mberman84

PRD.md - Product Requirements & Feature Inventory

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.

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Table of Contents

1. Operational Use Cases & Workflows

baaamn

<#
.SYNOPSIS
    Scans files or clipboard for URLs with customizable output fields and export capabilities.

.DESCRIPTION
    This function parses text for HTTP/HTTPS links. It allows granular control over 
    which metadata properties (Path, Line, Value, etc.) are included in the final output.
    
    It supports:
    1. Detailed Extraction: Returns specific properties for every match.

1827marketing

Coding without Language

Neurocode - Latent-Space Program Synthesis via Diffusion Models and Bespoke Compilation

Background - Vibes and Vectors

As someone who uses LLMs to generate code, I am used to seeing a Chain of Thought process as the LLM works through a coding challenge. These chains of thought are expressed in natural language, which means that the LLMs are constantly translating and committing to concrete expressions of a more latent and liminal process.

Coconut (Chain of Continuous Thought) is an approach which does not require LLMs to keep on resolving intermediate steps into natural language.

I wondered if this could be applied to code generation, and then I wondered if we could stay in latent space beyond code generation, reframing what we think of as an executable. I was interested in:

baaamn

# Install Scoop App
# scoop alias add i 'scoop install $args[0]' 'Install scoop app'
scoop alias add i 'foreach ($_ in $args) {scoop install $_ -su}' 'Install apps'

# Uninstall Scoop App
scoop alias add rm 'scoop uninstall $args[0]' 'Uninstall a scoop app'

# List Scoop Apps
scoop alias add ls 'scoop list' 'List installed scoop apps'

AndrewAltimit

AI Toolkit MCP Server (local diffusion AI model trainer)

Warning: Requires a powerful GPU!

A containerized AI Toolkit setup with MCP (Model Context Protocol) integration for training LoRA models and fine-tuning diffusion models. This provides a complete solution for training custom LoRA models with full MCP integration, allowing AI assistants to manage the entire training workflow.

Usage

See the template repository for a complete example. Also includes the ComfyUI MCP Server used for creating images/videos from the trained models.

baaamn

pip install -q -U google-generativeai

baaamn

###############################################################################
# Scoop Installation Script
# Author: Wesley
# Purpose: Automate Scoop setup, bucket registration, and app installation
###############################################################################

# -------------------------------
# 1. Configure Execution Policy
# -------------------------------
# Allow running local scripts for the current user

vielhuber

Universal

• OpenAI ChatGPT: https://chatgpt.com
• Anthropic Claude: https://claude.ai
• Google Gemini: https://gemini.google.com / https://aistudio.google.com
• Perplexity: https://perplexety.ai
• Microsoft Copilot: https://copilot.microsoft.com
• Grok: https://x.ai
• Meta Llama: https://www.llama.com
• Ai2 OLMo: https://allenai.org/olmo
• Mistral: https://mistral.ai

tkoz0

YT-DLP helpful options cheat sheet

basics:
--version
-U,--update
-i,--ignore-errors
--flat-playlist (list videos, no download)

video selection:
--datebefore YYYYMMDD

0xdevalias

AI / ML Toolkit

Some notes on AI / ML tools that seem interesting/useful (largely aiming to focus on open source tools)

Table of Contents

• See Also
  • My Other Related Deepdive Gist's and Projects
• OpenRouter