teej

There’s 100x more bots that don’t run JS than do, so the presence of a running JS environment is a helpful filter. User agent is not a good substitute.

Invest Like The Best Podcast. “The search for the truth with the anonymous master - Jesse Livermore”

https://open.spotify.com/episode/2POal0mOwBPtLQoeJGa0oL?cont...

Jesse talks at length about making decisions with intuition vs analysis. He breaks down when and why each of these work. It seems simple but it feels groundbreaking.

They also dig into an alternate valuation model for the stock market which I found interesting.

I don’t know about convenience store franchises but this is the model for every major fast food franchise in the US.

Cash that’s usable to invest in the business is different, and worth more, than cash that’s locked up due to liabilities. Interest rates are low, so earning < 2% on the float isn’t the best way to deploy capital.

No. I think when you are big enough, a feature with even 1% usage is big enough to dedicate resources to. Slack made $168M last quarter. 1% of that deserves at least 1 eng and 1 QA full time.

The site I work on has hundreds of millions of visitors a month. A double digit percentage of our overall traffic uses our desktop site on mobile. It’s not the same use case as Slack but I’d bet it’s bigger than you think.

Also, bug burden shouldn’t be an excuse if your market cap exceeds $10bn.

A lot of folks are moving to Dask, or Dask+Prefect these days.

Wikipedia has a really good history on the video game crash of 1983. https://en.wikipedia.org/wiki/Video_game_crash_of_1983

> The microlender is called Huabei, which means “just spend.”

You shouldn't be downvoted - there was a recent episode of This American Life talking about this trend. Pediatric Endocrinologists are prescribing growth hormone to otherwise healthy kids who are on the short side of the height spectrum. This is typically at the rich parents demand. The treatment is often not covered under insurance and can cost hundreds of thousands over a few years.

So yes, rich parents are already juicing their kids up on HGH to give them an extra inch or two of height. It's not the same exact treatment as the OP but it is happening today.

https://www.thisamericanlife.org/687/small-things-considered

This has interesting parallels to the issues folks have with autoscaling in AWS. When people first start using autoscaling it can be frustrating finding the right heuristic to scale on, with the automated system over-shooting or under-shooting what the capacity needs are.

What works well is when you calculate your own capacity needs, then just set the autoscaler to change to that new capacity number. In other words, using your knowledge of how your system works, you'll make better decisions than just looking at secondary metrics like resource utilization.

I know I've done manually triggered GC in Ruby and Java but I don't know enough about Go to say if the article's suggestion is reasonable.

You’ve just invented Universal Paperclips.

You’ve assumed JHH made a mistake but missed that Goldman is probably using a third party data source to verify the self-reported income figure.

I assume they pull income from a third party source to supplement the self-reported income. The third party income would be the source of the discrepancy.

I suspected the root cause of this situation was due to the income signal, not credit score. I bet that Goldman Sachs is not correctly accounting for California community property laws, where 50% of the household income is hers. This seems like the exact type of oversight that is:

- Not “technically” using gender as a signal

- Ends up practically causing gender-based unfairness

- Is simply bad data and I would consider a bug

- Is something customer service is not going to be helpful with

I’m glad this issue came to light. I hope it leads to productive conversations about black-box algorithms and underwriting.

Because most folks in DS/ML are working on predicting likelihood to press a button. You don’t need Fourier transforms for that.

Having family that contributes is also a major theme of the article, but isn't necessarily what you might think as "hidden wealth". That made me think the most, particularly about what contributions my extended family might have made (directly or indirectly) and also if there are any contributions I should be making back.

I love this approach because it starts from the position that the code writer knows what they are doing, instead of assuming they don't.

Workers do not have control over the fund choices they have in their 401k. Often, low cost index funds aren’t available. This can be fixed by working with HR and the 401k manager but it’s often an uphill battle and isn’t guaranteed to work.

Despite all that I agree that GP’s comment to abolish 401ks is throwing the baby out with the bath water.

I’ll give a short anecdote that might sway your opinion. I grew up poor in Baltimore City. I directly benefitted from the CTY program, where I was able to take accelerated math courses over the summer. My family didn’t pay a dime of the $3k+ per summer cost to attend. After several summers of CTY, I was able to take AP Calculus as a freshman in high school. I would not be who I am today without CTY.

I agree, that article written by the director of CTY is biased. And most who attended were rich kids, so maybe in aggregate their approach doesn’t make a difference. But it made a difference for me. CTY provided opportunities for me I never would have had otherwise.

> What is more important is having a deep market on Binance

It sounds like you are arguing that being popular is what matters more to being popular

Pushing computation back into the database has been a major trend in the last few years. Tools like DBT have made that transition easy in terms of clean code and testable SQL.

This article is talking about a role typically called “data analyst”. They help product managers, marketing, operations, etc by running reports, building dashboards, (business intelligence), building data models, running an exploratory analysis, analyzing A/B tests (product analytics) or things like building marketing attribution models or timeseries forecasting.

It’s the job that’s 80% of what a data scientist does but without Python or R. But mostly it depends because no one can decide what these roles should be called or what their responsibilities should be.

The move to columnar databases for BI and data science has thankfully eliminated a lot of this type of performance cruft. There are different issues now, but needing to add indexes just to run a new type of query is something I haven’t thought about in awhile.

I was always a side sleeper. My CPAP has made a significant difference in the quality of my sleep.

The author has chosen to monetize this article. They have the choice to make it free to everyone.

HN isn't typically responsive to jokes.

To make it worse - the shop, your credit card issuer (aka the bank) and the payment processor (aka Visa) are all each doing this.

The only way to know this would be if you read the entire discussion across 2+ threads on the Gitlab site for their event tracking MR. Basically this whole shit show started as so:

* Gitlab previously used 3rd party infrastructure for their user event tracking

* They did not send this 3rd party user id for GDPR and other reasons

* Because they did not have user id, they could not understand user behavior across sessions. Understanding user behavior across sessions is important, so they wanted to add it.

* Gitlab had just finished moving their event tracking infrastructure in house.

* The original MR was to add user id as an attribute to their event tracking

What proceeded was what I consider a very reasonable back and forth between data, infrastructure, and legal on the correct way to add user id. But somewhere along the line it went off the rails. How it turned from simply adding user id into including Pendo JS tags for on-prem customers, I have no idea.

Interest rate is something that gets regularly paid out on an investment.

Liquidation preference is something that typically pays out once when an illiquid investment (private company stock) becomes liquid (IPO or sells).

They both share aspects in that you are investing money up front but they have different rules to how you get a return on that investment.