> We’ll model it in Ballet on your real systems in a 30-minute working session
You drastically underestimate how bad most systems are. All of the vendors in your examples have good APIs. Those vendors already did the hard part.
All of the systems we need solutions for are moving targets that are breaking constantly. You can’t model those in 30 minutes because the ways in which they break don’t show up until months later.
Absolutely. And scale does not matter. I had issues with top tier systems like docusign, tableau etc in beforeGPT era. Many undocumented issues and when you write support usually they only were pushing to buy top tier support package, but never fix anything.
> I'm very intrigued by AI note takers, but I'm absolutely unwilling to expose me or my clients to this exact problem
Unfortunately it’s mostly not up to you. It’s a weakest-link problem. It doesn’t matter if you don’t use a note taker AI, if even one person on the call uses one. Their tool doesn’t notify you and usually the person doesn’t either.
It also has the reverse impact to the person using the note taker, where people say less around them. Same as if I'm talking to someone with Meta glasses.
I wonder if the people who use these tools know the people they meet with speak less during their meetings, and then all of the participants have a post-meeting call without them to say what they really thought.
You either buy a datastar license to get their debug tools, or you spend a couple of hours to build your own. I'm sure you can find something on GitHub too.
One of the cool things is how extensible d* is. Everything is a plugin, and you are free to extend it to suite your needs. We've invested a few days building a few specific data-* attributes for our application and it's super convenient.
> having a high signal-to-noise ratio for (open source) projects is a desirable goal?
It’s not obviously true. A higher number of attempts, a larger talent pool, typically doesn’t change the average much (or it might even make the average go down), but tends to produce higher peak outcomes.
We see this everywhere (science, startups, sports, chess, etc).
If you want the best spreadsheet, game, or whatever app you want, you’re only interested in the few highest peaks.
So you do actually get better signal to noise with a larger wasteland of discarded attempts. The higher peaks make it easier to filter out the noise.
The goal you’re intrinsically motivated by seems different than this. That seems to be the whole disagreement.
It would only solve crime if the police actually followed up on all areas where surveillance pointed out crime. Surveillance itself can't arrest a criminal.
Have you done iron infusions? My wife has to get these once in a while. She had gastric surgery years back and has issues with absorption of oral supplements.
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