When does it still make sense to use microservices, what are mental models to keep in mind when building resilient distributed systems, and how is much of the tech world fundamentally misunderstanding AI? With @samnewman. Timestamps: 00:00 Intro 03:16 Sam’s path into tech 09:32 Thoughtworks 20:55 The rise of microservices 33:37 Are specs becoming more important than code? 45:22 Building Resilient Distributed Systems (Sam’s new book) 52:35 Three rules of distributed systems 56:16 Observability 1:02:20 Resilience tradeoffs 1:07:54 Idempotency 1:15:58 Thundering herds 1:21:03 Business context and resilience decisions 1:25:51 Resilience engineering: four concepts 1:32:42 AI and resilience 1:36:26 AI’s limitations and where to use it 1:40:06 Cognitive debt and cognitive surrender 1:45:02 Modular architecture and AI software factories 1:51:53 Resources for learning software architecture 1:55:30 Where to find Sam Brought to you by: @turbopuffer - – vector and full-text search built on object storage: fast, 10x cheaper, and extremely scalable. Under the hood, the team are rewriting the tpuf storage engine. Follow along until it hits prod: @EntireHQ - Git hosting, rebuilt for the agentic era. Entire hosts your code in-region, and is up to 89x faster than any other competitor. Mirror from GitHub with a single click – I’ve already done so. @AntithesisHQ - Get a free introduction to property-based testing: through a rare, live webinar hosted by @hillelogram , author of Logic for Programmers. RSVP here: So, what does much of the tech world misunderstand about LLMs? Sam says: “I think the tech world in general is quite naive about AI and fundamentally misunderstands what an LLM is. For example: why did the LLM delete my database? Well, because it has no concept of causality. They have no concept that if I do A, B happens. LLMs are not world models. World models have been around for a long time. They predate LLMs. So if you had the appropriate model and the appropriate inputs, world models could at least get to the point of ‘if I do X, then Y happens and I’ve been told not to do Y.’ I think we expect LLMs to do more than they actually can do, because they seem so smart. This leads us to overestimate their capabilities a bit too much. But the nature of LLMs is why all the guardrails around LLMs are really not going to be the right long-term solution.”