Tech

Jonas Bonér, CTO of LightBend and creator Akka, discusses using Akka when developing distributed systems. He talks about the Actor Model, and how every Microservice needs to be viewed as a system to be successful. 

Why listen to this podcast:

 - Akka is JVM-based framework design for developing distributed systems leveraging the Actor Model - an approach for writing concurrent systems that treat actors as universal primitives and the most successful model with abstraction has been streaming
 - Circuit breakers in Akka are a backup and retry policy; they protect you by capturing failure data and allow you to roll back 
 - Every Microservice needs to be viewed as a system, it needs to have multiple parts that run on different machines in order to function and be fully resilient - thus is a Microsystem
 - Two different trends have emerged when it comes to hardware and environments: one is the trend toward Multi-core, the is a movement toward virtualized environments and the cloud
 - Saga pattern of managing long running transactions in a distributed system fits very well with messaging style architectures

Notes and links can be found on: http://bit.ly/2kwB2eB

Akka
The Actor Model
When Akka and the Actor Model is the perfect choice
Circuit breakers patterns in distributed systems
Two trends toward Multi-core
Reactive Manifesto
Event Driven vs. Message Driven
Reactive Programming and Streams
Microliths to Microsystems
What do you have to get right before you start trying to deploy a distributed systems?
Working with ML / AI at Lightbend to understand tracing through distributed system
Saga Pattern

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Quick scan our curated show notes on InfoQ http://bit.ly/2kwB2eB

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Show Notes

Jonas Bonér on the Actor Model, Akka, Reactive Programming, Microservices and Distributed Systems

Take a dive deep into digital privacy with Cory Doctorow — journalist, activist, and sci-fi author of Walkaway, Little Brother, and Homeland.  What is "the race between peak indifference and the point of no return"?  Are tech ecosystems facing a privacy crisis analogous to Climate Change?

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Cory Doctorow on Net Neutrality, Social Change, and Free Software

Today on the InfoQ Podcast, Wes speaks with ThirdLove’s Megan Cartwright. Megan is the Director of Data Science for the personalized bra company. In the podcast, Megan first discusses why their customers need a more personal experience and how their using technology to help. She focuses quite a bit of time in the podcast discussing how the team got to an early MVP and then how they did the same for getting to an early machine learning MVP for product recommendations. In this later part, she discusses decisions they made on what data to use, how to get the solution into production quickly, how to update/train new models, and where they needed help. It’s a real early stage startup story of a lean team leveraging machine learning to get to a practical recommendations solution in a very short timeframe.

Why listen to this podcast:

 - The experience for women selecting bras is poor experience characterized by awkward fitting experiences and an often uncomfortable product that may not even fit correctly. ThirdLove is a company built to serve this market.
 - ThirdLove took a lean approach to develop their architecture. It’s built with the Parse backend. The leveraged Shopify to build the site. The company’s first recommender system used a rules engine embedded into the front end. After that, they moved to a machine learning MVP with a Python recommender service that used a Random Forest algorithm in SciKit-Learn. 
 - Despite having the data for 10 million surveys, the first algorithms only need about 100K records to be trained. The takeaway is you don’t have to have huge amounts of data to get started with machine learning.
 - To initially deploy their ML solution, ThirdLove first shadowed all traffic through the algorithm and then compared it to what was being output by the rules engine. Using this along with information on the full customer order lifecycle, they validated the ML solution worked correctly and outperformed the rules engine.
 - ThirdLove’s machine learning story shows that you move towards a machine learning solution quickly by leveraging your own network and using tools that may already familiar to your team.

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Megan Cartwright on Building a Machine Learning MVP at an Early Stage Startup

Today on The InfoQ Podcast, Wes Reisz speaks with Grady Booch. Grady is well known as the co-creator of UML, an original member of the design patterns movement, and now work he’s doing around Artificial Intelligence. On the podcast today, the two discuss what today’s reality is for AI. Grady answers questions like what does an AI mean to the practice of writing software and around how he seems it impact delivering software. In addition, Grady talks about AI surges (and winters) of over the years, the importance of ethics in software, and host of other related questions.

Why listen to this podcast:

 - There have been prior ages of AI that has lead to immediate winters of where reality set in. It stands to reason, there will be a version of an AI winter that follows today’s excitement around deep learning.

 - AIs are beginning to look at the code for testing edge cases in software and do things such as looking over your shoulder and identifying patterns in the code that you write.

 - AIs will remove tedium for software developers; however, software developer is (and will remain) a labor-intensive activity for decades to come.nAI is another bag of tools in a larger systems activity.

 - Much of the AI developers are young white men from the United States. That has a number of inherent biases in this fact. There are several organizations that are focused on combating some of these biases and bringing ethical learning into the field. This is important for us to be aware of and encourage.

 - The traditional techniques of systems engineering we know for building non-AI systems will still apply. AI’s are pieces of larger systems. That might be really interesting parts, but it’s just a part of a larger system that requires a lot of non-AI engineering use cases.

 - Early machine learning systems were mostly learn and forget systems. You teach them, you deploy them, and you walk away. Today, we do continuous learning and we need to integrate these new models into the delivery pipeline.

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Grady Booch on Today’s Artificial Intelligence Reality and What it Means for Developers

At Google’s 2019 Cloud Next conference, I sat down with Stephen Thorne, site reliability engineer on Google’s customer reliability engineering team and co-author of "The Site Reliability Workbook," to talk about how organizations, both large and small, can use SRE to reduce operational costs, improve reliability, and create productive cross-functional teams.

How to Get Started With Site Reliability Engineering (SRE)

On this podcast, Wes talks to John Xmas. Johnny works for Kasada, a company that offers a security platform to help ensure only your users are logging into your web applications. Johnny is a well-known figure in the security space. The two discuss common attack vectors, the OWASP Top 10, and then walk through what hackers commonly do attempting to compromise a system. The show is full of advice on protecting your systems including topics around Defense in Depth, Time-Based Security, two-factor authentication, logging/alerting, security layers, and much more.

Why listen to this podcast:

 - While there are sophisticated web attacks out there that use things like PhantomJS or Headless Chome, the vast majority of the web application attacks are the same unsophisticated scripted attacks that you always hear about. These are simple scripts using tools like curl and BurpSuite with Python or JavaScript. These simple scripts are still incredibly effective.
 - OWASP Top 10 really hasn’t changed all that much in the last ten years. For example, despite being the number one approach used to educate defensive engineers on how to protect their apps, SQLI (SQL Injection) is still the most common attack. We continue to repeat the same mistakes that have exposed systems for a decade now.
 - Phishing is by and far the quickest way to compromise a system. Defensive in Depth, security boundaries, limiting local admin rights are all things that corporations can implement to minimize the blast radius.
 - Attackers have hundreds of gigs of actual username/password combinations that have been exposed from all the breaches over the past few years. These are often a first step when attempting to compromise a system. It’s more often likely that they will figure out a valid email pattern for a company and then feed actual names into that pattern to go after the username. From there, brute force attacks with those usernames against libraries of passwords is a common approach.
 - A common approach is to go after an email login. While the email can be a treasure trove of information, it’s more about using those credentials in other places. It’s pretty common, for example, to use those credentials to get into a network with a VPN.
 - Captcha/reCaptcha is not very effective and preventing these brute force attacks. There are a large number of bypasses and even Mechanical Turk companies that are available to bypass these tools. What can be effective is Time Based Security because it slows the attackers down. If you can slow them down, you can make the attack say long to succeed that they’ll go somewhere else.
 - Once inside the network, most companies often have little security on internal systems. Multi-factor authentication, not just on the front door, but on internal systems is a huge step in the right direction. Monitoring not only for failed login attempts but, in some situations, valid login attempts (such as when a domain admin logs into a domain controller) should absolutely be used.
 - When it comes to application security between services within a network, the best advice is to make sure developers really understand what is trying to be accomplished by something like JWT (JSON Web Tokens). Often its the lack of understanding of what they’re actually doing that leads to system vulnerabilities.

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Johnny Xmas on Web Security & the Anatomy of a Hack

Software engineers, architects and team leads have found inspiration to drive change and innovation in their team by listening to the weekly InfoQ Podcast. They have received essential information that helped them validate their software development map. We have achieved that by interviewing some of the top CTOs, engineers and technology directors from companies like Uber, Netflix and more. Over 1,200,000 downloads in the last 3 years.

Grady Booch on Today’s Artificial Intelligence Reality and What it Means for Developers

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