01 / THE SIGNAL

我们发现了什么

Launch HN:Mireye(YC S26)——面向现实世界 AI 智能体的基础设施。我正在构建 AI 智能体用于对现实场所做出决策的基础设施:为美国任何地点提供数据、丰富信息、工具和信号,通过一个 API 和 MCP 服务器提供服务。

  • 来源:Hacker News(发现于 2026-09-04)
  • 证据等级:D · 发现产品或需求信号,暂未获得可核验的商业证据。
  • 商业模式:API / Usage-based
  • 主题:AI Agent
  • 初筛评分:25.9/100 · 收录 1 次
#工作流自动化#待验证#产品发现
02 / SOURCE & EVIDENCE

证据,比故事更重要。

发现产品或需求信号,暂未获得可核验的商业证据。

规则清洗与初筛,未经人工商业核验。原文语境、实际客户和付费情况仍需自行验证。

引用与数字披露

来源类型(原作者自述/第三方测算/媒体转引)需采集端标注,本版尚未落字段。

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中文辅助译文(来源摘要)

Hi HN,我是 Ansh,Mireye 的创始人(https://www.mireye.com)。我正在构建 AI 智能体用来对物理场所做决策的基础设施:覆盖美国任意地点的数据、丰富化、工具与信号,全部封装在一个 API 和 MCP 服务器之后。演示视频:https://www.youtube.com/watch?v=haqO6UbUqU0 。试用时,把 https://www.mireye.com/skills.md 粘贴到你的智能体里,并在 mireye.com 领取一个免费密钥(5,000 credits,无需信用卡)。文档位于 https://docs.mireye.ai 。最快的上手方式是向该 API 提出任何关于美国任意地点的问题。挑一个你熟悉的地方测试一下,冷启动地对照你已知的事实给它打分。在做 Mireye 之前,我在做建筑领域的智能体,自己也撞上了这堵墙:我的智能体能在线推理任何事情,却对它脚下的实地一无所知。后来一家 Fortune 500 保险公司告诉我,他们的工程师因为同样的原因放弃了核保智能体。当被问及某个具体地点的具体问题时,前沿模型会持续产生幻觉。我的第一个产品是一个小众的选址筛查 App。客户在他们熟悉的地点上测试它,答案也站得住脚,但没人在乎这个 App 本身。他们想要的是它底层的引擎。使用数据也印证了这一点:目录里 317 个字段中有 311 个会被查询,且没有任何一种用例占据主导。所以我砍掉了那个 App,转而开始构建这套基础设施。Mireye 不是数据集上面套一层 API,因为单靠事实并不构成决策。

智能体通过它跑完整个任务:带引用的事实、把一个裸地址丰富化为业主、地块面积、构筑物和附近的电力,模型做不对的那些操作所需的工具,以及当情况变化时(例如重新分区申请)发出的提示。数据、丰富化、工具、信号。工具源自观察智能体如何失败。我们在自己的基础设施上构建智能体,而相同的问题反复出现:智能体会目测一段距离而不是去计算它,会为某个地址抓取错的地块,或者在一个批处理做到一半时把全部预算烧光。每一次失败都变成了智能体可以调用的东西:确定性的几何与行驶时间工具、地块解析、一个在运行前对任务进行定价的报价接口,以及把整个工作流(例如筛查一个场地或核保一个地址)打包好的技能。让我意外的是困难的部分。每个数据源都必须被采集(有时是逐县采集,无论各县以何种格式发布),归一化为同一份模式,订立契约(它来自哪里、每个值的意义、多久刷新一次),然后永远保持新鲜。我们今天为 366 个字段运行着这个循环,全部以多租户方式从同一个索引提供,而每一个数据源都以不同的方式抵抗。Maryland 发布了一个字面标题就叫 "Hidden Property Owner Names" 的数据集。我在 North Carolina 提交了公共记录申请,因为没人索引污水主管道。更深的问题是语义。两个县发布了一个同名字段,含义却不同。而最危险的取值是 null。它到底表示 "这里没有洪泛区",还是 "本县从未绘制过洪水图"?把模型放在这个缺口前面,它会用一组合乎情理的数字填满那片沉默。因此我们对缺失本身进行类型化。每个字段都会返回 ok、absent 或 failed。客户告诉我,正是这些拒绝让他们信任它。

最新的部分是按需索引。当你请求一个我们还没有的字段,一个长时运行的智能体会去研究数据源、采集数据、将其与真实情况对比测试,然后索引进去,通常在一天之内完成。我会从本帖中接受一些字段请求,回头报告它构建出的东西。人们用 Mireye 做出了我没预料到的事情:保险团队筛查洪水、风灾和野火的投资组合,一家 proptech 公司通过丰富化来清洗混乱的房源地址,一个健康品牌为海报投放点对街角打分,一家机器人公司采购仓库,数据中心的选址,无人机部署规划,为一座城市规划校车路线,以及为人口贩运调查提供信号。我们的使命是索引每一寸土地,让它和 Web 一样可查询。价格公开:免费层每月 5,000 credits,25,000 每月 $19,120,000 每月 $99,企业版可定制。我很想知道你们正在构建的智能体在哪里接触物理世界。更希望你在你熟悉的地方运行 Mireye,然后告诉我哪里做错了。

译文由上游机器翻译生成,可能有误;判断请以英文原文为准。

英文原文(来源本站未改写)

Hi HN, I'm Ansh, founder of Mireye ( https://www.mireye.com ).I'm building the infrastructure AI agents use to make decisions about physical places: data, enrichment, tools, and signals for any US location, behind one API and MCP server.Here's a demo video: https://www.youtube.com/watch?v=haqO6UbUqU0 To try it, paste https://www.mireye.com/skills.md into your agent, and grab a free key at mireye.com (5,000 credits, no card).Docs are at https://docs.mireye.ai .The fastest way in is to ask the API any question about any US location.Test it on a place you know, cold and grade it against what you know.

Before Mireye I was building construction agents and hit this wall myself: my agent could reason about anything online but knew nothing about the ground under it.Then a Fortune 500 insurer told me their engineers had given up on underwriting agents for the same reason.Frontier models keep hallucinating when asked a specific question about a specific place.My first product was a niche site-screening app.Customers tested it on places they knew and the answers held up, but nobody cared about the app.They wanted the engine underneath.Usage agreed: 311 of the 317 fields in the catalog get queried and no use case dominates.So I killed the app and started building the infrastructure instead.

Mireye is not a dataset with an API on top, because facts alone are not a decision.An agent runs the whole job through it: cited facts, a bare address enriched into owner, acreage, structures, and nearby power, tools for the operations models get wrong, and signals when something changes, like a rezoning filing.Data, enrichment, tools, signals.The tools came from watching agents fail.We build agents on our own infra, and the same things kept breaking: an agent would eyeball a distance instead of computing it, grab the wrong parcel for an address, or burn its whole budget halfway through a batch.

Each failure became something an agent can call: deterministic geometry and drive-time tools, parcel resolution, a quote endpoint that prices a job before it runs, and skills that package whole workflows like screening a site or underwriting an address.The hard part surprised me.Every source has to be gathered (sometimes county by county, in whatever format each county publishes), normalized into one schema, contracted (where it comes from, what each value means, how often it refreshes), and then kept fresh forever.We run that loop for 366 fields today, served multi-tenant from one index, and every source fought back differently.

Maryland publishes a dataset literally titled "Hidden Property Owner Names." I filed public records requests in North Carolina because nobody indexes sewer mains.The deeper problem is meaning.Two counties publish a field with the same name and it means different things.And the most dangerous value is null.Does it mean "no flood zone here" or "this county never mapped floods"?Put a model in front of that gap and it fills the silence with a plausible number.So we type absence.Every field returns ok, absent, or failed.Customers have told me the refusals are why they trust it.The newest piece is on-demand indexing.

Ask for a field we don't have and a long-running agent researches sources, collects the data, tests it against ground truth, and indexes it, usually within a day.I'll take a few field requests from this thread and report back with what it built.People have built things I didn't plan for: insurance teams screening portfolios for flood, wind, and wildfire, a proptech cleaning messy listing addresses through enrichment, a wellness brand scoring street corners for poster spots, a robotics company sourcing warehouses, data center site selection, drone deployment planning, school bus routes for a city, signals for human trafficking investigations.

The mission is to index every inch of the earth and make it as queryable as the web.Pricing is public: the free tier is 5,000 credits a month, $19/month for 25,000, $99/month for 120,000, custom for enterprise.I'd love to hear where the agents you're building touch the physical world.And I'd love it more if you run Mireye on a place you know and tell me what we got wrong.

出处https://news.ycombinator.com/item?id=49552616抓取日期 · 采集源 Hacker News

03 / EVIDENCE GAPS

这条还缺什么证据?

下面每条都由本条已有字段推出(等级、理由、商业模式、来源次数、是否演示), 本站不生成推测性结论;通用验证方法放在方法论页。

  • 可核验的收入或付费证据查官网定价页与付费口径;第三方数据源(如 GetLatka)只作旁证,需标注来源与时点。
  • 只有单一来源找一手站点或其他渠道是否重复出现同一产品;社区热帖数量不等于商业进展。

通用验证清单(谁有这个问题/谁愿意付费/一个人能交付哪一小步)见我们的筛选方法。

04 / SIGNAL HISTORY

发现时间线