u/Ambitious-Prompt-975

Hi, I dont even know if this subreddit is the right place to ask. If not, it would be awesome if you could give me a hint into the right direction.

The Background:
I have built a software platform over the past 16+ months that is similar to n8n, Flowise, or Dify: You visually build (or auto-generate) AI-Workflows that you can chat with - or use in automations.

I describe it on the landing page as "An open-source, local-first platform for building MCP-powered AI workflows: model management, MCP servers, a visual flow builder, chat, and headless automation in one app. Your keys and data never leave your machine."

imo, it is an extremly strong and capable tool and solves a lot of the pain points one experiences when working with AI. It's well past a Demo or MVP not only do I use it in its own production (an automated software development lifecycle where it improves and supervises itself), but it is also deployed at a customer site where it manages virtual machine statuses of their intranet systems.

It's a free app, open source and MIT-licensed, currently sitting at roughly 600 stars on Github. We had some initial traction after "Cline", an agentic-coding extension for VSCode twittered about us in the early development, but at the moment daily user numbers seem to low.

There is an online-demo, to click around and try - but the app, as it currrently is, is supposed to be self-hosted; that means you download , install and use it on your own computer or server. I can share the demo link in the comments, if that makes any difference.

Now my problem:
I want to do the next steps. I may be biased but I think a lot of features in this app are on-par or even superior to competitors. I want to 1) extend the reach and 2) build a business around that: a subscription service that lets you either sync your workspaces between machines or allows you to work online (or from your mobile device), on a private cloud computer we provide.

What it needs is more capital, more marketing, more users. But I am a technical person - I don't have a network, I dont know anything about seeding rounds, I suck at doing marketing. I could of course start working on all these pillars right now: create promotional material or hire an agency, do more market research, do some ad campaigns, get more people to talk about it.

But all that takes a whole lot of effort and time (and skill) - and if I could choose, I would rather spend that time building great solutions, because that's what I am really good at.

So I guess my question is:
What route should I take? Should I invest the time myself and figure all this out? Should I find someone to invest in my idea myself - or do I rather search for a team that supports me with that (i.e. I find someone to find someone to invest)?

tl;dr:
This MCP Server gives your AI a personal, slim, sandbox computer.

Defaults (can be configured):

• VM: Docker* with Debian-Slim
• Deletes when MCP Session ends
• Network access ON

Supports Local and Cloud. More below.
*(needs Docker installed & running!!!)

uvx mcp-sandbox-computer-vm-for-ai

----

Hello,

I recently found this MCP server called Kilntainers , which allows you to create vm-sandboxes for AI models to run terminal commands. This is a Fork. I added a persistent/temporary-flag through a parameter and FlyMachines.io support.

This MCP-Server has 1 tools that is important tool for your AI:

Tool	Purpose
sandbox_exec	Executes a terminal command in the Sandbox VM

There is a also few lifecycle tools, but these shouldn't always be exposed to your LLM; only if necessary.

Tool	Purpose
computer_dashboard	Open the MCP App and return the current inventory
computer_list	List state, backend, image, provider ID, and lifecycle mode
computer_create	Create/attach by ID; omission always generates a new slug
computer_restart	Restart while preserving writable state
computer_factory_reset	Erase writable state and recreate from the base image
computer_delete	Permanently remove the computer

https://github.com/flujo-app/mcp-sandbox-computer-vm-for-ai

• 🖥️ MCP-App based dashboard: List computers, run commands, restart, factory reset, and delete from FLUJO or another stable MCP Apps host.
• 🏷️ Named computers: Reconnect with a stable computer_id, or omit it to receive a readable random slug.
• 💾 Explicit lifecycle: Temporary computers are removed when the MCP session ends; permanent computers survive and can be reattached later.
• 🧰 Multiple backends: Docker/Podman, native Fly Machines, Modal, E2B, and WebAssembly.
• 🏝️ Isolated per agent: Every agent gets its own dedicated sandbox (computer_id parameter ).
• 🔒 Secure by design: The agent communicates with the sandbox over MCP - it doesn’t run inside it. No agent API keys, code, or prompts are exposed to the sandbox (except through .env vars you configure).
• 🔌 Tool and UI access: sandbox_exec simple for you model, while provider-neutral lifecycle tools power both models and the dashboard.
• 📈 Scalable: Scale from a few agents on your laptop to thousands running in parallel in the cloud.

- MCP-Server Command (For installation through UI's - e.g. Cursor, FLUJO)

uvx mcp-sandbox-computer-vm-for-ai

- Installation with json file (Claude Desktop, Cursor, Cline, etc.):

{
  "mcpServers": {
    "sandbox-computer": {
      "command": "uvx",
      "args": ["mcp-sandbox-computer-vm-for-ai"]
    }
  }
}

- Installation in Claude Code

claude mcp add --scope user sandbox-computer -- uvx mcp-sandbox-computer-vm-for-ai

- Installation with FLUJO

install from marketplace: mcp-sandbox-computer-vm-for-ai

install from github: https://github.com/flujo-app/mcp-sandbox-computer-vm-for-ai

I think its super useful with persistency and network by default and fly machines was needed,
thanks to the original creator, check Kilntainers on his Github as well.

Have fun!

PS: This is the mcp-apps-based management dashboard:

https://preview.redd.it/y4wt1wcod0jh1.png?width=1500&format=png&auto=webp&s=6631d97a4c7bb7827eeba0ffc482babfa8c12ba3

what are your choices?

I'm asking because it can mean a million things. And I am not sure my understanding is the same as everyone elses. I wanna get out of my bubble and see what people are actuall doing with AI.

So tell me about how you do AI:
Are you using an online service, or coding something yourself, are you just using Agents?

If you are building or deploying: Are you running your Agent(s) locally? Or hosted?
How is it "invoked"?

When you're coding yourself:
Are you using SDK's or Frameworks? A2A, MCP, OpenAI, LangChain? - And how much more complex was is than what you expected? Are you "done"?

If you are not building or coding yourself: What tools are you using?
Is running a session in claude code on ultramax effort already considered using Agents?

Do you care about security?
Or persistent memory?
Is your memory the conversation - or a special tool - or handled by the harness?

Are you using the Agents by yourself, or do others use them too?
Does your agent talk to other Agents?

How do you represent Agents visually? How do you interact with them? Chat, voice, video avatars? - or completly different.

Are you building Agents for fun, or for business? What Models are you using? What are you doing with your Agents? Do you do Text? or Image/Video/Audio?

Have you noticed a change in how you worked with AI from a year ago?

You go first, lets talk not make this an ad space!

I am looking for secret stars - MCP servers that nobody knows about that bring huge value (or are just funny/good) without complicated setup.

Creative MCP Servers, Social Media, Voice, Video, Image, Music, Office, Research, Automation, Meme - whatever comes to your mind.

I feel like the whole ecosystem evolves around paid API services and 402 transactions. Very little is local-first, and most of the rest is lazily wrapped.

Esto es algo que no he entendido desde el primer día. Un país que produce algunos de los mejores cafés del mundo. Caficultores y tostadores en todo el país, y sin embargo, la probabilidad de obtener esta basura de Nescafé en lugar de un buen tinto es siempre del 50:50. Por qué ?

https://preview.redd.it/7ojdgjm3r4ch1.png?width=623&format=png&auto=webp&s=fbd3a34ce9c8eaf1454cbf3cdb0311ea1dc55440

Yo sé que no hay esperanza, pero si alguien la encuentra: yo seguiré esperándola.

has anyone experience with this?
I was thinking about setting up some Workflows with local models, and I dont know how complicated that is to set up. This docker thing seems complicated

I was trying to figure out the best way to approach this.
Also I found this app called flujo and I am not sure if I should try that or rather n8n??

https://preview.redd.it/i83kdstj91ch1.png?width=720&format=png&auto=webp&s=55e790a95fcaf9bc82e1d03ec15ce648003c6f4d

Which way to go here? n8n seems very powerful but also kind of over my head.. and this flujo thing has only 600 stars on guthub I dont know if i can trust it

Hello everyone,

Since a year or so I’m building an open-source app for AI workflows and currently implementing a "self-improvement" loop: the app is supposed to improve its own code, using itself. I would highly appreciate your architectural critique and threat modeling ideas, as I am relatively new to actually securing LLM-based applications.

The Architecture:

1. The app polls my public GitHub Issues every 15 minutes. On a new issue, it triggers the first workflow.
2. A local, smaller model reads the issue to detect prompt injection or malicious intent.
3. If the local AI approves, the issue is passed to Claude. Claude evaluates the issue against the source code to create a plan. (As seen in the screenshot below, this step currently has access to PowerShell and filesystem tools).
4. This workflow ends and I have to manually approve of the plan and move it into a different folder.
5. Another trigger (a filewatcher) executes the next workflow: A separate coding agent that executes the approved plan, tests it, and commits it locally for my final review (and push).

My Core Concerns:

• Prompt Injection: Since the untrusted input (GitHub issues) is entirely public, I am relying heavily on a local AI to sanitize/block malicious prompts. How reliable is this in practice?
• Tool Abuse: Even though there is a manual review gate, the Planning Agent has MCP tool connections (PowerShell, Filesystem) to help it evaluate the codebase. I am worried a well-crafted issue could bypass the local AI and trick Claude into executing code immediately during the "planning" phase.

Repository: https://github.com/mario-andreschak/FLUJO

I am not asking for a free pentest or for anyone to attack the repo. I just want to know: What glaring holes am I missing in this setup? How would you approach hardening this workflow against malicious GitHub issues? What would be possible (other) attack angles?

Thanks for your time and expertise!

https://preview.redd.it/15vgrr3shkbh1.png?width=861&format=png&auto=webp&s=a577f32b76b701bff89d7c7f527698bda49b3740