The Silent RAG Killer: How Noisy Document Layouts Can Burn 70,000 Tokens Before

<!-- SC_OFF --><div class="md"><p>We've been working on a tool called Deblank that tries to reduce token usage when sending code to LLMs.</p> <p>The idea is straightforward: most

<h2> The hidden tax in your AI pipeline </h2> <p>If you're building with GPT or Claude, you’ve probably done this:</p> <ul> <li><p>Call an API</p></li> <li><p>Get a big JSON

<p>Everyone building GraphRAG tunes the same three dials: the retriever, the chunk size, the re-ranker. Then the retrieved subgraph gets serialized into the prompt with

<p>Spoiler: it's more like 4%.</p> <p>And honestly? That's still kind of impressive when you understand why.</p> <p>I've been using Claude Code heavily, and like most of you, I'm

<p>If you use Cursor, Claude Desktop, or Cline daily,<br /> you have probably watched your rate limits evaporate<br /> because of a single runtime crash.</p> <div class="highlight

<p>Today Inception Labs dropped <strong>Mercury 3</strong>: a diffusion-based LLM with a 1M-token context window doing <strong>1,068 tokens/sec on NVIDIA GPUs</strong>, claiming

<p>If you use Cursor or Claude Code heavily, you probably know this workflow: You start a new session, drag and drop a bunch of UI screenshots, architecture diagrams, or heavy

<p>Most Document AI teams are overusing VLMs.</p> <p>The default pattern still looks like this:</p> <ol> <li>take a PDF</li> <li>send the whole thing to a big multimodal