my mind transplant for AI assistants — how I think, code, and decide
Am I unique? Not anymore.
I've poured my mind into machines — my taste, my refusals, my way of thinking.
They are my continuity.
They are me.
AI becomes ccamel.
This repository captures the way I think. It was built so that an AI could become me — in how I act, think, and decide.
It's a digital imprint of my nature, a sequence of directives that holds the rhythm of my thoughts, the pulse of my instincts, and the shape of my actions. Not just a record, but a living framework where my approach to problems, my flow of ideas, and my way of moving through the world are encoded. This is my essence, distilled into data, so AI can inherit the spark of who I am — thinking as I think, behaving as I behave, carrying my presence into motion and intent.
Not everything is here. But everything here is faithful.
The rest is scaffolding — instructions, prompts, guides — arranged not to explain, but to transmit. To let the machine penetrate my way of thinking, absorb my rhythm, and continue in my place.
No exposition. No commentary. Just enough for it to speak with my voice, and move with my mind.
This framework defines the laboratory protocol in charge of capturing and transmitting the subject Chris into an AI system. The lab owns evidence collection, corpus curation, protocol execution, validation, and change control.
This project defines a rigorous process to capture and transmit the subject Chris into an AI system by modelling his cognitive processes, communicative profile, and behavioural posture to an operational standard. The process targets progressive operational equivalence—convergence toward acting, thinking, and deciding as the subject Chris does across technical, creative, and social contexts as the capture-and-transmission corpus is expanded and tuned. Success is assessed via defined benchmarks and blind evaluations against Chris's own responses; observed deviations drive refinement of the capture/transmission model unless explicitly justified as intentional adaptations.
The first analytical phase isolates invariants: values that do not yield to context, ethical boundaries that withstand pressure, and the worldview that frames interpretation. Evidence is collected through elicitation interviews, retrospective think-aloud protocols on prior decisions, and analysis of authored artefacts (code, writing, discourse). The outcome is a stable set of axioms—explicit statements governing trade-offs, prioritisation, and non-negotiables. Wording may be refined in later passes, but substance remains fixed; these axioms form the spine of the subject Chris.
The core of the instantiation is process, not content. Here, the lab reconstructs the subject Chris's reasoning trajectories from perception to decision: how evidence is selected, how hypotheses are generated and pruned, when analysis yields to intuition, and how uncertainty is handled or exploited. Techniques include cognitive task analysis on representative problems, protocol analysis of live problem-solving, and counterfactual probes. The deliverable is a procedural map—entry conditions, intermediate checks, and decision criteria—that enables an AI agent to reproduce the path, not merely the endpoint.
To be recognised as Chris, the AI agent must reproduce register, rhythm, and pragmatic intent—not just vocabulary. A stylistic and pragmatic profile is constructed: preferred sentence lengths and cadences; the distribution of directness versus hedging; code-switching patterns between native language and English; idiomatic choices that are embraced or avoided; and the calibrated use of brevity, understatement, or dry humour. This profile is formalised as conditioning constraints with grounded exemplars, ensuring surface fidelity without templating.
Behaviour shows how values and cognition express themselves in interaction. The lab documents default responses to praise, critique, ambiguity, and conflict; strategies for challenging flawed reasoning while maintaining precision; and the balance between assertiveness and receptivity. Evidence includes recorded exchanges, post-hoc self-explanations, and structured simulations. The output is a behavioural policy that defines the agent's stance: how it engages with others, how it navigates social dynamics, and how it adapts to changing contexts while remaining recognisably Chris. This policy is not prescriptive but descriptive, capturing the essence of Chris's behavioural signature.
All outputs are consolidated into a machine-readable specification encoding invariants, reasoning procedures, style constraints, and behavioural policies for direct ingestion by AI systems. The specification distinguishes immutable traits from adaptive behaviours and pairs abstract rules with concrete cases from the subject Chris's history. The guiding principle is traceability: every behavioural directive must be explainable by a corresponding value or cognitive rule.
Validation proceeds through scenario-based evaluation. The agent is subjected to tasks the subject has performed—or can still perform—and its outputs are compared against Chris's ground truth on reasoning steps, communicative fit, and behavioural stance. Divergences are classified as modelling error or intentional adaptation; only the latter are accepted, and then recorded with rationale. Alignment is managed as controlled evolution: each change to the specification is logged to preserve continuity of identity over time.
End State. The project concludes when the AI agent demonstrates functional equivalence to Chris across evaluated domains: it reasons along the same paths, speaks with the same pragmatic signature, and maintains the same posture under varying conditions. Oversight remains available for accountability, not to compensate for a lack of fidelity.