A comprehensive guide to architecting, implementing, and deploying applications powered by Large Language Models
llmaisoftware architectureproduction
page:/writingposts:6queued:4
> ls -t writing/
NOTES FROM PRODUCTION
AI agents, infra rot, full-stack reality, and what eight years taught me about leading engineers without breaking them.
idxdatereadtitle
Lessons learned from scaling a Go microservices architecture to handle millions of requests per day
gomicroservicesperformancescalability
A deep dive into React Server Components and how they change the way we build React applications
reactnext.jsserver componentsperformance
How to design, implement, and maintain production-grade machine learning pipelines on AWS
awsmachine learningmlopsinfrastructure
Leveraging TypeScript's type system to build more robust and maintainable enterprise applications
typescriptenterprisepatternsbest practices
Strategies and practices for fostering a collaborative and innovative engineering culture in distributed teams
cultureremote workleadershipteams
> queue
UPCOMING
- Q01Building RAG Applications with Vector Databases
A detailed guide on implementing Retrieval-Augmented Generation with various vector database options
planned - Q02Performance Optimization Techniques for React Applications
Step-by-step techniques to improve React application performance including code splitting, memoization, and more
in-progress - Q03Infrastructure as Code with Terraform and AWS
Video tutorial series on setting up a complete cloud infrastructure using Terraform
planned - Q04Implementing Event-Driven Architecture with Kafka
Comprehensive guide to implementing and scaling event-driven systems using Apache Kafka
planned
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