> For that you need to somehow make more money than you are spending.

I've had this idea of 'business as reducing entropy' floating around in my head for awhile. It's a neat way to think about the value a business offers to buyers; a washing machine manufacturer is selling reduced time to reduced entropy (clean cloths), spreadsheet software is selling reduced time to understanding (information from tabulated data), and so on.

From that perspective, a lot of AI-driven development is failing.

We're still in the phase of 'how do we get order out of semi-average chaos?' for LLMs. For ML we're largely past that point.

I've been using this framing as a means to guide me towards 'what is actually useful, what might someone actually buy'. I don't have my own business at this point, but its still fun to think about off and on.

Just stop thinking of software products. There are a million businesses offering solid value propositions that need a lot of software to run, but software isn't the product.

I think this is application dependent. LLM's are quite good for brainstorming, even if they are not arguably creative, at least they draw from a lot of information that is already out there, which saves me time in researching and learning.

> I've had this idea of 'business as reducing entropy' floating around in my head for awhile

You could generalize this to the purpose of life itself, probably.

This is disingenuous at best.

The man is an economist, not a crony operating at the federal level (one does not imply the other, and I know nothing of the man's background).

So in other words... he doesn't actually use the tools he's firmly convinced will automate the building of software.

I don't agree with the parent; I think capitalism is doing a lot of great things for us and will continue to, even with AI. But man I'm tired of these hot takes from people with limited practical experience.

> But man I'm tired of these hot takes from people with limited practical experience.

I hear you there. There's a reason I limit what I say online. I know very little about very little.

Steve Blank the startup whisperer and Steven Blank the economist are two very different people.

Python gets pretty heavy for old hardware. Once you hit your MVP you may want to look at compiled languages for better single-threaded perf.

This idea is one I floated for my agent framework; it can get a little complicated if the model needs to reason about the redacted data. If not, excellent.

The other half of this equation is correctly marking PII/etc. This is a problem I'm relatively familiar with, at least as far as brute-forcing from raw files. I'd be curious to hear about how you managed this. Or is that something that AWS handles for you?

I'd love to use this for not cannabis things. I'm looking at building a greenhouse soon, and having this kind of automation for tomatoes or carrots would be dream-like.

That's the idea - hence PlantLab, not CannaLab. Cannabis makes sense as the entry point because it's a cash crop with a big hobbyist scene, so there's enough interest to get real usage data early. But the goal is broader - tomatoes, grapes, whatever grows.

One crop at a time though. A so-so classifier across 50 species is way less useful than a really good one for the thing you're actually growing.

It's an Americanism that means 'it's always green' or 'always relevant'.

"taste" here is an intractable solution. Just take a look at how architecture has varied throughout the history of mankind, building materials, assembly, shape, flow, all of it boils down to taste. Some of it can be reduced to 'efficiency' -- like the 3 point system for designing kitchens, but even that is a matter of taste.

Find three professional chefs and they will give you three distinct visions for how a kitchen should be organized.

The same goes for any professional field, including software engineering.

> This is why I say that having a super clear vision up front is important, because it reduces this kind of directional churn.

I'm on my 6th or 7th draft of a project. I've been picking away at this thing since the end of January; I keep restarting because the core abstractions get clearer and clearer as I go. AI has been great in this discovery process because it speeds iteration much more quickly. I know its starting to drift into a mess when I no longer have a clear grasp of the work its doing. To me, this indicates that some mental model I had and communicated was not sufficiently precise.

Yep, for sure. Restarting is the right choice IMO, it's way easier than trying to untangle from a previous iteration.

At that point I would buy an old mini PC off of ebay and just put it on my desk.

If you're in the market, OpenCode is quite good and has become my daily driver. You may also consider pi[0], but that's (from what I've heard) more agenty.

[0] https://shittycodingagent.ai/