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2026-01-03 06:48:17 +00:00
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2016 - 2016 -
2026 2026
Eric X. Liu Eric X. Liu
<a href="https://git.ericxliu.me/eric/ericxliu-me/commit/f1178d3">[f1178d3]</a></section></footer></main><script src=/js/coder.min.6ae284be93d2d19dad1f02b0039508d9aab3180a12a06dcc71b0b0ef7825a317.js integrity="sha256-auKEvpPS0Z2tHwKwA5UI2aqzGAoSoG3McbCw73gloxc="></script><script defer src=https://static.cloudflareinsights.com/beacon.min.js data-cf-beacon='{"token": "987638e636ce4dbb932d038af74c17d1"}'></script></body></html> <a href="https://git.ericxliu.me/eric/ericxliu-me/commit/89dc118">[89dc118]</a></section></footer></main><script src=/js/coder.min.6ae284be93d2d19dad1f02b0039508d9aab3180a12a06dcc71b0b0ef7825a317.js integrity="sha256-auKEvpPS0Z2tHwKwA5UI2aqzGAoSoG3McbCw73gloxc="></script><script defer src=https://static.cloudflareinsights.com/beacon.min.js data-cf-beacon='{"token": "987638e636ce4dbb932d038af74c17d1"}'></script></body></html>

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2016 - 2016 -
2026 2026
Eric X. Liu Eric X. Liu
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2016 - 2016 -
2026 2026
Eric X. Liu Eric X. Liu
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2016 - 2016 -
2026 2026
Eric X. Liu Eric X. Liu
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<!doctype html><html lang=en><head><title>Eric X. Liu's Personal Page</title><meta charset=utf-8><meta name=viewport content="width=device-width,initial-scale=1"><meta name=color-scheme content="light dark"><meta http-equiv=Content-Security-Policy content="upgrade-insecure-requests; block-all-mixed-content; default-src 'self'; child-src 'self'; font-src 'self' https://fonts.gstatic.com https://cdn.jsdelivr.net/; form-action 'self'; frame-src 'self' https://www.youtube.com https://disqus.com; img-src 'self' https://referrer.disqus.com https://c.disquscdn.com https://*.disqus.com; object-src 'none'; style-src 'self' 'unsafe-inline' https://fonts.googleapis.com/ https://cdn.jsdelivr.net/; script-src 'self' 'unsafe-inline' https://www.google-analytics.com https://cdn.jsdelivr.net/ https://pagead2.googlesyndication.com https://static.cloudflareinsights.com https://unpkg.com https://ericxliu-me.disqus.com https://disqus.com https://*.disqus.com https://*.disquscdn.com https://unpkg.com; connect-src 'self' https://www.google-analytics.com https://pagead2.googlesyndication.com https://cloudflareinsights.com ws://localhost:1313 ws://localhost:* wss://localhost:* https://links.services.disqus.com https://*.disqus.com;"><meta name=author content="Eric X. 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Eric X. Liu Eric X. Liu
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<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Eric X. Liu's Personal Page</title><link>https://ericxliu.me/</link><description>Recent content on Eric X. Liu's Personal Page</description><generator>Hugo</generator><language>en</language><lastBuildDate>Mon, 29 Dec 2025 07:15:58 +0000</lastBuildDate><atom:link href="https://ericxliu.me/index.xml" rel="self" type="application/rss+xml"/><item><title>How I Got Open WebUI Talking to OpenAI Web Search</title><link>https://ericxliu.me/posts/open-webui-openai-websearch/</link><pubDate>Mon, 29 Dec 2025 00:00:00 +0000</pubDate><guid>https://ericxliu.me/posts/open-webui-openai-websearch/</guid><description>&lt;p&gt;OpenAI promised native web search in GPT‑5, but LiteLLM proxy deployments (and by extension Open WebUI) still choke on it—issue &lt;a href="https://github.com/BerriAI/litellm/issues/13042" class="external-link" target="_blank" rel="noopener"&gt;#13042&lt;/a&gt; tracks the fallout. I needed grounded answers inside Open WebUI anyway, so I built a workaround: route GPT‑5 traffic through the Responses API and mask every &lt;code&gt;web_search_call&lt;/code&gt; before the UI ever sees it.&lt;/p&gt; <?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Eric X. Liu's Personal Page</title><link>https://ericxliu.me/</link><description>Recent content on Eric X. Liu's Personal Page</description><generator>Hugo</generator><language>en</language><lastBuildDate>Sat, 03 Jan 2026 06:23:35 +0000</lastBuildDate><atom:link href="https://ericxliu.me/index.xml" rel="self" type="application/rss+xml"/><item><title>Why Your "Resilient" Homelab is Slower Than a Raspberry Pi</title><link>https://ericxliu.me/posts/debugging-authentik-performance/</link><pubDate>Fri, 02 Jan 2026 00:00:00 +0000</pubDate><guid>https://ericxliu.me/posts/debugging-authentik-performance/</guid><description>&lt;p&gt;In the world of self-hosting, there are many metrics for success: 99.9% uptime, sub-second latency, or a perfect GitOps pipeline. But for those of us running &amp;ldquo;production&amp;rdquo; at home, there is only one metric that truly matters: &lt;strong&gt;The Wife Acceptance Factor (WAF)&lt;/strong&gt;.&lt;/p&gt;
&lt;p&gt;My detailed Grafana dashboards said everything was fine. But my wife said the SSO login was &amp;ldquo;slow sometimes.&amp;rdquo; She was right. Debugging it took me down a rabbit hole of connection pooling, misplaced assumptions, and the harsh reality of running databases on distributed storage.&lt;/p&gt;</description></item><item><title>How I Got Open WebUI Talking to OpenAI Web Search</title><link>https://ericxliu.me/posts/open-webui-openai-websearch/</link><pubDate>Mon, 29 Dec 2025 00:00:00 +0000</pubDate><guid>https://ericxliu.me/posts/open-webui-openai-websearch/</guid><description>&lt;p&gt;OpenAI promised native web search in GPT‑5, but LiteLLM proxy deployments (and by extension Open WebUI) still choke on it—issue &lt;a href="https://github.com/BerriAI/litellm/issues/13042" class="external-link" target="_blank" rel="noopener"&gt;#13042&lt;/a&gt; tracks the fallout. I needed grounded answers inside Open WebUI anyway, so I built a workaround: route GPT‑5 traffic through the Responses API and mask every &lt;code&gt;web_search_call&lt;/code&gt; before the UI ever sees it.&lt;/p&gt;
&lt;p&gt;This post documents the final setup, the hotfix script that keeps LiteLLM honest, and the tests that prove Open WebUI now streams cited answers without trying to execute the tool itself.&lt;/p&gt;</description></item><item><title>From Gemini-3-Flash to T5-Gemma-2 A Journey in Distilling a Family Finance LLM</title><link>https://ericxliu.me/posts/technical-deep-dive-llm-categorization/</link><pubDate>Sat, 27 Dec 2025 00:00:00 +0000</pubDate><guid>https://ericxliu.me/posts/technical-deep-dive-llm-categorization/</guid><description>&lt;p&gt;Running a family finance system is surprisingly complex. What starts as a simple spreadsheet often evolves into a web of rules, exceptions, and &amp;ldquo;wait, was this dinner or &lt;em&gt;vacation&lt;/em&gt; dinner?&amp;rdquo; questions.&lt;/p&gt; &lt;p&gt;This post documents the final setup, the hotfix script that keeps LiteLLM honest, and the tests that prove Open WebUI now streams cited answers without trying to execute the tool itself.&lt;/p&gt;</description></item><item><title>From Gemini-3-Flash to T5-Gemma-2 A Journey in Distilling a Family Finance LLM</title><link>https://ericxliu.me/posts/technical-deep-dive-llm-categorization/</link><pubDate>Sat, 27 Dec 2025 00:00:00 +0000</pubDate><guid>https://ericxliu.me/posts/technical-deep-dive-llm-categorization/</guid><description>&lt;p&gt;Running a family finance system is surprisingly complex. What starts as a simple spreadsheet often evolves into a web of rules, exceptions, and &amp;ldquo;wait, was this dinner or &lt;em&gt;vacation&lt;/em&gt; dinner?&amp;rdquo; questions.&lt;/p&gt;
&lt;p&gt;For years, I relied on a rule-based system to categorize our credit card transactions. It worked&amp;hellip; mostly. But maintaining &lt;code&gt;if &amp;quot;UBER&amp;quot; in description and amount &amp;gt; 50&lt;/code&gt; style rules is a never-ending battle against the entropy of merchant names and changing habits.&lt;/p&gt;</description></item><item><title>About</title><link>https://ericxliu.me/about/</link><pubDate>Fri, 19 Dec 2025 22:46:12 -0800</pubDate><guid>https://ericxliu.me/about/</guid><description>&lt;img src="https://ericxliu.me/images/about.jpeg" alt="Eric Liu" width="300" style="float: left; margin-right: 1.5rem; margin-bottom: 1rem; border-radius: 8px;"/&gt; &lt;p&gt;For years, I relied on a rule-based system to categorize our credit card transactions. It worked&amp;hellip; mostly. But maintaining &lt;code&gt;if &amp;quot;UBER&amp;quot; in description and amount &amp;gt; 50&lt;/code&gt; style rules is a never-ending battle against the entropy of merchant names and changing habits.&lt;/p&gt;</description></item><item><title>About</title><link>https://ericxliu.me/about/</link><pubDate>Fri, 19 Dec 2025 22:46:12 -0800</pubDate><guid>https://ericxliu.me/about/</guid><description>&lt;img src="https://ericxliu.me/images/about.jpeg" alt="Eric Liu" width="300" style="float: left; margin-right: 1.5rem; margin-bottom: 1rem; border-radius: 8px;"/&gt;
&lt;p&gt;Hi, I&amp;rsquo;m &lt;strong&gt;Eric Liu&lt;/strong&gt;.&lt;/p&gt; &lt;p&gt;Hi, I&amp;rsquo;m &lt;strong&gt;Eric Liu&lt;/strong&gt;.&lt;/p&gt;

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<label class="menu-button float-right" for=menu-toggle><i class="fa-solid fa-bars fa-fw" aria-hidden=true></i></label><ul class=navigation-list><li class=navigation-item><a class=navigation-link href=/posts/>Posts</a></li><li class=navigation-item><a class=navigation-link href=https://chat.ericxliu.me>Chat</a></li><li class=navigation-item><a class=navigation-link href=https://git.ericxliu.me/user/oauth2/Authenitk>Git</a></li><li class=navigation-item><a class=navigation-link href=https://coder.ericxliu.me/api/v2/users/oidc/callback>Coder</a></li><li class=navigation-item><a class=navigation-link href=/about/>About</a></li><li class=navigation-item><a class=navigation-link href=/>|</a></li><li class=navigation-item><a class=navigation-link href=https://sso.ericxliu.me>Sign in</a></li></ul></section></nav><div class=content><section class="container post"><article><header><div class=post-title><h1 class=title><a class=title-link href=https://ericxliu.me/posts/debugging-authentik-performance/>Why Your "Resilient" Homelab is Slower Than a Raspberry Pi</a></h1></div><div class=post-meta><div class=date><span class=posted-on><i class="fa-solid fa-calendar" aria-hidden=true></i>
<time datetime=2026-01-02T00:00:00Z>January 2, 2026
</time></span><span class=reading-time><i class="fa-solid fa-clock" aria-hidden=true></i>
5-minute read</span></div></div></header><div class=post-content><p>In the world of self-hosting, there are many metrics for success: 99.9% uptime, sub-second latency, or a perfect GitOps pipeline. But for those of us running &ldquo;production&rdquo; at home, there is only one metric that truly matters: <strong>The Wife Acceptance Factor (WAF)</strong>.</p><p>My detailed Grafana dashboards said everything was fine. But my wife said the SSO login was &ldquo;slow sometimes.&rdquo; She was right. Debugging it took me down a rabbit hole of connection pooling, misplaced assumptions, and the harsh reality of running databases on distributed storage.</p><p>Here is a breakdown of the symptoms, the red herrings, and the root cause that was hiding in plain sight.</p><h2 id=the-environment>The Environment
<a class=heading-link href=#the-environment><i class="fa-solid fa-link" aria-hidden=true title="Link to heading"></i>
<span class=sr-only>Link to heading</span></a></h2><p>My homelab is designed for node-level resilience, which adds complexity to the storage layer. It is not running on a single server, but rather a 3-node <strong>Proxmox</strong> cluster where every component is redundant:</p><ul><li><strong>Orchestration</strong>: Kubernetes (k3s) managed via Flux CD.</li><li><strong>Storage</strong>: A <strong>Ceph</strong> cluster running on the Proxmox nodes, utilizing enterprise NVMe SSDs (<code>bluestore</code>) for OSDs.</li><li><strong>Database</strong>: Postgres managed by the Zalando Postgres Operator, with persistent volumes (PVCs) provisioned on Ceph RBD (block storage).</li><li><strong>Identity</strong>: Authentik for SSO.</li></ul><p>While the underlying disks are blazing fast NVMe drives, the architecture dictates that a write to a Ceph RBD volume is not complete until it is replicated over the network and acknowledged by multiple OSDs. This setup provides incredible resilience—I can pull the plug on a node and nothing stops—but it introduces unavoidable network latency for synchronous write operations. <strong>Keep this particular trade-off in mind; it plays a starring role in the investigation later.</strong></p><h2 id=the-symptom>The Symptom
<a class=heading-link href=#the-symptom><i class="fa-solid fa-link" aria-hidden=true title="Link to heading"></i>
<span class=sr-only>Link to heading</span></a></h2><p>The issue was insidious because it was intermittent. Clicking &ldquo;Login&rdquo; would sometimes hang for 5-8 seconds, while other times it was instant. To an engineer, &ldquo;sometimes slow&rdquo; is the worst kind of bug because it defies easy reproduction.</p><p>The breakthrough came when I put aside the server-side Grafana dashboards and looked at the client side. By opening Chrome DevTools and monitoring the <strong>Network</strong> tab during a slow login attempt, I was able to capture the exact failing request.</p><p>I identified the culprit: the <code>/api/v3/core/applications/</code> endpoint. It wasn&rsquo;t a connection timeout or a DNS issue; the server was simply taking 5+ seconds to respond to this specific GET request.</p><p>Armed with this &ldquo;smoking gun,&rdquo; I copied the request as cURL (preserving the session cookies) and converted it into a Python benchmark script (<code>reproduce_latency.py</code>). This allowed me to reliably trigger the latency on demand, turning an intermittent &ldquo;heisenbug&rdquo; into a reproducible test case.</p><p>The results were validating and horrifying:</p><div class=highlight><pre tabindex=0 style=color:#e6edf3;background-color:#0d1117;-moz-tab-size:4;-o-tab-size:4;tab-size:4><code class=language-text data-lang=text><span style=display:flex><span>Request 1: 2.1642s
</span></span><span style=display:flex><span>Request 2: 8.4321s
</span></span><span style=display:flex><span>Request 3: 5.1234s
</span></span><span style=display:flex><span>...
</span></span><span style=display:flex><span>Avg Latency: 4.8s
</span></span></code></pre></div><h2 id=investigation--red-herrings>Investigation & Red Herrings
<a class=heading-link href=#investigation--red-herrings><i class="fa-solid fa-link" aria-hidden=true title="Link to heading"></i>
<span class=sr-only>Link to heading</span></a></h2><h3 id=attempt-1-the-connection-overhead-hypothesis>Attempt 1: The Connection Overhead Hypothesis
<a class=heading-link href=#attempt-1-the-connection-overhead-hypothesis><i class="fa-solid fa-link" aria-hidden=true title="Link to heading"></i>
<span class=sr-only>Link to heading</span></a></h3><p><strong>The Hypothesis</strong>: Authentik defaults to <code>CONN_MAX_AGE=0</code>, meaning it closes the database connection after every request. Since I enforce SSL for the database, I assumed the handshake overhead was killing performance.</p><p><strong>The Fix Attempt</strong>: I updated the Authentik configuration to enable persistent connections:</p><div class=highlight><pre tabindex=0 style=color:#e6edf3;background-color:#0d1117;-moz-tab-size:4;-o-tab-size:4;tab-size:4><code class=language-yaml data-lang=yaml><span style=display:flex><span><span style=color:#7ee787>env</span>:<span style=color:#6e7681>
</span></span></span><span style=display:flex><span><span style=color:#6e7681> </span>- <span style=color:#7ee787>name</span>:<span style=color:#6e7681> </span><span style=color:#a5d6ff>AUTHENTIK_POSTGRESQL__CONN_MAX_AGE</span><span style=color:#6e7681>
</span></span></span><span style=display:flex><span><span style=color:#6e7681> </span><span style=color:#7ee787>value</span>:<span style=color:#6e7681> </span><span style=color:#a5d6ff>&#34;600&#34;</span><span style=color:#6e7681>
</span></span></span></code></pre></div><p><strong>The Reality</strong>: The benchmark showed a slight improvement (~4.2s average), but the random 5-8s spikes remained. The 300ms connection setup was a factor, but not the root cause. As a side note, enabling this without configuring TCP Keepalives caused the Authentik worker to crash with <code>OperationalError('the connection is closed')</code> when firewalls silently dropped idle connections.</p><h3 id=attempt-2-cpu-starvation>Attempt 2: CPU Starvation
<a class=heading-link href=#attempt-2-cpu-starvation><i class="fa-solid fa-link" aria-hidden=true title="Link to heading"></i>
<span class=sr-only>Link to heading</span></a></h3><p><strong>The Hypothesis</strong>: The pods were CPU throttled during request processing.</p><p><strong>The Reality</strong>: <code>kubectl top pods</code> showed the server using only 29m (2.9% of a core). Even increasing the Gunicorn worker count from 2 to 4 did not improve the latency of individual requests, though it did help with concurrency.</p><h2 id=the-root-cause-a-perfect-storm>The Root Cause: A Perfect Storm
<a class=heading-link href=#the-root-cause-a-perfect-storm><i class="fa-solid fa-link" aria-hidden=true title="Link to heading"></i>
<span class=sr-only>Link to heading</span></a></h2><p>I was stuck. The CPU was idle, network was fine, and individual database queries were fast (&lt;1ms). Then I looked at the traffic patterns:</p><ol><li><strong>Redis</strong>: Almost zero traffic.</li><li><strong>Postgres</strong>: High <code>WALSync</code> and <code>WALWrite</code> wait times.</li><li><strong>The Table</strong>: <code>django_postgres_cache_cacheentry</code> was getting hammered.</li></ol><h3 id=insight-the-breaking-change>Insight: The Breaking Change
<a class=heading-link href=#insight-the-breaking-change><i class="fa-solid fa-link" aria-hidden=true title="Link to heading"></i>
<span class=sr-only>Link to heading</span></a></h3><p>I checked the release notes for <strong>Authentik 2025.10</strong>:</p><blockquote><p><em>Breaking Change: Redis is no longer used for caching. All caching has been moved to the PostgreSQL database to simplify deployment.</em></p></blockquote><p>This architectural shift created a bottleneck specific to my storage backend:</p><ol><li><strong>The Change</strong>: Every API request triggers a cache write (session updates) to Postgres instead of Redis.</li><li><strong>The Default</strong>: Postgres defaults to <code>synchronous_commit = on</code>. A transaction is not considered &ldquo;committed&rdquo; until it is flushed to disk.</li><li><strong>The Storage</strong>: Ceph RBD replicates data across the network to multiple OSDs.</li></ol><p>Every time I loaded the dashboard, Authentik tried to update the cache. Postgres paused, verified the write was replicated to 3 other servers over the network (WAL Sync), and <em>then</em> responded.</p><h2 id=the-solution>The Solution
<a class=heading-link href=#the-solution><i class="fa-solid fa-link" aria-hidden=true title="Link to heading"></i>
<span class=sr-only>Link to heading</span></a></h2><p>I couldn&rsquo;t move the database to local NVMe without losing the failover capabilities I built the cluster for. However, for a cache-heavy workload, I could compromise on strict durability.</p><p>I patched the Postgres configuration to disable synchronous commits:</p><div class=highlight><pre tabindex=0 style=color:#e6edf3;background-color:#0d1117;-moz-tab-size:4;-o-tab-size:4;tab-size:4><code class=language-yaml data-lang=yaml><span style=display:flex><span><span style=color:#7ee787>spec</span>:<span style=color:#6e7681>
</span></span></span><span style=display:flex><span><span style=color:#6e7681> </span><span style=color:#7ee787>postgresql</span>:<span style=color:#6e7681>
</span></span></span><span style=display:flex><span><span style=color:#6e7681> </span><span style=color:#7ee787>parameters</span>:<span style=color:#6e7681>
</span></span></span><span style=display:flex><span><span style=color:#6e7681> </span><span style=color:#7ee787>synchronous_commit</span>:<span style=color:#6e7681> </span><span style=color:#a5d6ff>&#34;off&#34;</span><span style=color:#6e7681> </span><span style=color:#8b949e;font-style:italic># The magic switch</span><span style=color:#6e7681>
</span></span></span></code></pre></div><p><strong>What this does</strong>: Postgres returns &ldquo;Success&rdquo; to the application as soon as the transaction is in memory. It flushes to disk in the background. In the event of a crash, I might lose the last ~500ms of data (mostly cache entries), which is an acceptable trade-off.</p><h2 id=verification>Verification
<a class=heading-link href=#verification><i class="fa-solid fa-link" aria-hidden=true title="Link to heading"></i>
<span class=sr-only>Link to heading</span></a></h2><p>I re-ran the benchmark with <code>synchronous_commit = off</code>.</p><table><thead><tr><th>Metric</th><th>Before (<code>sync=on</code>)</th><th>After (<code>sync=off</code>)</th><th>Improvement</th></tr></thead><tbody><tr><td>Sequential (Avg)</td><td>~4.8s</td><td><strong>0.40s</strong></td><td><strong>12x Faster</strong></td></tr><tr><td>Parallel (Wall)</td><td>~10.5s</td><td><strong>2.45s</strong></td><td><strong>4x Faster</strong></td></tr></tbody></table><p>The latency vanished. The login became instant.</p><h2 id=key-insights>Key Insights
<a class=heading-link href=#key-insights><i class="fa-solid fa-link" aria-hidden=true title="Link to heading"></i>
<span class=sr-only>Link to heading</span></a></h2><ul><li><strong>Read Release Notes</strong>: The shift from Redis to Postgres for caching was a major architectural change that I missed during the upgrade.</li><li><strong>Storage Matters</strong>: Distributed storage (Ceph/Longhorn) handles linear writes well, but struggles with latency-sensitive, high-frequency sync operations like WAL updates.</li><li><strong>Tuning Postgres</strong>: For workloads where immediate durability is less critical than latency (like caching tables), <code>synchronous_commit = off</code> is a powerful tool.</li><li><strong>Observability</strong>: The &ldquo;Wife Test&rdquo; is a valid monitoring alert. If a user complains it&rsquo;s slow, investigate the P99 latency, not just the average.</li></ul><h3 id=references>References
<a class=heading-link href=#references><i class="fa-solid fa-link" aria-hidden=true title="Link to heading"></i>
<span class=sr-only>Link to heading</span></a></h3><ul><li><a href=https://docs.goauthentik.io/releases/2025.10/ class=external-link target=_blank rel=noopener>Authentik 2025.10 Release Notes</a></li><li><a href=https://www.postgresql.org/docs/current/wal-async-commit.html class=external-link target=_blank rel=noopener>PostgreSQL Documentation: Synchronous Commit</a></li></ul></div><footer><div id=disqus_thread></div><script>window.disqus_config=function(){},function(){if(["localhost","127.0.0.1"].indexOf(window.location.hostname)!=-1){document.getElementById("disqus_thread").innerHTML="Disqus comments not available by default when the website is previewed locally.";return}var t=document,e=t.createElement("script");e.async=!0,e.src="//ericxliu-me.disqus.com/embed.js",e.setAttribute("data-timestamp",+new Date),(t.head||t.body).appendChild(e)}(),document.addEventListener("themeChanged",function(){document.readyState=="complete"&&DISQUS.reset({reload:!0,config:disqus_config})})</script></footer></article><link rel=stylesheet href=https://cdn.jsdelivr.net/npm/katex@0.16.4/dist/katex.min.css integrity=sha384-vKruj+a13U8yHIkAyGgK1J3ArTLzrFGBbBc0tDp4ad/EyewESeXE/Iv67Aj8gKZ0 crossorigin=anonymous><script defer src=https://cdn.jsdelivr.net/npm/katex@0.16.4/dist/katex.min.js integrity=sha384-PwRUT/YqbnEjkZO0zZxNqcxACrXe+j766U2amXcgMg5457rve2Y7I6ZJSm2A0mS4 crossorigin=anonymous></script><script defer src=https://cdn.jsdelivr.net/npm/katex@0.16.4/dist/contrib/auto-render.min.js integrity=sha384-+VBxd3r6XgURycqtZ117nYw44OOcIax56Z4dCRWbxyPt0Koah1uHoK0o4+/RRE05 crossorigin=anonymous onload='renderMathInElement(document.body,{delimiters:[{left:"$$",right:"$$",display:!0},{left:"$",right:"$",display:!1},{left:"\\(",right:"\\)",display:!1},{left:"\\[",right:"\\]",display:!0}]})'></script></section></div><footer class=footer><section class=container>©
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<a class=title href=/posts/debugging-authentik-performance/>Why Your "Resilient" Homelab is Slower Than a Raspberry Pi</a></li><li><span class=date>December 29, 2025</span>
<a class=title href=/posts/open-webui-openai-websearch/>How I Got Open WebUI Talking to OpenAI Web Search</a></li><li><span class=date>December 27, 2025</span> <a class=title href=/posts/open-webui-openai-websearch/>How I Got Open WebUI Talking to OpenAI Web Search</a></li><li><span class=date>December 27, 2025</span>
<a class=title href=/posts/technical-deep-dive-llm-categorization/>From Gemini-3-Flash to T5-Gemma-2 A Journey in Distilling a Family Finance LLM</a></li><li><span class=date>December 19, 2025</span> <a class=title href=/posts/technical-deep-dive-llm-categorization/>From Gemini-3-Flash to T5-Gemma-2 A Journey in Distilling a Family Finance LLM</a></li><li><span class=date>December 19, 2025</span>
<a class=title href=/posts/the-convergence-of-fast-weights-linear-attention-and-state-space-models/>The Convergence of Fast Weights, Linear Attention, and State Space Models</a></li><li><span class=date>December 8, 2025</span> <a class=title href=/posts/the-convergence-of-fast-weights-linear-attention-and-state-space-models/>The Convergence of Fast Weights, Linear Attention, and State Space Models</a></li><li><span class=date>December 8, 2025</span>
@@ -9,9 +10,8 @@
<a class=title href=/posts/benchmarking-llms-on-jetson-orin-nano/>Why Your Jetson Orin Nano's 40 TOPS Goes Unused (And What That Means for Edge AI)</a></li><li><span class=date>October 2, 2025</span> <a class=title href=/posts/benchmarking-llms-on-jetson-orin-nano/>Why Your Jetson Orin Nano's 40 TOPS Goes Unused (And What That Means for Edge AI)</a></li><li><span class=date>October 2, 2025</span>
<a class=title href=/posts/flashing-jetson-orin-nano-in-virtualized-environments/>Flashing Jetson Orin Nano in Virtualized Environments</a></li><li><span class=date>September 28, 2025</span> <a class=title href=/posts/flashing-jetson-orin-nano-in-virtualized-environments/>Flashing Jetson Orin Nano in Virtualized Environments</a></li><li><span class=date>September 28, 2025</span>
<a class=title href=/posts/openwrt-mwan3-wireguard-endpoint-exclusion/>OpenWrt: Fix WireGuard Connectivity with MWAN3 by Excluding the VPN Endpoint</a></li><li><span class=date>September 22, 2025</span> <a class=title href=/posts/openwrt-mwan3-wireguard-endpoint-exclusion/>OpenWrt: Fix WireGuard Connectivity with MWAN3 by Excluding the VPN Endpoint</a></li><li><span class=date>September 22, 2025</span>
<a class=title href=/posts/unifi-vlan-migration-to-zone-based-architecture/>UniFi VLAN Migration to Zone-Based Architecture</a></li><li><span class=date>August 19, 2025</span> <a class=title href=/posts/unifi-vlan-migration-to-zone-based-architecture/>UniFi VLAN Migration to Zone-Based Architecture</a></li></ul><ul class=pagination><li>1</li><li><a href=/posts/page/2/>2</a></li><li><a href=/posts/page/3/>3</a></li><li class=hidden><a href=/posts/page/2/>&#8250;</a></li><li><a href=/posts/page/3/>&#187;</a></li></ul></section></div><footer class=footer><section class=container>©
<a class=title href=/posts/quantization-in-llms/>Quantization in LLMs</a></li></ul><ul class=pagination><li>1</li><li><a href=/posts/page/2/>2</a></li><li class=hidden><a href=/posts/page/2/>&#8250;</a></li><li><a href=/posts/page/2/>&#187;</a></li></ul></section></div><footer class=footer><section class=container>©
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<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Posts on Eric X. Liu's Personal Page</title><link>https://ericxliu.me/posts/</link><description>Recent content in Posts on Eric X. Liu's Personal Page</description><generator>Hugo</generator><language>en</language><lastBuildDate>Mon, 29 Dec 2025 07:15:58 +0000</lastBuildDate><atom:link href="https://ericxliu.me/posts/index.xml" rel="self" type="application/rss+xml"/><item><title>How I Got Open WebUI Talking to OpenAI Web Search</title><link>https://ericxliu.me/posts/open-webui-openai-websearch/</link><pubDate>Mon, 29 Dec 2025 00:00:00 +0000</pubDate><guid>https://ericxliu.me/posts/open-webui-openai-websearch/</guid><description>&lt;p&gt;OpenAI promised native web search in GPT‑5, but LiteLLM proxy deployments (and by extension Open WebUI) still choke on it—issue &lt;a href="https://github.com/BerriAI/litellm/issues/13042" class="external-link" target="_blank" rel="noopener"&gt;#13042&lt;/a&gt; tracks the fallout. I needed grounded answers inside Open WebUI anyway, so I built a workaround: route GPT‑5 traffic through the Responses API and mask every &lt;code&gt;web_search_call&lt;/code&gt; before the UI ever sees it.&lt;/p&gt; <?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Posts on Eric X. Liu's Personal Page</title><link>https://ericxliu.me/posts/</link><description>Recent content in Posts on Eric X. Liu's Personal Page</description><generator>Hugo</generator><language>en</language><lastBuildDate>Sat, 03 Jan 2026 06:23:35 +0000</lastBuildDate><atom:link href="https://ericxliu.me/posts/index.xml" rel="self" type="application/rss+xml"/><item><title>Why Your "Resilient" Homelab is Slower Than a Raspberry Pi</title><link>https://ericxliu.me/posts/debugging-authentik-performance/</link><pubDate>Fri, 02 Jan 2026 00:00:00 +0000</pubDate><guid>https://ericxliu.me/posts/debugging-authentik-performance/</guid><description>&lt;p&gt;In the world of self-hosting, there are many metrics for success: 99.9% uptime, sub-second latency, or a perfect GitOps pipeline. But for those of us running &amp;ldquo;production&amp;rdquo; at home, there is only one metric that truly matters: &lt;strong&gt;The Wife Acceptance Factor (WAF)&lt;/strong&gt;.&lt;/p&gt;
&lt;p&gt;My detailed Grafana dashboards said everything was fine. But my wife said the SSO login was &amp;ldquo;slow sometimes.&amp;rdquo; She was right. Debugging it took me down a rabbit hole of connection pooling, misplaced assumptions, and the harsh reality of running databases on distributed storage.&lt;/p&gt;</description></item><item><title>How I Got Open WebUI Talking to OpenAI Web Search</title><link>https://ericxliu.me/posts/open-webui-openai-websearch/</link><pubDate>Mon, 29 Dec 2025 00:00:00 +0000</pubDate><guid>https://ericxliu.me/posts/open-webui-openai-websearch/</guid><description>&lt;p&gt;OpenAI promised native web search in GPT‑5, but LiteLLM proxy deployments (and by extension Open WebUI) still choke on it—issue &lt;a href="https://github.com/BerriAI/litellm/issues/13042" class="external-link" target="_blank" rel="noopener"&gt;#13042&lt;/a&gt; tracks the fallout. I needed grounded answers inside Open WebUI anyway, so I built a workaround: route GPT‑5 traffic through the Responses API and mask every &lt;code&gt;web_search_call&lt;/code&gt; before the UI ever sees it.&lt;/p&gt;
&lt;p&gt;This post documents the final setup, the hotfix script that keeps LiteLLM honest, and the tests that prove Open WebUI now streams cited answers without trying to execute the tool itself.&lt;/p&gt;</description></item><item><title>From Gemini-3-Flash to T5-Gemma-2 A Journey in Distilling a Family Finance LLM</title><link>https://ericxliu.me/posts/technical-deep-dive-llm-categorization/</link><pubDate>Sat, 27 Dec 2025 00:00:00 +0000</pubDate><guid>https://ericxliu.me/posts/technical-deep-dive-llm-categorization/</guid><description>&lt;p&gt;Running a family finance system is surprisingly complex. What starts as a simple spreadsheet often evolves into a web of rules, exceptions, and &amp;ldquo;wait, was this dinner or &lt;em&gt;vacation&lt;/em&gt; dinner?&amp;rdquo; questions.&lt;/p&gt; &lt;p&gt;This post documents the final setup, the hotfix script that keeps LiteLLM honest, and the tests that prove Open WebUI now streams cited answers without trying to execute the tool itself.&lt;/p&gt;</description></item><item><title>From Gemini-3-Flash to T5-Gemma-2 A Journey in Distilling a Family Finance LLM</title><link>https://ericxliu.me/posts/technical-deep-dive-llm-categorization/</link><pubDate>Sat, 27 Dec 2025 00:00:00 +0000</pubDate><guid>https://ericxliu.me/posts/technical-deep-dive-llm-categorization/</guid><description>&lt;p&gt;Running a family finance system is surprisingly complex. What starts as a simple spreadsheet often evolves into a web of rules, exceptions, and &amp;ldquo;wait, was this dinner or &lt;em&gt;vacation&lt;/em&gt; dinner?&amp;rdquo; questions.&lt;/p&gt;
&lt;p&gt;For years, I relied on a rule-based system to categorize our credit card transactions. It worked&amp;hellip; mostly. But maintaining &lt;code&gt;if &amp;quot;UBER&amp;quot; in description and amount &amp;gt; 50&lt;/code&gt; style rules is a never-ending battle against the entropy of merchant names and changing habits.&lt;/p&gt;</description></item><item><title>The Convergence of Fast Weights, Linear Attention, and State Space Models</title><link>https://ericxliu.me/posts/the-convergence-of-fast-weights-linear-attention-and-state-space-models/</link><pubDate>Fri, 19 Dec 2025 00:00:00 +0000</pubDate><guid>https://ericxliu.me/posts/the-convergence-of-fast-weights-linear-attention-and-state-space-models/</guid><description>&lt;p&gt;Modern Large Language Models (LLMs) are dominated by the Transformer architecture. However, as context windows grow, the computational cost of the Transformer’s attention mechanism has become a primary bottleneck. Recent discussions in the AI community—most notably by Geoffrey Hinton—have highlighted a theoretical link between biological memory mechanisms (&amp;ldquo;Fast Weights&amp;rdquo;) and efficient engineering solutions like Linear Transformers and State Space Models (SSMs).&lt;/p&gt; &lt;p&gt;For years, I relied on a rule-based system to categorize our credit card transactions. It worked&amp;hellip; mostly. But maintaining &lt;code&gt;if &amp;quot;UBER&amp;quot; in description and amount &amp;gt; 50&lt;/code&gt; style rules is a never-ending battle against the entropy of merchant names and changing habits.&lt;/p&gt;</description></item><item><title>The Convergence of Fast Weights, Linear Attention, and State Space Models</title><link>https://ericxliu.me/posts/the-convergence-of-fast-weights-linear-attention-and-state-space-models/</link><pubDate>Fri, 19 Dec 2025 00:00:00 +0000</pubDate><guid>https://ericxliu.me/posts/the-convergence-of-fast-weights-linear-attention-and-state-space-models/</guid><description>&lt;p&gt;Modern Large Language Models (LLMs) are dominated by the Transformer architecture. However, as context windows grow, the computational cost of the Transformer’s attention mechanism has become a primary bottleneck. Recent discussions in the AI community—most notably by Geoffrey Hinton—have highlighted a theoretical link between biological memory mechanisms (&amp;ldquo;Fast Weights&amp;rdquo;) and efficient engineering solutions like Linear Transformers and State Space Models (SSMs).&lt;/p&gt;
&lt;p&gt;This article explores the mathematical equivalence between Hinton’s concept of Fast Weights as Associative Memory and the recurrence mechanisms found in models such as Mamba and RWKV.&lt;/p&gt;</description></item><item><title>vAttention</title><link>https://ericxliu.me/posts/vattention/</link><pubDate>Mon, 08 Dec 2025 00:00:00 +0000</pubDate><guid>https://ericxliu.me/posts/vattention/</guid><description>&lt;p&gt;Large Language Model (LLM) inference is memory-bound, primarily due to the Key-Value (KV) cache—a store of intermediate state that grows linearly with sequence length. Efficient management of this memory is critical for throughput. While &lt;strong&gt;PagedAttention&lt;/strong&gt; (popularized by vLLM) became the industry standard by solving memory fragmentation via software, recent research suggests that leveraging the GPU’s native hardware Memory Management Unit (MMU) offers a more performant and portable solution.&lt;/p&gt; &lt;p&gt;This article explores the mathematical equivalence between Hinton’s concept of Fast Weights as Associative Memory and the recurrence mechanisms found in models such as Mamba and RWKV.&lt;/p&gt;</description></item><item><title>vAttention</title><link>https://ericxliu.me/posts/vattention/</link><pubDate>Mon, 08 Dec 2025 00:00:00 +0000</pubDate><guid>https://ericxliu.me/posts/vattention/</guid><description>&lt;p&gt;Large Language Model (LLM) inference is memory-bound, primarily due to the Key-Value (KV) cache—a store of intermediate state that grows linearly with sequence length. Efficient management of this memory is critical for throughput. While &lt;strong&gt;PagedAttention&lt;/strong&gt; (popularized by vLLM) became the industry standard by solving memory fragmentation via software, recent research suggests that leveraging the GPU’s native hardware Memory Management Unit (MMU) offers a more performant and portable solution.&lt;/p&gt;

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2016 - 2016 -
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Eric X. Liu Eric X. Liu
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<a class=title href=/posts/quantization-in-llms/>Quantization in LLMs</a></li><li><span class=date>August 16, 2025</span>
<a class=title href=/posts/breville-barista-pro-maintenance/>Breville Barista Pro Maintenance</a></li><li><span class=date>August 9, 2025</span> <a class=title href=/posts/breville-barista-pro-maintenance/>Breville Barista Pro Maintenance</a></li><li><span class=date>August 9, 2025</span>
<a class=title href=/posts/secure-boot-dkms-and-mok-on-proxmox-debian/>Fixing GPU Operator Pods Stuck in Init: Secure Boot, DKMS, and MOK on Proxmox + Debian</a></li><li><span class=date>August 7, 2025</span> <a class=title href=/posts/secure-boot-dkms-and-mok-on-proxmox-debian/>Fixing GPU Operator Pods Stuck in Init: Secure Boot, DKMS, and MOK on Proxmox + Debian</a></li><li><span class=date>August 7, 2025</span>
<a class=title href=/posts/how-rvq-teaches-llms-to-see-and-hear/>Beyond Words: How RVQ Teaches LLMs to See and Hear</a></li><li><span class=date>August 3, 2025</span> <a class=title href=/posts/how-rvq-teaches-llms-to-see-and-hear/>Beyond Words: How RVQ Teaches LLMs to See and Hear</a></li><li><span class=date>August 3, 2025</span>
@@ -9,9 +10,8 @@
<a class=title href=/posts/mixture-of-experts-moe-models-challenges-solutions-in-practice/>Mixture-of-Experts (MoE) Models Challenges & Solutions in Practice</a></li><li><span class=date>June 1, 2025</span> <a class=title href=/posts/mixture-of-experts-moe-models-challenges-solutions-in-practice/>Mixture-of-Experts (MoE) Models Challenges & Solutions in Practice</a></li><li><span class=date>June 1, 2025</span>
<a class=title href=/posts/t5-the-transformer-that-zigged-when-others-zagged-an-architectural-deep-dive/>An Architectural Deep Dive of T5</a></li><li><span class=date>May 1, 2025</span> <a class=title href=/posts/t5-the-transformer-that-zigged-when-others-zagged-an-architectural-deep-dive/>An Architectural Deep Dive of T5</a></li><li><span class=date>May 1, 2025</span>
<a class=title href=/posts/espresso-theory-application-a-guide-for-the-breville-barista-pro/>Mastering Your Breville Barista Pro: The Ultimate Guide to Dialing In Espresso</a></li><li><span class=date>April 1, 2025</span> <a class=title href=/posts/espresso-theory-application-a-guide-for-the-breville-barista-pro/>Mastering Your Breville Barista Pro: The Ultimate Guide to Dialing In Espresso</a></li><li><span class=date>April 1, 2025</span>
<a class=title href=/posts/transformer-s-core-mechanics/>Transformer's Core Mechanics</a></li><li><span class=date>October 26, 2020</span> <a class=title href=/posts/transformer-s-core-mechanics/>Transformer's Core Mechanics</a></li></ul><ul class=pagination><li><a href=/posts/>&#171;</a></li><li class=hidden><a href=/posts/>&#8249;</a></li><li><a href=/posts/>1</a></li><li>2</li><li><a href=/posts/page/3/>3</a></li><li class=hidden><a href=/posts/page/3/>&#8250;</a></li><li><a href=/posts/page/3/>&#187;</a></li></ul></section></div><footer class=footer><section class=container>©
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<a class=title href=/posts/useful/>Some useful files</a></li></ul><ul class=pagination><li><a href=/posts/>&#171;</a></li><li class=hidden><a href=/posts/page/2/>&#8249;</a></li><li><a href=/posts/>1</a></li><li><a href=/posts/page/2/>2</a></li><li>3</li></ul></section></div><footer class=footer><section class=container>©
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@@ -25,4 +25,4 @@ where <code>δ_t = r_t + γV(s_{t+1}) - V(s_t)</code></p><ul><li><strong>γ (gam
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@@ -59,4 +59,4 @@ nvidia-smi failed to communicate with the NVIDIA driver modprobe nvidia → “K
2016 - 2016 -
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