teej

Back in the day they would prioritize high karma HN users for YC startup school invites. That’s how I first met pg.

Many folks don’t know or forgot that there are ISPs in the US that do deep packet inspection and inject banner ads into http traffic.

Aaron Harris and Adora Cheung leaving YC the same day. I wonder if that means there’s internal changes happening or if they’re founding something together.

I voted in my Bay Area city's local election in November. Property values, traffic, and environmental concerns were ALL policy justifications for curbing new housing.

My mind just cannot visualize what zip is doing. I’ve written production code with zip and I’ve finished leetcode problems with zip. But every single time I need a piece of paper to help me through it.

I’m fine with most of the rest of Python’s functional-style iterable helpers. But something about zip just doesn’t work for me.

Is there a reason that the copying process needs to be aware of how bytes are organized? Why can’t you just read the bytes from disk A and write those exact bytes to disk B?

“Would” is a bold word choice given that AWS hasn’t ever raised prices. Could, can, might - sure.

The bank across the street from me is 100% burning up in the same fire as my apartment if the California wildfires get to me.

The word “excessively” stands out like a sore thumb. Seems fishy to have a qualifier just on “violent” and not on any other term.

When you decide that a value N standard deviations away from the mean is an anomaly that must be investigated, you implicitly make an assumption about the underlying distribution of the data.

500k may seem like a lot, but it’s not in the context of the Mac App Store. Apple has sold over 100M Macs in the time this app has been around. Also, Apple’s policies are distinctly not driven by “if enough people are fine with it”.

Apple’s policies are bullshit, don’t get me wrong, but let’s not act like download numbers give the developer any leverage.

This article says breathing the air in Beijing on one day is 15 micromorts. Beijing seems to have a P2.5 AQI of 50-100. You can use that to compare to the hazard of your choice.

https://www.dnaindia.com/lifestyle/report-every-breath-you-t...

For my team, I decided we would go all in on Terraform for AWS resources. Lambda has turned out to be a particularly tricky one to fit into that mold. It took us some time to sort out where the “build” step lived in our deployment pipeline so that terraform config pointed at the right build artifact.

... you’re aware of the adage “the house always wins” right?

Yes, but the other Bay Area counties have chosen to shut down way in advance of that.

How many billionaires (paper or cash) has YC produced?

This is exactly how the flash games scene was for a long time.

I remember going over to a friends place who owned a Mac in order to play Warcraft 2 network games.

McDonalds hasn’t used beef tallow in their fries for 20+ years.

[edit] this seems disputed. The ingredients list includes “natural beef flavor” which it says is wheat and milk derivatives.

To clarify: old school McD fries were fried in beef tallow at the restaurant. Now it’s vegetable oil at the restaurant, but are par-fried at the processing plant in vegetable oil with some “natural beef flavoring”

I knew I’d get something wrong, thanks for the correction.

I'm going to try my best to explain these in an intuitive way. Statistics has lots of terms with names that are arbitrary and confusing.

To set the context, we are trying to use data to help us test a hypothesis. An example might be: "if we give this pill to a person, they will be cured of their disease". Statisticians test this by setting up two groups: Group A gets nothing (or a placebo), Group B gets the pill.

In statistics, you assume the "Null Hypothesis", in other words, that there is no difference between the two groups. You use hypothesis testing to help you "reject" the null hypothesis, to say that the groups are actually different. If the groups are different, that means the pill cures the disease. So we take a bunch of data about the two groups, run some math on that data, and use the result of that math to help us decide if we can reject the null hypothesis.

Statistics is a bunch of tradeoffs between certainty, making the wrong call, and data volume. The terms you have mentioned are either "knobs" (tradeoffs) we can make or measures that helps us understand our results.

Here's what those terms mean:

Type 1 Error: also known as "False Positive". You thought the pill cured the disease, but it does not.

Type 2 Error: also known as "False Negative". You thought the pill did nothing, but it actually works.

Power: the chance to avoid Type 2 error (false negative). The higher your power value, the lower chance you incorrectly assume your pill is ineffective.

N: The number of "observations", in our case, the number of patients in the trial for our pill.

The others are a little trickier to explain.

Alpha: Statisticians use a "confidence interval" as a way to communicate how uncertain they are about a particular result. In our trial we might say "patients were 15% less likely to have the disease after taking the pill, give or take 2%". We don't think the decrease is exactly 15% (what we observed) but is instead somewhere in that neighborhood. Alpha is a measure of the chance the real effect is OUTSIDE of your confidence interval. So in this case, the chance the effect is < 13% or > 17%.

Cohen's D: In our trial, we might measure "the number of times the patient coughed in a day" in addition to "do they have the disease anymore yes/no". In order to compare our two groups, we make look at the average number of coughs per day in group A vs group B. This is called measuring the "difference in means". Cohen's D is a formula to measure the difference in means that also encodes your uncertainty in the result.

They revert when it wears off.

It's insane to me that the only thing standing between me and having my life ruined is some minimum wage cell phone store employee.

Text message based 2FA needs to die.

Making some shitcoin or Pokémon-on-the-blockchain? Yes. ETH smart contracts? No. I’m bearish on crypto but ETH smart contracts seem really interesting.

You could make a case that the primary purpose of the internet is to download porn.

The book Discovering Prices by this year’s Nobel laureate is really fascinating. The very first chapter opens up to explain the boundless political, mathematical, and computational complexity involved with the FCC Broadcast Incentive Auction. I recommend it just for that alone.

It’s not a new phenomenon so much as it has emerged as an important shift from the status quo 20 years ago.

What’s changed in the last 10 years are the access patterns. There’s increased demand to have arbitrary query access over the raw data. The most impactful technology changes have been about pushing the access layer (queries, stream & batch processing, dashboards, BI tools, etc) down as close to the raw data as possible and making that performant. What’s fallen out of that are better MPP OLAP databases (snowflake), new columnar formats (parquet), SQL as the transform layer (dbt).

The biggest shift has been towards data lake (store everything) away from data cubes (store aggregates). This makes it orders of magnitude easier to diagnose, debug, and assert the correctness of data.

So these trends aren’t in a vacuum, they directly support the issues you discuss.

> Most of the projects I've seen or heard of are just people taking the same old data ...

I don’t disagree with you here. But in my experience it’s about getting Frank in marketing to use the same numbers as everyone else.

When you have 5 different ads platforms that all take revenue credit for a single conversion and have conflicting attribution models, and none of them add up to what accounting says is in the bank account. That’s a hairy problem.

There are different flavors of that class of problem at lots of companies.

UNLOGGED tables is new to me, thanks for sharing.

The table needed to never be visible as empty, so truncate wasn't a short term option.