecture is locked in. The carbon is already burnt. The only moment you can act
El problema real Cuando trabajas en DevOps, inevitablemente enfrentas el caos de gestionar infraestructura manualmente. Cambios undocumentados, configuraciones inconsistentes entre ambientes y actualizaciones que rompen lo que funcionaba. Terraform resuelve esto con un workflow estructurado que convierte tu infraestructura en código versionable y reproducible. El workflow de Terraform es el cicl
El Problema Real Cuando comenzamos a usar Terraform, muchos nos hacemos la misma pregunta: ¿cómo sabe Terraform qué recursos ya existen en la nube? La respuesta está en el state file, un archivo que frecuentemente causa dolores de cabeza innecesarios cuando no se entiende bien. El state file es un archivo JSON que Terraform mantiene como fuente única de verdad sobre tu infraestructura. Contiene
Purpose of Variables in Terraform Variables prevent repetitive hardcoding of values in Terraform configuration files. They reduce errors due to inconsistent value entries across multiple resources. Simplify updating environment-specific configurations (e.g., changing from dev to stage). Types of Variables Based on Purpose Input Variables: Accept values from users or other sources. Output Variables
Hello Developers! 👋 Most developers today pick a side: Let’s talk about combining C++ and JavaScript—the ultimate hybrid stack for high-performance applications. 👇 1. The Core Engine (C++) ⚙️ 2. The Browser Bridge (WebAssembly) 🌉 3. The Cinematic Experience (Vanilla JS + UI/UX) ✨ The Takeaway 🎯 Keep optimizing, keep building! 💻✨ ~ Ujjwal Sharma | @stackbyujjwal About the Author 👨💻 Ujjwal
I built a Vamana-based vector search engine in C++ called sembed-engine. Recently I made a pull request that sped up queries by 16x and builds by 9x. The algorithm stayed exactly the same. The recall stayed at 1.0. The number of visited nodes did not change. The speedup came from data layout. The original code stored vectors as separate objects pointed to by shared_ptr: struct Record { int64_t
The first time I implemented Vamana from the DiskANN paper, my approximate nearest neighbor index was slower than brute force. On tiny test fixtures, brute force took 0.27 ms per query. My Vamana implementation took 22.98 ms. That sounds absurd. ANN exists to skip work. The problem was not the algorithm. It was how I mapped the paper's abstractions to actual data structures. The DiskANN pseudocode