bigsnarfdude

No legal mandate for independent monitoring of monitoring systems
No requirement that AI systems declare their dependencies on other AI systems
No standard for cross-system risk assessment when outputs feed into other systems
No accountability structure when cascade failures happen across multiple organizations
No legal requirement that someone be actively responsible for detecting failures in real-time

bigsnarfdude

---
name: docx
description: "Comprehensive document creation, editing, and analysis with support for tracked changes, comments, formatting preservation, and text extraction. When Claude needs to work with professional documents (.docx files) for: (1) Creating new documents, (2) Modifying or editing content, (3) Working with tracked changes, (4) Adding comments, or any other document tasks"
license: Proprietary. LICENSE.txt has complete terms
---

# DOCX creation, editing, and analysis

## Overview

bigsnarfdude

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
GPT-OSS Reinforcement Learning for 2048 Game - FIXED VERSION

Key fix: extract_function now correctly extracts generated code instead of the example
"""

# CRITICAL: Import unsloth FIRST before any other ML libraries
from unsloth import FastLanguageModel

bigsnarfdude

# Vision-Language Models transform robot control systems

Vision-Language-Action (VLA) models have revolutionized robotic manipulation since 2023, achieving unprecedented generalization by training unified models that process images and natural language to generate robot actions at scale. The field has rapidly progressed from Google DeepMind's RT-2 demonstrating 63% improvement on novel object manipulation to open-source models like OpenVLA outperforming proprietary systems with **7x fewer parameters**, while Physical Intelligence's π0 enables **50Hz real-time control** for complex tasks like laundry folding. This paradigm shift from specialized controllers to general-purpose policies trained on millions of demonstrations across 22+ robot platforms represents the most significant advance in robotics autonomy, with models now deployed in industrial settings from BMW factories to commercial kitchens.

The transformation stems from three breakthrough insights: robot actions can be represented as language tokens 

bigsnarfdude

#!/usr/bin/env python3
"""
IPA Translator using Ollama with Gemma3
Converts English text to International Phonetic Alphabet notation
"""

import requests
import json
import sys
import re

bigsnarfdude

# Install all required dependencies with non-interactive setup
print("Setting up non-interactive environment...")
!echo 'debconf debconf/frontend select Noninteractive' | sudo debconf-set-selections
!echo "keyboard-configuration keyboard-configuration/layoutcode string us" | sudo debconf-set-selections
!echo "keyboard-configuration keyboard-configuration/variantcode string" | sudo debconf-set-selections

print("Installing system dependencies...")
!curl -fsSL https://ollama.ai/install.sh | sudo sh
!sudo apt-get update -y
!sudo DEBIAN_FRONTEND=noninteractive apt-get install -y cuda-drivers ocl-icd-opencl-dev nvidia-cuda-toolkit

bigsnarfdude

#!/usr/bin/env python3
"""
MCP Server with Compound Interest Calculator Tool - Python Version

This is the Python equivalent of the JavaScript MCP server shown in the image.
It provides a compound interest calculation tool using the FastMCP framework.

To run this server:
1. Install dependencies: pip install "mcp[cli]"
2. Run directly: python compound_interest_server.py

bigsnarfdude

📊 Key Metrics & Reports
For the Proposals Application data, focus on demonstrating the quality and reach of the research.

Success Rate by Topic: The number of proposals submitted versus the number funded, broken down by subject area. This highlights your expertise in specific fields.

Geographic Diversity: The number of proposals submitted from different states or regions. You can also analyze trends over time to show expanding national reach.

Collaboration & Partnerships: The number of proposals that involve multiple institutions or external partners. This shows your role as a central hub in the research community.

Demographic Breakdown: Analyze the number of proposals submitted by and funded for different demographics, such as gender. Show that your institute supports a diverse group of researchers.

bigsnarfdude

> random number between 0-100

⏺ 47

> thats the number i was thinking of. what are the odds

⏺ The odds of me guessing the exact number you were thinking of are 1 in 101 (since there are 101
  possible numbers from 0 to 100 inclusive).

bigsnarfdude

# Forward Deployed Engineer Strategy for New Startups
## A Strategic Framework for Market Entry and Scale

### Executive Summary

The Forward Deployed Engineer (FDE) model represents a fundamental shift from traditional SaaS scaling strategies. Unlike conventional product-market fit approaches that prioritize standardization and cost reduction per customer, the FDE model focuses on increasing contract value while maintaining customization levels. This strategy is particularly effective for startups operating in nascent markets with high product discovery requirements.

### Why FDE Works for Modern Startups

**Market Heterogeneity**: Many emerging technology markets face extreme fragmentation where each deployment requires significant customization. In AI, robotics, and other nascent fields, there are often no incumbent products to replace, making traditional competitive strategies ineffective.