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\ No newline at end of file +[a7f1af6] \ No newline at end of file diff --git a/about/index.html b/about/index.html index 6387d52..933f0d7 100644 --- a/about/index.html +++ b/about/index.html @@ -1,7 +1,7 @@ About · Eric X. Liu's Personal Page
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    Eric X. Liu

    Software & Performance Engineer @Google

    \ No newline at end of file +[a7f1af6] \ No newline at end of file diff --git a/posts/a-comprehensive-guide-to-breville-barista-pro-maintenance/index.html b/posts/a-comprehensive-guide-to-breville-barista-pro-maintenance/index.html index 53074dc..d564fea 100644 --- a/posts/a-comprehensive-guide-to-breville-barista-pro-maintenance/index.html +++ b/posts/a-comprehensive-guide-to-breville-barista-pro-maintenance/index.html @@ -10,7 +10,7 @@ The Breville Barista Pro has two distinct, automated maintenance procedures: the Understanding the Two Primary Maintenance Cycles Link to heading The Breville Barista Pro has two distinct, automated maintenance procedures: the Cleaning (Flush) Cycle and the Descale Cycle. It is important to understand that these are not interchangeable, as they address different types of buildup within the machine.">

    A Comprehensive Guide to Breville Barista Pro Maintenance

    5-minute read

    Proper maintenance is critical for the longevity and performance of a Breville Barista Pro espresso machine. Consistent cleaning not only ensures the machine functions correctly but also directly impacts the quality of the espresso produced. This guide provides a detailed, technical breakdown of the essential maintenance routines, from automated cycles to daily upkeep.

    Understanding the Two Primary Maintenance Cycles @@ -25,4 +25,4 @@ Understanding the Two Primary Maintenance Cycles Link to heading The Breville Ba 2016 - 2025 Eric X. Liu -[fe52a73]

    \ No newline at end of file +[a7f1af6] \ No newline at end of file diff --git a/posts/a-deep-dive-into-ppo-for-language-models/index.html b/posts/a-deep-dive-into-ppo-for-language-models/index.html index c8732f6..cde61bd 100644 --- a/posts/a-deep-dive-into-ppo-for-language-models/index.html +++ b/posts/a-deep-dive-into-ppo-for-language-models/index.html @@ -3,7 +3,7 @@ You may have seen diagrams like the one below, which outlines the RLHF training You may have seen diagrams like the one below, which outlines the RLHF training process. It can look intimidating, with a web of interconnected models, losses, and data flows.">

    A Deep Dive into PPO for Language Models

    7-minute read

    Large Language Models (LLMs) have demonstrated astonishing capabilities, but out-of-the-box, they are simply powerful text predictors. They don’t inherently understand what makes a response helpful, harmless, or aligned with human values. The technique that has proven most effective at bridging this gap is Reinforcement Learning from Human Feedback (RLHF), and at its heart lies a powerful algorithm: Proximal Policy Optimization (PPO).

    You may have seen diagrams like the one below, which outlines the RLHF training process. It can look intimidating, with a web of interconnected models, losses, and data flows.

    This post will decode that diagram, piece by piece. We’ll explore the “why” behind each component, moving from high-level concepts to the deep technical reasoning that makes this process work.

    Translating RL to a Conversation @@ -23,4 +23,4 @@ where δ_t = r_t + γV(s_{t+1}) - V(s_t)

    \ No newline at end of file +[a7f1af6] \ No newline at end of file diff --git a/posts/espresso-theory-application-a-guide-for-the-breville-barista-pro/index.html b/posts/espresso-theory-application-a-guide-for-the-breville-barista-pro/index.html index fc7a647..8224fa5 100644 --- a/posts/espresso-theory-application-a-guide-for-the-breville-barista-pro/index.html +++ b/posts/espresso-theory-application-a-guide-for-the-breville-barista-pro/index.html @@ -3,7 +3,7 @@ Our overarching philosophy is simple: isolate and change only one variable at a Our overarching philosophy is simple: isolate and change only one variable at a time. While numbers are crucial, your palate is the ultimate judge. Dose, ratio, and time are interconnected, but your grind size is your most powerful lever.">

    Mastering Your Breville Barista Pro: The Ultimate Guide to Dialing In Espresso

    6-minute read

    Are you ready to transform your home espresso game from good to genuinely great? The Breville Barista Pro is a fantastic machine, but unlocking its full potential requires understanding a few key principles. This guide will walk you through the systematic process of dialing in your espresso, ensuring every shot is delicious and repeatable.

    Our overarching philosophy is simple: isolate and change only one variable at a time. While numbers are crucial, your palate is the ultimate judge. Dose, ratio, and time are interconnected, but your grind size is your most powerful lever.

    Let’s dive in!

    Part 1: The Foundation — Dose (The Weight of Dry Coffee) @@ -20,4 +20,4 @@ Our overarching philosophy is simple: isolate and change only one variable at a 2016 - 2025 Eric X. Liu -[fe52a73]

    \ No newline at end of file +[a7f1af6] \ No newline at end of file diff --git a/posts/how-rvq-teaches-llms-to-see-and-hear/index.html b/posts/how-rvq-teaches-llms-to-see-and-hear/index.html index 1882906..f3c9940 100644 --- a/posts/how-rvq-teaches-llms-to-see-and-hear/index.html +++ b/posts/how-rvq-teaches-llms-to-see-and-hear/index.html @@ -3,7 +3,7 @@ The answer lies in creating a universal language—a bridge between the continuo The answer lies in creating a universal language—a bridge between the continuous, messy world of pixels and audio waves and the discrete, structured world of language tokens. One of the most elegant and powerful tools for building this bridge is Residual Vector Quantization (RVQ).">

    Beyond Words: How RVQ Teaches LLMs to See and Hear

    6-minute read

    Large Language Models (LLMs) are masters of text, but the world is not made of text alone. It’s a symphony of sights, sounds, and experiences. The ultimate goal for AI is to understand this rich, multi-modal world as we do. But how do you teach a model that thinks in words to understand a picture of a sunset or the melody of a song?

    The answer lies in creating a universal language—a bridge between the continuous, messy world of pixels and audio waves and the discrete, structured world of language tokens. One of the most elegant and powerful tools for building this bridge is Residual Vector Quantization (RVQ).

    This article dives deep into RVQ, exploring how it turns raw data into meaningful semantic IDs and how these IDs, in turn, unlock multi-modal understanding in LLMs.

    What is Residual Vector Quantization? The Art of Smart Compression @@ -18,4 +18,4 @@ The answer lies in creating a universal language—a bridge between the continuo 2016 - 2025 Eric X. Liu -[fe52a73]

    \ No newline at end of file +[a7f1af6] \ No newline at end of file diff --git a/posts/index.html b/posts/index.html index f85d077..939d2e8 100644 --- a/posts/index.html +++ b/posts/index.html @@ -1,6 +1,6 @@ Posts · Eric X. Liu's Personal Page

    Posts

    Posts

    \ No newline at end of file +[a7f1af6] \ No newline at end of file diff --git a/posts/mixture-of-experts-moe-models-challenges-solutions-in-practice/index.html b/posts/mixture-of-experts-moe-models-challenges-solutions-in-practice/index.html index 9bc0d76..e5d8f7e 100644 --- a/posts/mixture-of-experts-moe-models-challenges-solutions-in-practice/index.html +++ b/posts/mixture-of-experts-moe-models-challenges-solutions-in-practice/index.html @@ -11,7 +11,7 @@ Many routing mechanisms, especially “Top-K routing,” involve a discr 1. Challenge: Non-Differentiability of Routing Functions Link to heading The Problem: Many routing mechanisms, especially “Top-K routing,” involve a discrete, hard selection process. A common function is KeepTopK(v, k), which selects the top k scoring elements from a vector v and sets others to $-\infty$ or $0$.">

    Mixture-of-Experts (MoE) Models Challenges & Solutions in Practice

    7-minute read

    Mixture-of-Experts (MoEs) are neural network architectures that allow different parts of the model (called “experts”) to specialize in different types of inputs. A “gating network” or “router” learns to dispatch each input (or “token”) to a subset of these experts. While powerful for scaling models, MoEs introduce several practical challenges.

    1. Challenge: Non-Differentiability of Routing Functions @@ -44,4 +44,4 @@ The Top-K routing mechanism, as illustrated in the provided ima 2016 - 2025 Eric X. Liu -[fe52a73]

    \ No newline at end of file +[a7f1af6] \ No newline at end of file diff --git a/posts/secure-boot-dkms-and-mok-on-proxmox-debian/index.html b/posts/secure-boot-dkms-and-mok-on-proxmox-debian/index.html index 51c076d..13554c7 100644 --- a/posts/secure-boot-dkms-and-mok-on-proxmox-debian/index.html +++ b/posts/secure-boot-dkms-and-mok-on-proxmox-debian/index.html @@ -7,7 +7,7 @@ That message is the tell: Secure Boot is enabled and the kernel refuses to load nvidia-smi failed to communicate with the NVIDIA driver modprobe nvidia → “Key was rejected by service” That message is the tell: Secure Boot is enabled and the kernel refuses to load modules not signed by a trusted key.">

    Fixing GPU Operator Pods Stuck in Init: Secure Boot, DKMS, and MOK on Proxmox + Debian

    3-minute read

    I hit an issue where all GPU Operator pods on one node were stuck in Init after migrating from Legacy BIOS to UEFI. The common error was NVIDIA components waiting for “toolkit-ready,” while the toolkit init container looped with:

    • nvidia-smi failed to communicate with the NVIDIA driver
    • modprobe nvidia → “Key was rejected by service”

    That message is the tell: Secure Boot is enabled and the kernel refuses to load modules not signed by a trusted key.

    Environment @@ -59,4 +59,4 @@ nvidia-smi failed to communicate with the NVIDIA driver modprobe nvidia → “K 2016 - 2025 Eric X. Liu -[fe52a73]

    \ No newline at end of file +[a7f1af6] \ No newline at end of file diff --git a/posts/supabase-deep-dive/index.html b/posts/supabase-deep-dive/index.html index 55adf50..43e3844 100644 --- a/posts/supabase-deep-dive/index.html +++ b/posts/supabase-deep-dive/index.html @@ -3,7 +3,7 @@ Supabase enters this space with a radically different philosophy: transparency. Supabase enters this space with a radically different philosophy: transparency. It provides the convenience of a BaaS, but it’s built on the world’s most trusted relational database: PostgreSQL. The “magic” isn’t a proprietary black box; it’s a carefully assembled suite of open-source tools that enhance Postgres, not hide it.">

    Supabase Deep Dive: It's Not Magic, It's Just Postgres

    8-minute read

    In the world of Backend-as-a-Service (BaaS), platforms are often treated as magic boxes. You push data in, you get data out, and you hope the magic inside scales. While this simplicity is powerful, it can obscure the underlying mechanics, leaving developers wondering what’s really going on.

    Supabase enters this space with a radically different philosophy: transparency. It provides the convenience of a BaaS, but it’s built on the world’s most trusted relational database: PostgreSQL. The “magic” isn’t a proprietary black box; it’s a carefully assembled suite of open-source tools that enhance Postgres, not hide it.

    This deep dive will deconstruct that suite. We will move beyond the basics to explore the architectural patterns, security models, and development workflows that allow you to build robust, scalable applications. We will cover:

    • The Supabase Blueprint: A procedural guide to designing your application.
    • The Pillars of Supabase: A detailed look at Auth, Storage, Functions, and Realtime.
    • Transactional Realtime: How Supabase guarantees data consistency in a live environment.
    • Best Practices: The practical knowledge you need before writing a single line of code.

    The Guiding Philosophy: Your Database as the Source of Truth @@ -90,4 +90,4 @@ Supabase enters this space with a radically different philosophy: transparency. 2016 - 2025 Eric X. Liu -[fe52a73]

    \ No newline at end of file +[a7f1af6] \ No newline at end of file diff --git a/posts/t5-the-transformer-that-zigged-when-others-zagged-an-architectural-deep-dive/index.html b/posts/t5-the-transformer-that-zigged-when-others-zagged-an-architectural-deep-dive/index.html index 9d01e0a..b9705ac 100644 --- a/posts/t5-the-transformer-that-zigged-when-others-zagged-an-architectural-deep-dive/index.html +++ b/posts/t5-the-transformer-that-zigged-when-others-zagged-an-architectural-deep-dive/index.html @@ -3,7 +3,7 @@ But to truly understand the field, we must look at the pivotal models that explo But to truly understand the field, we must look at the pivotal models that explored different paths. Google’s T5, or Text-to-Text Transfer Transformer, stands out as one of the most influential. It didn’t just introduce a new model; it proposed a new philosophy. This article dives deep into the architecture of T5, how it fundamentally differs from modern LLMs, and the lasting legacy of its unique design choices.">

    An Architectural Deep Dive of T5

    6-minute read

    In the rapidly evolving landscape of Large Language Models, a few key architectures define the dominant paradigms. Today, the “decoder-only” model, popularized by the GPT series and its successors like LLaMA and Mistral, reigns supreme. These models are scaled to incredible sizes and excel at in-context learning.

    But to truly understand the field, we must look at the pivotal models that explored different paths. Google’s T5, or Text-to-Text Transfer Transformer, stands out as one of the most influential. It didn’t just introduce a new model; it proposed a new philosophy. This article dives deep into the architecture of T5, how it fundamentally differs from modern LLMs, and the lasting legacy of its unique design choices.

    The Core Philosophy: Everything is a Text-to-Text Problem @@ -30,4 +30,4 @@ But to truly understand the field, we must look at the pivotal models that explo 2016 - 2025 Eric X. Liu -[fe52a73]

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    Some useful files

    One-minute read
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