But as an enterprise buyer $50/m and $10K/m is the same bucket in terms of cost. No one will blink until around 100K, depending on what it is.

(The point I am making is; as an enterprise buyer I absolutely know how annoying it is for me to turn up and go "this random regulation, we're interpreting it in this highly specific and unique way, and we want it asap". Hence willingness to pay down that inconvenience)

They will pay $50 for your product... And probably $950 for the terms.

(Not saying that would have been the right thing for you but my advice to folks who find themselves in this position is always 20x or 40x the price - if that is enough to make it worth your bother, then go for it. Good chance theyll pay)

I fully agree that AI could extend access; but to build on what others have said too, lack of physical diagnostics is an issue as is the lack of physical tech infrastructure.

Besides; this article was about diagnosis not prescribing. It's pretty obvious, I think, that diagnosis is one area where AI will perform extremely well in the long run.

I think there are two metrics; the first is outright misdiagnosis, which studies put between 5 and 8% in US/Europe. That's a meaningful number to tackle.

Secondly; overdiagnosis. Where a Dr says on balance it could be X on a difficult to diagnose but dangerous problem (usually cancer). The impact of overdiagnosis is significant in terms of resources, mental health, cost etc.

For example; do you care if the UI has a purple theme or a blue one? Or if it's React or Vur. If you do that's part of your goals, if not it doesn't entirely matter if V1 is Blue and React, but V4 ends up Purple and Vue.

AI makes it cheap to implement complex first drafts and iterations.

I'm building a CRM system for my business; first time it took about 2 weeks to get a working prototype. V4 from scratch took about 5 hours.

They model what looks like intelligence but with very hard limits. The two advantages they have over human brains are perfect recall and data storage. They are also faster.

But the brain is vastly more intelligent:

- It can learn concepts (e.g. language) with an order of magnitude less information

- It responds in parallel to multiple formats of stimuli (e.g. sight/sound)

- LLMs lack the ability to generalise

- The brain interprets and understands what it experienced

That's just the tip of the iceberg. Don't get me wrong: I use AI, it is by far some of the most impressive tech we have built so far, and it has potential to advance society significantly.

But it is definitely, vastly, less intelligent than us.

They are vastly less intelligent than a human and logical leaps that make sense to you make no sense to Claude. It has no concept of aesthetics or of course any vision.

All that said; it got pretty close even with those impediments! (It got worse because the writer tried to force it to act more like a human would)

I think a better approach would be to write a tool to compare screenshots, identity misplaced items and output that as a text finding/failure state. claude will work much better because your dodging the bits that are too interpretive (that humans rock at and LLMs don't)

Which also suggests perhaps it is slightly more common than data suggests

If my children won't speak to each other I will refuse to be the go between because I become a proxy for one to the other. If one then punches the other they won't respect my perspective that this was wrong because I've set myself up as the proxy for the others feelings.

If you need a manger to resolve the above example, the org is broken and the engineers are poor engineers.

What I think is true is people cap out their technical competency, and look to shift their skillset and, globally, we are bad at a) training them to be good managers (because there is a wrong assumption it's an innate skill) and b) weeding out the many who also lack the ability to be a manager.

My reflection overall is; he's probably heard of servant leadership but not understood it? It's not about sweeping away problems but more a mindset that your role is to empower. I feel strongly that all new managers should embrace and get good at this because it instills the mindset that the best leaders ultimately only succeed through their team.

A servant leader who becomes overworked is either not doing their job well (delegation isn't contrary to the mindset!) or, more likely, has a poor leader themselvesw.

I actually love the concept of transparent leadership but sadly I can't see it come through in his points. They are all things a good leader, a good servant leader, should also do.

For me transparent leadership becomes more critical as you move up the stack. Once you get to multiple teams or teams of teams leaders must pivot strongly to strategy setting, and in this your servant leadership comes in painting a clear destination for everyone to get to.

At this point I believe the best leaders are genuinely transparent and the worst keep secrets. One of my most respected mentors framed it as deliberately over-sharing. Which I love, even if I get into trouble for it constantly!

(I do like the writers anarchic streak; the best leaders are radicals)

I'm struggling to see what you'd choose to do differently here?

Edit: actually I'll go further and say I'm guiding against accidental complexity. For example Auth.js is really boring technology, but I am annoyed they've deprecated in favour of better Auth - it's not better and it is definitely not boring technology!

Your last point; feels a bit idealistic. The point of code is to achieve a goal, there are ways to achieve with optimal efficiency in construction but a lot of people call that gold plating.

The setup these prompts leave you with is boring, standard, and something surely I can do in a couple of hours. You might even skeleton it right? The thing is the AI can do it both faster in elapsed time but also, reduces my time to writing two prompts (<2 minutes) and some review 10-15 perhaps?

Also remember this was a simple example; once we get to real business logic efficiencies grow.

> For large existing codebases, SDD is mostly unusable.

I don't really agree with the overall blog post (my view is all of these approaches have value, and we are still to early on to fnd the One True Way) but that point is very true.

System specs are non trivial for current AI agents. Hand prompting every step is time consuming.

I think (and I am still learning!) SDD sits as a fix for that. I can give it two fairly simple prompts & get a reasonably complex result. It's not a full system but it's more than I could get with two prompts previously.

The verbose "spec" stuff is just feeding the LLMs love of context, and more importantly what I think we all know is you have to tell an agent over and over how to get the right answer or it will deviate.

Early on with speckit I found I was clarifying a lot but I've discovered that was just me being not so good at writing specs!

Example prompts for speckit;

(Specify) I want to build a simple admin interface. First I want to be able to access the interface, and I want to be able to log in with my Google Workspaces account (and you should restrict logins to my workspaces domain). I will be the global superadmin, but I also want a simple RBAC where I can apply a set of roles to any user account. For simplicity let's make a record user accounts when they first log in. The first roles I want are Admin, Editor and Viewer.

(Plan) I want to implement this as a NextJS app using the latest version of Next. Please also use Mantine for styling instead of Tailwind. I want to use DynamoDB as my database for this project, so you'll also need to use Auth.js over Better Auth. It's critical that when we implement you write tests first before writing code; forget UI tests, focus on unit and integration tests. All API endpoints should have a documented contract which is tested. I also need to be able to run the dev environment locally so make sure to localise things like the database.

But there is a lot more complexity than, I think, you are glossing over. For example, you also likely have at least one technical services partner in the flows, probably two.

Additionally, money often doesn't move in real time, especially when credit cards are involved. The process is, intentionally, split.

Your point on that is fair, but remember, many credit providers are also not banks, and the money is in a bank account owned by a third party. So, as a trivial example, I can't just assume money coming to me from Bank A is related to transactions from Bank A's cards.

A lot of people don't realise that the main way all of this works is through very large batch files with lists of transactions in moving back and forth between various parties behind the scenes.

(We are on semantic points, though, but I just wanted to clarify the complexity behind the scenes that most people don't see or understand)

Your correct on the risk spread. I wasn't confident last night (I'm not totally versed on the terminals) but looked it up. As I understand if you choose to accept offline only payments then you accept the risk of the transaction failing. If it's the issuers choice they own the risk.