Almost every trader trades breakouts, yet very few are able to trade them properly.

In this article, we’ll be covering every aspect of breakout trading.

What are they?
Why do I prefer trading breakouts and not pullbacks?
And much more.
https://t.co/Mk5QW86x8s

A thread on HN about bad code in legacy projects both makes me think how little we've learned as a discipline over the years and, honestly, how little credit we give ourselves for some pretty major

Fun going down this list and thinking: "Hmm, plausible at a well-run modern software shop", "Hmm, possible, but requires implausible tradeoffs", "Literally disallowed by languages", and "If you were to attempt doing that our test suite wouldn't let you merge."

I think we as an industry celebrate (not quite the right word) failure too much and don't celebrate success nearly enough. There is no DailyWTF for competent execution, word of which generally stays pretty local to the source while incompetence passes into legend.

Alrighty let me try to thread the needle on being the change I want to see in the world while not giving away anything that will get me in trouble:

Ruby has wonderful developer ergonomics. Typed languages are easier for machines to guarantee the correctness of. We built a type checker for Ruby (and I believe it is slated for OSS release sometime).

c.f.

1) “Eliminationism” is a term you need not just to become familiar with, especially in today’s American rush toward authoritarianism. This will be a long, illustrated thread explaining what it means, how it works, and why Donald Trump is now our Eliminationist in Chief.

Warning: Some images contain violence and are disturbing.

Also warning: This thread is ridiculously long. 75 tweets is insane. I apologize. But this stuff is important. I hope you find it compelling anyway. If not, I understand.

2) So, what exactly do we mean by “eliminationism”? Here’s the academic definition:

3) It was largely coined by Holocaust scholar Daniel Jonah Goldhagen in his 1996 book Hitler’s Willing Executioners, about the role ordinary Germans played in the Holocaust, which he described as fueled by “eliminationist antisemitism.”

4) The book describes how in reality the largest numbers of Jews and other victims killed by Nazi directive were rounded up and gunned down or immolated en masse and buried in mass graves by ordinary Germans and non-Jewish residents of German-occupied territories.

So when it first appeared in Normandy, the Sherman Firefly and it's meaty 17 pounder represented an ideal combo of reliable chassis, great gun and devastating firepower right?

Ummm...

Truth is a bit more complex. /1
#WW2 #SWW #History

Deliveries of Firefly to units only commenced in April 1944,
just a few weeks before Overlord & left minimal time for familiarisation.

Armoured Regiments mostly utilised Fireflies on a basis of 1 per troop of 4 tanks. /2

Finally each armoured regiment troop had access to integral, meaty, anti-tank firepower capable of dealing serious damage to any German AFV likely to be encountered. /3

The dual purpose 75mm gun was quite capable of laying down a reasonable HE shell or AP round for dealing with Panzer III or IV.

Firefly's 17 Pounder had.... problems with HE, but excelled at tank killing. So Tigers and Panthers would need to be wary. /4

Firefly's design history dated back to March 1943 when the General Staff published "Policy on Tanks" which effectively stated the majority of tanks should have a good HE weapon and...

[See image below] /5

1 There's a chasm between an NLP technology that works well in the research lab and something that works for applications that real people use. This was eye-opening when I started my career, and every time I talk to an NLP engineer at @textio, it continues to strike me even now.

2 Research conditions are theoretical and/or idealized. A huge problem for so-called NLP or AI startups with highly credentialed academic founders is that they bring limited knowledge of what it takes to build real products outside the lab.

3 A product is ultimately a thing that people pay for - not just cool technology or user experience. But I’m not even talking about knowledge gaps in go-to-market work. I'm talking purely technical gaps: how you go from science project to performant + delightful user experience.

4 Most commoditized NLP packages solve well-understood problems in standard ways that sacrifice either precision or performance. In a research lab, this is not usually a hard trade-off; in general, no one is using what you make, so performance is less important than precision.

5 In software, when you’re making something for real people to use, these tradeoffs are a big deal. Especially if you’re asking those people to pay for what you’ve made (can’t get away from that pesky GTM thinking). They expect quality, which includes precision AND performance.