As LLM providers ship breaking changes on a near-monthly cadence, the difference between a 10-minute .env swap and a 400-line PR comes down to architecture. This post introduces seam-driven design for Python LLM apps - five narrow interfaces (provider, prompt, tools, config, observability) that can each be replaced independently - illustrated with a working FastAPI + LangChain reference app. Readers get a concrete checklist and two hands-on drills they can run against the open-source code immediately.
I've had that on my 'to buy' list for too long. In such a rapidly changing field like computing, books have to be really special to reach the 'classic' accolade.
This here. I think the author is confusing what a 10x developer is.
I took the 10x dev to mean somebody who may work efficiently but also improves all of the devs around them. Improving yourself by 2x is one thing but improving the team around you by 2x is where you get the 10x.
I got stuck on this problem a while ago. I was doing reinforcement learning and the environment needed to run in pypy to make it fast enough. I ended up using Pyro4 to bridge pypy (environment) and CPython (keras).