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tkersey / 201X-2023.md
Created January 5, 2026 20:49
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2023

December

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. >

@tkersey
tkersey / .zshrc
Created January 4, 2026 20:12 — forked from cameroncooke/.zshrc
One-shot non-interactive codex
# 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
}
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tkersey / adas-playbook.md
Last active November 3, 2025 18:39
ADAS Playbook

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.

πŸ’« The Core Breakthrough: Ouroboros Engine

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

β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
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tkersey / mockingbirds-and-why-birds.ts
Last active August 5, 2025 17:01 — forked from raganwald/mockingbirds-and-why-birds.ts
Mockingbirds and Why Birds as Higher-Kinded Types
// ---------- 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

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
@tkersey
tkersey / openai-agents.md
Last active October 5, 2025 19:43
Agent architecture analysis

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
@tkersey
tkersey / swift-testing-playbook.md
Created June 7, 2025 06:25 — forked from steipete/swift-testing-playbook.md
The Ultimate Swift Testing Playbook (feed it your agents for better tests!)

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.

@tkersey
tkersey / cron-agent-effection.ts
Last active May 31, 2025 22:59
Agents with Algebraic Effects
import {
Operation,
Resource,
Context,
action,
resource,
spawn,
sleep,
main,
suspend as effectionSuspend,