Vapor VS Node RED

Botter works in tandem with Vapor, which handles the server-side functions of your project. This powerful combination allows you to focus on what matters most - creating an engaging and effective chatbot.

// swift-tools-version: 5.8 // The swift-tools-version declares the minimum version of Swift required to build this package. import PackageDescription let package = Package( name: "MyServer", platforms: [.macOS("12.0")], products: [ // Products define the executables and libraries a package produces, and make them visible to other packages. .executable( name: "MyServer", targets: ["MyServer"]), ], dependencies: [ .package(url: "https://github.com/vapor/vapor.git", .upToNextMajor(from: "4.70.0")), // Dependencies declare other packages that this package depends on. // .package(url: /* package url */, from: "1.0.0"), ], targets: [ // Targets are the basic building blocks of a package. A target can define a module or a test suite. // Targets can depend on other targets in this package, and on products in packages this package depends on. .executableTarget( name: "MyServer", dependencies: [ .product(name: "Vapor", package: "vapor") ]), .testTarget( name: "MyServerTests", dependencies: ["MyServer"]), ] )

Otherwise there's https://github.com/vapor/vapor

The Swift/Vapor project is a relatively easy way to do it.

Opening this webpage (https://vapor.codes) cranks my CPU (5800x3d) to 100% instantly. Why?

Vapor is the most popular non-Apple-ecosystem Swift project. There have been a few others, but none particularly popular.

Server-side apps (typically via Vapor)

My first option other than PHP was using Swift and Vapor. I have made some projects with iOS and Objective-C, maybe I could also learn Swift and create both native iOS apps and backends with the same language.

StreamCam is written 100% in Swift, SwiftUI & Combine. The serverside is handled with Vapor.

Good question.

I expect that we're moving into a phase of AIs talking to AIs, and initially it'll be wasteful (because it'll be mostly English), but eventually, they'll derive their own language and seamlessly upgrade protocols when they determine they're talking to an AI. No clue how that will come about or what that language will look like, but honestly, it's kind of exciting.

Really interesting to think about how they might handle context, as well. Even though we have much bigger context windows (and they'll only get larger), context management is still a resource-management issue, which we'll probably continue to refine, as well. Imagine different strategies for managing both what is brought into the context of each request, as well as what form it could take (level of detail, additional references or commentary on it, etc). Things could get really unreadable even in English, and still be very interpretable for an LLM.

W.r.t. the graph-oriented interfaces, are you thinking something like Node-RED [1]? I'm seeing more and more people mention having LLMs produce non-text or structured outputs, like JSON, UI, and other things. Easy to imagine an LLM that wires together various open-source platforms, on-demand. Something like Node-RED for pipelines/functions, some UI tools for visualization/interactivity, other platforms for messaging, etc...

[1] https://nodered.org/

I skipped to chapter 9 in the article ("Clogged"), and it looked like Pipes failed because it didn't have a large enough team or a well-defined mission. As a result they couldn't offer a super robust product that would lure in enterprise users. "You could not purchase some number of guaranteed-to-work Pipes calls per month" is the quote from the article.

The reason I think that interesting is because that's the model these days for everything from AI tokens to Monday.com seats. It makes me feel like Pipes was before its time.

That said I've been collecting different "business glue" products that are similar to Pipes. To me, like you say, they aren't as interesting, exciting and intuitive as Pipes was, but maybe it just takes a little more digging. I tried to focus on open source tools but some aren't.

- n8n io: https://n8n.io/integrations/mondaycom/

- Node-RED: https://nodered.org/ (just read about this one in this thread)

- trigger dev: trigger.dev

- automatisch.io: https://automatisch.io/docs/

- Activepieces: https://www.activepieces.com/docs/getting-started/introducti...

- Huginn: https://github.com/huginn/huginn

- budibase: https://budibase.com/

- windmill: https://www.windmill.dev/

- tooljet: https://www.tooljet.com/workflows

- Bracket: https://www.usebracket.com/pricing (just SalesForce <-> PostgreSQL)

- Zapier: zapier.com/

Anyway I hope some of these are fun!

Source: https://github.com/node-red/node-red

I believe Node-RED (https://nodered.org/) the way to go. It's just an NPM package to install and you can run it how ever you wish (even on Windows). It has a friendly and helpful community with even the main developers tirelessly answering even beginner level questions. In fact the community forum its THE friendliest forum I've ever been a member of by a large margin. Node-RED's development is supported by the JS Foundation and it's completely free and open source. It's widely used in the industrial automation industry and even integrated by some PLC manufacturers such as Siemens.

Does anyone know if their are plans to implement something like this (or if there are already custom nodes out there). I'd like to experiment with things like looping and incrementing values (like a for loop) for a Ksampler for example. It's only an example though, so I am not looking for a ksampler specific solution; just a generic way to have a variable (e.g. Seed value), run some nodes that use that value, increment the value, and then loop back to the beginning until some sort of condition is met. Node-Red (an event driven node based programming language) has this functionality so it could defintely work in a node based environment such as ComfyUI (see here).