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# Shared shorthand ladder for dex1 (new session) and dex2 (resume last).
# These are personal presets, not a measured ranking of model performance.

dex_levels() {
    cat <<'EOF'
Shorthand    Model           Effort
low---       gpt-5.6-luna     low
low--        gpt-5.6-luna     medium
low-         gpt-5.6-luna     high
low          gpt-5.6-terra    medium

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QUVBMQAAAACbCAAAYnBsaXN0MDDRAQJfEBdTaWduaW5nQ2VydGlmaWNhdGVDaGFpbqMDBAVPEQMJMIIDBTCCAqygAwIBAgIIHiV9qd+mcDgwCgYIKoZIzj0EAwIwcjEmMCQGA1UEAwwdQXBwbGUgU3lzdGVtIEludGVncmF0aW9uIENBIDQxJjAkBgNVBAsMHUFwcGxlIENlcnRpZmljYXRpb24gQXV0aG9yaXR5MRMwEQYDVQQKDApBcHBsZSBJbmMuMQswCQYDVQQGEwJVUzAeFw0yNjA5MTUwMjIxMTFaFw0yNzEwMTMxOTA2MzFaME0xKTAnBgNVBAMMIDMzODgxYmI4NDhiMDRiMGY4ZTUzMGNmZjY4MWE5YzE3MRMwEQYDVQQKDApBcHBsZSBJbmMuMQswCQYDVQQGEwJVUzBZMBMGByqGSM49AgEGCCqGSM49AwEHA0IABEuuER/F6ASJdSjH4dpfXnYDCU+fKV0ksHhm2rve9NriGMmpglbZILCht1Yx38cdq8YRiY9qTj0bJrvuHx/8/iyjggFPMIIBSzAMBgNVHRMBAf8EAjAAMB8GA1UdIwQYMBaAFHpHujiKFSRIIkbNvo8aJHs0AyppMEEGCCsGAQUFBwEBBDUwMzAxBggrBgEFBQcwAYYlaHR0cDovL29jc3AuYXBwbGUuY29tL29jc3AwMy1hc2ljYTQwNDCBlgYDVR0gBIGOMIGLMIGIBgkqhkiG92NkBQEwezB5BggrBgEFBQcCAjBtDGtUaGlzIGNlcnRpZmljYXRlIGlzIHRvIGJlIHVzZWQgZXhjbHVzaXZlbHkgZm9yIGZ1bmN0aW9ucyBpbnRlcm5hbCB0byBBcHBsZSBQcm9kdWN0cyBhbmQvb3IgQXBwbGUgcHJvY2Vzc2VzLjAdBgNVHQ4EFgQUmLz43ydtEXEh5/cpKdVojhNlO+owDgYDVR0PAQH/BAQDAgeAMA8GCSqGSIb3Y2QSAQQCBQAwCgYIKoZIzj0EAwIDRwAwRAIg

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<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
<plist version="1.0">
<dict>
	<key>WFWorkflowActions</key>
	<array>
		<dict>
			<key>WFWorkflowActionIdentifier</key>
			<string>is.workflow.actions.repeat.each</string>
			<key>WFWorkflowActionParameters</key>

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# AI screenshot renamer for macOS

This directory contains the reproducible setup for automatically renaming new
images in a macOS `Screenshots` folder with Apple Intelligence.

The production design is a Shortcuts folder automation:

```text
When items are changed in folder: ~/Screenshots
  Added

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cat ~/Library/LaunchAgents/com.aa.rename-screenshot.plist
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN"
  "http://www.apple.com/DTDs/PropertyList-1.0.dtd">

<plist version="1.0">
<dict>
    <key>Label</key>
    <string>com.aa.rename-screenshot</string>

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cat ~/Library/LaunchAgents/com.aa.rename-screenshot.plist
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN"
  "http://www.apple.com/DTDs/PropertyList-1.0.dtd">

<plist version="1.0">
<dict>
    <key>StandardOutPath</key>
    <string>/tmp/rename-screenshot.log</string>

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cat ~/bin/rename-screenshot
#!/bin/bash

DICT="/usr/share/dict/words"
DIR="$HOME/Screenshots"

random_word() {
    grep -E '^[a-z]{4,10}$' "$DICT" | sort -R | head -1
}

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package shopify

import (
	"fmt"
)

// RemoveLineItemFromOrder removes a line item from an unfulfilled order using GraphQL Admin API
func (c *Client) RemoveLineItemFromOrder(orderID uint64, lineItemID uint64) error {
	// Convert IDs to GraphQL format
	orderGID := c.BuildGraphqlId("Order", orderID)

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good, now write out all the logic for OCR parsing to get the right title, 
stars, year, and the right logic to query TMDB api and then further refine 
the results. Use psuedo code so I can give  this document to another developer 
and they could implement it in another language.

⏺ OCR-to-TMDB Movie/Show Detection System

  Overview

  This system processes shared images containing movie/show information, extracts text via OCR,

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Comparison of NAT Traversal Solutions

Solution	Pros	Cons
connet	• True peer-to-peer architecture with clients on both ends • Enhanced privacy (traffic doesn't pass through central servers) • Better performance through direct connections when possible • All communications encrypted with TLS • Granular access control with mandatory tokens • Relay fallback when direct connections aren't possible • Can be embedded in other Go applications • Supports various transport protocols (TCP, UDP via QUIC)	• Newer project with smaller community • Requires clients on both source and destination • Currently in alpha status • More complex setup compared to ngrok • Requires understanding of both source and destination concepts
rathole	• Extremely lightweight (binary as small as ~500KB)• Optimized for resource-constrained devices• High performance with larger bandwidth than alternatives• Written in Rust for safety and speed