Locas.dev – 房地产和商业的人工智能位置适宜性分析器
我最近推出了 https://locas.dev，这是一个使用人工智能来回答房地产和商业中最关键问题的副项目：“这个地点适合我想做的事情吗？”
**功能介绍**
Locas.dev 接受一个输入——一个自然语言问题，例如：
“芝加哥市中心是购买土地的好地方吗？”
“迈阿密海滩适合开餐厅吗？”
然后，它会获取现实世界的地理标记数据，如学校评分、医院、警察局、餐厅、空气质量、交通节点、绿地，甚至花粉水平——使用自定义构建的 LLM + 规则引擎进行处理，并以结构化、可解释的方式进行回应。
每个答案包括：
- 在服务、设施、交通、环境和竞争等维度上的评分
- 指向附近地标的 Google Maps 链接
- 以人类可读的方式呈现的优缺点分析
- 清晰的总结和最终判决
**技术栈**
- 使用 Python、LangChain 和自定义向量查找引擎构建
- 基于 LLM 的推理，具有确定性的安全层
- 使用 Google Maps、OpenStreetMap 和公共数据集进行基础数据支持
- 完全无状态——每次分析仅需一个 GET 请求
**我为什么要构建它**
作为一名从事人工智能和教育工作的人，我经常面临选择“合适地点”的挑战，无论是用于项目、中心还是合作伙伴关系。现有的解决方案要么过于手动，要么基于地图，或者缺乏叙事推理。我希望能有一个像人类研究者或顾问那样思考地点的工具——但能够按需在几秒钟内提供结果。
**试用**
主页上有几个公开演示——欢迎您用自己的问题进行测试。
→ https://locas.dev
非常希望听到来自 HN 朋友们的反馈！我特别想了解：
- 您看到的潜在用例
- 您希望下一个推出的功能（例如，评分您自己的地址、邻里比较、投资建议）
- 集成（Slack、Notion 等）
感谢您的阅读！
– Azhar
查看原文
I recently launched https://locas.dev, a side project that uses AI to answer one of the most critical questions in real estate and business: “Is this location a good fit for what I want to do?”<p>What It Does
Locas.dev takes a single input – a natural language question like:<p>“Is Downtown Chicago a good place to buy land?”<p>“Would Miami Beach be a good spot for opening a restaurant?”<p>Then it fetches real-world geo-tagged data like school ratings, hospitals, police stations, restaurants, air quality, transit nodes, green space, and even pollen levels — processes it using a custom-built LLM + rules engine — and responds with a structured, explainable breakdown.<p>Each answer includes:<p>Ratings across dimensions like services, amenities, transport, environment, and competition<p>Google Maps links to nearby landmarks<p>Human-readable insights with pros and cons<p>A clear summary and final verdict<p>Tech Stack
Built with Python, LangChain, and a custom vector lookup engine<p>LLM-driven reasoning with deterministic safety layers<p>Google Maps, OpenStreetMap, and public datasets for grounding<p>Fully stateless – just one GET request per analysis<p>Why I Built It
As someone working in AI and education, I constantly run into the challenge of selecting the “right place” for programs, centers, or even partnerships. Existing solutions are either too manual, map-based, or lack narrative reasoning. I wanted something that thinks about a location the way a human researcher or consultant would — but on-demand, in seconds.<p>Try It
There are a few public demos on the homepage — feel free to test it with your own questions.<p>→ https://locas.dev<p>Would love feedback from fellow HNers! I'm especially curious about:<p>Potential use cases you see<p>What features you'd want next (e.g., scoring your own address, neighborhood comparisons, investment suggestions)<p>Integrations (Slack, Notion, etc.)<p>Thanks for reading!
– Azhar