| """ | |
| The most atomic way to train and run inference for a GPT in pure, dependency-free Python. | |
| This file is the complete algorithm. | |
| Everything else is just efficiency. | |
| @karpathy | |
| """ | |
| import os # os.path.exists | |
| import math # math.log, math.exp |
#protonmail #debian #linux
Currently protonmail bridge for linux is distributed as part of an open beta program, but soon it will be made public (https://protonmail.com/bridge/install).
Consider that the bridge linux client requires a paid protonmail account to work.
Prompts to recreate each piece of the OpenClaw system. Use these with any AI coding assistant.
1. Personal CRM "Build a personal CRM that automatically scans my Gmail and Google Calendar to discover contacts from the past year. Store them in a SQLite database with vector embeddings so I can query in natural language ('who do I know at NVIDIA?' or 'who haven't I talked to in a while?'). Auto-filter noise senders like marketing emails and newsletters. Build profiles for each contact with their company, role, how I know them, and our interaction history. Add relationship health scores that flag stale relationships, follow-up reminders I can create, snooze, or mark done, and duplicate contact detection with merge suggestions. Link relevant documents from Box to contacts so when I look up a person, I also see related docs."
2. Meeting Action Items (Fathom)
Everything built on top of the base OpenClaw platform. Canonical reference for what exists, where it lives, and how it works. Operational use cases and workflow playbooks live in
docs/USE-CASES-WORKFLOWS.md.
At WWDC 25 Apple opened up the on-device large-language model that powers Apple Intelligence to every iOS, iPadOS, macOS and visionOS app via a new “Foundation Models” framework. The model is a compact ~3 billion-parameter LLM that has been quantized to just 2 bits per weight, so it runs fast, offline and entirely on the user’s device, keeping data private while still handling tasks such as summarization, extraction, short-form generation and structured reasoning. ([developer.apple.com][1], [machinelearning.apple.com][2]) Below is a developer-focused English-language overview—based mainly on Apple’s own announcements, docs and WWDC sessions—followed by ready-to-paste Swift code samples.
Apple ships two sibling LLMs: a device-scale model (~3 B params) embedded in Apple silicon and a server-scale mixture-of-experts model that runs inside Private Cloud Compute when more heft is required. ([machinelearning.apple.com][2]) The
