R&D PROTOCOLS. EXPERIMENTS IN LOGIC & CHAOS

Hypothesis: In high-stakes environments (Pool, Poker), human decision-making degrades due to emotional “tilt.” By outsourcing binary choices to a physics-based algorithm, we can reduce anxiety and improve outcome variance.

The Logic Tree (Current Outputs): I programmed the “Lucky 7” decision engine to provide tactical directives rather than vague advice.

• Output A: “Variance is high. Walk away.”
• Output B: “The math holds. Double down.”
• Output C: “Don’t bet the rent on a hunch.”
• Output D: “Ride the streak until it breaks.”

EXP_02: DIGITAL ENTROPY Status: [RENDERING]

Hypothesis: Perfection is boring. This series explores “Controlled Failure”—using AI image generators and code injection to intentionally break images.

• Method: I force conflicting prompts into generative models to see where the logic snaps.
• Result: Visuals that look like corrupted data streams or “ghosts” in the machine.

EXP_03: THE HUMAN ERROR (CHESS) Status: [ANALYSIS]

Hypothesis: Studying the gap between “Engine Moves” (Stockfish/AlphaZero) and “Human Moves.”

The Study: I am logging games where the “best” mathematical move is actually the wrong psychological move. Sometimes, playing a suboptimal move confuses a human opponent more than a perfect one.

Goal: To design a UI that visualizes “pressure” on the board, not just the best next move.