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README.md

paperclip tool- paperclip_cost_audit.py

Instructions:

One-time setup

pip install psycopg2-binary

Run it

python paperclip_cost_audit.py

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The /probe Pattern: Catching AI Hallucinations Before They Become Code

I've been running a pattern called /probe against AI-generated code before I write anything, and it keeps catching bugs the AI had no idea it was about to cause.

The shape is simple. Before I write code based on AI output, I force each AI-asserted fact into a numbered CLAIM with an EXPECTED value, then run a command against the real system to check. I capture the delta. Surprises become tests.

The core move: claims are the AI's own prior confidence, made auditable.

The anchor story

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The REPL Compute Layer: Why AI Should Send Code, Not Data

The Problem

Most AI tool integrations follow the same pattern: fetch raw data, paste it into the prompt, ask the model to analyze it. A CSV with 500 rows. A database dump. An API response with nested JSON. The LLM reads every byte, burns tokens parsing structure it cannot see efficiently, and produces a summary that a three-line script could have generated.

This approach is expensive, slow, and lossy:

• Token cost scales with data volume. 50 Reddit posts at ~500 tokens each = 25,000 input tokens just to score and rank them. At Opus pricing, that is $0.42 per query.
• Context windows are finite. A 200K context window sounds large until you load a real dataset, a system prompt, conversation history, and tool definitions. The data competes with reasoning space.

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Agent Teams Guide: Sequential Ralph Loop vs Native Agent Teams

Date: 2026-02-11

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Two Ways to Execute PRDs

1. Ralph Loop (ralph.sh) — Sequential, one task at a time

2. Native Agent Teams (Claude Code feature) — Parallel, multiple agents

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PRD: Trade Analyzer

Introduction

Analyze trading snapshots from CSV to calculate P&L, win rate, and expected value metrics (Standard EV, Kelly Criterion, Sharpe Ratio) aggregated by ISO week. Pure functional core for calculations with console output.

Goals

• Load trade snapshots from CSV with type validation
• Calculate realized P&L by pairing ENTRY/EXIT events

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test to make public