Software Engineering Daily

In the 1960s, advertising agencies were high-dollar creative producers. A client would come to an ad agency and pay millions of dollars for artistic messaging that would convince a consumer to buy a product.

How could you measure the success of these advertising campaigns? Maybe you could see success in the sales data. Maybe people were starting to talk about the product. Ultimately, success was defined by how satisfied the client was.

When it comes to measuring outcomes, advertising has always been a messy business.

Bob Hoffman’s long career in advertising included CEO positions at three different agencies. He helped huge brands craft their messaging, and grab consumer attention. In Bob’s world of advertising, lots of money was spent on creativity. Were the campaigns successful? That depends who you ask. In the old world of advertising, everyone acknowledged that success was subjective.

As human attention moved online, the world of advertising changed. Advertising began to move from TV and magazines to websites. Technology companies were formed to enable this new type of advertising–known as adtech. These companies claimed to bring scientific accuracy to advertising campaigns. The biggest adtech player was Google, who perfected search advertising.

If you could imagine the opposite of what Bob Hoffman built his career doing, it might look like search advertising.

Bob’s campaigns were about creating a brand’s voice, with colorful art and subtlety and ambient messaging. Advertising was about turning a brand into an entity you recognize, teaching the consumer to associate Nike with fitness, or Dove soap with clean hands, or Cheetos with cheesy, salty attitude.

Search advertising, on the other hand, is just text. You enter a search query, you are looking for black socks, and the top link that comes back is a line of text that says “cheap black socks.” Search advertising catches people who have an intent to do something. They have stated their intent by typing into a box. With search advertising, a brand might not even need a sexy, flashy creative.

The idea of intent-based advertising was expanded with retargeting. As you navigate through the internet, adtech companies are watching you, gathering data on your intent. Maybe you aren’t typing your desires explicitly into a search box–but you are clicking on articles and blog posts and tweets. With all your online interactions, adtech companies can figure out that you are looking for black socks whether you say so or not.

Money poured into adtech for very good reasons: intent-based marketing works. The shift to adtech put agencies in an uncomfortable position. If they couldn’t capture the advertising market by selling highly produced, unmeasurable creativity, they would have to make their money doing something else.

The situation was this: big brands like Procter and Gamble were buying most of their advertising through agencies. Procter and Gamble decided it wanted digital advertising. The adtech companies were the ones who knew how to produce and distribute digital advertising. Since agencies had the relationships with the big brands, and adtech companies had the technology, agencies began to partner with adtech companies.

This is actually a simplified version of what happens. Agencies subcontract advertising deals to digital agencies. A digital agency buys technology from a slew of adtech companies: some technology tracks users around the internet, some technology places bids on advertising spots that will land in front of users. Because of all the middlemen, the incentives are aligned against the brands.

Contrast this world of agencies and adtech companies with the world of Google and Facebook. They are a duopoly because they earned that position. By creating a single monolithic purchasing process, they have removed much of the risk that comes from a purchasing process stocked with middlemen.

But back to Bob Hoffman.

As money poured into adtech, user tracking, and Google, brands started to care more about metrics. When Bob met with a brand, the brand wouldn’t be asking about the cool new advertising campaign featuring a young actress drinking a Coca Cola. The brand would be asking about the click-through rate of a display advertising campaign.

Brands moved their focus to statistics, and away from creativity. And technology companies were happy to provide them with statistics. Whether those statistics were true or not is another story altogether. The industry was moving from creative BS to outright lying, and Bob decided to leave.

In today’s episode, Bob explains how the state of advertising became so problematic, and the ways in which it harms us Internet users.

We have done lots of reporting about advertising fraud for the last year, and it is a popular topic because people are often shocked to find that online advertising is inextricably linked to organized crime, surveillance, and Twitter botnets. That’s not to say that online advertising doesn’t work–it certainly does! But understanding the dark underbelly of the Internet’s cash cow is a necessary precondition to finding a solution.

To find all of our old episodes about ad fraud, you can download the Software Engineering Daily app for iOS and for Android. With these apps, we are building a new way to consume content about software engineering. They are open-sourced at github.com/softwareengineeringdaily. If you are looking for an open source project to get involved with, we would love to get your help.

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In America, the tech companies we focus on are commonly known as FAANG: Facebook, Amazon, Apple, Netflix, Google. We all know what these companies do because they impact our daily lives. In Asia, there are three giant tech companies that have similar scale: Baidu, Alibaba, and Tencent, otherwise known as BAT.

Technology within a location is shaped by the pressures of that location. You might think we live in a global society, but tech in Asia is dramatically different than it is in America. Differences in culture lead to differences in product development.

In China, a different political system contributed to more rapid adoption of online payments. Because there is more payment data, people can be given loans more efficiently. Less of the population is “unbanked.” Online payments are mostly handled by WeChat, a social networking product from Tencent, and Alibaba, an ecommerce giant. If you live in the West, imagine that Facebook and Amazon handled most of your payments for everything. You would have a different relationship with those companies.

Bernard Leong is the host of Analyse Asia, a podcast about Asian developments in technology in business. After studying materials science in Singapore and theoretical physics at Cambridge, he made his way into business and journalism, and developed an interest in the Singularity–a subject that few people took seriously until recently (one topic we explored in this show is Masayoshi Son, the Japanese tycoon who wants to invest nearly a trillion dollars into technology companies; Masayoshi believes firmly that the Singularity is coming).

Shenzhen: The Silicon Valley of Hardware (Full Documentary) | Future Cities | WIRED

In the Plex by Steven Levy

The Hidden Forces Behind Toutiao: China’s Content King

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Machines understand the world through mathematical representations. In order to train a machine learning model, we need to describe everything in terms of numbers.  Images, words, and sounds are too abstract for a computer. But a series of numbers is a representation that we can all agree on, whether we are a computer or a human.

In recent shows, we have explored how to train machine learning models to understand images and video. Today, we explore words. You might be thinking–”isn’t a word easy to understand? Can’t you just take the dictionary definition?” A dictionary definition does not capture the richness of a word. Dictionaries do not give you a way to measure similarity between one word and all other words in a given language.

Word2vec is a system for defining words in terms of the words that appear close to that word. For example, the sentence “Howard is sitting in a Starbucks cafe drinking a cup of coffee” gives an obvious indication that the words “cafe,” “cup,” and “coffee” are all related. With enough sentences like that, we can start to understand the entire language.

Adrian Colyer is a venture capitalist with Accel, and blogs about technical topics such as word2vec. We talked about word2vec specifically, and the deep learning space more generally. We also explored how the rapidly improving tools around deep learning are changing the venture investment landscape.

If you like this episode, we have done many other shows about machine learning with guests like Matt Zeiler, the founder of Clarif.ai and Francois Chollet, the creator of Keras. You can check out our back catalog by downloading the Software Engineering Daily app for iOS, where you can listen to all of our old episodes, and easily discover new topics that might interest you. You can upvote the episodes you like and get recommendations based on your listening history. With 600 episodes, it is hard to find the episodes that appeal to you, and we hope the app helps with that.

Question of the Week: What is your favorite continuous delivery or continuous integration tool? Email [email protected] and a winner will be chosen at random to receive a Software Engineering Daily hoodie. 

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After raising $18 million, social networking startup Yubl made a series of costly mistakes. Yubl hired an army of expensive contractors to build out its iOS and Android apps. Drama at the executive level hurt morale for the full-time employees. Most problematic, the company was bleeding cash due to a massive over-investment in cloud services.

This was the environment in which Yan Cui joined Yubl. The startup did have traction. There were social media stars who would announce on Twitter that they were about to go on Yubl, and Yubl would be hit by an avalanche of traffic. 50,000 users suddenly logging on to interact with their favorite celebrity was a significant traffic spike.

How do you deal with a traffic pattern like that? Serverless computing. AWS Lambda allowed the company to scale up quickly in a cost efficient manner. Yan began refactoring the entire backend infrastructure to be more cost efficient, heavily leveraging AWS Lambda.

Unfortunately, Yan’s valiant effort was not enough to save the company. But there are some incredible engineering lessons from this episode–how to build cost-effective, scalable infrastructure. It’s also a case study worth looking at if you work at a startup, whether or not you are an engineer.

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Quantum computing is based on the system of quantum mechanics. In quantum computing, we perform operations over qubits instead of bits. A qubit is a vector, which can take on many more values than 0 or 1. The technology used to implement quantum computers is advancing such that it has its own Moore’s Law, but it can also leverage the classical advancements of Moore’s Law.

If classical computing advances at the exponential pace of 2^n, quantum computing advances at the pace of 2^2^n.

Quantum computing will advance technology in ways that will take us by surprise. If things feel like they are moving fast now, just wait until developers have access to quantum processing units. Machine learning, simulated chemical synthesis, and NP-complete problems are ripe for quantum computers.

Vijay Pande is a partner at Andreessen Horowitz and a board member at Rigetti Computing, a quantum computer company. In this episode, we explored what software engineers today need to know about quantum computers and some of the application domains that developers will be working on as quantum computers become available.

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Self-driving cars are here. Fully autonomous systems like Waymo are being piloted in less complex circumstances. Human-in-the-loop systems like Tesla Autopilot navigate drivers when it is safe to do so, and lets the human take control in ambiguous circumstances.

Computers are great at memorization, but not yet great at reasoning. We cannot enumerate to a computer every single circumstance that a car might find itself in. The computer needs to perceive its surroundings, plan how to take action, execute control over the situation, and respond to changing circumstances inside and outside of the car.

Lex Fridman has worked on autonomous vehicles with companies like Google and Tesla. He recently taught a class on deep learning for semi-autonomous vehicles at MIT, which is freely available online. There was so much ground to cover in this conversation. Most of the conversation was higher level. How do you even approach the problem? What is the hardware and software architecture of a car?

I enjoyed talking to Lex, and if you want to hear more from him check out his podcast Take It Uneasy, which is about jiu jitsu, judo, wrestling, and learning.

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“Culture fit” is a term that is used to describe engineers that have the right personality for a given company. In the hiring process, “lack of culture fit” is used to turn away engineers who are good enough at coding but just don’t seem right for the company. As today’s guest Ammon Bartram says, “lack of culture fit” usually means “lack of enthusiasm for what a company does.”

Ammon is the co-founder of Triplebyte, a company that is debugging the interviewing process. Triplebyte has interviewed thousands of engineers, and is discovering which aspects of the current hiring process make sense and which are based on superstition, or tradition. We had a great conversation about what culture really means, and how to hire effectively.

Check out our new topic feeds, in iTunes or wherever you find your podcasts. We’ve sorted all 500 of our old episodes into categories like business, blockchain, cloud engineering, JavaScript, machine learning, and greatest hits. Whatever specific area of software you are curious about, we have a feed for you. Check the show notes for more details.

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The history of computing can be thought of as a series of ideas rather than objects. From Aristotle’s formalization of the syllogism, to Alan Turing’s model for an all-purpose computing machine, to Satoshi Nakamoto’s distributed transaction ledger–these breakthroughs did not come in the form of polished, tangible objects. In fact, the objects which end up changing computing fundamentally are often built from ideas that seemed trivial at first glance.

Chris Dixon is a general partner at venture capital firm Andreessen Horowitz and is the author of the article How Aristotle Created the Computer. One job of a venture capitalist is to be early in identifying the ideas that will evolve into influential, tangible objects. In this article, Chris examined several instances in the history of computing where ideas that looked weird and impractical at first glance ended up being world-changing. Recent examples we discussed are blockchains and neural networks.

Chris recently wrote a great article about crypto tokens.

Check out our new topic feeds, in iTunes or wherever you find your podcasts. We’ve sorted all 500 of our old episodes into categories like business, blockchain, cloud engineering, JavaScript, machine learning, and greatest hits. Whatever specific area of software you are curious about, we have a feed for you. Check the show notes for more details.

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Search engines run our lives. The path we take to information is dictated by Google, Facebook, Amazon, and other forms of search. Search engines feel objective and truthful, but are built through ongoing experimentation and subjective decision making.

That’s what has kept Danny Sullivan writing about search engines for twenty years.

The content Google prioritizes, the ads that we see, the way that a product review changes how highly a search result appears on a search; these are the topics that Danny studies. He is the founder of Search Engine Land, an invaluable resource for news and updates about search engines and marketing. I’ve been reading Search Engine Land since college, so it was a treat to sit down for a conversation with him.

Software Engineering Daily is looking for sponsors for Q3. If your company has a product or service, or if you are hiring, Software Engineering Daily reaches 23,000 developers listening daily. Send me an email: [email protected]

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John Looney spent more than 10 years at Google. He started with infrastructure, and was part of the team that migrated Google File System to Colossus, the successor to GFS. Imagine migrating every piece of data on Google from one distributed file system to another.

In this episode, John sheds light on the engineering culture that has made Google so successful. He has very entertaining stories about clusterops and site-reliability engineering.

Google’s success in engineering is due to extremely high standards, and a culture of intellectual honesty. With the volume of data and throughput that Google responds to, 1-in-a-million events are likely to occur. There isn’t room for sloppy practices.

John now works at Intercom, where he is adjusting to the modern world of Google infrastructure for everyone. This conversation made me feel quite grateful to be an engineer in a time where everything is so much cheaper, so much easier, and so much more performant than it was in the days when Google first built everything from scratch.

I had a great time talking to John, and hope he comes back on the show again in the future because it felt like we were just scratching the surface of his experience.

Software Engineering Daily is looking for sponsors for Q3. If your company has a product or service, or if you are hiring, Software Engineering Daily reaches 23,000 developers listening daily. Send me an email: [email protected]

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