As someone who built a niche SaaS I can see both sides of this.
If you are looking to replicate the exact same feature set of an existing product - buying would almost always be a better choice.
But it's rare for avg customer needs to perfectly match product features. Most often they need 20-40% of the product + some custom, business specific logic that's missing and is later fixed with spreadsheets/integrations. In that case it depends on how mission critical this software is.
I'd say investing into the core software that runs your business might very well be worth the effort, even if it's 10x between build vs buy.
Finally, now I know I'm not the only one! These sharp edges constantly cut into my wrists to the point I was thinking of doing the same, or glueing some kind of kind soft padding to the edges. Great someone did it. I wonder how far can you cut them?
Sadly, memory bandwidth is abysmal compared to Apple chips - 273 GB/s vs 614 GB/s on M5 Max for similar price. Even though fp4 compute is faster, it doesn't help for all the decode heavy agentic workflows.
Because of a bad habit reading comments before the link I knew it was AI. I read it regardless, and... I still enjoyed it!
I'm very much not a writer or a critic, so my definition of good writing is likely very low. Yet I can't shake off this weird feeling that I truly enjoyed the writing and felt the emotions, _while_ knowing it's LLM.
I'm guessing that human after touch is what made it pleasant to read. I'd love to see the commit history of the process. Fun times we live in!
Just wanted to say that I've felt the same about the images. To me it's likely was the text that for some reason had AI-feel to it. Great story though, I was in awe learning it was AI generated.
In basic scenarios agree it’s possible. I’ve similarly toyed with building a small language and LLM picks it up. But that’s an additional context and it’s never felt like it worked “out of the box” vs say Typescript. Just saying that it’s an adoption barrier established languages don’t have.
It's a legitimate question to ask about any new language post AI - given there is no training dataset, any other language would work better with AI.
The bigger problem is maintainability over the long term, Deno is built by Node.js creator and is maintained for half a decade now, that's hard to compete with. In a way it's much more about social trust rather than particular syntax.
Great summary of the current state of AI coding, without all the hype.
I wonder though if we are fundamentally limited by LLMs architecture to come up with some novel architecture. Or is it just a limitation of current prompting and tools?
E.g. could your brute force the problem by asking "come up with 100 innovative, not yet tried ideas in compiler architecture" and run 100s of this experiments.
I feel like the answer to that is basically that "spark" that we have as human intelligence, but also... sometimes you get it by trying and failing many many times.
Fully off-grid using solar, batteries and Starlink for uplink. Focusing on AI inference at the beginning. Currently building our first prototype and testing cooling solutions.
Original Apple II manuals written by Chris Espinosa and Jef Raskin are a treat to read. Would highly recommend, just to get a sense of what it was like to get onboarded on Apple II back in the day.
And then obviously Programming the 6502 by Rodnay Zaks.
Out of all things I'm actually surprised they went straight to custom silicon, but gotta respect that decision. It's likely the only way to compete with Tesla right now.
I don't think we need that for 1,000x. We can building more solar, nuclear and there is still room for at least 10x improvement in efficiency for the chips. We are far far away from maxing out our compute capability as civilization before we start shooting satellites into the sun.
I agree scaling alone is not enough, and transformers itself is a proof of that - it was an iteration on the attention mechanism and a few other changes.
But no matter what the next big thing is, I'm sure it would immediately fill all available compute to maximize its potential. It's not like intelligence has a ceiling beyond which you don't need more intelligence.
> What high quality data sources are not already tapped?
Synthetic data? Video?
> Where does the next 1000x flops come from?
Even with Moore's law dead, we can easily build 1,000x more computers. And for arguments about lack of power - we have sun.
I see LLMs in a similar way - a new UI paradigm that "clicks the right buttons" when you know what you need, but don't know exact names of the buttons to click.
And from my experience there are lots and lots of jobs that are just "clicking the right buttons".
If you are looking to replicate the exact same feature set of an existing product - buying would almost always be a better choice.
But it's rare for avg customer needs to perfectly match product features. Most often they need 20-40% of the product + some custom, business specific logic that's missing and is later fixed with spreadsheets/integrations. In that case it depends on how mission critical this software is.
I'd say investing into the core software that runs your business might very well be worth the effort, even if it's 10x between build vs buy.