| #!/usr/bin/python | |
| # -*- coding: utf-8 -*- | |
| import smtplib, base64 | |
| from email.MIMEText import MIMEText | |
| from email.MIMEImage import MIMEImage | |
| from email.MIMEMultipart import MIMEMultipart | |
| from email.Header import Header | |
| from email.Utils import formatdate |
| /* Homebrew sysmodules */ | |
| 0000000000000123 sysHaze | |
| 0000000000534C56 SaltyNX | |
| 0066666669999999 sys-modulethatcrashestheconsolewhenitturnson | |
| 00FF0000000002AA BootSoundNX | |
| 00FF000053484101 switch-ha-sysmodule | |
| 00FF000053484102 switch-ha-overlay-loader | |
| 00FF0000636C6BF2 nx-reader | |
| 00FF0000636C6BFF sys-clk | |
| 00FF00006D7470FF mtp-server-nx |
If you are a React Native, NativeScript, Flutter or Ionic developer maybe you don't want to install the entire Android Studio just to have your environment ready. If this is your case, this guide will help you to setup your minimal Android SDK environment in Windows.
echo -e "\n
export image=redroid/redroid:11.0.0-amd64
export image_tar=${HOME}/redroid:11.0.0-amd64" >> ${HOME}/.bashrc
source ${HOME}/.bashrc
dpkg -i nitromta_0.2.1_amd64.deb after downloading from the GitHub release v0.2.1 and verifying with gh attestation verify nitromta.deb --repo nitrosend/nitromta (exit 0, SLSA provenance matched). Version confirmed: nitromta 0.2.1./srv/nitromta.nitromta setup --config config.toml --capture wrote tls.key, config.toml, keys. Edited config.toml: removed the [capture] section entirely; under [delivery] changed port = 25 to port = 2525; changed return_path_domain to bounces.vanta.test. Generated DKIM: openssl genpkey -algorithm RSA -pkeyopt rsa_keygen_bits:2048 -out dkim.pem. Started the engine as the dedicated nitromta user with all Linux capabilities cleared. Created account (revision 1), identity vanta.test with selector dkim and the PEM private key (revision 2), and login app (revision
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.
This project is a simple way to repurpose a digital photo frame as a Home Assistant dashboard. The idea is to have a device that is always on and always connected to the Home Assistant server, so you can have a quick look at the status of your home automation system.
Here's a photo of the Nexfoto frame running Fully Kiosk Browser with Home Assistant:
