Mar 27 · 7 min read · Here's a surprising fact: iOS apps with AI-powered search see 73% higher user engagement than traditional keyword-based search systems. Yet most iOS developers are still relying on basic Core Data queries and UISearchController when they could be del...
Join discussionMar 25 · 2 min read · At a glance: 4,100+ stars, 300+ forks, TypeScript, MIT license, 9 tools spanning semantic search, code search, company research, and deep research. Free tier: 150 calls/day (hosted) or 1,000/month (API key). Sub-200ms fast search latency. Rating: 4/5...
Join discussionMar 25 · 3 min read · At a glance: nickclyde/duckduckgo-mcp-server — 913 stars, Python, MIT, v0.1.2. 2 tools: search (up to 10 results, regional filtering) and fetch_content (webpage text extraction). No API key, no signup, no cost. Rating: 3.5/5. What It Does ToolDes...
Join discussionMar 20 · 5 min read · Full-text search breaks when users don't know the exact terms. A user searching "how to handle payment failures" won't find your article titled "Dunning Strategy for Involuntary Churn" even though it's exactly what they need. Semantic search fixes th...
Join discussionMar 19 · 11 min read · I noticed it the first time a "simple" search request turned into an argument between three databases. A sales rep asked for "forklifts in Texas with 10,000+ lb capacity under active warranty," and I realized I could answer that question three differ...
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Mar 16 · 10 min read · Beyond Embeddings: The Agent-Based Architecture Revolution In Part 1, I explored how embedding-driven search can understand user intent far better than traditional keyword matching. But understanding
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Mar 10 · 14 min read · I built my search stack backwards—on purpose. Most teams start with retrieval and ranking, then try to bolt “understanding” onto the front once users complain that the system returns something, just not the thing they asked for. I did the opposite be...
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Mar 10 · 11 min read · I didn’t switch to multi‑vector embeddings because it was trendy. I did it because a single pooled vector kept lying to my search. When you collapse a candidate into one embedding, you’re asking one point in space to simultaneously represent “career ...
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