<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet href="/rss-styles.xsl" type="text/xsl"?><rss version="2.0"><channel><title>Ping-Lin Chang</title><description>A space where my thoughts take flight.</description><link>https://pinglin.tw/</link><language>en-us</language><item><title>Collection Autofill at Scale</title><link>https://pinglin.tw/blog/collection-autofill-at-scale/</link><guid isPermaLink="true">https://pinglin.tw/blog/collection-autofill-at-scale/</guid><description>A design walkthrough of how Collection Autofill — a spreadsheet of AI-computed cells — stays fast, fair, and observable from a hundred rows to ten million.</description><pubDate>Thu, 28 May 2026 00:00:00 GMT</pubDate></item><item><title>Reimagine Unstructured Data ETL</title><link>https://pinglin.tw/blog/reimagine-unstructured-data-etl/</link><guid isPermaLink="true">https://pinglin.tw/blog/reimagine-unstructured-data-etl/</guid><description>Generative AI is revolutionizing software—how does it transform the way we utilize unstructured data like text, images, and videos?</description><pubDate>Fri, 14 Feb 2025 00:00:00 GMT</pubDate></item><item><title>The Shapes of Agent Memory – Files, Stores, and Experience</title><link>https://pinglin.tw/blog/the-shapes-of-agent-memory/</link><guid isPermaLink="true">https://pinglin.tw/blog/the-shapes-of-agent-memory/</guid><description>An agent that remembers across sessions can keep its memory as curated markdown files, as an auto-mined structured store, or as trained experience. I measured all of them: files against a structured store under one fixed model, a store-only head-to-head across the structured lineages, and an experience bank on the agentic benchmarks where the state of the art trains memory into the weights.</description><pubDate>Wed, 12 Aug 2026 00:00:00 GMT</pubDate></item><item><title>Why Steering Works – The Theory Beneath X Engineering for AI Agents</title><link>https://pinglin.tw/blog/why-steering-works/</link><guid isPermaLink="true">https://pinglin.tw/blog/why-steering-works/</guid><description>An agent run is stochastic gradient descent over solution space, and steering is how the true objective enters the loop. An optimization view and a Bayesian view that ground the industry ladder — prompt, context, harness, loop engineering — in foundation theory, and explain when a big model steering a small one beats distillation, and when it does not.</description><pubDate>Sun, 26 Jul 2026 00:00:00 GMT</pubDate></item><item><title>Optimizing Transformer Model Serving Parameters – An Apple Silicon GPU Case Study</title><link>https://pinglin.tw/blog/optimizing-apple-silicon-gpu-for-transformer-inference/</link><guid isPermaLink="true">https://pinglin.tw/blog/optimizing-apple-silicon-gpu-for-transformer-inference/</guid><description>One MacBook Pro M4 Max, an open-source MoE, and a hands-on exercise in optimizing it for inference: why the thing that collapses LLM throughput by 100× is prefill, not decode, and the two prefill-side fixes that got it back.</description><pubDate>Sat, 11 Jul 2026 00:00:00 GMT</pubDate></item></channel></rss>