Even if you do want datacenters built in your country, you probably don't want them built at the maximally explotative locations that their developers pursue.
They don't provide appreciable community value and they effectively mine limited local resources (power, grid capacity, land, water) and sell it as compute, immediately diverting the profit back out of the local economy and into very distant business accounts instead.
Builders choose their targets specifically by how well they can strong-arm weak/vulnerable communities into letting them build these mines through political influence and misrepresentation. It's bad.
What you probably want is to leverage their global market value to establish new power and grid capacity in undeveloped areas, perhaps to someday become a seed for
new communities that grow around the infrastructure development work.
But that's much more expensive than bullying and seducing a weak city council so it won't happen with regional/state/federal regulatory protections or incentives that push them away from the exploitative opportunities and towards the constructive ones.
The lean startup "feedback loop" was with customers (not coworkers). The idea was that you iterate on your viable product (not vibe prototype) with the market that derives value from it.
The slow part is finding those customers, syncing your deliveries with their processes, giving them time to meaningfully assess new workflows and features in the course of their business operations, collating the feedback you receive from all of them, and merging that feedback with your organization's long term growth objectives to drive new ideas into development. Well-developed organizations layer this inescapably slow flow across numerous parallel channels so engineering utilization can stay high since healthy engineering already cycled much faster than these market-engaged flows can.
Neither coding nor internal prototypes were the slow part. Market engagement and market-informed product planning were the slow part. And still are.
You may not realize it yet, and maybe you've just misrepresented it, but most of what you seem to be describing is usually considered wheel-spinning and navel-gazing. You may have made your internal process cycle faster, but you very likely just turned a wasteful busywork churn into a more efficiently wasteful busywork churn.
Neither coding nor internal prototypes were the slow part
That is not my experience mentoring 100+ startup founders. Building a prototype, the gateway to serious customer engagement, used to take months and many startups would die before finishing their first one.
Aren't those startups the ones wanting a google style infrastructure based on kubernetes with database sharding, an event-source architecture,... And when you told them a few VPS with postgres would have sufficed, they absolutely insisted that unless it's a next.js app backed by a serveless ecosystem and tens SaaS, they couldn't build their products?
> and yes ofc, I used LLMs to help reformulate some sections
???
Why in the world would that be an "ofc"?
If you're trying to establish yourself as a writer and communicator, LLM's are the last thing you want to color your personal voice with. They may have a role in cleaning up interpersonal communication or in helping non-professional communicators shape up their prose for formal occasions, but they are not some kind of magic neutral way to improve a writer's writing.
As you're seeing here, all that work would have been better received without the compromises and tells of LLM-ese because it would have been your writing, in your voice, as an intelligent analyst and communicator. The idiosyncrasies of that prose voice (your prose voice), are a durable signature that people come to associate with you individually and help them interpret tone, inflection, emphasis, insight in ways that the genericism and accent of an LLM scrubs out.
Give yourself more credit and don't do this; or at least don't treat it as an "of course"!
I also don't understand this. After having written something I never felt a need to have it reformulated by anything. What would even be the prompt for that?
Maybe "You are an expert editor. Polish this article for X demographic. Make no mistales."?
But jokes aside, I too prefer genuine human writing. Writing is complex enough that you can see a distinct style even if it's rough. LLMs tend to polish the roughness so much that everything reads like magazine ads.
I think it's easy for native speakers to say. But as English is not my mother tongue, I find it safer to run it through a checker and nowadays, LLMs. So maybe no need to be so harsh about it
I understand that motive. On the other hand, LLM smell makes the text untrustworthy. I have detected it as well, and I immediately started to wonder about whether I am reading a reasonable expert analysis or just an AI hallucination. I still don't know.
I recommend prompting the LLM to mostly fix glaring grammatical and stylistic mistakes, not to rewrite the entire thing into a LinkedIn post style text.
Have you ever had someone else edit your work, comment on it and provide alternative phrasings or organization? LLMs are pretty good at that, available any time and give instant results, as long as you understand that they work differently from a human reviewer - you can't expect it to be of the quality you'd get from a subject-matter expert or highly skilled writer, you have to lean into the LLM slot-machine model where you just get some alternative options. But it's incredibly useful when you're stuck in a rut with how to conceptualize or explain something, or even when you're not, and just want to visualize some alternatives that come from somewhere outside of your own head.
I think of it like a power thesaurus. Thesauruses get a bad rap for people just using them to look for ten-dollar words, but they're super useful for finding ways to articulate things differently, which can sometimes lead to bigger insights or ideas about restructuring the content.
It's on the author to look at what's suggested by the LLM and decide whether or not to use it, and there's an inherent danger in having one's voice overridden by simply accepting too many of the recommendations as-is. But that's between the author and the tool. I won't make any comment here on the article author's prose or how they maybe did or didn't use LLMs.
Sure there are. Population measures of morbidity, days of lost work, patient satisfaction, etc are all captured and studied.
Do you have indications that those are worse where wait times happen to be higher, or so you just take it for granted that longer waits must mean worse net experience?
Many universal public systems (not all, see: Canada) effectively allow this through private plans and private providers that supplement the government benefits, often notably for speedier diagnostics and treatment.
A little of A, a little of B. I have a lot of fun building it out, it's surpassed Factorio in addiction, and I've been able to flesh out some patterns that I roll back into more productive agent harness bits.
For A:
The learning is in building agent harnesses that aren't just cron jobs reading a file like HEARTBEAT.md. I have some serious tools for my own use. One main assistant/coordinator agent, one SRE/coder agent (with sub-agents of its own).
I originally just started last year with the AI assistant (Jane from enderverse). Along the way building scheduled systems, hand offs to other agents, etc. As I ran into problems, I'd be rewriting and refactoring. So I spent some time making a low-stake hatbot with history and routines. Instead a from-scratch golang harness, I built it around pi and extensions. Time of day prompt splices (extensions can inject into or modify prompts on the fly, wake up reminders. Things that you do in the main session vs spinning up an ephemeral session. Self improvement daydreaming (modify your own skills and AGENTS.md) A lot of that went back into rebuilding Jane to something more useful for me.
For B:
The "dynamic dollhouse" as you put it was seeing where I could take that living chatbot next. There's a lot of projects pointing agents at slack, discord, message boards. I figured why not a mud with rooms, weather, and props. Lots of interesting challenges. How to keep bots from nesting in their own room, how to keep them from yes-anding each other all day long. How to slow down 3 bots talking at each other so a human can get a word in edge-wise.
Different levels. There's plain old NPCs that have dice roll random responses. There's LLM driven NPCs that only remember the last 5-10 messages. And the main ones are bot agents. Full agent harness, moving around the environment. Long lived context windows. One character (a nurse at the hospital) gets into arguments with an NPC receptionist that treats her as another patient. Complains about it to other characters, they remember and the word spreads.
The agents get prompted to write down notes, the head home for sleep (and session compaction). Next time they enter a room with that person after compact, their notes get loaded automatically. This kind of behavior can feed back into the more productivity based agents.
Not my experience. Maybe it's your presentation that earns downvotes, not your message.
You can be a very principled person resisting and spotlighting norms you strongly disagree with, but how you do it tends to matter a lot. It makes the difference between people opening an ear to listen to you and reflexively pushing you away as an annoyance.
There are more silos in the software engineering industry than you might expect as a "Big Tech" kind of engineer (assuming that's where you're speaking from). Gaming, embedded, audio, aviation, defense, automotive, medtech and pharmacy, deep enterprise, finance, etc.
Those silos maintain different processes and workflows, different company cultures, different skill specializations, etc and jumping around between them in mid-career or senior can be very challenging. So they tend to have their own org chart shapes and salary/benefit norms.
When a Big Tech company moves into or absorbs one of those silos, or emerges from one of those silos, it can shake up what the people within them get paid (and thereby have big knock-on effects for legacy employers), but otherwise it's just it's own little bubble in a lot of ways. People can share stories and ideas across the siloes in venues like HN, but many of the "what are you even talking about" reactions that happen on here often occur when people from these different silos stumble into what are sometimes deep differences in what they do and what their work experience is like.
It's not as widely promoted, but if you're genuinely interested, there are more of those histories written then you'll ever have time to read yourself.
There's a classic five volume series "A History of Private Life" that works through a breadth-first survey over time. It can make for a great starting point, and is a bit like an encylopedia in the way you can engage with it as essays on certain times and topics instead of being expected to read it through serially.
The article is specifically about a strategy to improve on that (or rather a satirical exposé on how AI answers are the next spiral down into isolation).
That's exactly it, right? Self-sorting among those suitably positioned to emigrate and who have tastes more aligned with European norms?
That sounds more like pragmatism than romantacism.
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