Jorge1o1

Good point actually, thanks.

The problem with Design Thinking is that Design Thinking has become a cargo-cult of meaningless buzzwords and hot air. The same can be said about Agile -- it's just a collection of buzzwords loosely connected into a "framework" or "approach" or "school of thought" but it's certainly difficult to put an exact pin on what exactly DT and Agile entail.

But if we can't agree on the specifics -- at least we can agree on the broad points right? - Agile is about iterating quickly, making smaller changes more frequently, etc. - DT is about empathizing with the end user, considering the problem before solving it, and then attempting to fix it and asking for feedback

The problem is, once you cut through all the bullshit, you're left with what is really just common-sense wisdom. So they're faced with a choice: - Either you teach the core concept sans-BS, which can't be monetized. - Or you build an entire ecosystem of hot air, buzzwords, tooling, artifacts, blog posts and rituals around your obvious wisdom, which is how we ended up with Stories and Sprints and User Points and Epics and JIRA and Scrum and all the other crap. You can now sell $12,000 bootcamps, $100k software, etc.

Imagine, Casca and Brutus don't stab Caesar. Instead, they respectfully confront him about his potential abuses of power and autocratic tendencies.

I think the biggest issue is not even the left/right or political bias of Wikipedia but rather the fact that some committee of wiki editors decide along what seem to be fairly arbitrary/subjective lines that some sources are reliable and others aren't.

And then those claims make their way into Wikipedia where they inevitably (even though they shouldn't) are relied upon by students, politicians, journalists, who then perpetuate the claim.

https://xkcd.com/978/

Wow, what a list. Eye-opening really.

The American Conservative (yellow) ==> The American Conservative is published by the American Ideas Institute, an advocacy organisation. It is a self-identified opinionated source whose factual accuracy was questioned and many editors say that The American Conservative should not be used as a source for facts.

The New Republic (green) ==> There is consensus that The New Republic is generally reliable. Most editors consider The New Republic biased or opinionated. Opinions in the magazine should be attributed.

This seems like a somewhat arbitrary double standard to be applying. As a reader of both news sources they are both biased, opinionated sources, and I don't think you can trust one more than the other. But one is green with "be careful this might be biased" and the other is yellow for pretty much the same reason.

Interestingly, after months of chopping along at the whims of the FDA, the stock actually sold off a little today after the news came out at 2pm.[1]

A good example of what it means for news to to be "already priced in," and how in fact, too many traders in a name can overcrowd it and destroy the alpha. It's like game theory.

[1] https://yhoo.it/3pbC6aB

Great point!

For example, this Informational RFC is literally a 4 page cookbook on how to use Kerberos in a practical way.

https://datatracker.ietf.org/doc/html/rfc4559

At a 10,000 foot view (pedants will take offense) you should look to use multiprocessing for tasks which are CPU bound, and asyncio/threads (but really asyncio if you can) for problems which are IO-bound.

This is a massive simplification but most useful for a beginner.

Additionally, asyncio is not the same as multithreading, because typically asyncio is powered by a single-threaded event loop and use of a mechanism like select/kqueue/IOCP/epoll.

Nanomachines, son!

As a daily user of kdb+/q, I can say that at least for me, it's one of the "best feeling" and most ergonomic tools i've ever used.

I can actually query and understand tables in a very fast REPL in a way that would require 20-30 lines of SQL, nested subqueries, etc.

Even things like the way "left joins" work in KDB feels more natural than left joins in other SQL databases. https://code.kx.com/q/ref/lj/

You do an A/B test so even for the same tweet or same time period, you’re just comparing the new, “treatment” group against the old “control group.”

Haha you get to trade one scam against another.

I live in Miami and speaking anecdotally, the article is right about the facts and wrong about the conclusion.

It’s true that crypto money has dried up, the FTX arena has been re-renamed, that the crypto tide in Miami rose and fell in about a year’s worth of time.

I think the conclusion I would draw instead is that the rush of money and transplants into Miami hasn’t abated — it simply has a new face. Goodbye crypto degens, hello stockbrokers, hedgies, traders, quants, etc.

New skyscrapers are still going up, rent has yet to come down, and G wagons are still commonplace — just more Citadel / Millennium and less FTX.

> Approvers say “yes” or “not yet.” We want to encourage constructive comments, so the template doesn’t suggest “no.” Instead of just vetoing an idea, it’s better to be clear on what it would take to approve it.

I'm not trying to be facetious, but what if the idea really is a No? Not every change is for the better. What if someone proposes "we should rewrite X in Y language," or "we should use Z database instead." Sometimes you need to say "No."

Otherwise you end up with feature creep, endless rewrites and refactoring, code churn, and a bunch of other issues.

Reminds me of Vernor Vinge’s Rainbow’s End. There’s a subplot in which the Geisel Library wants to digitize thousands of books in a very fast amount of time, but the digitization machine to do so needs to destroy the books by tearing apart the binding.

You can still have thread safety issues with the GIL in place, because globals and other data is shared between threads.

For example, you can put a dictionary at the module level, thread A can set a key in that dictionary like “name”, thread B can overwrite it, and then thread A comes back, does dct[“name”] and gets an unexpected answer.

This is a relatively easy mistake to make, a lot of python code has module level variables.

>Arguing that an inflationary money is actually a better store of value than a deflationary one is not possible from an economic standpoint

See Krugman’s legendary article about the problem with deflationary currencies, such as the one used by the Capitol Hill Babysitting Co-op.

Long story short, he claims that people will hoard their deflationary currency because they expect it to go up in value over time. But if a large percentage of economic actors are hoarding their currency, there will be less economic activity and less output, and paradoxically, the value of the currency will go down rather than up.

There’s a solid amount evidence showing that there was some kind of abnormal volcanic winter in 536.

https://en.m.wikipedia.org/wiki/Volcanic_winter_of_536

Whether it was “the worst year in history” is probably tough to quantify and prove, though.

That’s forward stock market returns. AKA,__after__ everyone has been fired.

You need to look at what happens to stock prices during the recession/firing.

Cool video! It feels like the love child of Zapier and Airflow — in a good way!

While this article is mostly focused on collecting/gathering/visualizing cohort metrics, rather than on analysis, I would like to plug my college professor Peter Fader’s research on empirical Bayes modeling and analyzing Customer Lifetime Value.

One thing I learned from his class is that survival rates will naturally trend upward over time, which marketers erroneously attribute to (1) improving the product, (2) better customer service, (3) network effects / lock-in, etc.

However, if you have a heterogeneous customer base with latently better and worse customers, inevitably your worse customers will churn before your better customers, showing that “decrease” in churn.

These models also let you do cool things like conditional expectation: “if a customer has survived 13 months, what’s the probability they churn in the 14th?”

Here’s a paper of his from 2004: https://repository.upenn.edu/cgi/viewcontent.cgi?article=141...

Do as I say, not as I do.

I would have to argue that while community is a huge part (a lot of trades get done over IB), the biggest advantage is actually data.

Bloomberg has all sorts of connections with hundreds of different economic data providers and news orgs — including some they they own themselves.

It’s crazy how you can get CPI prints at 8:30:00.001 because presumably they have some embargo arrangement with the FOMC.

They have a corporate actions database with hundreds of thousands of mergers, dividends, minority stakes, etc.

They have millions of news articles, hundreds of thousands of securities, symbology for all of it.

And don’t even get me started on the alternative data in Bloomberg: weather data, maritime ship position, satellite maps, polling data, crypto prices, corporate hierarchies, holdings/investor data, millions of bios, etc.

It’s an empire of data and it’s all in one place.

This isn’t the intended use case, but one upside of cloud desktops is that if I ever forget to bring my work laptop with me, I can RDP from a friend’s computer, etc.

In one particular industry that is rife with cloud desktops you can be trusted to invest or trade $X mln dollars of someone else’s money on a daily basis, or to model out a $Y billion dollar M&A deal, but God forbid you try to install VSCode or MobaXTerm on your own.

IT presumably got tired of being bombarded with application install requests, so one solution is to use vendorized cloud desktops that come with pretty easy tools (for them) to install applications.

This is true. I ran a 3+ Sharpe and over 40% annualized strategy on PredictIt back when they had the tweet markets a few years ago. It was literally just fitting a Poisson model to Trump, Pence, White House, POTUS, and VP twitter accounts and then Kelly betting based on the difference between the market price and the Poisson model.

I strongly believe the only reason I got such a solid performance is not because I’m some kind of trading savant, but simply that the $850 per contract limit prevents smart money/institutional traders from moving the market towards efficiency.

Similar opportunities exist in the equity world — one huge advantage is that little guys don’t really incur market impact.

For a lot of systematic RV plays, you might only have 10bps alpha and then a 7bp trading cost each way.

Take a look at Wargame: Red Dragon. Might scratch the itch. In fact, the whole Wargame series by Eugen systems is set in the Cold War I believe.

Except for every single VR “metaverse” project (of which there are thousands) and all of the graphics hardware and software that goes into them.

Not today we’re not:

https://www.cnn.com/2022/08/29/world/nasa-artemis-1-launch-s...

Lyrics:

And she's buying a stairway to heaven There's a sign on the wall But she wants to be sure 'Cause you know, sometimes words have two meanings

Exactly right about the seeing the data and rerunning calculations.

The programmers who look down their nose at Excel are doing the exact same thing in Jupyter Notebooks and in their REPLs.

>I can see the state of any given variable at any time >I can rerun the same function on different inputs, or different functions on the same inputs >All without having to restart my program!

Remind you of anything?

Anybody who does print(df.head()) is pining for Excel…