Tim Kersey tkersey

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

The Ultimate Swift Testing Playbook (2024 WWDC Edition, expanded with Apple docs from June 2025)

Updated with info from https://developer.apple.com/documentation/testing fetched via Firecrawl on June 7, 2025.

A hands-on, comprehensive guide for migrating from XCTest to Swift Testing and mastering the new framework. This playbook integrates the latest patterns and best practices from WWDC 2024 and official Apple documentation to make your tests more powerful, expressive, and maintainable.

1. Migration & Tooling Baseline

Ensure your environment is set up for a smooth, gradual migration.

MCP's client-server architecture: Technical design for AI integration

The Model Context Protocol (MCP) represents a fundamental shift in how AI applications connect to external systems. Introduced by Anthropic in November 2024, MCP chose a client-server architecture over alternatives like peer-to-peer or monolithic designs to solve the "M×N problem" - where M AI applications need to integrate with N data sources, traditionally requiring M×N custom integrations. The client-server model transforms this into an M+N solution through standardized, secure, and scalable connections.

This architectural decision reflects deep technical considerations: security isolation between components, modular extensibility for diverse integrations, and protocol standardization that enables any MCP client to work with any MCP server regardless of implementation language or platform. The design philosophy prioritizes developer simplicity while maintaining enterprise-grade security boundaries - what Anthropic calls "

	// ---------- 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

	import {
	Operation,
	Resource,
	Context,
	action,
	resource,
	spawn,
	sleep,
	main,
	suspend as effectionSuspend,

	// Deep Dive: Prompts as Delimited Continuations in TypeScript

	// =============================================================================
	// 1. PROMPTS AS INITIAL CONTINUATIONS
	// =============================================================================

	/**
	* Prompts as Initial Continuations treats the prompt itself as the starting
	* continuation that establishes the computational context. The prompt becomes
	* a first-class continuation that can be captured, modified, and resumed.