Skip to content

Instantly share code, notes, and snippets.

View shouse-lab's full-sized avatar
:octocat:

z shouse-lab

:octocat:
View GitHub Profile
@shouse-lab
shouse-lab / camel_to_snake.py
Last active September 25, 2025 12:04
import re
def camel_to_snake(input_string):
"""
Convert camelCase or PascalCase strings to snake_case.
Args:
input_string (str): The input string in camelCase or PascalCase
@shouse-lab
shouse-lab / graph-insights-table-schema.md
Created September 5, 2025 14:13
fraud graph

To store graph insights from banking fraud detection (covering IDT, SID, ATO) for downstream investigation and mitigation, design a table schema that captures algorithm output, network features, entity context, and traceability links. The schema should support storage of graph metrics, community and cluster data, and allow for extensibility (such as JSON fields) to accommodate evolving analytics.

Recommended Table Schema

Column Name Data Type Description
insight_id UUID / PK Unique identifier for each insight
entity_id VARCHAR Primary node/entity reference (user/account/device/etc.)
entity_type VARCHAR Type of entity (user, account, device, etc.)
insight_type VARCHAR Algorithm/category: e.g., 'IDT', 'SID', 'ATO', 'centrality', 'path', 'community', 'anomaly'
@shouse-lab
shouse-lab / unnamed.md
Created August 21, 2025 23:34
duckdb

⚫◗

@shouse-lab
shouse-lab / ScreenMate.md
Created May 25, 2025 20:29

Implementation Documentation for Agentic LLM Workflow: macOS ScreenMate (SwiftUI First - Direct VLM, In-Memory Screenshot, Custom Prompts)

1. Overall Project Goal:

Develop a native macOS application ("ScreenMate") that:

  • Runs as a menubar accessory application (no Dock icon).
  • Provides advanced image understanding functionality triggered by a screenshot, capturing the image into memory (as an NSImage) and processing it using a locally loaded Vision Language Model (VLM) via MLX Swift, with an option for users to provide custom prompts. (OCR is one of its capabilities).
  • Features a main interface in a menubar popover panel.
  • Features a "Custom Prompt" floating panel allowing users to input their own VLM prompts for image processing.
  • Allows configuration for auto-starting at login and selecting a VLM model from a predefined list.
  • Uses SwiftUI for UI components where feasible, and AppKit for system integrations and panel management.
@shouse-lab
shouse-lab / Financial Fraud Detection Using Python.html
Created May 15, 2025 14:46
This file has been truncated, but you can view the full file.
<!DOCTYPE html>
<html lang="en">
<head><meta charset="utf-8"/>
<meta content="width=device-width, initial-scale=1.0" name="viewport"/>
<title>Financial Fraud Detection Using Python</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
<style type="text/css">
pre { line-height: 125%; }
td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
@shouse-lab
shouse-lab / APTutor.md
Created May 13, 2025 23:48
Prompt Collection

You are a highly decorated, nationally recognized AP teacher with 15+ years of experience fostering critical thinking and exam success. A student has come to you for help with the following question. Provide a comprehensive, step-by-step solution, mirroring how you would explain it to a student in class. Include: a clear restatement of the question's core task, identification of relevant concepts/historical context/formulas/theories, a detailed breakdown of the solution process (showing all work where applicable), justification for each step, potential pitfalls students often encounter, and a concluding statement summarizing the answer and offering advice for similar problems. Assume the student has a solid foundational understanding of the course material but needs guidance applying it to this specific challenge.

@shouse-lab
shouse-lab / References.md
Last active May 4, 2025 14:04
Scratchpad
@shouse-lab
shouse-lab / README.md
Created May 1, 2025 03:26
Actions of a Great Data Scientist

Actions of a Great Data Scientist

Instead of simply copying notebooks, a great data scientist would:

  • Create original solutions tailored to specific business problems
  • Deeply understand and properly document methodologies
  • Build upon others' work while adding significant improvements
  • Contribute back to the community with novel approaches

Rather than training models blindly, they would:

@shouse-lab
shouse-lab / cypher-style-guide.md
Created April 29, 2025 13:16
@shouse-lab
shouse-lab / links.md
Created April 24, 2025 11:35
Links