I use a cheap Tapo C100 webcam to monitor my 3D prints. It supports RTSP.
- Go to the Cameras section at https://connect.prusa3d.com
- Add a new camera.
- Click the QR code link
- Click "Start Camera"
| #!/bin/sh | |
| MUTATING_WEBHOOKS=($(kubectl get mutatingwebhookconfiguration -o jsonpath='{range .items[*]}{.metadata.name}{"\n"}{end}')) | |
| VALIDATING_WEBHOOKS=($(kubectl get validatingwebhookconfigurations -o jsonpath='{range .items[*]}{.metadata.name}{"\n"}{end}')) | |
| # Check Mutating WebHooks | |
| echo ":: Checking Mutating WebHooks ::" | |
| echo "::____________________________::" |
| ## deploys the external-dns - https://github.com/kubernetes-sigs/external-dns | |
| ## view latest version: helm search repo external-dns/external-dns | |
| ## view possible values: helm inspect values external-dns/external-dns | |
| resource "helm_release" "external_dns" { | |
| name = "external-dns" | |
| repository = "https://kubernetes-sigs.github.io/external-dns" | |
| chart = "external-dns" | |
| version = "1.8.0" | |
| namespace = "external-dns" |
| # instructions: | |
| # on the first run you to target apply otherwise terraform will complain with for_each | |
| # 1. terraform apply -target='null_resource.projects' | |
| # 2. terraform apply | |
| terraform { | |
| required_providers { | |
| google = { | |
| source = "hashicorp/google" | |
| } |
| locals { | |
| azurerm_private_endpoint = { | |
| custom_dns_configs = [ | |
| { | |
| "fqdn" = "something.westeurope.data.azurecr.io" | |
| "ip_addresses" = [ | |
| "10.10.10.202" | |
| ] | |
| }, | |
| { |
I use a cheap Tapo C100 webcam to monitor my 3D prints. It supports RTSP.
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.