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Computer Science

A collection of 33 issues

From Autopilot to Copilot: Designing Coding Assistants For Experts

Current assistants often generate large swaths of code in a single pass. This verbosity forces developers to reverse‑engineer the AI’s intent, verify subtle corner cases, and retrofit the output to project conventions. Because design rationale is rarely surfaced, reviewers struggle to trace decisions, and tests—if provided at

(Don't) Feed the Robots

"My site [with] modest traffic (~1,000 visitors per day) went from serving ~1GB/hr (to tons of AI crawlers!) to ~50MB/hr in traffic" Can you tell what was the point where I 1. Manually blocked a few AI crawlers 2. Moved my nameserver to Cloudflare plus

The GitHub Sampler

GitHub is the world's largest developer platform, making it a compelling place to build developer tools and study software development. However, with over half a billion repositories and an API that limits responses to a thousand results, it feels nigh impossible to answer even simple questions like, "

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 allows 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