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Computer Science

A collection of 37 issues

Shipping Fuzzy‑Joined Data

A growing share of empirical work relies on "data products" that integrate multiple administrative and survey sources via fuzzy joins. Researchers typically receive only the final merged panel and treat each cell as if it were directly observed. But upstream, the data producer has made substantive decisions—how

What Problem Is Traditional RAG Solving?

RAG solves a specific technical problem: it enables models trained on broad public text to answer questions about specialized content without retraining. But solving a technical problem doesn't automatically make RAG the right business solution. A useful way to reason about search-system design is to model its net

Conscious Decoupling: Separating Budgets For Thought and Action

Current LLM services bundle model intelligence with supporting infrastructure into fixed tiers. When you purchase access to GPT‑5 Thinking, Claude Opus 4.1, or similar offerings, you receive a package: some amount of model capability paired with some unspecified quantity of supporting compute. This bundling serves a purpose. It

Testing LLMs: Engineering Confidence Without Certainty

Software testing has always relied on a fragile assumption: that we can enumerate test cases that represent production behavior. We test specific inputs, verify outputs, and trust that production will behave similarly. This works until it doesn't. Search ranking degrades as user behavior evolves. Heuristics fail when traffic

How to Make Things Slower So They Go Faster

In a service with capacity $\mu$ requests per second and background load $\lambda_0$, the usable headroom is $H = \mu - \lambda_0 > 0$. When $M$ clients align, e.g., after a cache expiry, at a cron boundary, or as a service returns from an outage, the bucketed arrival

From Vibe Coding Assistants to Coding Assistants

Modern coding assistants often firehose code: a single prompt yields pages of plausible implementation that the developer must reverse‑engineer, reconcile with project conventions, and defend in review. That behavior is optimized for vibe coding—outputs that look complete and fluent—rather than for actual coding, where teams must understand,

(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