You’re right, I kept it sparse on purpose, but it may have come off too vague. Let me share more of the thinking.
We’ve been experimenting with a symbolic reasoning layer that sits beneath local LLMs, aimed at compressing their context demands by shifting some of the burden into structured memory and logic. The core idea: instead of infinitely scaling GPUs and tokens, we scaffold a learning loop that binds action to outcome, reflection to memory, and memory to improvement.
The architecture includes:
* A lightweight Prolog or Datalog engine to track symbolic execution paths
* Vector and symbolic memory fusion to keep both nuance and structure
* Outcome tests that update a learning score over time, enabling the agent to refine future actions
* Curiosity modules that bias the agent toward resolving ambiguity or closing feedback loops
One example: we had an agent running a simple multi-step task loop with an objective scoring function. First run: 0 percent success. But as outcome chains were logged and the reasoning engine updated its knowledge base, the same model climbed into the 70 percent range over 60 runs. No retraining, no fine-tuning, just structured feedback and symbolic state retention.
Still early, but the goal is not just to build a better chatbot or prompt wrapper. We’re aiming for something more like a persistent local intelligence that reasons through problems, remembers its missteps, and adapts without external retraining.
Sure but the assumption here is that the game stays the same. That the only worthwhile intelligence is one that optimizes for revenue capture inside an ad economy.
But there’s a fork in the road. Either we keep pouring billions into nudging glorified autocomplete engines into better salespeople, or we start building agents that actually understand what they’re doing and why. Agents that learn, reflect, and refine; not just persuade.
The first path leads to a smarter shopping mall. The second leads out.
Because once I have an intelligence that can actively learn and improve, I will out-iterate the market as will anyone with that capability until there is no more resource dependency. The market collapses inward; try again.
I assume that you're going to 3d print the mines that you use to build the oil rigs feed the chemical plants that you use for producing the filament, right?
The rip-off wasn’t just pricing. It was the whole model of scale-for-scale’s-sake. Bigger context, bigger GPUs, more tokens; with very little introspection about whether the system is actually learning or just regurgitating at greater cost.
Most people still treat language models like glorified autocomplete. But what happens when the model starts to improve itself? When it gets feedback, logs outcomes, refines its own process; all locally, without calling home to some GPU farm?
At that point, the moat is gone. The stack collapses inward. The $100M infernos get outpaced by something that learns faster, reasons better, and runs on a laptop.
I guess nobody cares? I really feel that this is important research and I would appreciate some sort of feedback from anyone, please. Let me know if more details are required and I'm happy to oblige.
Would appreciate anyone's thoughts. I know I didn't provide a lot of details but that was purposeful. I'm looking for feedback from fellow engineers who are interested in building a convergent system. The reason its called a "redprint" (as opposed to a "blueprint") is because in my mind this is an evolving layer of AI we're just beginning to grasp. The architecture itself is meant to shift as the agents self-improve. For decades the field of AI research has been split across many domains and experts. However I believe Redprint is a step towards fusing many of these ideas into a practical framework for Cognition itself.
Please chime in if you feel any interest in the subject. I'm not asking for money or hype or anything like that. Ideally someone who's ready to challenge what I have set forth and perhaps participate in the building.
We’ve been experimenting with a symbolic reasoning layer that sits beneath local LLMs, aimed at compressing their context demands by shifting some of the burden into structured memory and logic. The core idea: instead of infinitely scaling GPUs and tokens, we scaffold a learning loop that binds action to outcome, reflection to memory, and memory to improvement.
The architecture includes:
* A lightweight Prolog or Datalog engine to track symbolic execution paths
* Vector and symbolic memory fusion to keep both nuance and structure
* Outcome tests that update a learning score over time, enabling the agent to refine future actions
* Curiosity modules that bias the agent toward resolving ambiguity or closing feedback loops
One example: we had an agent running a simple multi-step task loop with an objective scoring function. First run: 0 percent success. But as outcome chains were logged and the reasoning engine updated its knowledge base, the same model climbed into the 70 percent range over 60 runs. No retraining, no fine-tuning, just structured feedback and symbolic state retention.
Still early, but the goal is not just to build a better chatbot or prompt wrapper. We’re aiming for something more like a persistent local intelligence that reasons through problems, remembers its missteps, and adapts without external retraining.