正在加载视频...

视频加载失败

On last week’s livestream we demo’d a powerful SQL query building library we are developing. It’s type safe, composable, constructs valid SQL and leverages some of Swift's most powerful features, such as key paths, macros and variadic generics.

13,066 次观看 • 1 年前 •via X (Twitter)

8 条评论

Point-Free 的头像
Point-Free1 年前

We will have a lot more to share about the library soon, but for now get a sneak peek from the recording of our livestream:

GitProtect.io 的头像
GitProtect.io2 年前

Automated GitHub, GitLab, Bitbucket and Jira backups, security compliance, data migration and every-scenario-ready Disaster Recovery for 360 cyber resilience. Schedule a custom demo or try 14 days for free.

Ian Keen 的头像
Ian Keen1 年前

Love this, I have a macro based typesafe query dsl as well that I’ve been using for a while. I would gladly stop building it in favour of a pointfree version 😂

Point-Free 的头像
Point-Free1 年前

Let us know if you want to compare notes sometime!

camsoft2000 的头像
camsoft20001 年前

Don’t know what I think about this. Very impressive but it feels very much like the Swift HTML DSLs everyone was making a few years ago. SQL is powerful because it’s concisely expresses complex scenarios using just a string.

Point-Free 的头像
Point-Free1 年前

The DSL tries to stay as true to SQL as possible but also provides an escape hatch for writing raw SQL anywhere in a query: .where { $0.date > .raw("datetime('now' , '-7 days')") } Can you explain your comment about HTML DSLs? Not sure we see the relevance here.

Max Smirnov 的头像
Max Smirnov1 年前

How do you make it to display and update in a preview? Simply #Preview { Text(query.sqlString) }? That’s a neat trick

Point-Free 的头像
Point-Free1 年前

Yep that’s it! 🙂

相关视频

Google open-sourced MCP Toolbox for Databases. I gave it access to everything else. For context, Google's MCP Toolbox for Databases is an open-source server that lets AI agents securely query structured databases like PostgreSQL and MySQL through the MCP protocol However, most enterprise knowledge doesn't actually live in databases. It's scattered across emails, Slack threads, GitHub repos, Salesforce records, customer reviews, and internal docs. So Agents can't see any of it, which means they're working with a fraction of the context they need. I fixed that using MindsDB. It acts as a universal SQL layer that sits on top of all your data sources: structured, semi-structured, and unstructured. This means you can query Salesforce, Gmail, GitHub, S3 files, Jira, and 200+ more sources using SQL syntax. The clever part is how it connects to the MCP Toolbox. MindsDB exposes everything through MySQL, so from the Agent's perspective, it's just running SQL and getting context back. It doesn't know or care that the data came from five different sources behind the scenes. This setup unlocks some powerful capabilities: → One SQL interface for dozens of enterprise sources → Cross-datasource joins (combine GitHub and CRM data in a single query) → Built-in ML capabilities for working with unstructured data → Simple MCP tools that now have massively expanded reach In the video below, the Agent queries GitHub data and a customer review database in one SQL query. So what used to require ETL pipelines and weeks of engineering effort now happens instantly. At the end of the day, AI agents are only as useful as the data they can access. This gives them a lot more to work with. I have shared the GitHub repo in the replies, where you can find more details about this.

Akshay 🚀

39,331 次观看 • 5 个月前