Software Engineering Daily

Robotics, genomics, and backend infrastructure: as an investor, it can be difficult to assess the viability of a startup that is on the cutting edge in any of these areas.

A robotics startup requires a team with an integrated understanding of hardware and software. A genomics company will not only have to develop a successful healthcare product, but will have to bring it to market through regulation. And in the world of backend infrastructure, building a business that will be differentiated from giant cloud providers gets harder every day.

Amplify Partners is a venture capital fund with an emphasis on technical investments. Their portfolio includes infrastructure companies like Datadog and Gremlin, as well as pharmaceutical and hardware companies.

Sunil Dhaliwal is the founder of Amplify Partners, and joins the show to discuss the thesis of Amplify. The investments that Amplify makes are in technical companies–which makes these financing decisions complex enough to require detailed, individualized research. But there are commonalities among the founding teams. Sunil lays out a useful rubric for anyone who is looking to learn about venture capital investing.

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Podcasts have surged in popularity, but the podcast ecosystem remains difficult to work with. Podcast listeners have difficulty finding episodes. Podcast creators have difficulty finding out how to get started. The advertising marketplaces for podcasts are immature, and it can be difficult to build a business as a podcaster.

Podcasting is unlike almost any other media format that we consume on the Internet. There is not an algorithmic feed of podcasts–we subscribe to podcasts we like and we see everything that gets published. Podcasting originated with Apple, who has not shown much interest in the podcast medium.

Anchor is a platform that makes it easy for users to publish podcasts. Today, a large percentage of the new podcasts created on the Internet are started on Anchor. Nir Zicherman is the CTO at Anchor, and he joins the show to discuss the strange world of podcast technology, and how Anchor is building a business.

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Slack is a real-time messaging system for work communication. On Slack, chat rooms as big as 100,000 people have productive conversations. This might sound like the same problem solved by social networks like Facebook, where billions of users communicate over a newsfeed. But the engineering constraints of a messaging system are different than that of a social network.

On a newsfeed, the order in which events appear is not chronological. Events can be out of order. You can miss events. When a user posts a message to a social network, there are not strict guarantees around when other people will see that message.

On Slack, messages have strong guarantees around arrival. When I send a message, everyone else who is in the room and connected should quickly receive that message as well. The messages need to be ordered and delivered exactly once. All messages on Slack are persisted.

We have covered the architecture and security model of Slack in previous shows. In today’s show, Keith Adams returns to discuss how messages are processed and broadcast in Slack. The problem of Slack’s messaging system is similar to the distributed systems problem of “atomic broadcast”, in which a single process broadcasts a message which needs to be received by all other processes correctly–or else received by none of them.

In Keith’s last show, he talked through the benefits of building a large system on PHP. He worked on infrastructure at Facebook, which was also a PHP application. It’s worth noting that both Slack and Facebook have scaled a monolithic architecture.

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Many people have saved some money which they want to invest for the future. Some people are happy investing their money in a roboadviser, which programmatically puts money into long-term investments. Other people want a more personal approach involving a certified financial planner (CFP®). A CFP® is a human who allocates capital for an individual based off of that individual’s preferences.

A CFP® spends time and effort researching the options for a client. If the client only has a small amount of money (say, $15,000), it is not worth it for the CFP® to spend much time on that account. As a result, there is a type of client who has saved a little bit of money, but has not saved enough to be an important client for a CFP®.

Facet Wealth is a software company that makes software for CFP®s to work more effectively with their client accounts. Facet Wealth has in-house CFP®s who work with this software to manage client accounts. In addition, Facet Wealth buys client accounts from independent CFP®s who have small accounts which they do not have time to manage. This is an innovative way to aggregate users onto the platform.

Gorkem Sevinc is CTO at Facet Wealth. He joins the show to describe the business and the software architecture of the company. We touched on many different areas–from human-computer interaction to the future of investing.

We recently launched a new podcast: Fintech Daily! Fintech Daily is about payments, cryptocurrencies, trading, and the intersection between finance and technology. You can find it on fintechdaily.co or Apple and Google podcasts. We are looking for other hosts who want to participate. If you are interested in becoming a host, send us an email: [email protected]

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Originally posted on June 23, 2016.

Pieter Hintjens grew up writing software by himself. The act of writing code brought him great pleasure, but the isolated creative process disconnected him from the rest of the world. As his life progressed he became involved in open source communities, and he discovered a passion for human interaction.

The post Death and Distributed Systems with Pieter Hintjens Holiday Repeat appeared first on Software Engineering Daily.

Originally posted on May 12, 2016.

When you are deciding who to marry, you are using an algorithm. The same is true when you are looking for a parking space, playing a game of poker, or deciding whether or not to organize your closet. Algorithms To Live By is a book about the computer science of human decisions. It offers strategies for how to think through everyday life like a computer scientist.

Brian Christian has a background in computer science and philosophy, and is an author of Algorithms to Live By. He joins the show to explain how the same algorithms and data structures we use for our computer programs can be applied to the real world.

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Featured Image Photo Credits

Originally posted on July 11, 2016.

Negotiation is an important skill for software engineers. The salary you negotiate at the beginning of your job could be a difference of tens of thousands of dollars over the course of an engineer’s career, but intimidating recruiters and exploding offers scare many engineers from negotiating at all.

Today, Haseeb Qureshi returns to the show to discuss his epic story of salary negotiation. On a previous episode, Haseeb discussed leaving his career as a poker player to join a coding boot camp and start down the path of a software engineer.

In this episode and his recent blog post, Haseeb describes his approach to the job search and salary negotiation process, which eventually landed him at Airbnb with a $250,000 annual salary–after about a year of learning to code.

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Policies define which users and applications can access and modify resources in a computer system.

In a file system, a user might have permission to read or write to a file. In a cloud infrastructure deployment, a user might have the rights to deploy a new server. One microservice may or may not have the necessary permissions to talk to another microservice. All of these are use cases where a “policy” defines the behavior within a computer system.

Policies in a company can be managed in a range of ways: configuration files, dashboards, and centralized permissions databases. A policy engine is a system for managing and automating the policy creation and deployment within an organization.

Microservices need to verify each request that comes in to ensure that the request has the correct permissions. To check those permissions, a microservice can contact the policy engine. The policy engine has all the information from the whole organization about who is allowed to do what. However, talking to the policy engine over the network can be a slow process.

Open Policy Agent is a deployable agent that can run as a sidecar next to a service, and check policies by looking inside of a cache. Torin Sandall is a core committer to the Open Policy Agent project, and he joins the show to talk about policy management, the Open Policy Agent, and the Kubernetes ecosystem (and surprisingly, WebAssembly).

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TLA+ is a formal specification language. TLA+ is used to design, model, and verify concurrent systems. TLA+ allows a user to describe a system formally with simple, precise mathematics.

TLA+ was designed by Leslie Lamport, a computer scientist and Turing Award winner. Leslie joins the show to talk about the purpose of TLA+. Since its creation in 1999, TLA+ has been used to discover bugs in systems such as Amazon S3, DynamoDB, Xbox, and Cosmos DB.

“TLA” stands for “temporal logic of actions”, a logical system that can be used to describe the behaviours of concurrent systems.

This podcast is meant as a brief introduction of TLA+. To go deeper, check out the TLA+ website and the TLA+ video course (note: these videos are highly entertaining because of Leslie’s dry, unpredictable sense of humor).

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Online labor marketplaces are widely used for one-to-one transactions. On Uber, a rider hires a driver for transportation. On TaskRabbit, a homeowner hires a cleaner to come clean their kitchen.

These types of marketplaces are not as widely used for one-to-many transactions, but they can be just as useful. A warehouse owner would want to hire a group of workers to help with holiday shipments. A conference organizer would want to hire a group of event staffers to help run the conference.

Wonolo is an on-demand staffing platform. Businesses post jobs and workers apply for those jobs. The types of work include event staffing, warehouse operations, merchandising, and other general labor tasks. In past shows, we have covered on-demand work platforms such as Fiverr, Thumbtack, Uber, and Instacart. Wonolo presents another variation in the business model and software architecture of the gig economy.

Jeremy Burton is the CTO and chief data scientist at Wonolo. He joins the show to talk about building and scaling Wonolo, and some of the key strategic decisions that Wonolo has made along the way. As with any successful marketplace business, Wonolo has solved the chicken and egg problem of how to get supply and demand on the platform simultaneously. The company has grown deliberately, setting up operations in one city at a time to make sure that they can provide a good experience in both sides of the market in each of the new geographies.

Jeremy and I also talked about the broader effects that the gig economy could potentially have on the labor market. Gig economy platforms use a 5-star rating system and written reviews to judge workers, instead of a resume system. The gig economy allows for rapid job liquidity, and the potential for workers to steadily “level up” more quickly than they might be able to in a typical corporate job. These aspects of the gig economy are rarely discussed, so it was enlightening to hear Jeremy’s views on them.

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