Agreed. Reading the rest of these comments are makes me feel crazy / like I’m missing something. It doesn’t sound like the interviewer was making the candidate divulge traumatic information - but rather assessing how they deal with adversity.
This is really awesome! Dream home project for me as well, but can't justify the cost of large e-ink displays so far (was shocked at the nearly ~$2k sticker price of that Boox Mira Pro!)
This week: look at Qwen3 Coder Next and GLM 4.7 but it's changing fast.
I wrote this for the scenario you've run out of quota for the day or week but want a back up plan to keep going to give some options with obvious speed and quality trade-offs. There is also always the option to upgrade if your project and use case needs Opus 4.5.
I think the idea is vaguely that the upper-upper class statistically must've done something wrong or have the power to cause extreme harm, therefore it's okay to snitch on them but not your regular Joe.
I'm just espousing the standard American middle class views about freedom here. Not trying to argue they are sound or rational.
Great read, thanks! Could you dive a little deeper into example 2 & pre-registration? Conceptually I understand how the probability of false positives increases with the number of variants.
But how does a simple act such as "pre-registration" change anything? It's not as if observing another metric that already existed changes anything about what you experimented with.
If you have many metrics that could possibly be construed as "this was what we were trying to improve", that's many different possibilities for random variation to give you a false positive. If you're explicit at the start of an experiment that you're considering only a single metric a success, it turns any other results you get into "hmm, this is an interesting pattern that merits further exploration" and not "this is a significant result that confirms whatever I thought at the beginning."
It's basically a variation on the multiple comparisons, but sneakier: it's easy to spend an hour going through data and, over that time, test dozens of different hypotheses. At that point, whatever p-value you'd compute for a single comparison isn't relevant, because after that many comparisons you'd expect at least one to have uncorrected p = 0.05 by random chance.
There are many resources that will explain this rigorously if you search for the term “p-hacking”.
The TLDR as I understand it is:
All data has patterns. If you look hard enough, you will find something.
How do you tell the difference between random variance and an actual pattern?
It’s simple and rigorously correct to only search the data for a single metric; other methods, eg. Bonferroni correction (divide p by k) exist, but are controversial (1).
Basically, are you a statistician? If not, sticking to the best practices in experimentation means your results are going to be meaningful.
If you see a pattern in another metric, run another experiment.
This doesn't make as much sense as you think it does. If you could predictably trade a flip from bearish to bullish (for example, of course there are other trend-based signals), you would not share that signal because others would overcrowd your trade (by buying/shorting and moving the price more quickly towards the trending direction than you).
A potential argument is that these signals are only applicable to a certain bracket of portfolio sizes (e.g. larger AUM funds would not be able to trade this strategy) -- but you are sharing this with folks presumably in your range of portfolio size.
The more highly liquid an asset, the more efficient it is and the fewer trading opportunities after accounting for transaction costs. In something like the S&P 500, everything is already priced in.
Meme stocks and shitcoins being manipulated by whales are not efficient and also not as liquid.
The larger point remains that none of the above considerations are discussed on this product's page.
I'm realizing there's a lot of confusion about what trend based models actually are. I was under the assumption the concept was more widely understood, but I'm realizing we need to explain it better.
To be clear, there's nothing new or innovative about a trend based model. It's one of the most commonly used investment strategies by intuitions, etc. It's been widely utilized for far longer than I've been alive.
There's no confusion about the type of edge. Just pointing out that if you are selling an edge rather than trading it yourself, you're either grifting or naive.
The discount is applied to $10/month and is said to make it effectively $8.5 per month, but is actually $8.25 per month, since they claim to charge $99 per year.
Then that would be $100 per year and none of your numbers for annual plans are accurate. Your website claims $99 per year and claims that is equivalent to $8.50 per month. None of these 3 numbers are equivalent.
You're correct about valuation, but the parent post was meant to address "how much liquid dollars should you expect to receive vs. 409a." You are likely to receive less in most cases (read: unless there are wildly successful public liquidity events) due to liquidation preferences.
Plenty of (non-VC backed) startups raise some money and then sell privately; it’s often the case that preference does not cause the common stock value to drop below the most recent 409a in these cases.
(In my experience, the 409a is on the order of 20% of the most recent raise, and preference is not more than 50%, in my area. And obviously you hope to sell for more than the last raise!).
