#PRAJIND
Price was indicating an ongoing period of consolidation.

Now that it has broken past the level of 363.10; Sustenance above the same would mark the targets as 388 followed by reclaiming ATH.

#StocksInFocus

Here is a special thread introducing our series on “The Making of the Islamic World.” It offers 10 episodes about the transformation of Islamicate societies between the 7th and 17th centuries. You can find the series page here: https://t.co/7mL9eWmMmz

This is Chris Gratien posting. I created this series during Fall 2020 for my students as asynchronous material supported by readings and discussion. This is going to be a long thread, but it will be a great shortcut for those looking to make similar use of these materials.

There are two items worth pointing out: 1. This is an overview of the political, social, and cultural history of Islamicate societies. It does not deal with the origins of Islam or many theological questions. It does discuss Islam’s spread and the transformation of Islamic law.

2. This is the first part of a two-part sequence that takes the history of the Islamic world into the present. It emphasizes developments and themes that have particular relevance for later periods.

This is really a multivocal narrative of Islamic history. We feature over 20 scholars from almost as many institutions. Many of those who worked on it, myself included, study the early modern and modern era but have teaching obligations that take us further back in history.

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.