Author: Brett Gilman
X Handle:@realTuckFrumper
Overview:
This post outlines a practical system I’ve built on top of ChatGPT called Logic Lookout (LL). It’s a memory-aware, assistant-grade framework that enables long-term recall, task continuity, and structured archiving, despite the current limitations of ChatGPT’s native memory and sidebar threading.
LL isn’t an app or a plugin. It’s a system-level design built with structured folders, persistent tagging, indexed summaries, and ChatGPT acting as the dynamic logic layer that tracks, surfaces, and manages it all.
Why I Built It:
The default ChatGPT interface (threads, shared links, saved chats) is great for individual prompts, but quickly breaks down when:
- You need continuity across multiple days or devices
- You work on layered, multi-topic projects
- You need a clean retrieval system months later
- You want to automate documentation and logic feedback loops
I needed something more persistent, structured, and system-aware, so I built Logic Lookout as a second brain ChatGPT could be with some scaffolding.
What Logic Lookout Does:
- Tags and stores major projects, solved problems, and experiments
- Builds structured summaries and permanent documentation from chat results
- Treats ChatGPT as the logic layer (not just a responder)
- Survives thread deletion
- Enables me to say: “File this to LL,” and the assistant understands where it lives, how to find it, and what it’s connected to
Example: Multi-System Automation + Debugging Use Case
Context: We manage billing workflows for multiple clients across five social media platforms with a combined audience exceeding 500,000 followers. Manual billing was becoming unsustainable and error-prone, so automation and traceability were mission-critical.
Problem: I needed to automate a multi-phase billing system based on social media engagement data, pulled from multiple Fedica CSV exports. The initial process involved manual data cleanup, duplicate rejection logic, click validation, billing triggers, and chronological stacking—all of which were prone to human error and version drift.
Solution:
- The assistant built a PowerShell-based ingest/merge script
- We added logic to detect duplicate posts, prioritize higher click counts, flag discrepancies, and sort post data into export folders
- Errors during testing were caught, logged, and used to refine automation logic iteratively across several work sessions
- Once finalized, the entire script set, troubleshooting logs, and validation logic were stored in
LL > Billing_Automation - Tagged with:
fedica-import,billing-script,click-verify,resolved-tested
Now, when I say “Let’s rerun May billing,” ChatGPT can re-pull the final folder, audit the result, and launch the relevant automation logic without me retraining it. The assistant remembers the entire process chain.
Structure:
Below are selected visuals that reinforce the structure and use of Logic Lookout in practice:
Why I’m Posting This:
I believe ChatGPT already has the bones to become a true memory-enhanced assistant. What it lacks structurally, it can make up for with logic, tagging, and consistent prompts. But that only works if:
- The user is willing to treat it like a system
- The assistant is trained to persist structure
If this resonates, I’d love to see more people push ChatGPT in this direction. If anyone at OpenAI is watching: the demand for memory-aware ops tools is real. And we’re already building with what you gave us.
Happy to share more details or examples. Just want to contribute a working model to this evolving space.