Year in Review - AI Tools That Changed Development in 2022
Reviewing the AI tools that transformed software development in 2022, from GitHub Copilot to Stable Diffusion and ChatGPT.
24 posts
Reviewing the AI tools that transformed software development in 2022, from GitHub Copilot to Stable Diffusion and ChatGPT.
My first week with ChatGPT - exploring its capabilities, limitations, and why this feels like a watershed moment in AI history.
Setting up comprehensive monitoring and alerting for production systems using Prometheus, Grafana, and Alertmanager.
Exploring the emergence of AI-generated art in 2022 and its profound implications for creativity, copyright, and the future of artistic expression.
Comprehensive guide to securing Kubernetes clusters in production, including RBAC, network policies, secrets management, and security scanning.
A practical comparison of GraphQL and REST APIs based on real-world projects, including performance benchmarks and use case recommendations.
An in-depth comparison of Midjourney and DALL-E 2 based on 200+ generated images, covering quality, style, pricing, and use cases.
Leveraging Python's asyncio and aiohttp to build high-performance async applications, handling 10K+ concurrent requests.
Implementing distributed tracing and metrics collection across microservices using OpenTelemetry, Jaeger, and Prometheus.
A comprehensive guide to setting up and running Stable Diffusion on your local machine, including hardware requirements, installation steps, and optimization tips.
Implementing a fast and relevant full-text search system using Elasticsearch, including indexing strategies, query optimization, and relevance tuning.
Building a scalable real-time chat application using WebSockets, Redis Pub/Sub, and Node.js with support for 10K concurrent connections.
Practical PostgreSQL performance tuning techniques that reduced query times from 500ms to 20ms, including indexing strategies, query optimization, and configuration tuning.
Setting up a production-ready CI/CD pipeline with GitHub Actions, including testing, building, security scanning, and deployment.
Testing AI-powered code review tools including GitHub Copilot, DeepCode, and CodeGuru to see if they can replace or augment human code reviews.
MongoDB schema design patterns and anti-patterns learned from managing a 500GB database with 100M+ documents.
How I optimized a Rust data processing pipeline from 2 seconds to 50 milliseconds through profiling, algorithmic improvements, and Rust-specific optimizations.
Implementing effective API rate limiting using Redis, token bucket algorithm, and distributed rate limiting across multiple servers.
Master the art of prompt engineering for GitHub Copilot to get higher quality code suggestions and boost your productivity.
Advanced Docker Compose patterns for production deployments, including health checks, secrets management, and high availability configurations.
Optimizing a React application's load time from 3 seconds to 300ms through code splitting, lazy loading, memoization, and bundle optimization.
How we scaled our microservices architecture from 500 to 10,000 requests per second using Kubernetes, including real metrics, challenges, and lessons learned.
How we migrated from manual AWS console management to Terraform, managing 50+ resources across multiple environments with version control and automation.
After 3 months of using GitHub Copilot in production, here's an honest review of its impact on development workflow, productivity gains, and unexpected challenges.