How Yelp Built "Yelp Assistant" (10 minute read)
Yelp built an AI assistant that answers specific questions about businesses (like "Is the patio heated?") by pulling evidence from reviews, photos, and business attributes instead of relying on the LLM's own knowledge. The real challenge wasn't the initial prototype, but rather making it production-ready, which meant splitting a single bloated LLM call into specialized stages, keeping data fresh in near real-time, and cutting latency from 10+ seconds down to under 3.5. |
The purpose of Continuous Integration is to fail (6 minute read)
Continuous Integration (CI) provides value not when it passes, but specifically when it fails, as its primary purpose is to catch mistakes early in the development process. Without CI, errors are only detected after deployment, resulting in long, manual, and dangerous feedback loops where problems can cause significant damage. A failing CI run acts as a safety net, preventing erroneous code from reaching users and offering a much shorter, automated, and safer feedback mechanism. |
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Nobody knows how the whole system works (4 minute read)
No single person truly understands how complex technological systems, like telephones or the internet, work completely. While building without understanding underlying mechanisms is risky, modern systems are inherently too complex for any individual to grasp entirely. |
Negative Externalities of Gen-AI within Software Teams (10 minute read)
Generative AI, despite potential individual productivity gains, creates "negative externalities" that reduce collaboration within software teams. These issues include LLM-generated communication that is overly verbose and lacks contextual hierarchy, making crucial information difficult to find. Additionally, AI-produced code introduces novel types of bugs that necessitate increased vigilance during code reviews and obscures authorship, making it harder to identify team members with specific knowledge. |
Stop Using Icons in Data Tables (4 minute read)
Icons in data tables actually increase cognitive load and visual entropy, making complex data harder to process. This is because non-universal icons require users to decode intricate shapes, creating a "cognitive tax" unlike the predictable, rhythmic "texture" of text. |
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LiftKit: The UI Framework for Perfectionists (4 minute read)
LiftKit is an open-source UI framework designed for perfectionists, primarily solving common visual symmetry problems in user interfaces. It applies golden-ratio proportions with subpixel accuracy to elements like buttons, cards, and inputs, correcting issues such as icon spacing and padding. |
Transformers.js v4 Preview: Now Available on NPM! (6 minute read)
Hugging Face has released a preview of Transformers.js v4 on NPM. Transformers.js enables the execution of machine learning models through JavaScript on the web. This version introduces a new WebGPU Runtime, completely rewritten in C++, which allows WebGPU-accelerated models to run across JavaScript environments like browsers, servers, and desktop applications. |
Claude Skills (GitHub Repo)
This repository contains 97 production-ready skills for the Claude Code CLI. These skills cover token savings, prevention of common coding errors, and tools like Context Mate for project analysis and deep debugging. |
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Opus 4.6, Codex 5.3, and the post-benchmark era (10 minute read)
OpenAI and Anthropic recently released updated coding assistant models, GPT-5.3-Codex and Claude Opus 4.6. While Codex 5.3 has improved a lot, offering faster feedback and broader task capabilities, Claude Opus 4.6 still holds an edge in usability and reliability, making it more approachable for a wider audience. Traditional benchmark scores are increasingly irrelevant for assessing these new agentic models. |
What Is an Async Agent, Really? (8 minute read)
No agent is inherently asynchronous. Rather, its asynchronous nature depends on whether the user chooses to wait for its completion. A true "async agent" should be defined as an agent capable of managing and orchestrating multiple other sub-agents concurrently. |
Python Only Has One Real Competitor (5 minute read)
Python is the leader in data science, a position solidified by its straightforward native code interop that helped create essential libraries like NumPy and Pandas, alongside its general-purpose utility for deploying models and other applications. While specialized languages like R and MATLAB exist, they don't have Python's versatility, struggling to compete as end-to-end solutions. Clojure is Python's only genuine competitor, especially as it runs on the Java Virtual Machine, which overcomes Python's inherent performance limitations. |
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six thoughts on generating c (9 minute read)
A compiler engineer shares practical strategies for generating robust and performant C code, going over techniques such as utilizing `static inline` for abstraction, explicit type management, and manual register allocation to use C efficiently as a compilation target. |
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