A common misunderstanding about Agile and “Big Design Up Front”:

There’s nothing in the Agile Manifesto or Principles that states you should never have any idea what you’re trying to build.

You’re allowed to think about a desired outcome from the beginning.

It’s underremarked upon that stablecoins accomplish basically all of the original design goals for Bitcoin qua a transactional mechanism (instant free value transfer anywhere), but that nobody cares about this because nobody ever cared about that part of the pitch.

“Why do people care about stablecoins then?”

A mix of “they encourage dollar-denominated liquidity in the cryptocurrency ecosystem and discourage withdrawal of the same” and “they’re good for money laundering.”

“But they make value transfer between exchanges much faster!”

This was a solved problem in traditional finance, too, mostly through the extension of credit. (It doesn’t matter how long settlement takes if there is sufficient trust to enable credit.)

The Bitcoin ecosystem is *positively allergic* to credit, so you have to call it a coin for them to accept it. And after you call it a coin they ignore everything the world has learned about credit, like risk management.

“Stablecoins aren’t credit!”

They’re pretty much exactly credit? A tether is a zero-coupon Bitfinex bond with a non-functioning call option. I

So, today, for the first time in 25 (!) years of Apple, I downgraded. From the 2016 MacBook Pro to my 2013, which I had kept in a drawer...

It was pretty simple to do—Apple Time Machine backups let me do it with one click.

That first tweet captures, in two pictures, how badly Apple has “lost the plot” (to quote @wylieprof). On the right is the Apple MagSafe adapter, from 2013. On the left, what I had “upgraded” to.

Thanks, Apple! I really was nostalgic for worrying about yanking my computer off the table.

Oh and I really appreciated not knowing if my computer was charging. What was great was the little whoop sound you used, so that the speaker before me could be informed I was charging my laptop.

Today we’re going to talk statistics and semantics. I’m going to do so via the medium of one of Twitter’s favourite stats 1.7% (aka “intersex is as common as red heads.”). The statistic comes from Fausto-Sterling’s work, which you can see a review of

To understand the statistic, we have to drill down into what the meaning of the word intersex is. Most people think of hermaphrodites. Let’s stress at the beginning there has been no example ever of a human being with both sets of working reproductive organs.

There is a very tiny percentage of people who are born with both testicular and ovarian tissue (what Fausto-Sterling calls “true hermaphrodites”). And when I say tiny, we’re talking intersex tiny, as in 0.0117/1,000 live births, or one in 1,000,000. These are very complex cases.

So, how do we arrive at 1.7%? What Fausto-Sterling does is apply a wider scope. Instead, she argues that intersex is anything that doesn’t fit into a platonic ideal. The platonic ideal, in this case, being a phenotypically perfect XX female and a phenotypically perfect XY male.

This means that intersex, used in the context of the 1.7% statistic, includes people for whom there is no sexual ambiguity. Take for example Klinefelter syndrome (XXY males). As Fausto-Sterling acknowledges, many males can go their whole life and not realise they are XXY.

For decades, Jeff Bezos shunned ads.

But now, Amazon’s ad business is huge: it’s on a $30B+ annual run rate (more than 2x the combined sales of Twitter, Snap and Pinterest).

Here’s how it happened 🧵

1/ Amazon doesn’t break out its ad business separately in financial filings. However, most analyst believe ads make up the majority of its “Other” revenue segment.

This figure has exploded from $1B in 2015 to a current annual run rate of ~$32B.

2/ In 2009, Bezos said “ads are the price you pay for a crappy product”.

But the business is so lucrative that Amazon’s interface has been swamped with ads:

◻️ First 3-7 search results (left in red)
◻️ Most of the product page (right in blue)

3/ Prior to the rise of ads, Amazon set out to build the ultimate organic customer recommendations engine.

The approach was codified in a famed 2003 research paper: “Amazon Recommendations: Item-to-Item Collaborative Filtering.”

4/ In recent years, Amazon organic recommendations:

◻️"Customers who bought this also bought this"
◻️"Customers who viewed also viewed"

Have been replaced w/ ads:

◻️ "Sponsored products related to"
◻️ "Brands related to this category"

(1) Since some people laugh at Cardano project and it's peer review aspect, let me share a story from work for all of you
(2) There is company called Databricks and they open sourced recently a technology called: https://t.co/gpZyKKiMVD . This technology is super important

(3) In BigData delta allows one to perform DELETE and MERGE operations contrary to Hadoop + Hive where this is not possible, seeking through many partitions finding customer data to remove and then rewriting it is not only expensive as an operation but also very dangerous

(4) This promising technology is something that many companies, which have big data turn into as they need to be GDPR compliant.
(5) I was always reading and accepting all details from their website as given and the "truth"

(6) There is however an amazing Principal Developer at our company that found some of the claims from their website as dubious, e.g. ACID claims on such a distributed file system
(7) It turns out this is not the first time this company advertises something that is simply not true

(8) on another occasion this company made untrue claims about Cassandra database and this then became source of disputes e.g by dr. @martinkl - my fave expert in distributed systems
(9) All professional developers also know of course famous Jepsen tests ->