Hey, who wants to read a story about @Upwork fucking over freelancers in order to upsell their clients on unnecessary services the clients don't want and can't afford? Strap in!

Some people really benefit from hearing advice that everyone knows, for the same reason we keep schools open despite every subject in them having been taught before.

In that spirit, here's some quick Things Many People Find Too Obvious To Have Told You Already.

Your idea is not valuable, at all. All value is in the execution. You think you are an exception; you are not. You should not insist on an NDA to talk about it; nobody serious will engage in contract review over an idea, and this will mark you as clueless.

Technologists tend to severely underestimate the difficulty and expense of creating software, especially at companies which do not have fully staffed industry leading engineering teams ("because software is so easy there, amirite guys?")

Charge more. Charge more still. Go on.

The press is a lossy and biased compression of events in the actual world, and is singularly consumed with its own rituals, status games, and incentives. The news necessarily fails to capture almost everything which happened yesterday. What it says is important usually isn't.

Companies find it incredibly hard to reliably staff positions with hard-working generalists who operate autonomously and have high risk tolerances. This is not the modal employee, including at places which are justifiably proud of the skill/diligence/etc of their employees.

~8yrs ago (Dec’12) I got a job @Google. Those were still early days of cloud. I joined GCP @<150M ARR & left @~4B (excld GSuite). Learned from some of the smartest ppl in tech. But we also got a LOT wrong that took yrs to fix. Much of it now public, but here’s my ring-side view👇

By 2008, Google had everything going for it w.r.t. Cloud and we should’ve been the market leaders, but we were either too early to market or too late. What did we do wrong? (1) bad timing (2) worse productization & (3) worst GTM.

We were 1st to “containers” (lxc) & container management (Borg) - since '03/04. But Docker took LXC, added cluster management, & launched 1st. Mesosphere launched DCOS. A lot of chairs were thrown around re: google losing this early battle, though K8 won the war, eventually 👏

We were 1st to “serverless” (AppEngine). GAE was our beachhead -- it was the biggest revenue source early on but the world wasn’t ready for serverless primitives. We also didn’t build auxiliary products fast enough. Clients that outgrew GAE wanted “building block” IaaS offerings.

1st to hadoop (map-reduce ‘04) but our hosted Hadoop launched in ‘15. AWS EMR was ~200M ARR by then. 1st to cloud storage (GFS ’03), but didn’t offer a filestore till ‘18! Customers were asking for it since 2014. Didn’t launch archival storage or direct interconnect till v. late.

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.

An apostle was addressing his own members
If not for technology, you wouldn't be privy to his discussion with his members
He was sharing with them within the context of their understanding of his message, nature, and personality how he thrived in the middle of a season of casting

down
He mentioned what he achieved and even though it might sound like a boast, he was not boasting to you
He was boasting to his church members and as such, you cannot call it a boast
A father does not need to boast to his children and a shepherd surely does not need to boast to

his sheep
Newspapers needed a scandal and felt the message of the apostle was the right fit in their quest to get eyeballs
You read it and you began to insult the apostle
You, the son of a dog from Abatan refuse dump decided to drag an apostle on Twitter
Do you understand what it

means to be an apostle?
The angels working with an apostle are innumerable, they do not only work at his command, but they also work at his behest!
They are entrusted with the duty of ensuring that the Lordship of his office is respected by both spiritual and mundane entities

In the days of Jesus, many of the Pharisees and religious leaders treated his words and actions the same way many of the gutter bred stone casters with 100 Naira data and stolen or London used phone treated the words of the apostle
Jesus warned them at the time that they were

A quick thread on intelligence analysis in the context of cyber threat intelligence. I see a number of CTI analysts get into near analysis paralysis phases for over thinking their assessments or over obsessing about if they might be wrong. (1/x)

Consider this scenario. A CTI analyst identifies new intrusions and based on the collection available and their expertise note that the victims are all banks. Their consumer wants to know when threats specifically target banks (not just that banks are victims).

The CTI analyst has, from their collection, at this time, and based on their expertise enough to make an activity group (leveraging the Diamond Model in this example) that meet's the requirement of their consumer. So what's the problem?

The CTI analyst begins to over think it. "What if I had more collection? Would my analysis change? I really don't *know* they aren't also targeting mining companies in Australia as I don't have collection there."

The analyst knows their analysis is going to be shared. Maybe even public. "What if another team or professional intelligence firm has more collection and ends up noting that it isn't banking specific at all. Banks are victims, not targets. Will my consumer distrust me later?"