If I had to summarise Google’s effort it would be: stay close but let the others burn themselves out. Position for the long game until you see something worth betting the company on.
I made something to ping an AWS service to tell me the uptime of my internet connection. The idea was to sprinkle them around our area, connected to various home WiFi’s, and get a better triangulation of outages. Eg is whole pipe out, just one ISP etc.
I made and tested it but didn’t care enough to continue.
The benefits were in communication and relationship arbitrage? Surely both of those can be automated over time.
This is just one aspect but it’s still useful. Many people want to see a house and say “please help me make that”. In Australia there’s a set of house patterns that reduce the overhead of just landing a design and pushing it through the admin hurdles.
I generally accuse LLM’s of having no sense of value. The machine will make a complicated plan but entirely lose sight of eg the fact that response time matters to humans.
Not always, but enough that I consider it a thing to fire in a direction, not a thing that aims.
I assume the bet is that as you swap humans for machines, this pays for itself. Swap entire devs and teams and frankly, managers, and you make up a lot of 5%’s fast.
If it works. And I’m not sure who is going to buy the stuff the machines produce, but shrug. Presumably some bots click ads for NFT’s that other bots generate.
Hard problem. I find myself adding in filler to stop the thing from jabbering.
I also think it spends most of its iq on sounding good rather than thinking about the problem. “Yeah absolutely I can see why you’d like to…” etc. This is likely because it’s on a timer and maybe voice is more expensive to process? Text responses spend more time on the task.
> The easiest way would be a straight tax on AI usage, and using that tax to pay a universal basic income
Problem for jobs is that there are 200 countries and all the earnings will go to a few. Universal basic income for everyone? Or just the US?
Who gets to keep their house locations in a new fair world? The person whose parents bought in the right place 50 years ago? Who pays the money these models earn, if nobody clicks ads or does a job? What is income for if we don’t work and can just ask the AI for everything we want?
What happens when the super smart AI comes up with “better” (more fair, consistent, etc) answers than you think you have to questions like the above? What if they end up socialist? Do we force it (and invite risk it escapes and fights us for the greater good) or give in to the presumably more thorough reasoning?
The cost between an A500 and a VGA-enabled PC in 1987 ($699 vs $3500-ish?) would have put them in such different categories and customer segments that they would rarely interact.
I remember seeing a PC one of the rich kids brought to boarding school in 1990 and realising it was just crisper than my A500. The PC’s in the school lab were all green and orange screens with one colour CGA, so this was quite a surprise. Still took me some time to accept reality :)
I’m moving away from Claude for anything complicated. It’s got such nice DX but I can’t take the confident flaky results. Finding Codex on the high plan more thorough, and for any complicated project that’s what I need.
Still using Claude for UX (playgrounds) and language. OpenAI has always been a little more cerebral and stern, which doesn’t suit those areas. When it tries to be friendly it comes off as someone my age trying to be a 20-something.
Daniel is a very impressive guy. Well within the realm of “fund the people not the idea” that YC seems to do. Got a few bucks from them and probably earning from collaborations etc. Odds of them not figuring out a business model seem slim.
I have this as a skill Claude created to run the rest. It mentions each skill in turn, see below. It’s not deterministic but it definitely runs each skill and it’s raised a bunch of issues, which I then selectively deal with. Where I can, once an issue is identified, I make deterministic tests.
Text includes:
Invoke each review/audit skill in sequence. Each skill runs its own comprehensive checks and returns findings. Capture the findings from each and incorporate them into the final report.
IMPORTANT: Invoke each skill using the Skill tool. Each skill is independently runnable and will produce its own detailed output. Summarize findings per skill into the unified report format.
I found that when I have “infinite” tokens my behaviour changed. 3-5 tabs so I’m not waiting, free side quests, huge review skills over whole codebase, skills that wrap 10 other skills. It’s like going from expensive data to uncapped.
I think these token doubles are there to kick you into a abundance mindset (for want of a better term) so going back feels painful. Stop counting tokens, focus on your project and the cost of your own time.
Apple similar, without the “stay close” bit.