Semantic Search Is an Architecture Problem

<p><a class="article-body-image-wrapper"

<p>Our careers page search often felt broken. Searching for “Java” could return JavaScript roles. A misspelling could produce no results at all. Searching for “manager” could

<p>Your RAG application works perfectly with 1,000 documents. You push it to production, upload 10 million vectors, and suddenly your query latency jumps from 50ms to 5 seconds.

<!-- SC_OFF --><div class="md"><p>We spent the last year building a code indexing system and tried pretty much every retrieval approach along the way. Sharing what actually worked

<p>Whenever someone asks me to add search to an application, I try to find ways around it. The implementation itself isn't the problem, it's everything that comes with it: extra

<p>This week’s headlines highlight the rapid evolution of AI in China, with models like Qwen and DeepSeek pushing the frontier of capability. But as these models grow more

<p>How I built browser-native semantic vector search using WASM — no server, no API keys, no per-query cost. Full code walkthrough with React. </p> <p>My documentation site had

<blockquote> <p>Originally published on <a href="https://freedevkit.com/blog/vector-embeddings-the-foundation-of-semantic-ai/" rel="noopener noreferrer">FreeDevKit</a>.</p>