mshron

Fair, but I think for the purposes of this article, social media accounts (could be friends or corporations) that talk about breaking events would also count as "the news". Both in the plusses and the minuses! Upside, you're more aware of what's going on. Downside, you still can't do anything about it. Only faster.

In a lot of ways, current-events Twitter is more like cable news than it is like reading a weekly. All hot takes, no time for context. But like you said, for idiosyncratic folks, the "lifestyle" content is going to be much more relevant on Twitter than on CNN.

It seems like a weird time to post this article to HN. Aren't we all living through an extremely good example of why paying attention to the news pays off? Generally speaking, people I know who read the news prepared for SARS-CoV-2 by mid-February, and those who were disconnected from the news were caught off guard a month later when everything shut down.

One thing I will happily grant is that _watching_ the news is a terrible idea. TV twists everything into being frivolous entertainment, regardless of the topic. Newspapers aren't much better. Too short a time horizon, too much blow-by-blow, meaning no time for context and low signal to noise.

I find that weekly and monthly magazines strike a better balance. Authors have time to write up background and provide context, and little blips and false starts get smoothed over by the passage of time.

Great question. Writing skill is generally underdeveloped in engineers, and it's absolutely worth getting good at.

Based on your other comments, you're already starting with drafting, which is great. The next step is to learn to revise well so you feel more comfortable with your early attempts and don't need to toss them out.

The book _Style: Lessons in Clarity and Grace_ by Williams and Bizup is about how to rework a draft into something more coherent. They talk about revising sentences, and paragraphs, and then whole structures to be as coherent as possible.

Once you have a mental model for how people read, and what makes reading easier or harder, it's easier to get into the habit of writing and revising. Then you get stuff out of your head and revise it until it's in a form you're happy with. Rinse and repeat.

Warby Parker | New York, NY | Engineering Manager, Data Engineering | Full-time | Onsite preferred but remote possible

As one of the original "clicks to bricks" retailers, Warby Parker has more interesting retail data than almost anybody else. We run our own supply chain, including an in-house optical lab; we built our own ERP; and, with a strong e-commerce site and close to 100 stores, our omnichannel game is on point.

One of our biggest challenges is getting all of that data into a coherent form and making it as easy as possible for analysts to use it. We're transitioning right now from an in-house PostgreSQL database to BigQuery, and in the process developing more mature data governance, data modeling, and general processes around data quality. And at the same time, we're working to take our ETL infrastructure to the next level.

We have a team of seven plus a Product Manager tackling this challenge, with more to come in the future.

What we need next is an Engineering Manager [0] to nurture this team into its next phase of development. Coach them to the next level, identify where we need to grow and succeed, and make it happen. Let's talk!

[0] https://boards.greenhouse.getrake.io/warbyparker/jobs/175520...

Solar is already down to $0.065/kWh for industrial applications [1]. That's $3.90/gallon at today's prices, and solar prices continue to fall.

The DOE's 2020 targets came three years ahead of schedule. Their 2030 target is $0.04/kWh [2], which would work out to $2.40/gallon. Mix in the fact that a bunch of countries (and US states) have implemented carbon taxes and you've got yourself a good long term investment.

[1] http://solarcellcentral.com/cost_page.html [2] https://www.energy.gov/eere/solar/articles/2020-utility-scal...

I gave a relevant talk a few years ago: How to Kill Your Grandmother with Statistics[1].

The authors are spot on that the problem is not p-values per se but dichotomous thinking. People want a magic truth box that doesn’t exist. Unfortunately there are a ton of people in the world who continue to make money off of pretending that it does.

https://www.youtube.com/watch?v=iRpAHS5_hDk

The Weeds, by Vox, and Why is This Happening, by MSNBC. Both of them take an hour to explore one topic, usually related to politics or a cultural moment. But it's not just a summary of recent news; they go back into history and provide context from different sides of an issue.

They're staffed by people who are well read and bring much needed nuance to a lot of discussions. I learn something fascinating every time

Respectfully, I think you're working from an outdated definition of racism and sexism.

People have already been treated differently on the basis of their race and sex for their entire lives. Starting at the end, and assuming that being colorblind will solve the problem (which has been basically the official policy in our society for the last 40 years), empirically hasn't worked out that well.

Instead, we should be taking active measures to improve opportunities, including examining how to make our companies more welcoming places to be, as per the OP:

> Sometimes the interventions are much more work: creating a company culture that is not hostile for black women.

> But the net result is the same: more inclusion, better talent, and a better end product.

For anyone interested in this extremely important point (that markets need a lot more than just enforcing contracts to work), two book recommendations:

* Rob Reich, _Saving Capitalism_ [1]

* Steven K Vogel, _Marketcraft_ [2]

What kinds of contracts can be enforced? How much personal liability people should have? Is bankruptcy allowed? and so on. All trade-offs between different groups, and there’s no one “free market” answer to any of them.

[1] https://www.penguinrandomhouse.com/books/227780/saving-capit...

[2] https://global.oup.com/academic/product/marketcraft-97801906...

I'll give it a shot.

Every city has rules about what you can and can't build within its boundaries. Even lightly-regulated cities like Houston still have rules about density, lot sizes, and what you can build where. There isn't such a thing as a city without "government intervention."

The current rules were put into place by previously powerful people who wanted the Bay Area to be low density. There are new powerful people who want to change the rules so it can be higher density.

Other people (not the people who are the main focus on this article) also want the local government to help pay to build homes for poorer residents. There are families which have been living in these areas for a long time who can no longer afford to stay. Many communities are only strong because there are people who have been there for a long time, who know each other and have worked hard to build that community. Kicking them out to make room for new comers, who may only be there temporarily, in time weakens the quality of life for everyone.

I don't think you're disagreeing with what the authors are saying.

They are not suggesting that the money comes from the recent MBA grad. They're suggesting a typical private equity approach where new company debt and outside money are used to buy out the current owners. Then cash flow repays debt, investors, then finally the person who put the deal together who will earn 20% of profits going forward.

This type of management (as you said, running an existing company instead of starting one from scratch) seems exactly like what an MBA prepares someone for, much more than it prepares them to launch new ventures.

Through no fault of your own, I believe you are misreading this key paragraph:

> That’s a net deficit of $147,174, which Edwards covered with his own discretionary research and personal funds. The Flint Water Study team of 25 students and research scientists also estimated their total donated labor at more than 6,000 hours, which they valued at over $165,000 based on the hourly rates of the team members.

So it was an estimated $165k in free labor on top of the rest of the costs. You are confused because their lab costs were $166,487, which is a similar number.

What about companies that have additional data sources? For example, a CRM. It seems like it's hardly a data warehouse if it's _only_ event data. Or am I missing something?

I went back and read it more carefully, and it's linked to an email address one character off from the known Satoshi one. satoshin@vistomail.com vs satoshi@vistomail.com.

Interesting argued. I say: let's try it somewhere that is game, and see what happens. As Bob Reich says, we've been running an experiment in trickle down economics for the past 30 years and it hasn't done most of us much good. I'm not going to buy much in theory either way until I see some attempts in practice.

The two people mentioned in the article both say they make about $500 a month from social security.

Well said. Advanced math is mostly about working with properties higher up the chain of abstraction, and then seeing what happens when you bring the insights learned up there back down to more concrete examples.

From an OO point of view, the real numbers inherit almost every useful trait: they're a field, they have a topology, they have a measure. Studying the parent classes, so to speak, gives you abstract algebra, topology, and analysis, respectively.

Once you get the basics of each, you can study how they interact. Then, once that stuff is clear, they can be recombined in beautiful ways to give you new objects to study.

The "default" alpha and beta are not the correct ones for a website A/B test.

If you're designing a drug, you'd better be very careful not to accidentally approve something that is useless. It would cost a ton of money and lives in the long run if it was no better than placebo. False rejection of the null is very bad. False acceptance of the null is not so bad.

By contrast, if you're doing an A/B test on a website, you're actually not in bad shape if you accidentally think that a red button is a bit better than a blue button, assuming that they're pretty close. False rejection of the null is okay.

However you are screwed if you miss out on the chance that a red button gives you 50% more conversion. With websites, false acceptance of the null is very bad. It's okay to mistakenly think your button is effective but it's very bad to mistakenly think that the button is ineffective.

Websites have the opposite cost benefit calculation to science generally and shouldn't use the same parameters.

Love the idea!

I would ask for a little more separation of concerns. One package for raw but cleaned data with a collection of schemas, and a second for loading arbitrary data + schemas in to Django (and probably accomplishing all of the extra administrative steps provided in the example).

That way if I want to add other schemas for a non-Django use in the same package (say if I care more about analysis than clicky-interfaces) or not use Django, I can still use a package manager for the same data.

This is the key result in this conversation; this should be higher. Basically: if we insists on Deutsch's causal fixed point propety of CTCs, they do not give you P=NP, they give you P=PSPACE. That is, all problems which can be solved in polynomial space can now be solved in polynomial time, and vice versa.

So for a time-travel computer to give P=NP or P=O(1), it would have to violate Deutsch's causality criterion. I have almost zero knowledge of either field, but it sounds to me like it makes Deutsch's model more plausible.

Ehhh... the term "adversary" is the standard term in any crypto paper or book for people who you are designing your system to be safe against. I'm very concerned about NSA overreach, but this is normal crypto terminology. It's true that this isn't quite algorithm design, but it's the most natural word I could imagine for such a scenario.

Living in Brazil, working with the Guardian, a UK Newspaper. Not saying that OP was right, just saying that there isn't much reason to split hairs here.

I went to the Restore the Fourth rally in NYC. It was, by a long shot, the most cohesive march I have ever been on.

It helps that the context was threefold: NSA spying, NYPD Stop & Frisk, and NYPD unlawful surveillance of Muslims throughout the tri-state area. All clear 4th Amendment issues. We had a diverse crowd, with a mix of geeks, Occupy-types, Tea Partiers, and members of the Muslim community.

We marched, we chanted, we even took some streets in lower Manhattan. Hard to say how big, but probably about a thousand there. The farthest anyone got off-topic was "Free Bradley Manning." Very rare to see that kind of discipline in a diverse crowd. I was very impressed.

I agree, this is the question. And the answer is "both". From the NY Times article:

> FISA orders can range from inquiries about specific people to a broad sweep for intelligence, like logs of certain search terms, lawyers who work with the orders said. There were 1,856 such requests last year, an increase of 6 percent from the year before.

Imagine a 2x2 matrix of intelligence requests, with one side being "narrow" vs "broad" and the other being "short" vs "long" duration. Verizon was long and broad, the worst kind.

The tech companies are trying to imply that they were short and narrow, at least most of the time, and the journalists are trying to imply that they were long and broad, but never come out and say it because they have no evidence. If we had better transparency about FISA, we could answer this question ourselves. Just knowing how many requests were for individual user's data would be extremely helpful.

Finally a demo that doesn't make me think "Wow, the web has really caught up to the 1995 desktop!"

Maybe if you're already an after-effects wiz this doesn't seem as magical, but it certainly felt like magic to me.

Called. Thanks for putting this together! Slick experience.

I had some complaints that I was hard to hear; not sure if that was Twilio or that I was calling through Google Voice.

Right. My larger point though is that there are lots and lots and lots of jobs that can be eliminated without strong AI. We may succeed in destroying the demand side of the economy long before we solve the problem of making the supply side pointless.

There are many, many jobs that you could eliminate without inventing sentient AI. Anything that requires implementing a plan, rather than coming up with the knowledge, should be fair game. Doctors, most of contract law, teaching, lots of logistical jobs. I imagine we're going to get there awhile before we get to strong AI.

I agree. It seems to me that the Kevin Drum article is much clearer on this topic. AI doesn't need to be that good in order to oust humans from their jobs. His first line example is automated cars, not sentient robots.

In the case where most jobs can be automated like driving can be automated, we're not talking a technological singularity, we're just talking a massive transfer of wealth to holders of capital. Personally, I think that's the more realistic scenario, and the one that it's easiest to think through the consequences of.

Jig was excellent, some very clear talent involved there. Curious to see what comes next. Best of luck to all of you!