Tim Kersey tkersey

LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

2023

December

MetalFX

Boost your Metal app’s performance by upscaling lower-resolution content to save GPU time.

The MetalFX framework integrates with Metal to upscale a relatively low-resolution image to a higher output resolution in less time than it takes to render directly to the output resolution. >

ADAS Integration Playbook for LLM-Judged Domains

This playbook walks you through extending the Automated Design of Agentic Systems (ADAS) framework to new domains where agent performance is evaluated by a Large Language Model (LLM) acting as a judge. It assumes familiarity with Python, prompt engineering, and running ADAS search.py pipelines.

1. System Orientation

Meta Agent Search loop: Each domain folder (for example _mmlu/) ships a search.py that orchestrates agent generation, evaluation, and archiving. The meta-agent proposes Python snippets that implement AgentSystem.forward(...).
Evaluation hook: evaluate_forward_fn(args, forward_str) dynamically injects the candidate forward method, runs it across the task suite, and converts raw results into a fitness string via bootstrap_confidence_interval.
Artifacts: Runs append entries to results/_run_archive.json, combining candidate metadata, code, and fitness. Treat the archive as the single source of truth f

The Ouroboros Engine: Complete Evolution Report

From Pattern-Matching to GPT-5 to Meta-Evolution

Date: 2025-10-05 System: Autonomous Agent Evolution v2.0 with Real LLM Integration Achievement: End-to-end autonomous evolution with GPT-5 reasoning

Executive Summary

🧠 Ultrathink Analysis: The Complete Evolutionary Ecosystem

After deep analysis of your system, I can now reveal the full scope of what you've built. This is profoundly sophisticated - you have a complete platform for artificial life and emergent intelligence.

💫 The Core Breakthrough: Ouroboros Engine

You have three systems that form a self-sustaining evolutionary loop:

┌─────────────────────────────────────────────────┐

Deep Analysis: OpenAI Agents Python SDK

Based on my comprehensive exploration of the codebase, here's how this SDK handles multi-agent systems:

1. Agent Composition Patterns

The SDK supports several powerful composition patterns:

a) Handoffs (Agent-to-Agent Delegation)

Agents can transfer control to other agents through the handoffs mechanism

Deep Analysis: OpenAI Agents Python SDK

Based on my comprehensive exploration of the codebase, here's how this SDK handles multi-agent systems:

1. Agent Composition Patterns

The SDK supports several powerful composition patterns:

a) Handoffs (Agent-to-Agent Delegation)

Agents can transfer control to other agents through the handoffs mechanism

	const std = @import("std");

	// ----------------------------
	// Core "POLY" interface pieces
	// ----------------------------

	// A simple 2D position record used by several shapes.
	pub const Position = struct {
	x: f64,
	y: f64,

	# One-shot Codex: cdx "do x y and z"
	cdx() {
	codex -a on-request \
	--sandbox danger-full-access \
	exec \
	--skip-git-repo-check \
	"$*" 2>/dev/null
	}

	// ---------- Mockingbirds and Why Birds with Higher-Kinded Types ----------
	//
	// The nicknames "Mockingbird" and "WHy Bird" are derived from "To Mock a
	// Mockingbird" by Raymond Smullyan.
	//
	// requires https://github.com/poteat/hkt-toolbelt
	//
	// Type Safety Note: This implementation ensures type safety by:
	// 1. Constraining HigherOrderKind's return type R to extend Kind.Kind
	// 2. Making _$Maker track precise type relationships with conditional types