Build-Series

Build a Notification Service from Scratch — Part 3: Retries, Rate Limiting, and Monitoring

Part 3: Implement rate limiting per channel, dead letter queue for failed notifications, and monitoring with Prometheus metrics.

Build a Notification Service from Scratch — Part 4: Scaling and Deployment

Final part: Scale notification workers horizontally, implement batching for high throughput, Dockerize, and deploy to production.

Build a Notification Service from Scratch — Part 2: Queue and Worker System

Part 2: Implement Redis-backed queue with Celery workers for email, SMS, and push notification delivery. Build channel-specific dispatchers.

Build a Notification Service from Scratch — Part 1: Architecture and Database Design

Part 1: Design a multi-channel notification service architecture. Covers database schema for email, SMS, and push notifications with template management.

Build a URL Shortener from Scratch — Part 5: Analytics, Docker, and Deployment

Final part: Add click analytics with IP geolocation, Dockerize the application, and deploy to production with docker-compose.

Build a URL Shortener from Scratch — Part 4: Redis Caching for Fast Redirects

Part 4: Add Redis caching to the URL shortener for sub-millisecond redirects. Implement cache-aside pattern with read-through and write-through strategies.

Build a URL Shortener from Scratch — Part 3: API Endpoints with FastAPI

Part 3: Build FastAPI endpoints for creating short URLs and redirecting. Includes request validation, error handling, and Pydantic schemas.

Build a URL Shortener from Scratch — Part 2: Database Schema and Base62 Encoding

Part 2: Design PostgreSQL schema for URL shortener with Alembic migrations. Implement Base62 encoding/decoding for short code generation.

Build a URL Shortener from Scratch — Part 1: Requirements and Architecture

Part 1 of building a production URL shortener. Define requirements, architecture, and technology choices for a scalable link-shortening service.

Build a RAG Chatbot from Scratch — Part 5: Deployment and Production Considerations

Final part: Deploy the RAG chatbot to production with Docker, including cost optimization, rate limiting, and monitoring setup.

Build a RAG Chatbot from Scratch — Part 4: Evaluation and Quality Metrics

Part 4: Evaluate RAG chatbot quality with retrieval metrics, answer faithfulness, and hallucination detection. Build an evaluation pipeline.

Build a RAG Chatbot from Scratch — Part 3: Streaming Responses with SSE

Part 3: Add real-time streaming responses using Server-Sent Events (SSE). Stream LLM tokens as they're generated for a ChatGPT-like experience.

Build a RAG Chatbot from Scratch — Part 2: Retrieval and Prompt Assembly

Part 2: Implement semantic retrieval from ChromaDB and assemble prompts with retrieved context. Build the core Q&A endpoint with source citations.

Build a RAG Chatbot from Scratch — Part 1: Architecture and Embeddings

Part 1: Design the architecture for a Retrieval-Augmented Generation chatbot and implement document embedding with OpenAI's text-embedding models.