نظرة عامة
رصد مجتمع Hacker News هذا الخبر الذي حصد 12 نقطة و0 تعليق خلال ساعات قليلة، مما يجعله من أبرز أخبار الذكاء الاصطناعي اليوم. المصدر الأصلي: armature.tech.
في هذا المقال نستعرض أبرز ما جاء في هذا الخبر، تحليله من منظور عربي، وما يعنيه للمستخدمين العرب المهتمين بأدوات الذكاء الاصطناعي.
التفاصيل
Hi HN! We’re Theodore and Louis, founders of Armature (YC P26). We reconstruct the entire session behind the MCP tool calls you receive, including what the user asked their agent to do and what the agent thought.<p>You wrap your MCP in 3 lines of code (our SDK is available in Typescript, Python and Go) and start seeing in your dashboard: - All sessions reconstructed: it’s like reading the real conversation the user had inside Claude or ChatGPT! - A ranking of your MCP most popular use cases, built from sessions clustering - The most frequent issues your users’ agents encounter so you can fix them.<p>Here is a quick demo: <a href="https://youtu.be/ZFlvquhyNMQ" rel="nofollow">https://youtu.be/ZFlvquhyNMQ</a><p>The story behind this is that we initially launched Armature as a standalone testing tool (<a href="https://www.ycombinator.com/launches/QQc-armature-making-your-app-finally-usable-by-ai-agents">https://www.ycombinator.com/launches/QQc-armature-making-you...</a>) that could naturally be used through an MCP itself. We quickly realized we had no idea how our users were using Armature MCP and if they were satisfied with it or frustrated. It’s something we had also experienced in our previous companies: Louis built MCPs exposed to millions of users and Theo was a Forward Deployed Engineer at Palantir before joining a Datadog spin-off as Founding Engineer. Both testing and product analytics had always been real pains when exposing a product to agents but we always thought there wasn’t much we could do about analytics because the conversation lived in our users’ AI client.<p>Then it struck us: what if we asked the agents why they were making this or that tool call? And what’s the user's intent or potential frustration? So we started experimenting with MCP instrumentation and the use-cases actually surprised us! Many of our first customers had implemented workarounds for their CI to trigger new tests or for their coding agents to fetch the results efficiently. Even though we talked to our first users regularly, they had never shared this feedback with us. We then built automations to automatically cluster use-cases, identify issues frequently encountered and let our own coding agents fix them. When our CTO friends heard about this, they wanted to try it for themselves so we gave them access to a cloned version of our internal product and they started sharing feedback like they never did on our “real” product!<p>That’s when we decided to start working seriously on MCP Analytics as a product. At first we were afraid of degrading MCP performance so we iterated until we reached the exact same success rate as without our instrumentation (89.17 % vs 89.15 % pass rate out of 870 runs). Then privacy was an obvious constraint so we applied the
المصدر الأصلي
هذا الخبر مأخوذ من منصة Hacker News — المجتمع التقني الأكثر متابعة في العالم.