Source? How recently?

You can reach much higher spend through the API (which you can configure `$claude` to use)

> Like, Copilot could watch my daily habits and offer automation for recurring things.

We're working on it at https://github.com/openadaptai/openadapt.

See also: https://audionotch.com/app/tune/

> But that one year of knowledge building, distribution, early customers, then dominates who has control over the cap table for a decade.

Can you recommend any resources for learning how to do this work yourself?

I'd recommend:

- the lean startup: https://www.amazon.com/Lean-Startup-Entrepreneurs-Continuous...

- Amy Hoy's blog: https://stackingthebricks.com/

- working in the domain you are interested in for a year. Go be a realtor. Go work in a lawncare business. etc. etc.

- read Patrick's advice about how he found customers for Appointment reminder: https://www.kalzumeus.com/2010/05/14/unveiling-my-second-pro...

edit: added link to Patrick's piece

One shortcut would be to:

1. pick a vertical (construction, legal, accounting, etc)

2. look at how to apply ai (compliance, voice customer service, email)

3. find sales people from a would be competitor or old school b2b saas in that area, and pay them 150$ an hour to tell you what customers are paying for

4. at the direction of how to do sales, come up with the product and either do the sales yourself without building, or pay someone to do cold calls and pitch the product

5. build the product

> While the LLMs get to blast through all the fun, easy work at lightning speed, we are then left with all the thankless tasks: testing to ensure existing functionality isn’t broken, clearing out duplicated code, writing documentation, handling deployment and infrastructure, etc.

I’ve found LLMs just as useful for the "thankless" layers (e.g. tests, docs, deployment).

The real failure mode is letting AI flood the repo with half-baked abstractions without a playbook. It's helpful to have the model review the existing code and plan out the approach before writing any new code.

The leverage may be in using LLMs more systematically across the lifecycle, including the grunt work the author says remains human-only.

That's my experience as well. The LLM is great for what I consider scaffolding. I can describe the architecture I want, some guidelines in CLAUDE.md, then let it write a bunch of stubbed out code. It saves me a ton of time typing.

It's also great for things that aren't creative, like 'implement a unit test framework using google test and cmake, but don't actually write the tests yet'. That type of thing saves me hours and hours. It's something I rarely do, so it's not like I just start editing my cmake and test files, I'd be looking up documentation, and a lot of code that is necessary, but takes a lot of time.

With LLMs, I usually get what I want quickly. If it's not what I want, a bit of time reviewing what it did and where it went wrong usually tells me what I need to give it a better prompt.

Corporate income taxes are treated differently than personal income taxes. You absolutely can deduct corporate expenses in Canada.

I didn't mention Canada in order to nonsensically compare corporate taxes in the USA versus personal income tax in Canada, but because it's a good idea to mention where you are if you're writing about taxes.

Yes, corps get all kinds of tax breaks, whereas individuals don't.

As a Canadian I still don’t understand what the point of your original comment was. We already know this thread is about a US rule.

Try disabling Javascript.