tinymollusk

Just a quick vote to reinforce what you did. You made something, and asked for the most absurd audience to critique it.

Keep being you. HN hated Dropbox, of all things. I am hesitant to announce my product here because I struggle with as widespread of negativity as I perceive here.

If someone isn't going to buy what you're selling, I feel they should be silent and just not buy. If they would buy with a small tweak, there are ways for Bad PMs to put up their credit card. Otherwise, ignore their advice, ignore mine, and keep pushing.

For the people who ask for more, yeah. There's always room for improvement. But if you take down people within our community, you can go sit and spin.

Jon, I run a few sites in a similar space. I feel 1MB is fine for my customer base. If HN isn't your customer, don't worry about them. If HN is your customer, you have the hardest job in the world because none of us will ever pay you anything you can build a business off of.

Go forth and prosper amongst the people who will pay you for the value you provide. Ignore us.

With 100k grants to give, I think you'd want quite a few crackpots, and maybe even some lazy mathematicians. Those could be the types who come up with a game changing result.

I switched to 90+% audiobook after being an avid reader my whole life. I think my eyes have gotten worse, so physically reading because tiring quickly.

The secret for audiobooks, for me, is to listen to them while walking. Currently doing about 50 miles / week, which gets me through 1-2 audiobooks (1.5x, but I rewind and replay a LOT).

I notice my attention is crucial, and varies depending on interactions while walking. When by myself, not crossing streets (down by the walking areas of my town), I can retain a ton.

YMMV, but for me the sweet spot of information consumption is while walking, so audiobooks + exercise beat reading, hands down.

I interpreted that remark as a commentary on humans preference for failing in a conventional manner being perfectly acceptable, versus possibly failing in an unconventional one.

(I also grew up professionally in the 2000s, well after IBM was widely considered elite by the tech nerd community.)

Something I've been wondering about for a while now... Would it be possible to train an algorithm to identify similar characteristics of data from different schema? Looking at the actual data, I mean, and inferring a translation table or the like?

I have a background in data engineering and don't really know where I'd get started. But if you could figure out a way to throw differently schema'd data at an algorithm and have it try to create a universal schema, you'd be wealthy.

There would be a ton of challenges, but the problem you described seems like a societally valuable one to solve.

Richard Feynman's famous quotation is applicable here: "The first principle is that you must not fool yourself — and you are the easiest person to fool".

False positives are often more dangerous than false negatives. This is why the "fake it until you make it" trope can come back to bite you in the read end.

Madoff said he is happier in prison than he was while fearing his scheme would be discovered. [0]

That doesn't really make me any more sympathetic to his criminal and unethical activities.

[0] https://www.cbsnews.com/news/bernie-madoff-happiest-ive-been...

Humans, on the other hand, have all kinds of biases (intentional and unintentional, conscious or not) that creep in because they do have context.

The important thing is that we build systems that account for the process-based problems, not that we build components that are perfect. Things that matter more are how frequently these mistakes happen? What's the impact? Can we eliminate these errors with multiple layers of processes designed to identify the exploits?

The company decision makers could be making a bad decision. They're likely in dire straits to be in this position in the first place. Add in shareholders, some slick marketing/badmouthing from Bain reps, and you have a good opportunity for people to act irrationally, in their personal dis-interest.

Also, you can imagine that Bain has a few inside managers who do very well personally from this kind of deal.

Your second paragraph is problematic. First, truly free markets are unlikely to be an accurate statement of where someone earned their money (e.g. the US is nowhere near that, so that excludes every wealthy American). Second, the use of earn -- does that exclude people who extract a large amount of money, like Martin Shrekli? Finally, it does not address the elephant in the room, that most people who have lots of money have inherited it, not necessarily earned it themselves.

My perspective is that unit economics guesses are important because you want to estimate the scaling factor. You might not know where they are now, but it's important for runway goals to know where the unit economics will breakeven.

Directionality rather than absolute value.

My approach is to spend 98% on substance, then use a template (like Beautiful.ai) or contractor to quickly put together something visually appealing. Doesn't have to be a unique design, but for under $100 or a couple hours time, I can get something that's just pleasant to look at.

I'm not suggesting it's true, but it does fit the narrative of his (and his employer's!) incentives. People usually act in line with their immediately obvious incentives.

Not a smoking gun, but to me it's definitely an indication.

I agree about this particular comment -- your interpretation is likely correct. I was just trying to connect it larger, to the decision making skills that I'm working on (and thought might be interesting to a HN audience).

FWIW, the Pigs incident is widely cited as an example within this sphere. It has nothing to do with whether the advisor was previously skilled -- it has more to do with thinking about how people understand and use the information I communicate. (and that using numbers instead of words seems to be considered "best practice" in the decision making community).

Is that true, and is that objectively more impressive?

To the former, I always assumed there were in flight adjustments after launch. Elon's roadster had inaccurate calculations, I heard they were off by like 2%. I assumed the moon landing was similar.

I can't wait to see what AI can do with the collective time and resources it took for that seagull to evolve.

Doesn't his "99/100" give us a much better idea of his confidence in his own knowledge, compared to something more vague like "usually" or "the vast majority of the time"?

I ask because I've been studying how to make better decisions, and one of the techniques is to use numbers instead of the easily misinterpreted words. The famous example is the Bay of Pigs decision -- Kennedy was told the invasion plans had a "fair" chance at succeeding. The advisor was later interviewed and said that he meant 3-1 against as "fair odds", which could easily have changed Kennedy's decision.

When you say economic consensus, do you mean there are testable predictions based on the consensus that have been shown to be reproducible in multiple real world experiments?

Lots of economics seems to me like it's models that appear to work but don't generate testable predictions that turn out to be accurate.

I suppose I believe it's less important whether or not we collectively determine the correctness of the other person, and more important that I use their contrarianism to question my own knowledge. I'm less interested in judging their argument and more interested in using their comments to evaluate my own. Kind of a "less wrong" approach, but focused on my own model of reality.

Outside of the slightly distasteful attitude ("I didn't bother" and "I bet" are slightly abrasive sounding), isn't the attitude one of the skeptic? It jibes with my understanding of how to make better decisions: don't start with the assumption something is true, question information before you store it in your brain.

Annie Duke talks about this in her latest book.

I think your analogy would be more apt if you were spitting seeds onto public property, and the state took the trees. Or even further: you spit seeds (your data) onto Google's property while they gave you a free tram ride to the other side.

It also adds human redundancy and specialization, both of which reduce risk for a company. In the "product/market fit" words, before fit, a company is all risk. After fit, during growth and long term profit extraction, companies try to reduce risk as much as possible (and optimize for greater margins/efficiency).

How much should you bet to never be ruined but maximize the long term expected value of your bets?

I didn't find "Everybody Lies" particularly compelling. It was an interesting piece of exploratory research, but I didn't see a lot of effort on generalizability. Several of his examples were using rare occurrence rates.

In short, the whole book felt like he got great access to data, and poked around in it until something interesting popped up. Then overarching conclusions were drawn without significant effort to describe whether or not the data was representative.

I'm very sorry that you have to suffer through this alone. All I can offer is my electronic compassion -- I hope you are able to find even 1 small thing in this world that brings you joy. Sometimes that seed can flower into something beautiful :).

I also encourage you to post part or all of your story tomomrrow. I look forward to reading it.

As a dense primer on the subject, I'm reading Sapolsky's book "Behave". It was recommended by Gates and Mike Mouboussin as the best book they read in 2017.

Yoga practice, 3-5 times per week has done wonders for my posture and how I physically feel over the course of a day. Doesn't have to be hot or boot camp; I prefer restorative classes.

I agree on an individual level. In order to develop policies, however, it's important to base decisions on the truth.

(Not that this always happens)

I have no question that people who have built brands believe that it was successful. We can even look at "big brands" like Coke -- they sell sugar water + a brand -- surely that must be real.

My nuanced take is that a brand "exists" in the sense that we are able to understand it, what it represents, etc. What we do not know is if that's the best way to spend resources. All the money that went into Intel Inside -- what if they spent it on something else? No marketer can know that; the question as a marketer: "is this the best thing I can do with my resources?" Nothing about "building a brand" provides objective feedback or testable predictions about the usage of that dollar.

Suggesting that TV advertising can't be a scam because so many people have put so much effort into it is another fallacy. Old school TV advertising was done by people whose jobs dependent on the revenue stream -- even if branding were completely ineffective; if they got paid, I wouldn't consider that irrational.

Brands happen. They can have value, but we can only try to prove that value after the fact.

Direct response, on the other hand, tends to be less full of b.s.

https://conversionxl.com/blog/cro-vs-branding/