Louis Wang
ML engineer at Netflix, previously at Snap. I build reasoning recommender systems and AI agents — from generative retrieval and semantic IDs to autonomous agents, multi-agent systems, and LLM-powered applications.
Recent posts
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Two Bets on Generative Recommendation: Semantic IDs vs. Fine-Tuned LLMs
A head-to-head comparison of the two paradigms remaking recommendation — semantic ID autoregressive models and fine-tuned LLMs — with trade-off analysis and a look at how they're converging.
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The Attention Bottleneck: How Modern LLMs Solved a Problem That Nearly Broke the Transformer
From vanilla multi-head attention to Flash Attention 3 — the engineering bottlenecks that drove every major attention variant and the math behind each fix.
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The Harness Is the Moat: Why Autonomous AI Agents Live or Die by Their Architecture
Model quality is commoditising. The durable competitive advantage in 2026 is harness architecture — the deterministic enclosures that make probabilistic agents reliable. A deep analysis of the four architectural primitives every production harness must implement, and how Autoresearch, Ralph Loop, Superpowers, and GSD each solve them differently.
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Generative Recommendation in Production: HSTU, OneRec, and What Every Major Platform Is Building
From semantic IDs to OneRec Think — how Meta, Kuaishou, Google, Alibaba, ByteDance, and LinkedIn are replacing two-stage retrieval pipelines with generative models. What's in production and where the field is heading.
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From Vibe Coding to Harness Engineering: How to Actually Ship AI-Assisted Software
Vibe coding gets you a working prototype in 10 minutes. Harness engineering is how you ship it to production. Here's the difference, why it matters, and how to make the transition.