Using Codex to make Claude better is exactly the kind of cross-tool workflow that's becoming the actual edge.
AI
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Optimizing LLM token usage by removing polite filler
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The bigger point: you're paying for "Certainly!" and "I'd be happy to help with that" on every single response. Those tokens cost real money at scale. And they cost you time at every scale. Caveman mode isn't a hack. It's a correction. LLMs were trained on polite human text.
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Performance Benchmarks of AI Plugin Token Reduction
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The actual benchmarks from the original plugin: → Average 65% output token reduction across 10 real prompts
→ Range: 22-87% depending on task type
→ Coding explanations saw the highest compression
→ Code blocks themselves stayed identical One tester ran it for 6 days on a -
Optimizing LLM Performance Through Brevity and Prompt Engineering
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Why does talking like a caveman actually work? Three things happening at once: → LLMs spend 40-60% of output tokens on filler. Greetings, restatements, encouragement, transitions. You scroll past all of it. Caveman strips it. → A March 2026 paper found that brevity
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How to optimize Claude’s output using custom skills
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Julius Brussee's caveman plugin hit 51,000+ GitHub stars in two weeks. It cuts Claude's output tokens by 65%. I turned it into a Claude skill you can build in 30 seconds. Copy the prompt below. Ask Claude to build the skill. Save it in your Customize settings.
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Inference demand will eventually dwarf training requirements
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inference demand is going to dwarf training within a few years and most forecasts don't reflect that
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Professor says transformers paper would fail his student
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If the transformers paper was written by one of my students, I wouldn’t let him graduate until he did a better job.
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The Growing Business Risk of Addictive AI in Digital Products
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Addictive AI Could Become The Next Big Business Risk #AI is making digital products more engaging, but that also creates a growing #risk around #addiction, #mentalhealth and harmful customer behavior. As regulators, courts and consumers pay closer attention, #businesses need to
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Deep Learning Papers Confusing Because Researchers Are Confused
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Deep learning papers are confusing because deep learning researchers are confused.
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Instruct vs Thinking Models: Cost, Speed, and Trade-offs
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Instruct vs thinking models, in one line:
— Satya Mallick (@LearnOpenCV) 19 mai 2026
System 1 vs System 2 — with a 5–20x cost-and-speed gap.
If the task doesn't need planning or multi-step reasoning, the thinking model isn't smarter. Just slower, pricier, and more likely to hallucinate.
— Dr. Satya Mallick, CEO @ OpenCV… pic.twitter.com/QcHpeXyVHDInstruct vs thinking models, in one line:
System 1 vs System 2 — with a 5–20x cost-and-speed gap.
If the task doesn't need planning or multi-step reasoning, the thinking model isn't smarter. Just slower, pricier, and more likely to hallucinate.
— Dr. Satya Mallick, CEO @ OpenCV