Ingestion Architecture
SELECT ENGINE
Desk Rig Contention Telemetry (32GB DGX Spark)
ENGINE: RUST MICRO
Engine Footprint
30 MB
Extraction Rate
98%
Exec Latency
142 ms
Clean LLM Tokens
1,480 tok
System Memory Utilization: 22.3 GB / 32 GB
OOM SAFE
OS (4GB)
LLMs + SDXL Rig (18GB)
Ingestion Engine
Free Capacity
# Deep Reinforcement Learning from Human Feedback (Next.js Blog)
Recent architectures optimize policy gradients with minimal memory contention...
### Summary of Innovations
- Direct preference optimization with zero offloaded policy parameters
- AST-level pruning guarantees sub-100ms response times for summary pipelines
- Stripped 14 cookie overlays, 2 floating header elements, and 3 inline tracking beacons.
<!-- Pre-filtered Client Hydration Shell -->
<div id="__next">
<header class="cookie-consent-modal-v2">...</header> <!-- [STRIPPED BY AST] -->
<nav class="sticky-nav">...</nav> <!-- [STRIPPED] -->
<main class="article-body">
<h1>Deep Reinforcement Learning from Human Feedback</h1>
<p>Recent architectures optimize policy gradients...</p>
</main>
</div>
[06:00:01] Starting morning ingestion cron (thinkidiot.com/digest/daily)
[06:00:01] Active models in memory:
- Digest Writer 8B (Q4_K_M) -> 5.8 GB
- Fact Checker 3B (Q8_0) -> 3.4 GB
- Diffusion Illustrator SDXL -> 8.8 GB
[06:00:02] Engine spawned: lightweight-rust-headless-browser (v0.4.2)
[06:00:02] RSS feed resolved: 14 target URLs
[06:00:03] Scraping target: Next.js Client Hydrated Technical Article
[06:00:03] Executed QuickJS micro-runtime (DOM ready in 38ms)
[06:00:03] AST filter: 24 noise nodes removed
[06:00:03] Context ready for Digest Writer 8B: 1,480 tokens (0 memory thrashing)