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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>No Need to Train Your RDB Foundation Model · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2602.13697/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:27:26 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/2602.13697/feed.xml" rel="self" type="application/rss+xml" /><item><title>No Need to Train Your RDB Foundation Model — HN submission</title><link>https://news.ycombinator.com/item?id=47313564</link><guid isPermaLink="false">urn:hackerpapers:submission:2602.13697:47313564</guid><pubDate>Mon, 09 Mar 2026 18:48:12 GMT</pubDate><description>&lt;p&gt;Why Most Valuable AI Systems Are Still Tabular Models&lt;/p&gt;&lt;p&gt;The Hard Part of Predictive AI Isn’t the Model I’ve spent most of my career building predictive systems on tabular data. The highest-value AI systems I’ve seen in production aren’t LLMs. They’re predictive models that operate on structured operational data: customers, orders, shipments, transactions, support events, etc. These systems quietly generate millions in value by replacing expensive third-party services, improving operational decisions, and turning predictions into products. Examples include churn prediction, fraud detection, ETA prediction, inventory demand forecasting, and…&lt;/p&gt;&lt;p&gt;6 points; 2 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/2602.13697/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>cs.AI</category></item></channel></rss>
