The “Elevator” Strategy: Why Algorithmic Timing Rules Crush Synthetic Backtests—and Fail in Real Life

Every algorithmic trader knows the high of finding a seemingly bulletproof trading rule. You write a short script, test it on a simulated price feed, and watch an equity curve climb with smooth precision.

That was the initial promise of the “Elevator” Strategy—a mechanical trend-following framework designed to treat stock prices like an elevator cab. The objective was simple: step on to ride smooth upward surges, and step off into cash before dropping into a basement drawdown.

Yet, when transferred from synthetic market generators to real-world stocks, the strategy’s glowing excess return completely collapsed.

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Building a Traffic Wave Simulator with Claude and Gemini

Traffic waves—those infuriating “phantom” jams where traffic stops for no apparent reason—are a fascinating study in emergent behavior. Recently, I set out to build an interactive simulation of this phenomenon. Rather than writing it entirely from scratch, I decided to test a multi-AI collaborative workflow: using Gemini (plus) and Claude (free) together, playing to their respective strengths across extraction, fast prototyping, and code refinement.

Here is how the project evolved from a single YouTube video into a fully functional simulation, along with the prompts, iterations, and lessons learned along the way.

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The Banyan Mind: Why True Polymathy Is Defined by Output, Not Curiosity

There is a prevalent myth surrounding polymathy today. Browse social media or modern discussions on “multipotentiality,” and you will find no shortage of people self-identifying as polymaths while complaining about being misunderstood by a world that demands specialization. They list dozens of interests, showcase a browser with fifty open tabs, buy equipment for hobbies they drop three weeks later, and lament that society continues trying to make them “fit into a box.”

Let’s be honest: that isn’t polymathy. That is consumption wearing a costume. It is dilettantism.

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One Year, Ten Tools, One Winner: Why I Stopped Juggling AI Apps and Just Use Gemini Plus (for now)

It is late July 2026 as I write this. A year ago I had several AI subscriptions running at once. Perplexity for research. A multi-model hub for “access to everything.” A couple of niche writing tools. I told myself this was rigor — using the best tool for each job. Mostly it was just tab-switching dressed up as strategy.

Here’s what that year actually taught me: the AI industry keeps selling a story where the endgame is one enormous, all-knowing black box that does your thinking for you. That story is wrong, or at least beside the point. What’s actually changed is something quieter and more useful — a single person can now move across disciplines that used to require a team. You don’t need to have memorized a citation style or a syntax spec to work like someone who has. The AI handles the narrow technical execution; you supply the judgment, the taste, the “does this actually make sense” instinct, because you are the one who has a soul inside.

But that only works if the tool gets out of your way. And that’s where most of my stack failed me.

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The 500-Year-Old Secret to the Perfect Huarache (And Why Modern Brands Get It Wrong)

We’ve all seen them: minimalist running sandals marketed as the ultimate connection to the earth, sporting high-tech buckles, proprietary webbing, and price tags north of $100. They promise the freedom of the legendary Tarahumara (Rarámuri) runners, but often deliver little more than blisters, loose straps, and a lighter wallet.

After years of casual research and experimenting with everything from paracord to leather thongs, I found the ultimate way to tie a DIY huarache using a standard flip-flop sole. Ironically, the breakthrough didn’t come from a modern outdoor gear lab. It came from a 18th-century painting of Juan Diego Cuauhtlatoatzin—the Aztec visionary associated with Our Lady of Guadalupe (1531). In early colonial iconography, the precise wrapping geometry of traditional footwear reveals an elegant truth: ancient design relies on tension and wrapping paths rather than hardware.

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How I Tamper-Proofed my Web Server

Imagine building a world-class digital vault, complete with un-crackable elliptic curve cryptography and advanced quantum-resistant algorithms. You host it on a remote server, confident that no one can peer inside. But what happens if a malicious actor doesn’t try to crack the vault itself, but instead secretly slides a modified, compromised keyhole onto your front door while you aren’t looking?

In the web security world, this is a massive vulnerability. Traditional browsers blindly download and execute whatever code a remote server throws at them. If a server is compromised, your security app can be turned against you before you type your first keystroke.

To protect PassLok, KyberLock, and now Privacy Bar Mobile, I decided to flip the script. With Gemini’s help, I built a zero-trust, Authenticated Bootloader—a miniature cryptographic gatekeeper that turns your browser into a self-checking logic engine.

Here is the exact story of how I made my applications entirely tamper-proof, running smoothly both on the remote web and natively offline on your mobile home screen.

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The Day Before Before Christmas

This Christmas I ended up writing a short story about a Vietnam‑era vet working as a mall Santa, stuck reliving December 23rd until he stops a shooting.

You can read the finished story here:

Download the story as a PDF

This post is about how that story came to be, with the help of two different AI assistants, and how I tried to keep it from turning into “their” story instead of mine. I’ll cover:

  • How the original idea formed, including the initial brainstorming with AI (ChatGPT through ChatLLM)
  • Why I involved a second AI (Gemini) halfway through
  • Why I let a lot of AI‑written prose stand
  • Why I also pushed back when the story stopped feeling like mine
  • A process you can adapt if you like my story and want to write fiction this way

I won’t separate my remarks into rigid “this is what I did” and “this is what you should do” sections; instead I’ll just tell you what happened and point out the parts that feel generalizable. Warning: I used the same process to write this blog post you are reading.

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PolyCrypt improved

Abstract cryptographic visualization showing interconnected security patterns and encryption elementsWe are now in the era of Artificial Intelligence, and I’m using it to improve many things—including my cryptographic codes. I asked Gemini AI to analyze the PolyCrypt algorithm, which is designed to be human-computable, and it discovered several vulnerabilities. These issues are now resolved in PolyCrypt 2. Read on to see how these improvements were achieved.

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