AI engineering has emerged as a distinct discipline built on three layers: application development, model development, and infrastructure. Unlike traditional ML engineering which focuses on training custom models, AI engineering emphasizes adapting existing foundation models through prompt engineering and fine-tuning. The field requires less deep ML knowledge but more focus on evaluation, inference optimization, and building user interfaces. AI engineers work with larger, more compute-intensive models that produce open-ended outputs, making evaluation significantly more challenging. The role bridges software engineering and ML, with many practitioners coming from full-stack development backgrounds rather than traditional ML research. 

Bracket is a self-hosted tournament system designed for easy use, leveraging async Python with FastAPI for the backend and Next.js with Mantine for the frontend. It supports various tournament formats such as single elimination, round-robin, and swiss, and allows for dynamic scheduling and management of tournaments and teams. The system can be run using Docker or independently with pipenv and yarn, and is configured using .env files or environment variables.

Frontend engineering goes beyond styling buttons and layouts; it involves building systems that serve human experiences. Key aspects include data flow, state models, component architecture, user experience flow, and accessibility. It combines design with logic to create scalable and user-friendly products.

Typesense is presented as an approachable search engine that makes advanced search functionality accessible to developers. The introduction emphasizes how Typesense eliminates the confusion and frustration often associated with search tools, making developers feel capable rather than overwhelmed. A comprehensive 22-episode series covers everything from basic queries and setup to advanced features like semantic search, vector queries, and cloud deployment.

This post describes a simple three-step process to build an MCP server using tools like Gitingest and Google AI Studio, enabling the transformation of FastMCP repository data into LLM-readable text. It also highlights the capabilities of the Firecrawl framework, which converts websites into structured formats for AI applications.

Schej is an inclusive scheduling platform designed to help groups find optimal meeting times. It offers free availability polling, integrates with various calendar systems, and supports features like time zone management and email notifications. Built with Vue 2, MongoDB, Go, and TailwindCSS, it provides functionality to match availabilities, duplicate polls, and export data in CSV format.

A technical lead shares their experience rebuilding a failing e-commerce system in 30 days, transforming it from a single-server monolith to a scalable microservices architecture. The rebuild involved migrating to Google Cloud Platform with Kubernetes, breaking down the Node.js monolith into Java and Go microservices, implementing Redis caching and Kafka for background jobs, rebuilding the frontend with Next.js, and adding comprehensive monitoring. The result was a system that could handle 50K concurrent users with 3x faster response times and reduced page load times from 3.7s to 1.2s. 

The post introduces the concept of semantic coupling as an innovative method to manage schema evolution in event-driven architectures, replacing traditional schema coupling. By using semantic tags instead of versioning or altering event schemas, it allows consumers to focus on the meaning of data rather than its representation. This approach prevents breaking changes and eliminates the need for event versioning, encouraging smoother schema transitions. Tools like Orbital and Taxi can facilitate these transformations by decoupling the data representation from consumers