<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: druide67</title><link>https://news.ycombinator.com/user?id=druide67</link><description>Hacker News RSS</description><docs>https://hnrss.org/</docs><generator>hnrss v2.1.1</generator><lastBuildDate>Sat, 13 Jun 2026 13:46:04 +0000</lastBuildDate><atom:link href="https://hnrss.org/user?id=druide67" rel="self" type="application/rss+xml"></atom:link><item><title><![CDATA[New comment by druide67 in "Flash-MoE: Running a 397B Parameter Model on a Laptop"]]></title><description><![CDATA[
<p>The finding about removing the 9.8 GB Metal LRU cache for a 38% speedup is the most interesting part. Same lesson as PostgreSQL's advice against application-level buffer pools that compete with the OS page cache : the hardware memory compressor doing 130K decompressions/sec was pure overhead.<p>Curious about the remaining gap: 5.7 tok/s vs 18.6 theoretical (from SSD bandwidth). Is the ~70% overhead mostly GPU compute on non-expert layers (attention, norm), or is there I/O scheduling room left?</p>
]]></description><pubDate>Mon, 23 Mar 2026 22:14:27 +0000</pubDate><link>https://news.ycombinator.com/item?id=47495878</link><dc:creator>druide67</dc:creator><comments>https://news.ycombinator.com/item?id=47495878</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=47495878</guid></item><item><title><![CDATA[New comment by druide67 in "Skills Manager – manage AI agent skills across Claude, Cursor, Copilot"]]></title><description><![CDATA[
<p>Interesting tool. One thing I've noticed managing rules across Claude Code and Copilot: the same instruction produces very different results depending on the agent. Claude follows multi-step rules well, Copilot tends to ignore anything beyond the first line.<p>Does Skills Manager handle this at all, or is it purely format/distribution?
Seems like the hard problem isn't syncing files — it's that the same "skill" needs different phrasing per agent to actually work.</p>
]]></description><pubDate>Wed, 18 Mar 2026 13:03:54 +0000</pubDate><link>https://news.ycombinator.com/item?id=47425295</link><dc:creator>druide67</dc:creator><comments>https://news.ycombinator.com/item?id=47425295</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=47425295</guid></item><item><title><![CDATA[New comment by druide67 in "How I write software with LLMs"]]></title><description><![CDATA[
<p>This resonates. I spent years thinking I enjoyed coding, but what I actually enjoy is designing elegant solutions built on solid architecture. Inventing, innovating, building progressively on strong foundations. The real pleasure is the finished product (is it ever really finished though?) — seeing it's useful and makes people's lives easier, while knowing it's well-built technically. The user doesn't see that part, but we know.<p>With AI, by always planning first, pushing it to explore alternative technical approaches, making it explain its choices — the creative construction process gets easier. You stay the conductor. Refactoring, new features, testing — all facilitated. Add regular AI-driven audits to catch defects, and of course the expert eye that nothing replaces.<p>One thing that worries me though: how will junior devs build that expert eye if AI handles the grunt work? Learning through struggle is how most of us developed intuition. That's a real problem for the next generation.</p>
]]></description><pubDate>Mon, 16 Mar 2026 17:56:13 +0000</pubDate><link>https://news.ycombinator.com/item?id=47402402</link><dc:creator>druide67</dc:creator><comments>https://news.ycombinator.com/item?id=47402402</comments><guid isPermaLink="false">https://news.ycombinator.com/item?id=47402402</guid></item></channel></rss>