diab0lic

Being a multi millionaire isn’t really relevant here. If you’re not the type of person who spends $4k on a tech toy then you’ll have a hard time justifying it.

> Japanese non-disposible saws seem quite beefy and more comparable to western saws in thickness.

I’ve got a handful of forged 240mm ryoba annd dozuki in my shop that are notably thinner than disposables according to my digital calipers

> And 2x4 stick framing with metal fasteners is also a thing in Japan, having gained popularity b/c of its simplicity in recent decades.

Agreed. I’ve heard nearly all new construction is stud construction these days.

> Most japanese planes wouldn't be able to handle Maple or Hickory, to say nothing of the harder exotics.

I often see this repeated in online woodworking forums but my experience here has been different albeit limited to kanna blades from two makers.

> I'd put 18th century european fine furniture against japanese fine furniture any day. It's not until the 20th century that you saw a decline in western skills.

Agreed here too. Furniture studies from that time period make this evident. Western tools from that time period don’t differ as much either.

I have no take on whether or not this specific situation is priced in or not. However I’d challenge you on the second comment. I’ve found Taleb’s advice very actionable but it did take a lot of thinking about it to move from abstract thoughts into practice.

Probably not but they’ll have a harder time finding capital the next time they need to borrow. It is generally unwise to default on debt if you think you may need to borrow again.

The HN take is highly sensationalized. It’s a great place to work with fantastic work / life balance, great pay, and friendly coworkers. I was there for nearly a decade and would never describe it as toxic.

> The appropriate layman's forecast of a recession within the next year is something like a constant 11 %. I'm willing to bet this outperforms most "predictions" out there.

I think we agree here but the unspoken measure is the amount of uncertainty in each forecast. This is still a very inaccurate prediction compared to the collision detection system which nearly always gets it right.

You hit the nail on the head in your last paragraph. Doing something useful doesn’t require an accurate prediction. I agree entirely that assigning an appropriate cost function and responding accordingly guides you to useful actions.

Edit: Just discovered your blog through your profile. The topics look super relevant to my interests. Thank you for sharing your thoughts online, I’m looking forward to reading them!

Much of the discussion here including the linked article fail to make an important distinction between domains. Prediction can be done quite effectively on thin tailed processes. A lot of the counter examples listed in the comments here are physical systems which are thin tailed. I see aircraft autopilot, collision detection, ozone depletion. These are all well understood physical phenomenon in which large deviations do not occur — your car doesn’t get teleported elsewhere in the middle of avoiding a collision. If a large deviation did occur, say a meteor striking between your car and the object it is attempting to avoid, the collision avoidance system would almost certainly fail. These events occur so infrequently that the system can just assume they won’t and boast a high success rate.

Meanwhile the examples from the linked article are fat tailed processes. Recessions, GDP, interest rates, exchange rates. These are all subject to large discontinuous jumps. Anyone doing a 5 year rate prediction in July 2019 would have been required to predict the pandemic in order to accurately forecast. This is a single example but predictions in this domain are regularly blown out by being teleported to a completely different world. Unlike the thin tailed domain these events happen frequently enough that they’re the only thing that matters for the forecast.

Knowing which class your generating process belongs to is critical to understanding whether forecasting will be effective or not. I’ll take collision detection and leave economic forecasts at the door any day.

> We got him a pretty beefy Alienware so he wouldn’t be tied to the cluster GPUs.

Probably this.

It’s a bit more complicated than that, it’s also a bit more complicated than what I’m about to say.

Functionally speaking when a company issues a dividend of $x the stock price S will drop to S-x on the ex dividend date. This makes sense as the company now has $x less per share. The shareholder is at a wash as well. They had S before and now they have S-x+x. No value was created, money was simply moved.

Given the above you can more or less value dividends using discounted cash flow. A ~3% dividend is CSCO’s case isn’t worth paying more (increasing the share price over) on its own because the cash used to buy the shares is worth ~5.5% today according to the risk free rate.

In fact if one were considering the dividend alone then the company should be worth less. Obviously factoring in future growth, etc… the market has landed on the current price.

It can both serve a function (help build extended relationships) and be something people don’t want to do because those relationships can be severed in an instant thanks to at will employment.

Barter economies also make it next to impossible to save for the the future. Most goods produced by common are perishable and therefore one cannot be “saved” in any meaningful way. This keeps people at sustenance level and working until they die.

It is anything but strict. In fact they’ll help you circumvent it.

https://amp.cnn.com/cnn/2023/03/27/business/binance-cftc-law...

Binance from the article most certainly does: https://amp.cnn.com/cnn/2023/03/27/business/binance-cftc-law...

I suspect the post you’re replying to disagrees with the 1% figure.

From the perspective of the bank this isn’t a black swan event, from the perspective of a smaller farmer or winery it is certainly a black swan event.

Everyone withdrawing when they have money gets $1, everyone trying to withdraw after insolvency hits gets $0. If this were to occur the secondary market would bear some small but nonzero value representing the off chance that it is a liquidity issue and not a solvency issue.

The commenter likely meant “fail whale”[0]

[0] https://en.m.wikipedia.org/wiki/Twitter#Outages

I like your explanation for why the word might be drift prone but I think the explanation is much simpler: In contemporary social and political conversations disagreement isn’t an acute occurrence. People who share even a slightly different view are not just wrong, they’re rotten to the core and consciously systemically undermining good in the world.

Words like wrong, disagree or even lying don’t express this sentiment. They give the person who disagreed with you a pass on what they really are: A manipulator who is attempting to assault your psyche and break you down.

I really hope we as a society get out of this mode of thinking soon. I’d love to be able to have rational discourse again in my lifetime outside of a very small circle of trusted people.

I can’t tell if you’re serious or sarcastic but there’s no way my mother or father would be able to do this. My wife could but would but would find it annoying enough to feign ignorance and make me do it if I told her it had to be done.

I’ve got crypto enthusiast friends who refuse to use hardware wallets because of the inconvenience.

> All that's really happening, as someone else on HN aptly put it, is we're speed-running the history of finance out again in crypto-land.

I’d be surprised if the person who said this meant it was a good thing. The sentence literally acknowledges a similarity between crypto and traditional finance. Yet instead of taking those lessons and implementing the solutions the crypto space insists on making the mistakes over again and maybe learning the lessons. The cognitive dissonance in this space is intense.

There is a lot of missing information in their probability of binary events presented. Presumably they polled N forecasters and are presenting an x/N prediction. The fact that each forecaster is estimating in a continuous space and then binarizing their result means that a lot of information has been lost.

To look at an extreme example… were all the “yes” votes 95%+ certain and the “no” votes just under the line 49%? Or was it more like a bunch of no votes at 49% and a bunch of yes votes at 51%?

Binarizing forecasts necessarily discards information. Aggregating a bunch of binary predictions into a percentage does no recapture said information, unfortunately.

I don’t generally place much stock in forecasts but… I’m unaware of what the 8 questions were last year but this years includes a few non binary outcomes. If this was the case last year then their performance was a fair bit better than random.

I am a little disappointed to see that the results of “super forecasters” in the economist and on the underlying Good Judgement open website does not present a 95% credible interval, or even a good old fashioned confidence interval.

Would love to see results presented with the uncertainty quantified. Especially given that the yes/no questions are aggregated binarized predictions from what is almost certainly a collection of continuous models. A lot of information is lost between the people performing the analysis and either of these pages.

Kelly maximizes the long run returns as the number of bets approaches infinity. If the variance is extremely high on f(x) and you get a single bet it can be rational to go very heavy. (Here x is the Bernoulli with p=0.75 and f(x) the payoff of 10000:1 on x = 1)

This problem is usually considered from the other angle: if variance is high but the odds are unfavorable you should favor fewer bigger bets over many smaller bets. Why? Because the more bets you make the more likely you are to converge onto the expectation. A single big bet leaves you either running way over expectation or slightly under it. All of this assumes that you have to bet though, the safest thing to do with negative expectation is to just not bet.

His example is unusual in that it has positive expectation (so you want to bet) and high variance (because you’re limited to a single bet). f(x) = 0b or 10000b so the single sample has a mean of 5000 and a variance of 50,000,000 but the E[f(x)] is 7500. A bigger than Kelly bet is rational here but if you had unlimited (or a large number of) bets you should revert to Kelly.

Food for thought.

> [Even] in [our] darkest days, Tether has never once failed to honor a redemption request from any of [our] verified customers.

Bold statement by tether given that they, if I recall correctly had a very extended period in their past (months) where they were not allowing withdrawals. Wether they openly stated that or not is irrelevant, plenty of people could not get money out.

I’m not saying people sell these for insurance. One of the two options in this spread IS insurance.

A short call spread is someone selling calls (option A in the link) and buying insurance on said sale (option B in the link).

Nobody ever claimed they did, but it is an investment, with insurance.

If you want something more financial a short vertical spread [0] is a strategy in which you are purchasing insurance on the risk (investment) you just took (made). There are many such investments of this form.

[0] https://www.optionsplaybook.com/option-strategies/short-call...

Plenty of good investments are insured. There are whole classes of investment that simply would not be sane to do without insurance. Taking an example from every day life: People buy investment properties and most of the time they insure them.

Right? Netflix is churning out a profit with a ratio on the interval [20, 25] depending on the day. I really don’t see how it belongs in the same category as the other two who don’t have a finite P/E ratio.

It makes it more expensive for those using it, thus making alternatives more appealing.