The Starting Point When we first built our data pipeline for scraping investment intelligence from sources like Crunchbase, LinkedIn, and SimilarWeb, we optimized for simplicity. Our initial architecture served us well for the first few customers: Original Architecture: * SSH into a VM and manage multiple tmux sessions * Run bash scripts

Building RAG (Retrieval-Augmented Generation) applications in 2024 feels like navigating a minefield of framework choices. After months of experimentation and some production incidents I'd rather forget, we've finally landed on a stack that works. Here's our journey through the LLM framework landscape and why

I've been using Docker since 2016. Nine years of docker-compose up and docker build, shipping containers to production, debugging the occasional "works on my machine" issue. I was dangerous enough to get things working, but if you asked me about the nuances of layer caching or

Last year, our Heroku bill hit $3,000/month. The breakdown was painful: $500/month for managed Postgres, $200 for Redis, and over $2,000 for dynos running our Django REST API and Celery workers. The compute costs were especially galling—we were paying premium prices for dynos that sat

When we started building Sugar Connect (our SugarCRM integration platform), we had a few hundred users syncing data between SugarCRM and their other business tools. Fast forward two years, and we're handling tens of thousands of active users, processing millions of tasks daily, and orchestrating complex sync workflows.