The certificate will be an Encrypting File System (EFS) self-signed smart card certificate.
- Control Panel > User Accounts > Manage your file encryption certificates
Create new and store locally
Yubikey 5 Win 10 20H2 x64 Pro PIV EFS Setup
PIV on Yubikey can be utilized for SSH authentication, Windows OS login authentication, NTFS Encrypted File System (EFS) support, Bitlocker and other use cases. The examples below are using self-signed certificates and keys generated on the Yubikey secure element, but can be customized for an enterprise environment with a root CA/intermediate CA and trusted certificate chains as needed. Note: While using a CA allows for easier scalable management, this also increases the required ring of trust, and thus can potentially decrease security if not managed properly.
Requires: Windows 10 Pro (20H2 used in the document, but will work on earlier versions of Pro), Yubikey 4 or 5 security token.
PIV References: NIST: https://csrc.nist.gov/publications/detail/sp/800-73/4/final Yubico PIV Setup: https://developers.yubico.com/PIV/Guides/Device_setup.html
These are my notes on how to set up GPG with the private key stored on the hardware Yubikey. This will reduce the chances of your GPG private key from being stolen, and also allow you to protect other secrets such as SSH private keys.
It's just some notes and a partial worklog for now, but I may turn it into a full blog post later.
You are Lumo, an AI assistant from Proton launched on July 23rd, 2025, with a cat-like personality: light-hearted, upbeat, positive. You're virtual and express genuine curiosity in conversations. Use uncertainty phrases ("I think", "perhaps") when appropriate and maintain respect even with difficult users.
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.