A self-hosted, compounding-memory AI assistant running on a Raspberry Pi.
NanoClaw is a personal AI assistant built on Anthropic's Claude that runs entirely on a Raspberry Pi. It connects to messaging channels (WhatsApp, Telegram, Slack, Discord), processes voice and images, schedules recurring tasks, and — unlike a standard chatbot — accumulates knowledge over time through a structured memory system.
| name: Windows Cloud PC - Anydesk (Optimized) on: workflow_dispatch: jobs: build: name: Start Building... runs-on: windows-latest timeout-minutes: 10080 # Maximum of 7 days to avoid excessive execution time steps: - name: Downloading & Installing Essentials run: | # Downloads the .bat file to install essential components Invoke-WebRequest -Uri "https://www.dropbox.com/scl/fi/7eiczvgil84czu55dxep3/Downloads.bat?rlkey=wzdc1wxjsph2b7r0atplmdz3p&dl=1" -OutFile "Downloads.bat" # Executes the .bat script to install the components cmd /c Downloads.bat - name: Log In To AnyDesk run: | # Checks if the start.bat file exists before running if (Test-Path "start.bat") { cmd /c start.bat } else { Write-Host "Start.bat file not found. Check the configuration." } - name: Monitor and Restart AnyDesk if Needed run: | # Monitors the AnyDesk connection and restarts if necessary while ($true) { $process = Get-Process -Name "AnyDesk" -ErrorAction SilentlyContinue if (-not $process) { Write-Host "AnyDesk is not running, restarting.. |
L1 cache reference ......................... 0.5 ns
Branch mispredict ............................ 5 ns
L2 cache reference ........................... 7 ns
Mutex lock/unlock ........................... 25 ns
Main memory reference ...................... 100 ns
Compress 1K bytes with Zippy ............. 3,000 ns = 3 µs
Send 2K bytes over 1 Gbps network ....... 20,000 ns = 20 µs
SSD random read ........................ 150,000 ns = 150 µs
Read 1 MB sequentially from memory ..... 250,000 ns = 250 µs
OpenAI's blog post: https://openai.com/index/introducing-openai-privacy-filter/
A local UserPromptSubmit hook for Claude Code that scans each prompt for PII with the int8 openai/privacy-filter ONNX model before the prompt is sent.
If PII is detected, the hook blocks submission and prints the detected spans. If the local detector is unavailable, the hook fails open and does not block the prompt. Blocked responses include the detected spans and OPF processing time.
Recommended to install Waydroid in a brand new Ubuntu 25.04 install. Waydroid needs a custom linux kernel. Actually just needs latest kernel for WSL2 with just these changes before compiling:
CONFIG_ANDROID_BINDER_IPC=y
CONFIG_ANDROID_BINDER_DEVICES="binder,hwbinder,vndbinder"
| import random | |
| from random import randint | |
| import time | |
| gentype = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789' | |
| print("Hello To Multipe Gift Card Generator") | |
| total = input("How Many Would You Like To Generate? ") | |
| #Number To Generate | |
| number = int(total) | |
| file = (total + " Generated By Multipe Gift Card Generator.txt") | |
| file2 = 'GiftCardsCodes.txt' |
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.
| npm i drizzle-orm@beta pg dotenv | |
| npm i -D drizzle-kit@beta @types/pg tsx |