<rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Hacker News: searchguy</title><link>https://news.ycombinator.com/user?id=searchguy</link><description>Hacker News RSS</description><docs>https://hnrss.org/</docs><generator>hnrss v2.1.1</generator><lastBuildDate>Thu, 16 Apr 2026 00:03:20 +0000</lastBuildDate><atom:link href="https://hnrss.org/user?id=searchguy" rel="self" type="application/rss+xml"></atom:link><item><title><![CDATA[New comment by searchguy in "Ask HN: Anyone using knowledge graphs for LLM agent memory/context management?"]]></title><description><![CDATA[
<p>I've certainly thought about this problem a lot, and knowledge graphs invariably come up as a solution. I've built something that automatically extracts facts/RDF triples from documents and interactions, and indexed them into a vector DB. The quality and utility of the facts can vary though.</p>
]]></description><pubDate>Tue, 13 May 2025 19:33:01 +0000</pubDate><link>https://news.ycombinator.com/item?id=43976727</link><dc:creator>searchguy</dc:creator><comments>https://news.ycombinator.com/item?id=43976727</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=43976727</guid></item><item><title><![CDATA[New comment by searchguy in "EM-LLM: Human-Inspired Episodic Memory for Infinite Context LLMs"]]></title><description><![CDATA[
<p>do you have references to<p>> TTT, cannon layers, and titans</p>
]]></description><pubDate>Tue, 13 May 2025 18:25:31 +0000</pubDate><link>https://news.ycombinator.com/item?id=43976070</link><dc:creator>searchguy</dc:creator><comments>https://news.ycombinator.com/item?id=43976070</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=43976070</guid></item><item><title><![CDATA[New comment by searchguy in "Meilisearch – search engine API bringing AI-powered hybrid search"]]></title><description><![CDATA[
<p>I'm a little confused by your statement that "Meilisearch decided to use hybrid search and avoid fusion ranking" when your website [1] says "Hybrid search re-ranking: The final step involves re-ranking results from both retrieval methods using the Reciprocal Rank Fusion (RRF) algorithm."<p>Can you clarify what you mean by "fusion ranking"?<p>All hybrid search requires a method to blend keyword and vector search results. RRF is one approach, and cross-encoder-based rerankers is another.<p>[1]: <a href="https://www.meilisearch.com/blog/hybrid-search" rel="nofollow">https://www.meilisearch.com/blog/hybrid-search</a></p>
]]></description><pubDate>Tue, 15 Apr 2025 05:03:16 +0000</pubDate><link>https://news.ycombinator.com/item?id=43689132</link><dc:creator>searchguy</dc:creator><comments>https://news.ycombinator.com/item?id=43689132</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=43689132</guid></item><item><title><![CDATA[New comment by searchguy in "Show HN: Chonky – a neural approach for text semantic chunking"]]></title><description><![CDATA[
<p>Hey, thanks for unpacking what you did at ecodash.ai.<p>Did you manually curate the queries that you did LLM query expansion on (generating a large number of diverse queries), or did you simply use the query log?</p>
]]></description><pubDate>Mon, 14 Apr 2025 06:40:46 +0000</pubDate><link>https://news.ycombinator.com/item?id=43678641</link><dc:creator>searchguy</dc:creator><comments>https://news.ycombinator.com/item?id=43678641</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=43678641</guid></item></channel></rss>