
LinkedIn Content Strategy & Writing Style
AI Tech Consulting at Every
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Mike Taylor positions himself as a high-stakes practitioner at the intersection of AI technical consulting and strategic resource management. His content strategy centers on "stress-testing" new models through ambitious, personal benchmarks-such as cloning interview subjects or automating book writing-to move beyond hype into tangible ROI. He is notable for his pragmatic, budget-conscious transparency, often discussing token costs and data privacy with the same rigor he once applied to digital ad spend. By blending his background in performance marketing with cutting-edge LLM experimentation, Mike offers a unique value proposition: he doesn't just explain AI, he treats AI agents as a new asset class to be optimized and audited.
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I'm not a journalist, so when I was told I'd be interviewing GitHub's COO I made an AI version of them to practice on. Studies have shown that AI agents can replicate real human responses to 85% accu…

PSA: If you used Claude Fable-5 today with memory turned on you just violated all your NDAs. Anthropic requires a 30 day retention policy including human review, and the memory feature (on by default)…

Alright here's my mythos / fable take after testing it all weekend: This was my headline after testing it on everything I could throw at it: “This model just squeezes so much more juice out of the…

I lived through the quantization of marketing with the rise of digital ad spend portfolio optimization, and I see the same thing happening in AI. Soon token budgets won't be 20% of your salary, your…

I interviewed GitHub's COO recently after practicing on AI version of him! Here's a checklist you can follow to prepare for your next high-stakes meeting:

2.5 posts/week
Posts / Week
28
Total Posts Analyzed
LOW
Posting Frequency
29.5%
Avg Engagement Rate
STABLE
Performance Trend
45
Avg Length (Words)
HIGH
Depth Level
ADVANCED
Expertise Level
0.92/10
Uniqueness Score
NO
Question Usage
0.4%
Response Rate
Writing style breakdown
<start of post>
I spent the weekend trying to break the new Claude model and I think I finally found its limit.
I started with my usual "PowerPoint benchmark"—asking it to generate a structured deck outline from a messy transcript. Usually, models hallucinate the slide count or miss the nuance of the speaker's tone.
Claude didn't just get the count right; it suggested three slides I hadn't even thought of that actually improved the narrative flow.
Then I got ambitious. I fed it 40,000 words of raw interview notes from a project I'm working on and asked it to find the "hidden thesis" that connects them all.
It cost about $14 in tokens and took less than two minutes. The result was so coherent it felt like I was reading a summary written by a senior editor who had spent a week with the material.
Accuracy of technical terms (95%+)
Tone consistency (Matched my previous writing)
Logic gaps (None found)
Funnily enough, when I showed the results to my co-founder, he thought I’d spent all Sunday writing it myself. I’m not sure if I should be impressed or worried about my job security!
If you're going to test this, don't waste it on simple summaries. Give it something messy, something you've been procrastinating on because it's too much "grunt work."
<end of post>
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