<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>AI Systems on Yosgi</title><link>https://yosgi.github.io/en/tags/ai-systems/</link><description>Recent content in AI Systems on Yosgi</description><generator>Hugo</generator><language>en</language><lastBuildDate>Mon, 29 Jun 2026 09:20:51 +0000</lastBuildDate><atom:link href="https://yosgi.github.io/en/tags/ai-systems/index.xml" rel="self" type="application/rss+xml"/><item><title>Using AI Agents for Asset Recognition and Annotation in 3D Scenes (Part 3)</title><link>https://yosgi.github.io/en/post/using-ai-agents-for-asset-recognition-and-annotation-in-3d-scenes-part-3/</link><pubDate>Sun, 21 Jun 2026 10:00:00 +1200</pubDate><guid>https://yosgi.github.io/en/post/using-ai-agents-for-asset-recognition-and-annotation-in-3d-scenes-part-3/</guid><description>Making scan results land in the right place</description></item><item><title>Using AI Agents for Asset Recognition and Annotation in 3D Scenes (Part 2)</title><link>https://yosgi.github.io/en/post/using-ai-agents-for-asset-recognition-and-annotation-in-3d-scenes-part-2/</link><pubDate>Sat, 20 Jun 2026 19:11:11 +1200</pubDate><guid>https://yosgi.github.io/en/post/using-ai-agents-for-asset-recognition-and-annotation-in-3d-scenes-part-2/</guid><description>How we split 2D detection and 3D verification into two stages to reduce false positives in digital twin asset annotation, and how that revealed the next recall bottleneck</description></item><item><title>Using AI Agents for Asset Recognition and Annotation in 3D Scenes</title><link>https://yosgi.github.io/en/post/using-ai-agents-for-asset-recognition-and-annotation-in-3d-scenes-part-1/</link><pubDate>Fri, 19 Jun 2026 09:46:11 +1200</pubDate><guid>https://yosgi.github.io/en/post/using-ai-agents-for-asset-recognition-and-annotation-in-3d-scenes-part-1/</guid><description>How we turned free-form 3D exploration into measurable coverage scanning and improved valve inventory recall in a digital twin factory from around 40% to around 90%</description></item><item><title>From Extracting Drawing Text to Placing 2D Annotations in a 3D Scene</title><link>https://yosgi.github.io/en/post/from-extracting-drawing-text-to-placing-2d-annotations-in-a-3d-scene/</link><pubDate>Fri, 12 Jun 2026 11:28:21 +1200</pubDate><guid>https://yosgi.github.io/en/post/from-extracting-drawing-text-to-placing-2d-annotations-in-a-3d-scene/</guid><description>A practical engineering recap of how a simple drawing text extraction task turned into a 2D-to-3D mapping workflow for digital twins, combining SVG parsing, vision models, geometry rules, registration math, and agent tool design.</description></item><item><title>Why Tool and Code Fail Differently in Agent Systems（2）</title><link>https://yosgi.github.io/en/post/why-tool-and-code-fail-differently-in-agent-systems2/</link><pubDate>Mon, 25 May 2026 14:50:20 +1200</pubDate><guid>https://yosgi.github.io/en/post/why-tool-and-code-fail-differently-in-agent-systems2/</guid><description>Tool and Code do not just differ in expressiveness; they also fail at different times. This article explains why multi-round Agent systems amplify that difference.</description></item><item><title>Tool for Control, Code for Analysis(3)</title><link>https://yosgi.github.io/en/post/tool-for-control-code-for-analysis3/</link><pubDate>Mon, 25 May 2026 14:50:20 +1200</pubDate><guid>https://yosgi.github.io/en/post/tool-for-control-code-for-analysis3/</guid><description>In large-scale Agent systems, pure Tool-based approaches tend to collapse under context pressure, while pure Code-based approaches introduce excessive latency. Based on real-world experiments, this article introduces a dual-path execution model that uses a “Context Off-Ramp” to switch between Tool and Code execution.</description></item><item><title>An MCP-as-Code Refactor, and Why It Did Not Work the Way I Expected</title><link>https://yosgi.github.io/en/post/an-mcp-as-code-refactor-and-why-it-did-not-work-the-way-i-expected1/</link><pubDate>Mon, 25 May 2026 14:50:20 +1200</pubDate><guid>https://yosgi.github.io/en/post/an-mcp-as-code-refactor-and-why-it-did-not-work-the-way-i-expected1/</guid><description>A real-world MCP-as-Code refactor on where Code helps, where Tool still fits better, and why Agent runtime needs both.</description></item><item><title>AI Won’t Replace Software Engineers. It Will Replace the Parts of Coding We Never Loved.</title><link>https://yosgi.github.io/en/post/ai-won-t-replace-software-engineers-it-will-replace-the-parts-of-coding-we-never-loved/</link><pubDate>Thu, 21 May 2026 23:28:56 +1200</pubDate><guid>https://yosgi.github.io/en/post/ai-won-t-replace-software-engineers-it-will-replace-the-parts-of-coding-we-never-loved/</guid><description>AI will not replace software engineers as a whole. It will remove much of the repetitive, mechanical work and give engineers more leverage to focus on judgment, systems, users, and building better products.</description></item><item><title>Your Agent Is Not Inconsistent Because It Is Dumb. It Just Has Too Many Tool Paths.</title><link>https://yosgi.github.io/en/post/your-agent-is-not-inconsistent-because-it-is-dumb-it-just-has-too-many-tool-paths/</link><pubDate>Thu, 21 May 2026 18:20:04 +1200</pubDate><guid>https://yosgi.github.io/en/post/your-agent-is-not-inconsistent-because-it-is-dumb-it-just-has-too-many-tool-paths/</guid><description>Multi-tool agents become unreliable when the same task takes different tool paths each time. This article explains why Skills should preserve validated execution paths, not one-off results, and why those Skills need to evolve over time.</description></item><item><title>Early Experiences with Building MCP Tools</title><link>https://yosgi.github.io/en/post/early-experiences-with-building-mcp-tools/</link><pubDate>Thu, 29 Jan 2026 17:45:36 +1300</pubDate><guid>https://yosgi.github.io/en/post/early-experiences-with-building-mcp-tools/</guid><description>How Cut 6,000 Tokens Down to 500</description></item></channel></rss>