Did any of you submit this successfully? I first tried in Firefox and it seemed to just not work when clicking the final submit. Then I tried again in Safari and the same, but the UI worked a little differently (a dropdown for the product model) and the submission still didn't work. So I tried one more time in Safari and it said the serial number was already submitted.

But never got an email affirming my submission so... I dunno.

This power bank is actually an old one that I don't really use anymore, but if I get a chance to get a replacement, I sure don't mind. So hopefully it went through?

I feel like people are suspending their reasoning in order to maximally shit on airlines in this thread (because yes, they do have a history of predatory pricing practices).

The problem with this isn't the difference in prices - charging less for buying in bulk is a normal thing that's probably been done by merchants since the invention of money.

The problem with this is the lack of communication. There's no advertisement of a bulk/family discount at any point during the pricing process, you just see a different price. That's the problem here, not the price difference itself.

First Windsurf and now this. OpenAI is spending billions like there's nothing else to use this money for while being seemingly cash strapped for model training since they already signaled more investment rounds would be needed to remain competitive. They're trying to become too big to fail before they have a moat which won't work well.

Thanks :)

I create the tools that our researchers use to run the experiments. I typically don't run the experiments themselves. I wouldn't want to mis-speak or say something non-public and have it be picked up in the press, so I'll only respond at a very high level.

In quantitative research (which is to say, showing a survey to hundreds of participants), there are what are called participant panels. Companies go recruit people to take surveys. The companies get paid for this - some of the money goes to incentivize participants, and some the companies keep as profit. Amazon's Mechanical Turk, UserTesting, Cint, and Prolific are examples of participant panels and/or the companies that run them.

We package the experiment as a web app and give it to the provider. They go show it to the requested number of participants, whose responses we log and analyze.

In quantitative research, there's a thing called "power analysis," which tells you how many participants you need to have statistically significant answers to your questions. The more ways you want to be able to slice the data, the more participants you need.

Participant panels vary in quality. Ideally, a panel is comprised of honest people who want to be helpful, and who represent the population you're trying to model.

You can imagine that a stay-at-home mom who's killing time while the kids are at school might be a very good participant. She's someone who might use your product in real life, and her primary motivation is to give you her honest response so you make the thing she might use better for her. The financial incentive is a thank you for her time, but she's not chasing it.

You can also imagine someone who's trying to chain together these incentives to form an income stream - the online equivalent of a food delivery person. That person's primary motivation is to get through the task as quickly as possible to maximize the number of incentives he receives. He might always choose "A" when asked for his preference between two alternatives, not because he likes A, but because it's faster to not move the mouse. (This is called "straight-lining.") That person would be a bad participant. We try to detect this and screen that person out.

Panels compete on quality. For a long time, Mechanical Turk had a reputation for having a preponderance of young Indian men who were trying to game the system. You'd have to design your experiment so the fastest way to complete it was to be honest, to try to dissuade cheating. (There are whole forums of Mechanical Turk workers trading scripts etc. to try to complete as many experiments as possible.) Even if you get honest responses, there's still a problem of representation. Unless the population you're modeling is mostly young Indian men, that panel's opinions might not match your users.

Age, gender, and location are basic demographics that are frequently used to stratify data, so I'm using them as examples here, but to your point - there are a lot of different factors that might impact how representative someone is of a population.

There's a challenge to all of this (which again, I'm writing in one draft, off the top of my head - there are surely others) - panels are made of a finite number of people, and the more specifically you want to analyze someone's demographics, the more participants you need (power analysis).

Using the demographics you listed as example filters, let's go from a generic to a specific population:

- People

- Young people

- Young women

- Young Japanese women

- Young tech-savvy Japanese women

- Young affluent tech-savvy Japanese women

- Young rural affluent tech-savvy Japanese women

(Assume that we assigned a quantifiable threshold to each adjective, so e.g. "young" means "under 35.")

A participant panel is going to have many thousands of people, but how many young, rural, affluent, tech-savvy, Japanese women does it have? How many people does your power analysis say you need to speak confidently about the opinions of the people in the group? How many experiments do you want to run that need the opinions of that group?

The more you filter a panel, the longer it takes to complete an experiment. If you just need 300 people, you can get your data back in a few hours. If you need 300 people who meet a specific demographic profile, it's going to take substantially longer.

Over time, that problem turns into panel exhaustion. You want the panel to be representative of your users, and people who have been in a lot of similar experiments might be less representative of your users. There was another comment that was concerned about the representation of women over 70. Say there are 50 active participants in a panel who are women over 70 and your power analysis says you need 10 before you can estimate their preferences (again, hypothetical numbers I am making up). As soon as you give another experiment to that panel, the likelihood that you're going to have repeat responses from women over 70 goes up. Pretty soon, all your experiments are asking the opinions of the same small group of people.

To caveat one last time: I'm just a guy who works with the researchers cited in these articles. I'm not the one running the experiments or deciding how the data gets sliced. I've intentionally used hypotheticals and obscure demographic intersections because I don't want to imply anything about how the actual experiments are run; but instead to give a broad overview of the kinds of problems you encounter when you work in this space.

Research is the art+science of studying a subset of people to estimate the behavior of people at large, because it's not practical to ask everyone everything, all the time. Part of the art is figuring out which demographics are the most impactful to the things you want to measure, because as you add intersections, the quantities of data you need to speak credibly about those intersections explode.

This kind of take is only true if you haven't given the tax system any thought at all and just want to dunk on the IRS to get upvotes

The govt only has an idea of what you might owe from "official" information (W2s), Since we don't live in a totalitarian society where the govt knows everything about you.

Filing taxes is the govt telling you to make sure to report anything they do not know about.

Also you do not go to jail for getting your taxes wrong, you go to jail if you fail to fix them after being audited or fraud.

I've actually looked at Ann Arbor before as I adore college towns and was looking at trying to find one in the great lakes region as a forever / retirement destination. Given you're from the area would you have any others to recommend?

Things I'm mainly looking for:

* A climate change refuge

* No 'lake effect' snow

* Continuing education opportunities (i.e. auditing classes as a retiree)

* A good public / uni library system

* Walkable density

* Reasonable cost of living (yea, this is gonna be higher in college towns)

I realize that moving to the great lakes region and wanting to avoid snow are naturally in conflict. I have a disability so I'm just bearing in mind my balance and ability to shovel snow in my old age.

I had a few use cases with searching and organizing emails I would have used. For example, I wanted a table of all my Lyft rides from a certain year with distances driven, start/end locations, cost, etc. All that info is available in the email you get after riding, so I figured Gemini could read my emails and organize the info.

Turns out it doesn't work at all. It gave me a random selection of rides, was missing info in some of them, and worst didn't realize it was giving me bad info. Pretty disappointing.

Make no mistake, in a private equity acquisition like this where the company is earning around $50mil profit on a billion dollars revenue, they will cut costs to make their money back.

This PE firm led acquisition of Zendesk for $10bil in 2022 and McAfee for $14bil in 2021

They've spent over $30billion dollars on just these 3 acquisitions in 3 years. Many of which would be part financed.

Culture after zendesk goes acquired (including 8% layoffs 2023) https://www.teamblind.com/post/Zendesk-changes-after-going-p...

Squarespace financials https://stockanalysis.com/stocks/sqsp/financials/

More info on this PE firm https://en.m.wikipedia.org/wiki/Permira

Full disclosure, I'm a YC alum whose last start-up was acquired by Google, who applied to this batch and didn't get an interview.

YC is not the stamp of quality it once was, to be sure, though it still works as social proof, because investors (especially VCs) want to invest in companies other investors like (or failing that, companies they imagine other investors would like). YC would say that they aren't trying to be a stamp of quality or social proof, they are just trying to help start-ups.

If I had to take a stab at articulating how YC has changed, it's that it's become a VC, picking ideas to generate returns.

This rejection email from 2022 that someone posted online cements the idea for me (excerpted):

"Unfortunately, we've decided not to fund {Company} this batch. We enjoyed our conversation today and were impressed by you as founders building something they are passionate about bringing into being. However we weren't convinced that this product strategy is going to yield a big company in its current form. ...

Of course, things are very early and you are still figuring out the right way to build and structure your business. If you're able to make significant progress with it, we'd be very interested in hearing about it for a future batch."

Original YC would not reject founders over their current product strategy and because they haven't figured out the right way to structure their business--haven't put together the "proof" that they could be on track to be a unicorn, etc. That's a VC rejection. Of course, YC gets many thousands of applications and has to reject most of them. You could say they shouldn't be criticized for trying to give a little feedback.

It's just hard to convey the sense from the early days of YC that they really didn't care about the return, or the progress so far, or VCs, or fads, or anything. That said, I really was at the right place at the right time and got very lucky.

Finally, I want to call out the phenomenon in the world of VC where the ability to generate hype alone is enough to make you and your investors a lot of money, even if there isn't a lot of substance in your company (and even if things are morally or ethically questionable), through the mechanism of greater fools. Cryptocurrency is a whole exploration of this effect, turning hype into money by building a streamlined mechanism for bringing in greater fools, but we can also look at examples like Theranos. (There's actually a ton of money at the top of society looking for somewhere to go, which ideally would be routed to more worthy ventures than it is, but that's a whole topic in itself.) The point is, the greater fool "strat" works. In a moral vacuum, if you are trying to maximize returns, you lean into it.

If I were running a fund like YC with basically unlimited money at my disposal, balancing the goals (strategies?) of 1) make money for money's sake, 2) advance technology for technology's sake (let's go to Mars, etc), and 3) make the world a better place, I would focus on (3). Like can we at least try to build a unicorn that feeds people, and maybe fail, rather than trying to build FooBarBazCoin that eats the word, and failing? We mostly see a mix of (1) and (2).