轻量本地搜索引擎的需求
问题概述
当前主流搜索工具（如 macOS Spotlight）资源消耗大、性能差，且难以满足本地化、低功耗的需求。用户希望在本地运行高效的搜索+推理引擎，以实现快速、私密的内容检索，特别是在学习和内容适配场景中。
痛点标签
- 现有搜索系统资源 hog，耗电高，响应慢
- 缺乏能够在本地运行且兼容推理功能的轻量级搜索引擎
- 用户需要根据自身水平动态检索合适内容，现有工具无法实现适配
待验证问题
- 用户是否愿意为此类本地搜索工具付费？
- 当前搜索需求主要集中在哪些具体场景（如学习、文件管理）？
- 用户对现有搜索工具的主要不满点是什么？
证据
原话
Before joining the team I started developing a comprehensible input curation engine for my own language learning purposes built on Antfly.
原话
Yeah open question what "perfect" search would even be, like would that just end up being indistinguishable from a kind of magical omniscience? And then there's "can I literally just find that one freaking slideshow from a while ago with that one client... or is it in Google Drive...?" And I really don't want the solution to be that we just plug everything into Claude
原话
I've actually been using this to build a local file search agent. I started building it on the Go version of Antfly, but the new Zig runtime is a huge improvement (better resource utilization, reliability, recovery, ...)Anyone who has used Spotlight search on macOS knows that (1) it can be an absolute resource hog, and (2) it's relatively useless (even with Siri stuff they added in macOS 27). So I was eager to take a stab at a native app that did both better and kept everything on-device (no external inference providers), and building it on Antfly meant I could run it all from one engine (way simpler to coordinate than a whole RAG pipeline).
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