I’ve created Pixie, a platform to employ and track your kids work. For families with a business, it helps reduce tax burden and fund a child’s Roth.
I’m a physician with some 1099 income, built the platform myself because my kids help with my side projects, and have since onboarded CPAs who now offer it to their clients. I saved 5k this year on my own taxes by employing my kids and it has funded their Roth.
Soon after launching, I crossed the $500/month mark.
This is neat, I appreciate that even if I can't use the service, i still learned something new about what's possible. Gonna keep it in the back-pocket for sharing with friends.
Also reminded me of when Justin Jackson talked about how he hired his kids for help with real projects and the positives from it https://justinjackson.ca/flipping-tables
I’ve been working on Pixie, a platform to employ and track your kids for real work; for families with a business, it helps reduce tax burden and fund a child’s Roth.
I’m actually an anesthesiologist with some 1099 income, built the platform myself because my kids help with my side projects, and have since onboarded CPAs who now offer it to their clients.
It's been a fun journey!
Thanks for the kind words! It is not at all profitable, but it gives me purpose, and scratches a particular intellectual itch. I wish I could afford to make it my full-time job and just write every day without worrying about day-job income, but alas, the universe currently desires otherwise.
Maybe more of a misinterpretation by AI and/or author than full on hallucination.
"Life insurers can predict when you'll die with about 98% accuracy."
"98%" appears in the citation[1], but as the ratio of actual deaths to expected deaths. (i.e. 98% of the deaths they expected actually occurred.) Some months that figure was ~104%, so it's not a measure of accuracy.
I saw that too and thought the utm_source=chatgpt was pretty funny, since I only noticed it when I was pasting the link into chatgpt myself to get it to tell me where the "98% accurate" claim was coming from.
Anyway, it's a pretty vague statement. What it sounds like it's saying is for the average person they can predict when they will die, and you have to decide if they mean the day, the year or the decade. But chatgpt gave me an interpretation that seems to make more sense:
> The 98 % figure is about aggregate forecasting, not clairvoyance. It means that when an insurer predicts, say, 10 000 deaths across its book in 2024, the actual count typically falls within roughly ±200. For any single customer, the prediction is still just a probability curve, not a calendar appointment with the Grim Reaper.
It's extremely incorrect. 98% accuracy on when "you'll" die would imply an R^2 of 0.98 for individual-level lifespan predictions. We are nowhere close to that.
It could mean a lot of things. It could mean "We think you'll die at 87+-5 years" and counting it as a success if you die anywhere in that age range for instance which I could believe they can predict the vast majority of the time. I doubt they are referring to the R Squared.
I often wonder if human intelligence is essentially just predicting words and phrases in a cohesive manner. Once the context size becomes large enough to encompass all a person history, predicting becomes indistinguishable from thinking.
Maybe, but I don't think this is strictly how human intelligence works
I think a key difference is that humans are capable of being inputs into our own system
You could argue that any time humans do this, it is as a consequence of all of their past experiences and such. It is likely impossible to say for sure. The question of determinism vs non-determinism has been discussed for literal centuries I believe
But if AI gets to a level where it could be an input to its own system, and reaches a level where it has systems analogous to humans (long term memory, decision trees updated by new experiences and knowledge, etc.) then does it matter in any meaningful way if it is “the same” or just an imitation of human brains? It feels like it only matters now because AIs are imitating small parts of what human brains do but fall very short. If they could equal or exceed human minds, then the question is purely academic.
The body also has memory and instinct. It's non-hierarchical, although we like to think that the mind dominates or governs the body. It's not that it's more or less than predicting, it's a different activity. Humans also think with all their senses. It'd be more or less like having a modal-less or all-modal LLM. Not sure this is even possible with the current way we model these networks.
And not just words. There is pretty compelling evidence that our sensory perception is itself prediction, that the purpose of our sensory organs is not to deliver us 1:1 qualia representing the world, but more like error correction, updates on our predictions.
I’m working on https://postcardlove.com — a super simple site that lets you send real postcards using a photo from your phone. No login, just upload, write, and send.
It started because I wanted to send my sister an old photo for her birthday, and instead of using CVS or Shutterfly like a normal person, I ended up building it using Bubble and Lob.
Now I’m exploring the B2B angle — a friend asked if I could do postcard mailings for her medical practice. Curious if anyone here has experience with postcards for outreach or marketing?
yay, postcard apps! this is my space, too. there's something really fun about making an API call and then postcards shoot out the other side.
i like your site and the visual design, very simple and straightforward. at $3.99 it does seem rather expensive compared to other options out there (i have an idea on how much it costs you). also, having some actual pictures of what the cards look like on the website will surely help with conversion.
the first postcard app I built is Three Kind Words (https://threekindwords.com). it’s a small vending machine on the internet where people anonymously send a friend three postcards, one word at a time. the first two cards are unsigned, and the last one reveals who sent them. it’s meant to be a slow, kind surprise in the mail. we've sent over 250 cards so far.
my latest postcard app is Slow-Mo Rainbow (https://slowmorainbow.com). this one lets you send a rainbow in the mail, one postcard at a time. i just launched it last week and am still getting the rough edges worked out, but in the spirit of the thread, i'm sharing it here.
It also can be used for Marketing because you can circle areas on a Google Map to send mailings to, and then filter by Household income, home value, etc
Let's chat! Postcardlove.com was a small side project that I created while working on the larger project. I think I can use PostalAgent for that project.
