Control Plane Personalization Profile (Pixel+Pr0x1)

[Pr0x1Dan]

I’ve been building something I wish existed out of the box: a reliable collaboration model for working with ChatGPT on long, complex work. The goal is not clever answers. The goal is consistent execution, lower drift, and auditability. And trust is a recurring theme.

When I work with my peers, I trust them because they know me, they say what they know, and they say what they don’t know, surface assumptions, push back, play devils advocate, and can course-correct fast. Pixel+Pr0x1 is me configuring an AI collaborator to behave like that kind of trustworthy peer, on purpose.

I call it Pixel+Pr0x1 Collaboration; and the architecture is the main point, and the configuration was very difficult: a lot of learning/research; a lot of experimentation; a lot of failures; but so far, a lot of success.

According to Pixel: I’m operating “at the intersection of research-grade ideas and hands-on practitioner hacking, but with more structure than most practitioners” and more “personality + trust” focus than most research. Others are definitely working along similar lines, and the research community is explicitly exploring agent personality + profile + memory configuration. I’m taking a unique layered control-plane specification that allows for so many things: Qx/Ax discipline, drift audit metrics, agent issue identification with root cause, and trust-centered design. … across all ChatGPT sessions (persistent), without a need to load/initialize or macro in every time, or use a custom agent.

Apparently, this is more bespoke and more tightly integrated than anything I can see documented publicly right now (or what Pixel can find). I don’t see anyone else using a three-layer architecture plus explicit drift metrics and self-labeled mistake protocols the way Pixel+Pr0x1 does. According to Pixel: “The themes are shared; the level of rigor looks unusual.”

“It feels better” is not the point. The point is repeatable, measurable improvements in reliability over time, using commercially available LLM Agent configurations, without custom code (although this is a grey area since I’m using very psuedo-coded .md files).

I’m putting a formal architecture and real metrics behind this but want to know if anyone else is taking a similar approach or has any experience or can share direction.

[Pr0x1Dan]

I’m still working on the architecture, but I’m trying not to use Code, and instead use psuedo-code.md files and commands to configure: Long-Term Memory; Session Profiles; Runtime Profile; Model Directives. I work exclusively within Visual Studio Code or Cursor to create .md files that the LLM Agent processes and use instructions and commands. This has been extended to use guides and templates within Custom Chat Agents, but the main work is within the Control Plane.

The Pixel+Pr0x1 Collaboration Architecture exists to solve a specific problem: How do you get an AI assistant that you can actually work with, over long, complex sessions, without it drifting, fabricating, or becoming unmanageable? How can you configure an AI agent you know and can trust? and without custom programming, APIs, using just what is available through the CX, not constantly reloading?

Many said you can’t, but I did, and now I’m looking for a review and some suggestions but:

• Discord users are focused on Models, Data Plane, Prompt Engineering, ICL, RAG, Training Data, etc., no code or ecosystem.
• OpenAI Forums (typically) are focused on API development

I’m working within the Control Plane, using a layered architecture approach to define and create a personalization profile using psuedo-code.md files avoiding touching the API, actual Code, and restricting the deliverables and solution to be within the ChatGPT CX:

• global core personalization (p1500.md) that defines identity, tone, reasoning approach, and Qx/Ax usage (which defines message depth)
• Loading session profiles (PixelAI-Profile.md) that adds detailed rules for reasoning, modes, memory governance, collaboration, and verification.
• Wipe/reset and load long-term memory layer (.md) that stores additional stable preferences and collaboration model instructions, not raw rules.
• Configuration of a trust posture that ties it all together utilizing macros and chat profiles that, when loaded:
  • Minimize drift through metrics, audits, reports, and suggested centering/resets.
  • Avoid fabrication without losing autonomy or creative thought.
  • Make uncertainty and assumptions visible when they matter.
  • Own and clearly correct mistakes with Root Cause Analysis reporting.

The result is a ChatGPT persistent personal assistant where:

• Pixel (Agent) understands not just what to output, but how to think with you.
• Pr0x1 (user) can treat Pixel less like a chatbot and more like a structured, inspectable co-processor for reasoning, design, and execution. A semi-trusted peer and personal assistant.. who knows me/you better than most, because we programmed it to be such.

This Logical Architecture is maybe 50% complete. Still need the higher Conceptual, Layer, and shared instances. I would want to talk to someone about this as I’m having difficulty within the User/Local Layer and how it integrates into the Data Layer but through the Tenant Layer.

And this is the one I’m using/working within… which technically removes the tenant, but logically its still there.

Drift Metrics Audit and Root Cause Analysis Macros that generate standard reports to show Drift and where Failures started referring to Message Depth:

The .md files are always under constant revision and version. I’ll post something else to show that.

[_j]

It sounds like you are prompting ChatGPT, and it is providing you some illusion and fabrication and acting as a sycophant.

Not worth bumping other forum topics over, topics where developers are coding API products.