there definitely are some areas in software-land where graphing data and/or directly eyeballing it genuinely helps to spot patterns where statistical methods might be cumbersome/tricky/otherwise annoying, like log analysis[1]

[1] https://jvns.ca/blog/2022/12/07/tips-for-analyzing-logs/

This sharp uptick in LLM in-context "learning" capabilities means I'm more excited than ever to try to get to grips with "new" languages like Nim or Gleam (but worried that using LLMs to help me get to a working end state will rob me of some of the experience of learning).

Every MCP vs CLI argument I've seen really glosses over _where_ the agent is running, and how that makes a difference. For individual users where you're running agents locally, I totally agree that CLIs cover the vast majority of use cases, where available.

I think something I've not seen anyone mention is that MCPs make much more sense to equip agents on 3rd party platforms with the tools they need - often installing specific CLIs isn't possible and there's the question of whether you trust the platform with your CLI authentication key.