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@dhel_tamayo: Rubber hot compress bag #hotcompress #hotcompressbag #rubberhotcompress #hotwaterbag

Dhel’s OLshop
Dhel’s OLshop
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Monday 14 September 2026 03:55:00 GMT
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Claude can't watch a video, so my Mac measures it first and Claude reads a one-page summary. Roughly 3,000 tokens instead of about 50,000. Here's the prompt to set up the same thing: Build me a token-saver mod for Claude Code. Start by auditing my setup for token efficiency: 1. Look through my recent sessions and list the 5 tasks where you spend the most tokens. For each, tell me what you were reading (images, video frames, long files, transcripts, web pages) and roughly how many tokens it costs each time. 2. For each one, tell me whether my Mac could do the heavy lifting locally, for free, before you ever read it. Check what's built in first: Apple's Vision framework (text recognition, face detection, person cutouts) through a small Swift script, Spotlight (mdfind) for finding files, and sips for image details. Then free local tools: ffmpeg for one contact sheet instead of separate frames, Whisper for transcripts, and simple scripts that summarize long files. 3. For the top two, build a local prep step that saves a one-page summary and at most two images, so you read that instead of the raw files. Show me a before-and-after token estimate for each, and wait for my OK before installing anything. #claudecode #claude #ai #automation #mac
Claude can't watch a video, so my Mac measures it first and Claude reads a one-page summary. Roughly 3,000 tokens instead of about 50,000. Here's the prompt to set up the same thing: Build me a token-saver mod for Claude Code. Start by auditing my setup for token efficiency: 1. Look through my recent sessions and list the 5 tasks where you spend the most tokens. For each, tell me what you were reading (images, video frames, long files, transcripts, web pages) and roughly how many tokens it costs each time. 2. For each one, tell me whether my Mac could do the heavy lifting locally, for free, before you ever read it. Check what's built in first: Apple's Vision framework (text recognition, face detection, person cutouts) through a small Swift script, Spotlight (mdfind) for finding files, and sips for image details. Then free local tools: ffmpeg for one contact sheet instead of separate frames, Whisper for transcripts, and simple scripts that summarize long files. 3. For the top two, build a local prep step that saves a one-page summary and at most two images, so you read that instead of the raw files. Show me a before-and-after token estimate for each, and wait for my OK before installing anything. #claudecode #claude #ai #automation #mac
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