Alex shares why he chose to found a machine learning company with Clojure, what worked, what didn’t, and how they scaled their technology team from 3 people to over 40. Alex explains where they use Clojure, and where they don’t, and why. How Clojure’s strengths and weaknesses change with your team size. Which assumptions he made about Clojure when founding the company that changed over time, and which turned out to be correct.
Related
People keep asking how companies like Ramp, Linear, and Anthropic ship so much software with agents. There is…
the world hasn’t figured out yet that you can literally just fix everything with a Nix overlay
Nix has been single-handedly the best thing I have added to my dev toolbox when I found out about it 6 years ago. More…
LLM Architecture in 2026: What You Need to Know with Sebastian Raschka
Sebastian Raschka, Independent AI Researcher and author of Build a Large Language Model from Scratch, joins Hugo to…
AI Concierge
Miles is right. AI Concierge is one of the best ways to make money as an AI consultant right now. I'm making over…
From One-Shot to Agentic: Optimizing Shop Intelligence with DSPy @ Shopify Scale
Kshetrajna Raghavan, a principal engineer from Shopify, walks through how they evolved their approach to extracting…
Headroom: A Context Optimization Layer for LLM Applications - Tejas Chopra, Netflix, Inc.
