privacy

How Handwright turns a scanned worksheet into a custom .ttf font — OpenCV glyph extraction, potrace vectorization, and fonttools assembly. Local-first, no cloud required.

Implement post-quantum cryptography with CRYSTALS-Kyber and Dilithium—prepare homelab for quantum threats using NIST-approved algorithms.

Build privacy-first AI lab with local LLMs—run models up to 34B on RTX 3090 (24GB VRAM) with network isolation, traffic monitoring, and real privacy controls.

Migrate to self-hosted Bitwarden—deploy secure vault with backup strategies, SSL certificates, and database encryption for full control.

Deploy DNS-over-HTTPS with Pi-hole and dnscrypt-proxy—encrypt DNS queries for home network privacy and prevent ISP monitoring with DoH implementation.

Deploy local LLMs for privacy-first AI—run language models on homelab hardware with model selection, optimization, and deployment strategies.

Secure personal AI experiments with model isolation and network segmentation—protect LLM deployments using privacy controls and threat modeling.

Deploy federated learning across homelab with granular-ball computing—train privacy-preserving models with 82% reduced network transfer.

Implement privacy-preserving authentication using ZK-SNARKs for homelab SSO. No passwords transmitted, cryptographic proof of identity without revealing credentials.