

The idea of putting Cameras on City Vehicles and using AI to find problems, including Code Enforcement issues, is a good one IMO. The problem, as the article notes, is what that data is going to be used for in the future. We used to call this a Slippery Slope argument but the past 10 years have proven that if data is harvested that it will eventually be misused; it’s some kind of parallel to Godwin’s Law that I am going to coin as “Flock’s Law”.
In this specific instance Flock’s Law tells us that the authorities will inevitably start scanning the photos / videos for people in order to track movements, the Police will be in the database stalking people, and you’ll start getting tickets for over-filling your trash can.
Meanwhile all people really want, and what won’t happen, is someone to come by to get that absolutely jungle of a lawn mowed, fix a broken sidewalk, replace a missing street sign, or move the damn boat / camper / trailer / junk car that has been parked in the street for the last year.












You’ve never used an LLM against an MCP server with a properly built API. The ad hoc shit you can do is amazing.
Imagine you are managing 1,500 endpoints. A mix of Windows (both clients and servers), Mac, and Linux. Now imagine that one day there’s a CVSS 10.0 in Eclipse Temurin 17 and you suddenly need to know everywhere it’s installed and which precise 17.x version.
You could spend all damn day fussing with SQL -or- you could fire up $LLM and tell it “Give me a report showing all installed instances of Temurin below version 17.3. Sorted by endpoint type and operating system”.
Suddenly want to know how many windows workstations haven’t rebooted in more than 14 days? You can do that and get an answer in not much more time than it takes you to type the question.
Suddenly need to change a setting in 15 different Microsoft Tenancies? Snap, it’s done.
LLMs are NOT magic bullets but they are useful for WAY more than what you listed. You just don’t know that because you don’t do that kind of work.