This website and article promote the destruction of property. If you disagree with something, you can engage civily, encourage people to vote with you, run for elections. Violence is not the answer.
Hi. The mayor of Denver pushed through flock cameras despite them being unpopular and not even getting enough votes to buy them. He got them to change the price enough that he didn't need the votes to get them installed.
How do you have a civil society when the people in power cheat?
It sounds like he worked within the legal constraints of the system he was elected to work within.
This kind of discretionary spending authority can used for things that are good, bad, or indifferent. When it gets used to cut through the red tape and buy a new swingset for a neighborhood park, then that's good; nobody complains about that. (Except someone would surely complain about that, but come on man.)
And when it gets used to install government tracking systems, that's bad.
> How do you have a civil society when the people in power cheat?
The problem isn't that the mayor can spend some money. Rather, the problem here is that government tracking systems are completely legal to buy.
The laws need adjusted so that government tracking systems are completely illegal, instead.
"Yeah, good luck getting the government to do that!"
The people of Colorado are free to initiate their own legislation and constitutional amendments and then vote them into force.
Destroying a camera isn't violence. It's destruction of property, sure. But property isn't inherently good and sometimes it degrades society.
If some goober installs massive floodlights that blast into windows of some houses, I think everyone would support a kid with a slingshot busting a few bulbs. If some guy is blasting music from a speaker at 3 AM every single day, I don't think anyone will complain about a cable being cut. If cameras are installed that sell data to companies like Palantir, companies that say they want to kill you and they're going to kill you and it's just a matter of time until they kill you, destroying those cameras is the non-violent option.
"Violence" is a word normally used when the victim is sentient, but I'll go along with it:
Violence against inanimate objects is morally neutral. Violence against instruments of violence is self-defense. Violence against oppression is how the USA was founded.
A corporation has unfair political advantages including a deep purse, an unlimited lifespan, and more recently all the rights of personhood. The only advantage the people have is their numbers, and yeah numbers of votes would be great, I agree, but when votes are ignored, or never solicited in the first place, it often comes down to numbers of pitchforks, as it were.
On voting harder, see the lead incident mentioned: "This happened weeks after the city council voted to keep the cameras despite overwhelming public opposition." I also advocate patiently working through the process, but people are not blind to the trends: the democratic process is failing as government increasingly sidelines voters and the richest have the levers of power.
I don't see anything on the site or article that promote the destruction of property. It's an aggregation of public information regarding the history of vandalism towards a specific target.
The website largely documents the current state of privacy and provides resources for (digital) services that help maintain privacy. This is an encouraging civil engagement which educates and empowers the audience.
Everything I've read and learned in my 50 or so years on this planet leads me to believe that the times injustice can be corrected purely by civil engagement and voting are massively outweighed by the times that they can't. So depending on how bad the thing is - people make choices.
Without having thought about it for more than about 10 seconds: I guess I associate violence with something more personal: an actual person or living thing, or personal property. I guess "corporate property" is where it gets more into the grey zone for me.
But I see your point. Destroying a thing (even corporate) is a pretty extreme reaction that I can only see making sense after having exhausted all other "peaceable" avenues.
People that see these things as detrimental to society though are likely pretty motivated.
> FOSTA-SESTA is the source of this. A well intentioned bill, that, once again, has unintended consequences beyond it's original intention.
You're right that these are connected to FOSTA/SESTA, but you're missing the actual connection.
FOSTA/SESTA were not "well-intentioned". They were the product of lobbying from explicitly religious, anti-sex, anti-pornography groups. Those same groups are behind recent campaigns to require providing government ID to access pornography, to allow attorneys general to prosecute LGBTQ content, and to ban pornography from platforms like Steam and Itch.io.
FOSTA/SESTA have worked exactly as they were intended to! The intention was to make it harder to conduct sex work legally and safely, and they accomplished that goal!
These policies have little to do with FOSTA/SESTA themselves, in that the text of those laws has no bearing here. But those bills were the first big, national victory of these campaigns, and they used that momentum to raise absurd amounts of money to lobby for the other laws mentioned above, and to target financial infrastructure as an easy point of leverage to accomplish their goal of banning pornography across the Internet.
> Those bills are the reasons banks/credit card companies are pushing this since it holds them liable.
You are either misreading those bills or confusing them with other similar bills which did target banking infrastructure (and which thankfully did not pass).
FOSTA/SESTA did increase liability for platforms, but the applicability of those laws to this specific case is minimal to nonexistent.
Banks didn't do this before 2018 as aggressively. Reputational stuff is always been there and chargebacks have always been there. This law is why they've been so aggressive over the last few years. There's nothing else that has changed
> This law is why they've been so aggressive over the last few years. There's nothing else that has changed
There is so much that has changed! If you think FOSTA/SESTA are the only thing that have changed, you're clearly not up to date on this topic!
> Banks didn't do this before 2018 as aggressively
Because after FOSTA/SESTA passed in 2018, the groups that lobbied for it started targeting financial infrastructure as the front in their war. This is not some secret; they've been very open about it and their lobbying efforts have been extremely well-documented.
The best way to prompt an LLM is to describe the outcome you want, that's it. They are trained as task completers. A clear outcome is way better than a process.
If the LLM fails, either you didn't describe your outcome sufficiently or is misinterpreted what you said or it couldn't do it (rare).
Common errors should be encoded as context for future similar tasks, don't bloat skills with stuff that isn't shown to be necessary.
> The best way to prompt an LLM is to describe the outcome you want, that's it. They are trained as task completers. A clear outcome is way better than a process.
This is not true for anything complex. They’re instruction followers, of which task completion is just one facet.
They’re also extremely eager to complete tasks without enough information, and do it wrongly. In the case of just describing task completion, despite your best efforts, there are always some oversights or things you didn’t even realize were underspecified.
So it helps a lot to add some process around it, eg “look up relevant project conventions and information. think through how to complete the task. ask me clarifying questions to resolve ambiguities. blah blah”. This type of prompt will also help with the new Opus 4.7 adaptive thinking to ensure it thinks through the task properly.
Agreed, and further, I'd argue the OP's division of LLM instructions into either process or outcome specification is a false dichotomy. My agentic process specification is about automatically specifying the outcomes that I would otherwise repeatedly have to tell the LLM to consider, like making sure test coverage is maintained, or that decisions are documented on the original Github issue. Or it's about correcting common failure modes, like when the agent spends an enormous amount of time running repo-wide tests while debugging a focused change, because the agent doesn't consistently optimize around the time-to-implement as an outcome. Arguably part of addressing those failure modes boils down to pure process in the sense that I specify a logical order for achieving the outcomes, e.g. creating a plan before implementing. But that is mostly to organize approval gates for my convenience, rather than structuring the agent's work per se.
If there is anything we have learned in decades of Software engineering, it's "A clear outcome" is not easy to describe. In many cases, it's impossible unless people from 4 different domains collaborate. That's why process matters. It allows for software to be built is a "semi-standardized" way that can allow iterations to get us closed towards the expected outcome, that might emerge over time.
Yes, not everything I use LLMs for going to have the same level of ambiguity or complex requirements. Optimizing by choosing to skip over parts of the process is exactly Addy is talking in this article.
This seems like common sense but it does not work in practice.
Prompting is just the first part. To get the outcome, you need to have other systems to steer the agent as it get things wrong. Proper deterministic tests work. But there is also stuff that need to happen during the LLM execution like cyclic detection etc. All of this adds up.
You cannot just prompt an LLM an hope for a good outcome. It might work in small isolated scenarios but it just does not work consistently enough to call it reliable.
Without further guardrails enforced by the process or the harness, LLMs do not have sufficient capabilities to complete a task up to a certain standard.
I agree that many skills are overblown and unnecessary. But there's a lot of value in giving AI the right process. See how much more effective Claude can be for moderate or large changes when using the superpowers skill.
a skill is just reusuable/shareable context. It's just text, really. It's useful for things like documentation on how to use an API (this works better than MCP in my opinion), or a non consensus way of doing something. For example, you can use remotion to generate video. There are useful remotion skills that allow you to reliably generate specific types of videos. Captions of a certain style, for example.
That seems a bit reductive. Even with humans, there’s a range of interpretations and ways that something can be built or a task completed. Engineers remember stuff so you don’t have to keep repeating yourself. Skills are a way to describe your outcome without similar repetition.
MCPs are basically just JSON-rpc. The benefit is that if you have applications that require an API key, you can build a server to control access (especially for enterprise). It's the same as REST apis, except by following a specific convention we can take advantage of generic tools (like the one I built) and means you don't need to rely on poor documentations to connect or train a model to use your very specific CLI.
But if you have customer facing APIs then all of these problems were already solved in an enterprise context. You can force an oauth flow from skills if you want.
I don’t think that CLIs are the path forward either, but you certainly don’t have to teach a model how to use them. We’ve made internal CLIs that adhere to no best practices and expose limited docs. Models since 4o have used them with no issue.
The amount of terminal bench data is just much higher and more predictable in rl environments. Getting a non thinking model to use an MCP server, even hosted products, is an exercise in frustration compared to exposing a cli.
A lot of our work is over voice, and I’ve found zero MCPs that I haven’t immediately wanted to wrap in a tool. I’ve actually had zero MCPs perform at all (most recently last week with a dwh MCP and opus 4.6, where even the easiest queries did not work at all).
LLMs don't care about mcp vs CLI. CLIs enable LLMs to fetch/mutate data and build scripts with the same program. I think of it like a Linux dev in a box. Sometimes you want to just call a tool, sometimes you want to write a small program that calls that tool instead.
How many kernel devs does the world need? A dozen or two?
It will be the same with software. AI will be writing and consuming most software. We will be utilizing experiences built on top of that, probably generated in real time for hyper personalization. Every app on your phone will be replaced by one app. (Except maybe games, at least for a short while longer).
Everyone's treating writing code as this reverent thing. No one wrote code 100 years ago. Very few today write assembly. It will become lost because the economic neccesity is gone.
It's the end of an era, but also the beginning of a new one. Building agentic systems is really hard, a hard enough problem that we need a ton of people building those systems. AI hardware devices have barely been registered, we need engineers who can build and integrate all sorts of systems.
Engineering as a discipline will be the last job to be automated, since who do you think is going to build all the worlds automation?
How wildly dismissive of the foundation of the X$ billion dollar software industry. You think humans just stumbled into writing code by accident or something?
How does building agentic systems, a "really hard" problem, not just end up a "regular code" problem? Because that is what it is. A distributed systems problem with non-deterministic run lengths. How do you switch agent contexts? Similar to how you solve regular program context switching. How do you search tool capabilities and verify them? How do you effectively manage scheduled tasks?
Oh, look, you've just invented the operating system kernel. Suddenly, those 'dozen or two' experts don't seem so archaic after all!
Does it even make sense to build everything on top of machines that are 70% reliable? The sheer orchestration and validation overhead at scale risks being more expensive than just keeping most software engineers and having them manage a few AI agents.
Also, 200 years ago we didn't have bike mechanics. Car mechanics. Boat mechanics. Plumbers. Electricians. Not all new professions fade away.
Every problem you described is solvable and while it may not be solved right now or even in 6 months it'll probably be solved within 18 months. It's just scaling and tuning the models
You can’t “tune models” to get people willing to get on a zoom call with an agent and the agent asks them questions and talk through strategy and understand human emotions.
Are they also going to interact with the model for a design review session?
Tell the model where it got it wrong and the model is going to make the changes?
In 18 months AI agents will be able to accurately infer people's emotional state from the subtle facial expressions they make in a sales meeting, in real time?
I deleted vscode and replaced with a hyper personal dashboard that combines information from everywhere.
I have a news feed, work tab for managing issues/PRs, markdown editor with folders, calendar, AI powered buttons all over the place (I click a button, it does something interesting with Claude code I can't do programmatically).
Why don't I share it? Because it's highly personal, others would find it doesn't fit their own workflow.
Technical people (which is by far the minority of people out there) building personal apps to scratch an itch is one thing.
But based on the hype (100x productivity!), there should be a deluge of high quality mobile apps, Saas offerings, etc. There is a huge profit incentive to create quality software at a low price.
Yet, the majority of new apps and services that I see are all AI ecosystem stuff. Wrappers around LLMs, or tools to use LLMs to create software. But I’m not really seeing the output of this process (net new software).
I worked in an industry for five years and I could feasibly build a competitor product that I think would solve a lot of the problems we had before, and which it would be difficult to pivot the existing ones into. But ultimately, I could have done that before, it just brings the time to build down, and it does nothing for the difficult part which is convincing customers to take a chance on you, sales and marketing, etc. - it takes a certain type of person to go and start a business.
Nobody’s talking about starting businesses. The article is specifically about pypi packages, which don’t require any sales and marketing. And there’s still no noticeable
uptick in package creation or updates.
There is no money in mobile apps. It came out in the Epic Trial that 90% of App Store revenue comes from in app purchases for pay to win games. Most of the other money companies are making from mobile are front end for services.
If someone did make a mobile app, how would it get up take? Coding has never been the hard part about a successful software product.
I think this is the great conundrum with AI. I find it's most useful when I build my own tools from models. It's great for solving last-mile-problem types of situations around my workflow. But I'm not interested in trying to productize my custom workflow. And I've yet to encounter an AI feature on an existing app that felt right.
Problem is that all these companies trying to push AI experiences know that giving users unfettered access to their data to build further customization is corporate suicide.
> Yet, the majority of new apps and services that I see are all AI ecosystem stuff.
The same was true of all this computer science stuff too. We built parsers, compilers, calculators, ftp and http, all cool stuff that just builds up our own ecosystem. Look how that turned out.
An ecosystem has to hit a critical mass of sophistication before it breaks out to the mainstream. It's not going to take very long for AI.
> But based on the hype (100x productivity!), there should be a deluge of high quality mobile apps, Saas offerings, etc. There is a huge profit incentive to create quality software at a low price.
1. People aren't creating new apps, but enhancing existing ones
2. Companies are less likely to pay for new offerings when the barrier to entry is lowered due to AI. They'll just vibe code what they need.
I don't think the 2nd point will make a huge impact on software sales. Who is vibe coding? Software developers or business types? They aren't going to vibe code a CRM, or their own bespoke version of Excel, or their own Datadog APM.
Maybe they will vibe code small scripts, but nobody was really paying for software to do that in the first place. Saas-pocalypse is just people vibe investing, not really understanding the value proposition of saas in the first place (no maintenance, no deployments, SLAs, no database backups, etc).
Perfect example of what you’re talking about: today a coworker of mine showed off a vibecoded data viewer app that lets him view our analytics in a way that works well for his job, using our analytics platform’s API. A nice little personal productivity boost for him, but not something that will ever replace the analytics platform itself.
Why on earth would you publish and monetize software anybody can reproduce with a $20 subscription and an hour of prompting? Why would you ever publish something you vibe coded to PyPI? Code itself isn’t scarce anymore. If there is not some proprietary, secret data or profound insight behind it, I just don’t think there is a good reason to treat it like something valuable.
> Wrappers around LLMs, or tools to use LLMs to create software. But I’m not really seeing the output of this process
Because it's better to sell shovels than to pan for gold.
In the current state of LLMs, the average no-experience, non-techy person was never going to make production software with it, let alone actually launch something profitable. Coding was never the hard part in the first place, sales, marketing & growth is.
LLMs are basically just another devtool at this point. In the 90s, IDEs/Rapid App Development was a gold rush. LLMs are today's version of that. Both made developer's life's better, but neither resulted in a huge rush of new, cheap software from the masses.
For SaaS, the bottleneck is still access to data. Everything else already has been made in the past 5-10 years, so if you can't find a way around data moats you don't really have a product 99% of the time - especially now that people can vibecode their own solutions (and competitors.)
Beyond that, marketing is harder than ever. Trying to release an app on Shopify app store without very strong marketing usually just means you drop it into a void. No one trusts any of the new apps, because they're inevitably vibecoded slop and there's no way to share your app on social media because all the grifting and shilling have totally poisoned that avenue.
Take a look at Show HN now - there are tons of releases of apps every day, but nothing gets any traction because of the hostile/weird marketing environment and general apathy. Recently, I saw the only app to graduate from New Show HN likely used a voting cartel to push it to the top. And take a guess at what that app did? It summarized HN's top stories with an AI. Something any dev could make in about 10 minutes by scraping/requesting the front page and passing it through an LLM with a "summarize this" prompt.
The entire "indiehacker" community is just devs shilling their apps to each other as well. The entire space is extremely toxic, basically. Good apps might get released but fall into a void because everyone is both grifting and extremely skeptical of each other.
Well it’s mostly explained by the fact that most people lack imagination and can’t hold enough concepts about a particular experience to think about how to re-imagine it, to begin with.
Oh and sadly, llm’s are useless for the imaginative part too. Shucks eh.
I have a list of ideas a mile long that gets longer every day, and LLMs help me burn through that list significantly faster.
However, the older I get, the more distraught I get that most people I meet "IRL" are simply not sitting on a list of problems they simply lack time to solve. I have... a lot of emotions around this, but it seems to be the norm.
If someone doesn't see or experience problems and intuitively start working out how they would fix them if they only had time, the notion that they could pair program effectively ideas that they didn't previously have with an LLM is absurd.
> most people I meet "IRL" are simply not sitting on a list of problems they simply lack time to solve. I have... a lot of emotions around this, but it seems to be the norm
This sounds unnecessarily judgmental. Doing this is your hobby. Other people have different ways they want to spend their time. That doesn't make you superior, just different.
You're describing arrogant superiority, which is a real thing. It is definitely me sometimes! I can own that.
What I'm describing is an observation that despite my closely held progressive ideals, most people simply do not appear to see the world through the lens of "lots of things could be better and I see several ways to start fixing them".
I could say a lot of [probably shitty and judgmental] things about perhaps why this divide exists and the myth of the wisdom of crowds vs the genius inventor.
We can concede that all great ideas are inevitably built on the shoulders of previous great ideas by people who often go uncredited - bless the lab techs - but this thread is about the fact that most people aren't even trying to be near where the innovations happen.
Yeah and frankly the innovation would occur irrespective of llm’s.
Would it be harder? Sure. And perhaps the difficulty adds an additional cost of passion being a necessary condition to embark on the innovation. Passion leads to really good stuff.
My personal fear is we get landfill sites of junk software produced. To some extent it should be costly to convert an idea to a concept - the cost being thinking carefully so what you put out there is somewhat legible.
As I’ve said in my other post, I’m very confident that imagination is the true bottle neck.
Writing lines of code? Nope. If one can imagine… trust me, writing lines of code is trivial.
Most people have no imagination. So sure they can produce more stuff with llm’s but it’ll just be mostly garbage.
Perhaps they can produce some peculiar workflow that works ‘for them’. Sure. But I think about the money invested into the LLM-based projects and I highly doubt we are going to see any returns that justify the spend. What we are going to see is a felling on the profession of software engineers, since the pipe dream of AGI isn’t coming and imagination is scarce.
Most businesses do not have the capacity to use LLMs to produce software. If you have an idea that you can create into real high quality software that there is a demand for, then you should absolutely do it.
This is probably my favorite gain from AI assisted coding: the bar for "who cares about this app" has dropped to a minimum of 1 to make sense. I recently built an app for grocery shopping that is specific to how and where I shop, would be useless to anyone other than my wife. Took me 20 minutes. This is the next frontier: I have a random manual process I do every week, I'll write an app that does it for me.
More than that. Building a throwaway-transient-single-use web app for a single annoying use kind of makes sense now, sometimes.
I had to create a bunch of GitHub and Linear apps. Without me even asking Codex whipped up a web page and a local server to set them up, collecting the OAuth credentials, and forward them to the actual app.
Took two minutes, I used it to set up the apps in three clicks each, and then just deleted the thing.
Same energy here. I was sitting on 50+ .env files across various projects with plaintext API keys and it always bothered me but never enough to actually fix it. AI dropped the effort enough that I just had a dedicated agent run at it for a few days — kept making iterations while I was using it day to day until it landed on a pretty solid Touch ID-based setup.
This mix of doing my main work on complex stuff (healthcare) with heavy AI input, and then having 1-2 agents building lighter tools on the side, has been surprisingly effective.
That's fine and all, but how much are you ready to pay to Anthropic and OpenAI to be able to do this? Like, is it worth 100 bucks a month for you to have your own shopping app?
Haha great. I guess my wider point is that most people won't be ready to pay for it, and in the end there will be only two ways to monetize for OpenAI et al: Ads or B2B. And B2B will only work if they invest a lot into sales or if the business owners see real productivity gains one the hype has died one.
It's not worth 100 bucks a month for me to have my own shopping app, but maybe it's worth 100 bucks a month to have ready access to a software garden hose that I can use if I want to spew out whatever stupid app comes to my mind this morning.
I'd rather not pay monthly for something (like water) that I'm turning on and off and may not even need for weeks. But paying per-liter is currently more expensive so that's what we currently do.
I think the future is going to be local models running on powerful GPUs that you have on-prem or in your homelab, so you don't need your wallet perpetually tethered to a company just to turn the hose on for a few minutes.
Me, and photo editor tool to semi-automate a task of digitizing a few dozen badly scanned old physical photos for a family photo book. Needed something that could auto-straighen and auto-crop the photos with ability to quickly make manual adjustments, Gemini single-shotted me a working app that, after few minutes of back-and-forth as I used it and complained about the process, gained full four-point cropping (arbitrary lines) with snapping to lines detected in image content for minute adjustments.
Before that, it single-shot an app for me where I can copy-paste a table (or a subsection of it) from Excel and print it out perfectly aligned on label sticker paper; it does instantly what used to take me an hour each time, when I had to fight Microsoft Word (mail merge) and my Canon printer's settings to get the text properly aligned on labels, and not cut off because something along the way decided to scale content or add margins or such.
Neither of these tools is immediately usable for others. They're not meant to, and that's fine.
My buddy and I are writing our own CRUD web app to track our gaming. I was looking at a ticketing system to use for us to just track bug fixes and improvements. Nothing I found was simple enough or easy enough to warrant installing it.
I vibe'd a basic ticketing system in just under an hour that does what we need. So not 20 mins, but more like 45-60.
I built a small app to emit a 15 kHz beep (that most adults can't hear) every ten minutes, so I can keep time when I'm getting a massage. It took ten minutes, really, but I guess it's in the spirit of the question.
For 20 minutes of time, I had a simple TTS/STT app that allows me to have a voice conversation with my AI assistant.
I've been getting close to that myself, I've been using VSCode + Claude Code as my "control plane" for a bunch of projects but the current interface is getting unwieldly. I've tried superset + conductor and those have some improvements but are opinionated towards a specific set of workflows.
I do think there would be value in sharing your setup at some point if you get around to it, I think a lot of builders are in the same boat and we're all trying to figure out what the right interface for this is (or at least right for us personally).
I'm guessing it's not a hard coded function, the button invokes. Instead it spawns a claude code session with perhaps some oredefined prompts, maybe attaches logs, and let's claude code "go wild". In that sense the button's effect wouldn't be programmatical, it would be nondeterministic.
I have had the thought to write little "programs" in text or markdown for things which would just a chore to maintain as a traditional program. (I guess we call them "skills" now?) Think scraping a page which might change its output a bit every so often. It the volume or cadence is low, it may not be worth it to create a real program to do it.
But it requires A LOT of work to make sure it is actually safe for people and organizations. And no, an .md file saying “PLEASE DONT PWN ME, KTHX” isn’t it at all. “Alignment” is only part of the equation.
This all reads, to put it politely, like it's being written by someone who is not all there and being convinced by letting AI write everything that they have a coherent idea. Or just trying to put a bunch of buzzwords together to get people to buy something. Do you have any code or actual demos of "your" "work" to share? Your homepage's "See It in Action" section is just more AI slop articles in video form.
Kind of. I'm finding that my terminal window in VSCode went from being at the bottom 1/3rd of my screen to filling the whole screen a lot of the time, replacing the code editor window. If AI is writing all of your code for you based on your chat session, a lot of editing capabilities aren't needed as much. While I wouldn't want to get rid of it entirely, I'd say an AI-native IDE would deemphasize code editing in favor of higher-level controls.
Sorry, I'm not sure how this relates to the content of the article. Sounds like an interesting experience, but this is an analysis of the Python ecosystem pre+post ChatGPT.
One craft is automated and a new one is just beginning.
Building AI agents is really fun and the problem of having them be reliable adaptable efficient is actually really challenging and I'm having a lot of fun with it trying to figure it out.
To me it's a lot like factorio or my personal favorite Dyson sphere program where at first you do everything by hand and then you automate and then you automate the automation.
For the first time in human history we can automate intelligence with a computer but just because we can automate it doesn't mean all the good automation is good and we need engineers who can figure out how to automate it reliably scale it deploy it maintain it.
And yes eventually we will automate the automation too.
Unregulate the insurance industries problem solved. Let people actually buy insurance for it's intended purpose. No insurance company would pay these rates willingly, they do it because of all the regulations. They aren't allowed to profit normally, so they find ways around it. Just let them operate normally, like all sorts of other insurance programs.
Yeah no thanks, let’s do the tried and true universal healthcare that literally every else does. They get better results AND it’s cheaper. We’re literally paying more for something worse.
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