u/Syosse-CH

Hello Zämme,

ich hoffe Ihr chönt mir da witerhelfe, ich wott en Schloss bi mir nacherüste mit Nuki Smart Lock PRO (5th Gen) CH-Zylinder. Demit ich au vo 2 Stock us Türe ufmache cha oder mit Autolock, falls ich ide nöchi bi Türe ufgot.

Mini Türe macht mir vo usse mit dem Schlüssel uf (Rundprofilzylinder) und vo inne also im Hus inne gits eh Drehknauf.

Ich ha so es dürenand und ha denkt die Nuki chömed ah usse site, will ich die Nukis scho paar mal gseh ha ade Wohnigstüre im Gang.

Was isch jetzt korrekt und gits defür eh anderi Lösig ?

Ussesite Türe, sorry für Qualiy

Innesite Hus

Aleitig

Danke viel mal!

Hi everyone,

I have a door with a key cylinder on the outside and a thumbturn/knob on the inside.

I’m interested in the Nuki Smart Lock Pro 5.0, but I understand that it normally mounts over a key on the inside.

Does the Pro 5.0 work with this type of lock, or would I need the Nuki Ultra?

Ideally, I’d like to keep using a key from the outside.

Swiss Doors

Inside

Outside

Thanks alot!

Hi everyone,

I'm curious how you use AI with ClickUp. Do you use ClickUp Brain, Claude, ChatGPT, or something else?

I've recently started explaining my tasks to Claude and having it help me create and structure tasks in ClickUp.

It already works quite well, but I'm sure there are many more possibilities for automation and workflows.

How are you using AI with ClickUp? I'd love to hear your ideas and setups!

Thanks!

Hi everyone,

I suddenly can’t see, purchase, or manage any licenses anymore. I’m logged in as an Admin, but my emails have now stopped working because of this.

Previously, I could see all the licenses and their status (e.g. active, stopped, etc.). I could also see the payment methods and credit card information.

Now, I can’t see any of this anymore.

Has something changed recently with the admin permissions or billing/license management? Why can’t I access these options anymore even though I’m an admin?

Any help would be greatly appreciated!

https://preview.redd.it/ax3vv2p3lalh1.png?width=1793&format=png&auto=webp&s=c01549c6c0858c236ac41dc1ef5271730608f160

Hello

I’m building a SaaS that automatically registers domains for Swiss SMEs after checkout no manual steps.

I’m currently testing Openprovider, but I’m running into issues with their sandbox/API and domain registrations, so I’m looking for alternatives.

Main requirements:

• REST API for domain availability + registration
• A working sandbox for testing
• Registrar only DNS is handled by Cloudflare, hosting by Vercel
• Ability to set nameservers during registration
• No large upfront deposit or monthly minimum
• Webhooks for domain status would be a nice bonus

Who are you using in production for automated domain registrations?

How reliable has the API been?

Thanks alot!

Hello everyone,

Unfortunately, while searching through my hardware, I only found one RTX 3090 Ti instead of two.

For my planned setup, I need at least two GPUs, or generally more than 48 GB of total VRAM for running local LLMs.

My current hardware setup:

Motherboard: ASUS ProArt B850-Creator WiFi

CPU: AMD Ryzen 9 9950X

Are there any graphics cards available in the $1,300–$1,700 price range that offer around 48 GB VRAM per card?

I would appreciate any recommendations or experiences.

Thanks alot!

Hello everyone,

Unfortunately, while searching through my hardware, I only found one RTX 3090 Ti instead of two.

For my planned setup, I need at least two GPUs, or generally more than 36 GB of total VRAM for running local LLMs.

My current hardware setup:

Motherboard: ASUS ProArt B850-Creator WiFi

CPU: AMD Ryzen 9 9950X

Are there any graphics cards available in the $1,300–$1,700 price range that offer around 48 GB VRAM per card?

I would appreciate any recommendations or experiences.

Thanks alot!

Edit: Wrong Title 36GB VRAM..

Hi everyone,

I'm currently building my local AI server and have the following hardware planned:

• CPU: AMD Ryzen 9 7950X
• GPU: 2× RTX 3090
• RAM: 64 GB DDR5-6000 (4×16 GB Kingston Fury Beast)
• Motherboard: ASUS ROG Strix B650E-E Gaming WiFi
• SSD: Samsung 990 Pro

My only concern is the motherboard. Right now, two GPUs are enough, but I'm thinking a few years ahead. If I want to add more GPUs or increase my total VRAM in the future, I'd need more PCIe slots.

I've been searching for alternatives, but I can't seem to find many AM5 motherboards for the Ryzen 9 7950X with 4 or more PCIe slots that are actually suitable for multiple GPUs.

Do you have any motherboard recommendations?

My budget is ideally under $500.

Thanks in advance for your advice!

Hi everyone,

I'm planning to build a local AI server for business automation and would appreciate some feedback on the hardware before I buy the remaining parts.

The workflow will use n8n for orchestration, Ollama + Qwen3-30B-A3B (Q8) for local inference, PostgreSQL + pgvector for RAG, and maybe later Open WebUI as the interface.

A typical use case would be:

• Salesforce triggers an event (e.g. low stock).
• n8n retrieves supplier data and pricing from PostgreSQL.
• Qwen generates a supplier email text based on (n8) company rules and historical data (PostgreSQL).
• n8n validates the data (supplier, pricing, business rules, etc.).
• An employee receives a confirmation request, reviews the generated email, and approves it.
• n8n sends the final email.

I already own 2× RTX 3090 (24 GB each, 48 GB total VRAM).

Planned hardware:

• CPU: AMD Ryzen 9 7950X
• GPU: 2× RTX 3090
• RAM: 64 GB DDR5-6000 (4×16 GB Kingston Fury Beast)
• Motherboard: ASUS ROG Strix B650E-E Gaming WiFi
• SSD: Samsung 990 Pro

From what I've read, Qwen3-30B-A3B (Q8) should require around 33 GB VRAM, so it should fit on the dual 3090 setup.

My questions:

• Does this hardware make sense for this workload?
• Is the B650E-E a good choice for two RTX 3090s (PCIe x8/x8), or would you recommend a different motherboard/platform?

Hi everyone,

I'm planning to build a local AI server for business automation and would appreciate some feedback on the hardware before I buy the remaining parts.

The workflow will use n8n for orchestration, Ollama + Qwen3-30B-A3B (Q8) for local inference, PostgreSQL + pgvector for RAG, and maybe later Open WebUI as the interface.

A typical use case would be:

• Salesforce triggers an event (e.g. low stock).
• n8n retrieves supplier data and pricing from PostgreSQL.
• Qwen generates a supplier email text based on (n8) company rules and historical data (PostgreSQL).
• n8n sends the final email automatically.

I already own 2× RTX 3090 (24 GB each, 48 GB total VRAM).

Planned hardware:

• CPU: AMD Ryzen 9 7950X
• GPU: 2× RTX 3090
• RAM: 64 GB DDR5-6000 (4×16 GB Kingston Fury Beast)
• Motherboard: ASUS ROG Strix B650E-E Gaming WiFi
• SSD: Samsung 990 Pro

From what I've read, Qwen3-30B-A3B (Q8) should require around 33 GB VRAM, so it should fit on the dual 3090 setup.

My questions:

• Does this hardware make sense for this workload?
• Is the B650E-E a good choice for two RTX 3090s (PCIe x8/x8), or would you recommend a different motherboard/platform?
• Would you change anything before I buy the remaining parts?

https://preview.redd.it/7mymepdkhrdh1.png?width=1751&format=png&auto=webp&s=b2c6a43f2ada7cd1e210cc05549d0e834605d55c

Thanks for your feedback!

Hello everyone,

I’m not sure if this is the right place to ask, but I noticed something interesting with the Just Eat app.

On a new phone, I installed Just Eat and used the welcome voucher code successfully. After that, I uninstalled the app, created a new email, used a different name, address, and voucher code, but it didn’t work anymore.

I also tried installing the app inside Android Secure Folder, but it still didn’t work. However, when I used a completely different phone, it worked again.

Does anyone know what exactly Just Eat checks to identify a device or account? Is it based on device ID, IP address, or something else?

Thanks!!

Hello everyone,

I’m not sure if this is the right place to ask, but I noticed something interesting with the Just Eat app.

On a new phone, I installed Just Eat and used the welcome voucher code successfully. After that, I uninstalled the app, created a new email, used a different name, address, and voucher code, but it didn’t work anymore.

I also tried installing the app inside Android Secure Folder, but it still didn’t work. However, when I used a completely different phone, it worked again.

Does anyone know what exactly Just Eat checks to identify a device or account? Is it based on device ID, IP address, or something else?

Thanks!!

Hi everyone,

I'm currently building a local AI architecture with multiple layers and I'm trying to understand where n8n ends and MCP begins.

One use case is automated supplier negotiations. We'll have a mailbox like buy@mail.. where supplier offers arrive.

The planned flow is:

• Supplier email arrives.
• n8n sends it to a local Qwen LLM.
• The LLM extracts the supplier, product and offered price and send to n8n.
• n8n looks up our PostgreSQL database (last agreed price, target price, negotiation rules, etc.).
• The information is sent back to Qwen, which drafts either an acceptance or a negotiation email.
• If the offered price is acceptable (same or lower than the target), it drafts an acceptance.
• If the price is too high, it drafts a negotiation email, for example explaining that George previously supplied the product at a significantly lower price and asking whether he can improve the offer.

This seems like a perfect use case for n8n right ?

My second use case is a local workshop assistant. A technician can ask repair-related questions, and the AI first searches our local documentation and database. If nothing relevant is found, it could optionally query Claude (depending on company policy).

After reading about MCP (Model Context Protocol), I'm wondering if I'm approaching this correctly.

Would you:

• Keep n8n as the orchestration layer for both use cases?
• Replace most of n8n with MCP?
• Or use both: n8n for deterministic workflows like email processing and MCP for the AI assistant, where the LLM needs to intelligently choose tools and data sources?

And if only the MCP is available, where does it get the rules it should follow? For example, rules about what it is allowed or not allowed to do such as not sending sensitive data to the internet or excluding certain sources. Or do you have to provide these rules every single time?

How would you architect these two use cases, and where do you see the practical boundary between n8n and MCP in production systems?

Thanks alo!!

Hi everyone,

I'm currently building a local AI architecture with multiple layers and I'm trying to understand where n8n ends and MCP begins.

One use case is automated supplier negotiations. We'll have a mailbox like buy@mail.com where supplier offers arrive.

The planned flow is:

• Supplier email arrives.
• n8n sends it to a local Qwen LLM.
• The LLM extracts the supplier, product and offered price and send to n8n.
• n8n looks up our PostgreSQL database (last agreed price, target price, negotiation rules, etc.).
• The information is sent back to Qwen, which drafts either an acceptance or a negotiation email.
• If the offered price is acceptable (same or lower than the target), it drafts an acceptance.
• If the price is too high, it drafts a negotiation email, for example explaining that George previously supplied the product at a significantly lower price and asking whether he can improve the offer.

This seems like a perfect use case for n8n right ?

My second use case is a local workshop assistant. A technician can ask repair-related questions, and the AI first searches our local documentation and database. If nothing relevant is found, it could optionally query Claude (depending on company policy).

After reading about MCP (Model Context Protocol), I'm wondering if I'm approaching this correctly.

Would you:

• Keep n8n as the orchestration layer for both use cases?
• Replace most of n8n with MCP?
• Or use both: n8n for deterministic workflows like email processing and MCP for the AI assistant, where the LLM needs to intelligently choose tools and data sources?

And if only the MCP is available, where does it get the rules it should follow? For example, rules about what it is allowed or not allowed to do such as not sending sensitive data to the internet or excluding certain sources. Or do you have to provide these rules every single time?

How would you architect these two use cases, and where do you see the practical boundary between n8n and MCP in production systems?

Thanks alotèè

Hi everyone,

I’ve used Outlook’s PST export/import feature many times in the past, and it usually worked fine.

However, during my last migration (IMAP → Exchange), something went wrong. For some reason, the PST export/import didn’t include all emails. It became a mess I had to export and import multiple times just to recover most of the missing emails on the new mail provider.

Now I need to move an Archive folder (32 GB) from one Exchange mailbox to another Exchange mailbox. Both mailboxes are under the same domain, just different email addresses.

My question:

Would you still recommend using the classic Outlook PST method (export to PST, add the second mailbox in Outlook, then import into the target mailbox)?

Or is there a better / safer way for Exchange → Exchange migration to avoid missing emails again?

Thanks alot!

Hello Zämme

Ich ha spontan welle uf Arosa aber ha grad no erfahre das det irgendeh event stattfindet mit über 2000 Persone. Darum kennet Ihr schöne Ort Umgebig Graubünde zum abkühle mit Hotel und möglichst ohni Gondle zum ufecho. Fahre vo Zentralschwitz los.

Thanks!

Hi everyone,

I’ve been using Clickup for about 6 years, and over time it has become pretty chaotic.

I created a lot of tasks quickly, but many were never updated or moved to “Done,” so a huge number are still sitting in “To Do.” That makes the whole system messy and less useful.

I’m now planning to start fresh with a completely new Space.

I’m a one-man IT business with a few freelancers, so I manage almost everything myself and handle very different types of work:

• Client IT work (network, servers, support)
• Admin, orders, appointments
• Internal business tasks
• Projects like local AI/LLM setups and startup ideas

My old setup was:

• Separate Spaces for clients, company, and projects
• One Folder per client
• Lists like IT Tasks / Admin

Now I’m wondering: what’s the best way to structure this in the future so it stays simple and manageable?

How do you organize ClickUp for mixed workloads like this?
What are your best practices?

Also, do you use List, Board, or Gantt view for IT tasks or general to-do tasks?

Also, I’m curious about your daily workflow in Clickup. Do you actually use the Inbox? Mine often feels overloaded, so I rarely look at it. I’m also wondering what your Home page or Dashboard looks like, because I’ve honestly never really used either of them. The first thing I usually do when opening ClickUp is go straight to the Calendar. Do you regularly use Dashboards as part of your workflow?

Would love to hear how other solo business owners or IT people are using ClickUp.

Thanks alot!!

Hi everyone,

I'm currently putting together the concept for a local LLM and I'd love to get your input before I get started.

Our use cases:

1. Email communication with suppliers: The AI should help with price negotiations over email. To do that, it looks through my mailbox (Exchange) for previous communication with the respective supplier, pulls out the most recently quoted prices, and negotiates further on that basis. Basically, it should search the existing email history with a supplier and take the manual work of looking things up and replying off my plate.
2. Internal chatbot: We should be able to ask it questions about certain processes, products, etc. So essentially a company assistant that knows our internal knowledge.
3. Local-first with a cloud fallback: The idea is that everything runs locally on Ollama by default. But when something is too complex or needs knowledge the local model doesn't have, the system should reach out to an external AI (e.g. the Claude API) over the internet, pull in that answer, and feed it back into the flow. So local for the bulk of the work, external only as a controlled exception and only the specific snippet that's needed leaves the server.

Here's the setup that was recommended to me, all running via Docker on an on-premise server:

• Server: 2× RTX 3090 Ti with 24 GB VRAM each
• PostgreSQL: as the database
• n8n: for automations (e.g. read emails → send to Ollama → have it draft a reply → back to n8n → send out via email/IMAP)
• NocoDB: as the interface
• Ollama: as the local AI
• External AI (optional): Claude API, called only for complex cases or missing knowledge

As far as I understand, each component has its own job. But here's what I'm still not fully clear on:

1. Do I really need every component? From what I understand, the local AI itself has no database – so the data (e.g. our customer data) has to live somewhere else, right? Is that why PostgreSQL is in there?
2. What exactly is n8n for? My understanding: n8n handles the interface to the outside world – email, Salesforce/ERP, other providers, and it would also be the thing that calls out to the external AI when needed. The local AI / Ollama can't do that itself, or am I getting that wrong?
3. Company chatbot: If I also want to build a chatbot, I can use the same local AI for it, right? And would I need n8n again for that even though I just want to chat with the AI directly?
4. Local-first + cloud fallback: Is routing things to a local model first and only escalating to an external API (Claude etc.) for hard cases a sensible approach? How do you decide when to escalate, and how do you keep sensitive data from leaking out in those calls?

I'm still not quite sure which components I actually need and which I don't.

And my main question: Would you recommend n8n, or do you know other tools I can set up locally/self-hosted?

Thanks in advance for your thoughts!

https://preview.redd.it/e8sqbey6nz8h1.png?width=1572&format=png&auto=webp&s=9e9b4cb1cf32de20f6e0c0731bda51d65af0dc8e

Hi everyone,

I'm currently putting together the concept for a local LLM and I'd love to get your input before I get started.

Our use cases:

1. Email communication with suppliers: The AI should help with price negotiations over email. To do that, it looks through my mailbox (Exchange) for previous communication with the respective supplier, pulls out the most recently quoted prices, and negotiates further on that basis. Basically, it should search the existing email history with a supplier and take the manual work of looking things up and replying off my plate.
2. Internal chatbot: We should be able to ask it questions about certain processes, products, etc. So essentially a company assistant that knows our internal knowledge.
3. Local-first with a cloud fallback: The idea is that everything runs locally on Ollama by default. But when something is too complex or needs knowledge the local model doesn't have, the system should reach out to an external AI (e.g. the Claude API) over the internet, pull in that answer, and feed it back into the flow. So local for the bulk of the work, external only as a controlled exception and only the specific snippet that's needed leaves the server.

Here's the setup that was recommended to me, all running via Docker on an on-premise server:

• Server: 2× RTX 3090 Ti with 24 GB VRAM each
• PostgreSQL: as the database
• n8n: for automations (e.g. read emails → send to Ollama → have it draft a reply → back to n8n → send out via email/IMAP)
• NocoDB: as the interface
• Ollama: as the local AI
• External AI (optional): Claude API, called only for complex cases or missing knowledge

As far as I understand, each component has its own job. But here's what I'm still not fully clear on:

1. Do I really need every component? From what I understand, the local AI itself has no database – so the data (e.g. our customer data) has to live somewhere else, right? Is that why PostgreSQL is in there?
2. What exactly is n8n for? My understanding: n8n handles the interface to the outside world – email, Salesforce/ERP, other providers, and it would also be the thing that calls out to the external AI when needed. The local AI / Ollama can't do that itself, or am I getting that wrong?
3. Company chatbot: If I also want to build a chatbot, I can use the same local AI for it, right? And would I need n8n again for that even though I just want to chat with the AI directly?
4. Local-first + cloud fallback: Is routing things to a local model first and only escalating to an external API (Claude etc.) for hard cases a sensible approach? How do you decide when to escalate, and how do you keep sensitive data from leaking out in those calls?

I'm still not quite sure which components I actually need and which I don't.

And my main question: Would you recommend n8n, or do you know other tools I can set up locally/self-hosted?

Thanks in advance for your thoughts!