Iran knows that the US population really really doesn’t want a ground invasion. Right now, we have lost a handful of lives from missiles hitting US bases, but it’s not the same as a ground war.

Cuba, however, would very much get a ground invasion if they start striking the US with missiles. It’s not even a question. And I also assume their leaders are not religious fanatics with any interest in martyrdom.

The crazy thing is we often cite an Apple Tax, but in this case, I think they actually have a cheaper product.

We do have some idea. Kimi K2 is a relatively high performing open source model. People have it running at 24 tokens/second on a pair of Mac Studios, which costs 20k. This setup requires less than a KW of power, so the $0.8-0.15 being spent there is negligible compared to a developer. This might be the cheapest setup to run locally, but it's almost certain that the cost per token is far cheaper with specialized hardware at scale.

In other words, a near-frontier model is running at a cost that a (somewhat wealthy) hobbyist can afford. And it's hard to imagine that the hardware costs don't come down quite a bit. I don't doubt that tokens are heavily subsidized but I think this might be overblown [1].

[1] training models is still extraordinarily expensive and that is certainly being subsidized, but you can amortize that cost over a lot of inference, especially once we reach a plateau for ideas and stop running training runs as frequently.

Being Apple is just a structural disadvantage. Everyone knows that open claw is not secure, and it’s not like I blame the solo developer. He is just trying to get a new tool to market. But imagine that this got deployed by Apple and now all of your friends, parents and grandparents have it and implicitly trust it because Apple released it. Having it occasionally drain some bank accounts isn’t going to cut it.

This is not to say Apple isn’t behind. But OpenClaw is doing stuff that even the AI labs aren’t comfortable touching yet.

> Cost: $1,000 Case 1 (90%): OpenAI goes bankrupt. Return: $0 Case 2 (9%): OpenAI becomes a big successful company and goes 10x. Return: $1,000 + 5% interest = $1,050 Case 3 (1%): OpenAI becomes the big new thing and goes 100x. Return: $1,000 + 5% interest = $1,050

The actual math is that if OpenAI succeeds, then there's a nod and a wink that JPM will land the lead role in the IPO or any mergers/acquisitions, which translates into huge fees.

Comments URL: https://news.ycombinator.com/item?id=42523192

Points: 8

# Comments: 3

There is definitely ageism in tech, but 39 isn't old. I'd be happy to take a look at your resume and provide advice if you give me a way to contact you. But it sounds like the issue isn't your resume... indeed you are getting lots of interviews, so maybe it's something you are doing in the interview process. Do you have a sense of where things go wrong? Are you often getting to the final stage before hearing no?

Well, they certainly _might_ be functioning in ten years from now. Conservatively, you get 5 years of use out of this, which isn't bad for $15-20, depending on your use case.

[1] https://www.lifehealth.com/top-25-us-companies-ranked-by-pro...

The second one is more important than the first one. If you don't build the right product, it doesn't matter how well it scales or how it has amazing test coverage or wonderful documentation. To that end, I think that too many managers (and companies) do too much shielding of engineers from customers. If you are just given a figma mockup and told "build this", it's easy to get bogged down for a week with the details of building a search bar at the bottom of the page only to realize that the stakeholders would have been OK with a dropdown select. Better to understand the problem you are solving and the only way to really do this is to have some kind of interaction with customers. As an engineering manager, I try to encourage engineers to get on sales calls and see product demos. When you see it from a high level, you a) almost always notice things that need fixing or can be improved and b) see where the piece that you are working on fits into the larger picture.

That said, I find that many engineers don't want to get on customer calls, and usually there's room for those engineers in an organization as well. For example, "build a new video conferencing service for artists to collaborate" would be a very challenging problem (I think) that is not well defined and therefore requires deep customer understanding. "Make Google searches run with 10% fewer CPU milliseconds" is arguably a much harder problem to solve, but it's so well defined that it really doesn't need customer understanding (setting aside the initial decision about whether it makes sense to work on).

Word to the wise... as of Python 3.7, the regular dictionary data structure guarantees order. Declaring an OrderedDict can still be worthwhile for readability (to let code reviewers/maintainers know that order is important) but I don't know of any other reason to use it anymore.