Tag: SJL

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AI AI: Large Language Models Investing

The Ledger of Curiosity

We often romanticize the “back of the napkin” idea. It is the symbol of spontaneous genius—the startup mapped out in a coffee shop, the ticker symbol hurriedly scribbled during a dinner party. But we rarely talk about what happens to the napkin afterwards.

Usually, it gets thrown away. Or lost. Or stuffed into a drawer, becoming just another artifact of a fleeting thought that had momentum but no direction.

In the first two parts of this experiment, I used Gemini 3 Pro to solve the friction of entry (transcribing my messy handwriting) and the friction of analysis (stress-testing the ideas against 10-K realities). But there was one final gap: Permanence.

An analysis that lives and dies in a chat window is barely better than one that lives and dies in a notebook. It is still ephemeral. To truly build a “Second Brain” for investing, the data needs to leave the conversation and enter a system.

“The goal of technology should be to stop us from losing the work we’ve already done.”

I tweaked my workflow one last time. I asked the AI to not just judge the stocks, but to format its judgment into a raw CSV block.

With a simple copy-paste, my handwritten scribble wasn’t just digitized; it was database-ready. It went from a piece of paper to a row in Google Sheets with columns for “Market Cap,” “P/E Ratio,” and “Primary Risk.”

Suddenly, I wasn’t just looking at a list; I was building a ledger. I can now track these ideas over months. I can see if the “Red Flag” the AI identified actually played out. I can measure my own batting average.

The goal of technology shouldn’t just be to make us faster at doing work. It should be to stop us from losing the work we’ve already done. By turning ink into data, we stop treating our ideas as disposable. We give them the respect of memory.

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AI AI: Large Language Models Investing

The Digital Devil’s Advocate

There is a seduction in the handwritten note. When I scribble down a company name in a notebook, it is purely additive. It represents potential upside, a future win, a brilliant insight caught in ink. The notebook is a safe harbor for optimism because it lacks a “Reply” button. It doesn’t argue back.

But optimism is an expensive luxury in investing.

After my initial experiment—using Gemini 3 Pro to transcribe my messy list into tickers—I felt a surge of productivity. But productivity is not the same as discernment or understanding. I had a list of stocks, but I didn’t have a thesis. I just had digitized hope.

So, I took the next step. I didn’t ask the AI for validation; I asked for a fight. I fed the tickers back into the model with a specific directive: “Act as a contrarian hedge fund analyst. Find the red flags. Kill my enthusiasm.”

“I didn’t ask the AI for validation; I asked for a fight.”

The results were immediate and sobering. The “promising tech play” I had noted? The AI highlighted a massive deceleration in user growth hidden in the footnotes of their latest 10-Q. The “stable dividend payer”? It flagged a payout ratio that was mathematically unsustainable.

In seconds, the warm glow of my handwritten discovery was doused with the cold water of 10-K realities. And it was fantastic.

We often view AI as a tool for creation—generating text, images, and code. But its highest leverage application might actually be destruction. By using it to stress-test our assumptions, we outsource the emotional labor of being the “bad cop.” It allows us to kill bad ideas quickly, cheapy, and privately, before we pay the market tuition for them.

My notebook is still where the dreams live. But the digital realm is now where they go to survive the interrogation.

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AI AI: Prompting

Carving Away: Part II

Well, that escalated quickly.

It looks like the wood carving prompt from my recent post struck a chord. I’ve seen some incredible results floating around, and since traffic is still high, I wanted to share a few “remixes” of the original prompt that I’ve been experimenting with this weekend using Gemini 3 Pro.

If you mastered the basic “hand-carved miniature” look, here are three ways to push the aesthetic in different directions.

1. The “Dark Walnut” Aesthetic

The original prompt tends to produce a light, pine-like wood. Use this variation if you want something moodier, richer, and more polished. It works exceptionally well for portraits or architectural subjects where you want a high-end feel.

The Prompt Addition:
Append this to your subject line: ...carved from dark polished walnut wood, rich deep grain texture, rim lighting, subsurface scattering, mahogany tones, smooth finish.

2. The “Painted Folk Art” Look

Sometimes raw wood is a bit too monochromatic. This variation pushes the model to apply a distressed paint job, making the image look like a vintage toy or traditional folk art found in an attic.

The Prompt Addition:
Append this to your subject line: ...faded hand-painted wooden figurine, chipped paint revealing wood underneath, vintage folk art style, muted primary colors, distressed texture.

3. The “Rough Hewn” Sketch

This is my personal favorite. It forces the AI to leave “tool marks,” making the object look like a work-in-progress rather than a finished product. It adds a tactile, human imperfection that many AI images lack.

The Prompt Addition:
Append this to your subject line: ...rough hewn unfinished wood, visible chisel marks, splintered edges, raw timber texture, sawdust specs, harsh directional lighting.

A Note on Models

I am still finding that Gemini 3 Pro handles the texture mapping better for the “Rough Hewn” look, capturing the jagged edges convincingly. However, Nano Banana seems to prefer the polished “Dark Walnut” style, producing really nice specular highlights on the wood grain.

If you are getting results that look too “plastic,” try lowering the guidance scale slightly or adding varnish to your negative prompt.

Let me know what you carve out of the latent space next.