[Links of the Day] 28/06/2018 : Operational CRDT & causal trees, the story ISPC and Larabee compiler, Limitation of gradient descent

• Causal Trees& Operational CRDTs : Educational project showing how to use CRDT for real-time document sharing and editing.
• The story of ispc : Intel Larrabee compiler history. It seems that Intel missed the mark there, and was significantly too early for the deep learning onslaught. It seems that to some extent that the ISPC model would have significantly bridged the gap between GPU and CPU for machine learning computation. 
• The limitations of gradient descent as a principle of brain function : looks like emulating more complex brain function will not work by using gradient descent methods. While this strategy was quite successful for deep learning it seems that there is some inherent limitation to a more generic brain functions emulation as the authors describe. 

[Links of the Day] 26/06/2018 : How economist got Brexit wrong, Driving data set, CRDT @ redis

• How the economics profession got it wrong on Brexit : Economist got the economy wrong... News at 11 .. Anyway, it's a very good analyse of the pitfalls that the various group fell into. And a good read to get a better understanding of the UK economy and how to reacts to large socio-economic events. 
• BDD100K : want data for your driverless car ?? Berkeley got you covered.  [data][paper]
• CRDT @ redis : I love CRDT and this talk about their use in Redis.

[Links of the Day] 21/06/2018 : TCP's BBR , Hierarchical convoluted neural network, The Government IT self-harm playbook

• BBR : BRR seems like a great alternative to CUBIC or RENO for server-side optimization. Even if you have to be a little bit careful if you run in a mixed environment as a server running BBR will literally asphyxiate other server running CUBIC within the same environment. 
• Tree-CNN : the authors describe a Hierarchical Deep Convolutional Neural Network and demonstrate that they are able to achieve greater accuracy with a lower training effort versus existing approach. 
• Government IT Self-Harm Playbook : this is a must-read for anybody in IT, be a small or large corporation, private or public organization. To be honest I can see these type of mistakes happening all over the corporate world. It's easier to spot them in large corporation undergoing "digital transformation". Anyway, read it, learn from it.

[Links of the Day] 19/06/2018 : Facebook network balancer, Open policy agent, Intel NLP libs

• OPA : an open source policy agent that decouple policy from actual code logic. This is essential to provide great flexibility with fine-grained control of resources. These kinds of features are a key building block for secure and robust API based solution. [github]
• Katran : facebook scalable network load balancer. It relies on eBPF and XDP from the Linux kernel to deliver impressive performance at low-cost thanks to its capability to run on off the shelf hardware. [github]
• NLP Architect : Intel NLP library and solution. Sometimes I feel that Intel has some great hardware and software but the release cycle is rather decoupled. Which often leave the user in an odd situation, where the hardware is out but the software is not there yet. And sometimes it's the opposite. I really feel that Intel should work on this. Maybe externalise the software to a separate entity as the hardware culture might be impeding the software side of the company.

[Links of the Day] 14/06/2018 : GDPR documentation template, Survey of Vector representation of meanings, Supervised learning by quantum neural networks

• A Survey on Vector Representations of Meaning : the papers present an overview of the current state of word vector model research space. The survey is quite useful when you need to choose a vector model for your NLP application as each model comes with different tradeoffs.
• EverLaw GDPR documentation Template: Highly practical and down to earth document helping you classify your current status regarding GDPR and understand what exposure you have to it. To some extent, this is almost a must fill the first step for any company out there that deals with individuals information. 
• Supervised learning by Quantum Neural Networks:  what's better than neural networks? Quantum neural networks !!! 

[Links of the Day] 12/06/2018 : Type checking for Python, Golang Web scrapper , Google Style Guide

• Pyre : Fast Type Checking for Python by Facebook crowd. Written in Ocaml
• Colly : web scrapper and crawler framework in Golang. I really like Scrappy but I think colly has some good potential. Even if often speed is not the main characteristic of scrappers. Actually, you really want to have good rate limiting mechanism if you want to avoid crashing the website you scrap
• Google Style Guides : All style guide for the different programming languages used at Google 

[Links of the Day] 07/06/2018 : Quantum algo for beginners, Dynamic branch prediction and Running Python in Go

• Quantum Algorithm Implementations for Beginners : this paper present a lot of the basic algorithm used for quantum computation. It's a good start if you want to check out what quantum computer can do and test it on a real one!
• A Survey of Techniques for Dynamic Branch Prediction :  with all the spectre and meltdown attack, this paper is a good refresher on what is dynamic branch prediction, how it works and why we need these techniques. 
• Cgo and Python : When you want to run python in your go, and you realise that the python threading model is still a pain in the $"!$*(^!& .

[Links of the Day] 05/06/2018: All about kubernetes - kops and descheduler

Today is all about k8s

• Kops : Production Grade K8s Installation, Upgrades, and Management
• Kops terraform : HA, Private DNS, Private Topology Kops Cluster all via terraform on AWS VPC
• Descheduler :  this aim at solving the issue of overprovisioning nodes with k8s. This descheduler checks for pods and evicts them based on defined policies. Ideally, these policies aim at maximising resource usage without compromising availability.

Microsoft aim at undercutting AWS strategic advantage with its Github acquisition

Microsoft acquired Github code sharing platform. This is a brilliant move. It allows Microsoft to offset some of the insane advantages that AWS gained over the last couple of year via its innovate, leverage, commoditise strategy. 

ILC model by Simon Wardley

ILC relies on the following mechanisms: the larger the ecosystem, the higher the economy of scale, the more users, the more products being built on to of it, and the more data gathered. AWS continuously use this data trove to identify patterns and apply it to determine what feature they are going to build and commoditise next.  The end goal is to offer more industrialised components to make the entire AWS offer even more attractive. It's a virtuous circle, even if sometimes AWS cannibalise existing customer product and market share on the way. Effectively, AWS customers are AWS R&D department that feedback information into the ecosystem. 

As a result, AWS methodically eat away at the stack by standardising and industrialising components built on top of their existing offer. It further stabilises the ecosystem and enables them to tap further into the higher level of the IT value chain. As a result, AWS can reach more people while organically growing their offer at blazing speed with minimal risk. Because, apparently, all these startups are taking all the risks instead of AWS. 

How does Microsoft acquisition play into this?  Well, Microsoft with its Azure platform is executing a similar play to the one that AWS is delivering. However, Microsoft has a massive gap to bridge to catch up to AWS. And the difference is widening at incredible speed as the economy of scale offers an exponential advantage. AWS has a significant head start in the ILC game, which confers them a massive data collection advantage over its competitor. However, Microsoft can hope to bridge that gap by directly undercutting AWS and instantly tap into the information pipeline coming from GitHub. By doing so, Microsoft can combine the information coming from its Azure platform with Github. Providing them with an invaluable insight that combines actual component usage and developers interest and use. Moreover, this will also offer valuable insight into AWS, and other cloud platforms as a majority of projects ( opensource or not) deploying onto these are hosted on Github.

Cloud Wardley Map with Github position

I quickly drew the Wardley map above to demonstrate how smart the acquisition of Github is. You can clearly see how the code sharing platform enables Microsoft to undercut AWS strategic advantage by gaining ecosystem information straight from the developers and the platforms above.  As Ballmer once yelled: Developers, developers, developers!