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Computer Science

A collection of 35 issues

Coding At a Distance

Senior professors have long lived the future of coding. Professors ask. Underlings code. Professors critique the output, e.g., "I prefer statistical significance stars for the betas but not the constant term." Underlings revise the code. The cycle continues. Something similar can play out at companies. Now, thanks

Building Together, Separately: Challenges of Software Development

With Atul Dhingra Microservices, Macroproblems A single page on Doordash can make upward of 1000 gRPC calls (see the interview). For many engineers, upward of a thousand network calls nicely illustrate the chaos and inefficiency unleashed by microservices. Engineers implicitly diff 1000+ gRPC calls with the orders of magnitude fewer

Bad Service: Missing Functionality in Web Services

A host of cloud services are missing the core functionality needed to build businesses on top of the services. Powerful services on mature platforms like Google Vision, etc., have a common set of deficiencies—they do not allow clients to send information about preferred latency and throughput (for a price)

Reducing Friction in Selling Data Products: Protecting IP and Data

Traditionally software has been distributed as a binary. The customer “grants” the binary a broad set of rights on the machine and expects the application to behave, e.g., not snoop on personal data, not add the computer to a botnet, etc. Most SaaS can be delivered with minor alterations

Back to the Future: Engineering Without Data (Models)

Software engineering has changed dramatically in the last few decades. The rise of AWS, high-level languages, powerful libraries, and frameworks increasingly allow engineers to focus on business logic. Today, software engineers spend much of their time writing code that reasons over data to show something or do something. But how

ML (O)Ops! Keeping Track of Changes (Part 2)

The first part of the series, “Improving and Deploying On-Device Models With Confidence”, is posted here. With Atul Dhingra One way to automate classification is to compare new instances to a known list and plug in the majority class of the exact match. For such instance-based learning, you often don’

ML (O)Ops! Improving and Deploying On-Device Models With Confidence (Part 1)

With Atul Dhingra. Part 1 of a multi-part series. It is well known that ML Engineers today spend most of their time doing things that do not have a lot to do with machine learning. They spend time working on technically unsophisticated but important things like deployment of models, keeping

Build Software for the Lay User

Most word processing software helpfully point out grammatical errors and spelling mistakes. Some even autocorrect. And some, like Grammarly, even give style advice. Now consider software used for business statistics. Say you want to compute the correlation between two vectors: [100, 2000, 300, 400, 500, 600] and [1, 2, 3,

What’s the Next Best Thing to Learn?

With Gaurav Gandhi Recommendation engines are everywhere. These systems recommend what shows to watch on Netflix and what products to buy on Amazon. Since at least the Netflix Prize, the conventional wisdom is that recommendation engines have become very good. Except that they are not. Some of the deficiencies are

everywhere: meeting consumers where they are

Content delivery is not optimized for the technical stack used by an overwhelming majority of people. The technical stack of people who aren’t particularly tech-savvy, especially those who are old (over ~60 years), is often a messaging application like FB Messenger or WhatsApp. They currently do not have a