1. Policing is yet another example of Baumol’s effect[1]; sectors where automation lags become expensive

2. Policing can be automated effectively, including via Flock:

> Hunters Point rapper Dreamllife Rizzy described how police tech has made it more difficult to get away with bipping and other crimes. “Crime in San Francisco, period, that shit over with, brother,” the rapper said. “They got drones, hella shit.” He described how Flock cameras clock the plates on a stolen car and send an alert to police stations, which send drones to follow the vehicle.[2]

3. Policing, like major surgery, ambitious construction, military invasions, and other high-consequence endeavors, is not immune to perfidy, incompetence, or other human weaknesses.

4. Argument by implicit synecdoche (here is an example of an abuse, therefore the general behavior is abusive) is ubiquitous and invalid; you need to actually collect wide evidence and weigh major tradeoffs. How does Flock affect total crime rates? Total abuse rates? That only matters if you think that we should be open to the possibility that widespread use of Flock cameras might be good.

5. WRT privacy and policing-the-police in particular, there is a small but highly vocal community of libertarians whose perspective is sharp, detailed, and not reflective of broader society’s perspective except in motte-and-bailey form, and who will never be persuaded by evidence because for them what matters are the principles (n.b.: this observation is not per se a criticism — sometimes principles should trump evidence around tradeoffs).

6. HN’s 10^5 (?) active commenters include 10^4(?) members of that libertarian vanguard

7. Everyone has heard all the libertarian and contra-libertarian arguments going back decades; no one is learning from yet another go-round

[1] https://acjsissons.medium.com/the-baumol-effect-not-a-diseas...

[2] https://sfstandard.com/2026/08/13/rest-peace-bip-city/?utm_s...

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There is a movement parallel to formal methods to define acceptance criteria at high resolution but not logico-mathematically, which at least grapples with the mapping problem but can’t resolve it where the map isn’t the territory, which is most places. Has Google’s results page, with its extremely evolved internal optimization frameworks really hit an optimum? Could that prototype you whipped up to capture a hazy idea have better illustrated it? These questions are best answered by looking outside the system to what the system serves.

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1. Free Software / Open Source are Good and True by assertion. There is no God but source code, and Stallman is its prophet. 2. Questions whose answers tend to contradict point 1., such as “Gee, the world runs on Python — as wonderful as job as Guido and his inner circle have done, is it time to ask what an ideal management structure for a technology worth (tens? hundreds? of) billions of dollars might be?” are not welcome — are largely not asked.

An original sin of Free Software which carried through to Open Source and infects HN via its many Open Source believers is a reluctance to take project management seriously. OP shows that Jellyfin’s dictat... er, maintainer is not effectively managing the project. Open Source has no adequate answers (“fork” is not adequate).

Welcome to the human condition, my friend. The good news is that a plurality of novels, TV shows, country songs, etc. can provide empathy for and insight into your experience.

Regarding water specifically, we now have multiple desalination projects of 1MM m^3/day, enough to support a city of 4MM people. They are expensive, but getting cheaper, and real (rich) polities in the Middle East are relying on them.

9. Python got lucky that its inevitable screwups (Python3) didn’t quite kill it.

10. Swift and Kotlin both define programming as serving the compiler (specifically LLVM) rather than serving the coder’s problem. (I haven’t discussed Rust so far since it isn’t attempting to compete with 98% of Python use cases, but if you squint you can see it as going one step further than Swift and Kotlin and in effect forcing the coder to be a sort of human compiler who thinks in types and memory management. This is not a criticism of Rust, BTW.)

0. And behind all of this is Moore’s Law and the demographic explosion of programmers. Python was an implicit, perhaps unconscious bet that if you served people thoughtfully, the tradeoffs with serving the needs of contemporary silicon wouldn’t matter as much.

2. Python is conceptually compact, dominated by hash tables with string keys. The initial leader in the ecosystem, Perl, is conceptually sprawling and difficult to reason about.

3. Python also took lessons from the Unix shell, a mature environment for accommodating beginners and experts.

4. Python had a formal process for integrating C modules from early on.

5. Python’s management has an elegant shearing layer structure, where ideas can diffuse in from anywhere.

6. $NEXT_GENERAL_PURPOSE_LANG (Ruby, Go) weren’t enough better to displace Python. Both were heavily influenced by Python’s syntax, but ignored the community-centric design process that had created that syntax in favor of We Know Best.

7. Speaking of open source entrepreneurialism, JavaScript has become a real rival thanks to the Web (and node), but it is handicapped by the inverse failure mode: where Go is dominated by a handful of Googlers, JavaScript was effectively unmanaged at the STDLIB level for a crucial decade, and now it can’t recover. (I’d also guess that having to write a module system that works well in the chaos that is Web clients and simultaneously the Unix world is a daunting design problem.)

8. Python got lucky that data science took off.

[1] https://ospo.gwu.edu/python-wasnt-built-day-origin-story-wor...

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