My Journey to a reliable and enjoyable locally hosted voice assistant

I have been watching HomeAssistant’s progress with assist for some time. We previously used Google Home via Nest Minis, and have switched to using fully local assist backed by local first + llama.cpp (previously Ollama). In this post I will share the steps I took to get to where I am today, the decisions I made and why they were the best for my use case specifically. Links to Additional Improvements Here are links to additional improvements posted about in this thread. New Features Security C...

Can you slim macOS down?

In a quest to reduce the number of processes running in macOS Tahoe, consider the example of Time Machine backups, which can easily be replaced by 3rd party alternatives.

When O3 is 2x slower than O2

An deep dive into a pathological case.

The Secret Life of a Local-First Value

A deep dive into how CRDT-powered local-first apps track, merge, and sync every INSERT, UPDATE, and DELETE inside SQLite.

The Beginner's Textbook for Fully Homomorphic Encryption

Fully Homomorphic Encryption (FHE) is a cryptographic scheme that enables computations to be performed directly on encrypted data, as if the data were in plaintext. After all computations are performed on the encrypted data, it can be decrypted to reveal the result. The decrypted value matches the result that would have been obtained if the same computations were applied to the plaintext data. FHE supports basic operations such as addition and multiplication on encrypted numbers. Using these fundamental operations, more complex computations can be constructed, including subtraction, division, logic gates (e.g., AND, OR, XOR, NAND, MUX), and even advanced mathematical functions such as ReLU, sigmoid, and trigonometric functions (e.g., sin, cos). These functions can be implemented either as exact formulas or as approximations, depending on the trade-off between computational efficiency and accuracy. FHE enables privacy-preserving machine learning by allowing a server to process the client's data in its encrypted form through an ML model. With FHE, the server learns neither the plaintext version of the input features nor the inference results. Only the client, using their secret key, can decrypt and access the results at the end of the service protocol. FHE can also be applied to confidential blockchain services, ensuring that sensitive data in smart contracts remains encrypted and confidential while maintaining the transparency and integrity of the execution process. Other applications of FHE include secure outsourcing of data analytics, encrypted database queries, privacy-preserving searches, efficient multi-party computation for digital signatures, and more. As this book is an open project (https://fhetextbook.github.io), we welcome FHE experts to join us as collaborators to help expand the draft.

Real-world performance comparison of ebtree/cebtree/rbtree

In the previous synthetic comparison between ebtree and cebtree it was quite clear that performance varies quite a bit between key types, k...

Pwning the Ladybird browser | Jess's Cafe

Noise Explorer

Noise Explorer is an online engine for reasoning about Noise Protocol Framework Handshake Patterns. Noise Explorer allows you to design and validate Noise Handshake Patterns, to generate cryptographic models for formal verification and to explore a compendium of formal verification results for the most popular and relevant Noise Handshake Patterns in use today.