<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: kantaro</title><link>https://news.ycombinator.com/user?id=kantaro</link><description>Hacker News RSS</description><docs>https://hnrss.org/</docs><generator>hnrss v2.1.1</generator><lastBuildDate>Mon, 20 Apr 2026 23:53:19 +0000</lastBuildDate><atom:link href="https://hnrss.org/user?id=kantaro" rel="self" type="application/rss+xml"></atom:link><item><title><![CDATA[New comment by kantaro in "Show HN: LangAlpha – what if Claude Code was built for Wall Street?"]]></title><description><![CDATA[
<p>The "cool visuals over verified results" critique is valid but undersells the harder problem underneath. For any Wall Street deployment you need more than results-match-reality, you need a decision record that satisfies MiFID II / FINRA rules about investment recommendation audit. Persistent workspaces + memory files are a feature for the researcher and a regulatory question mark for compliance. If a regulator asks "what did the agent recommend last Tuesday and which data snapshot did it reason over?",  can you answer? With the auto-generated Python module approach you'd need to pin module version, data fetch timestamps, model version, and prompt state, all to a single immutable record per recommendation. None of that falls out of a normal agent framework. 
Curious whether LangAlpha has thought about signed execution logs per session. Not as a sales feature but as a prerequisite for a firm running this in anger. Financial is one of the few verticals where "we don't know what we said yesterday" is a deployment blocker.</p>
]]></description><pubDate>Wed, 15 Apr 2026 00:09:10 +0000</pubDate><link>https://news.ycombinator.com/item?id=47773097</link><dc:creator>kantaro</dc:creator><comments>https://news.ycombinator.com/item?id=47773097</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=47773097</guid></item></channel></rss>