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Mike Taylor
LinkedIn Content Strategy & Writing Style
AI Tech Consulting at Every
United States
250 Viral ScoreView on LinkedIn
LinkedIn Content Strategy & Writing Style
AI Tech Consulting at Every
0 people tracking this creator on ViralBrain
Mike Taylor positions himself as a high-level practitioner-consultant who bridges the gap between raw AI capabilities and sophisticated business workflows. His content strategy centers on rigorous, "in-the-trenches" experimentation, where he uses himself as a guinea pig to test benchmarks like cloning personas for interview prep or automating the production of technical books. He is notable for his unfiltered transparency regarding the trade-offs of AI, frequently highlighting hidden risks like data privacy violations or the rising opportunity cost of automation. By intersecting technical authorship with a community-building "AI degenerate" persona, Taylor offers a rare blend of deep-tech evaluation and pragmatic systems-building that appeals to professionals who have moved past the hype and into production.
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This is a thoughtful essay on how AI impacts jobs. The key point is that models make older expert work easy and lead to a flood of sameness. To differentiate, we'd have to rely on human expertise fo…
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…
If you come to my event I guarantee I'll save you enough on claude tokens to pay for your beer.
2.5 posts/week
Posts / Week
11
Total Posts Analyzed
MEDIUM
Posting Frequency
467.6%
Avg Engagement Rate
STABLE
Performance Trend
850
Avg Length (Words)
HIGH
Depth Level
ADVANCED
Expertise Level
0.9/10
Uniqueness Score
YES
Question Usage
0.6%
Response Rate
Writing style breakdown
<start of post>
I spent the morning trying to break the new reasoning models by giving them my "impossible" logic puzzles.
Usually, LLMs trip up on the third layer of inference and start hallucinating about the rules. They get confident, but they get it wrong.
This time was different. I watched the "thought" trace for about 45 seconds and it actually caught its own mistake mid-stream. It realized it had miscalculated the spatial relationship of the objects and corrected itself before outputting the final answer.
I'm starting to think that the "vibes" era of AI evaluation is officially over. We're moving into a phase where the delta between a good prompt and a great one is measured in how much room you give the model to think.
I've put together a doc with the three puzzles I used and the specific traces that surprised me. Link in the comments if you want to run these against your own workflows.
Give it a try on something you previously thought was too complex for a single prompt.
<end of post>
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