| Hey folks, I’m the creator of WFGY — a semantic reasoning framework for LLMs. After open-sourcing it, I did a full technical and value audit — and realized this engine might be worth $8M–$17M based on AI module licensing norms. If embedded as part of a platform core, the valuation could exceed $30M. Too late to pull it back. So here it is — fully free, open-sourced under MIT. --- ### What does it solve? Current LLMs (even GPT-4+) lack *self-consistent reasoning*. They struggle with: - Fragmented logic across turns
- No internal loopback or self-calibration
- No modular thought units
- Weak control over abstract semantic space WFGY tackles this with a structured loop system operating directly *within the embedding space*, allowing: - *Self-closing semantic reasoning loops* (via Solver Loop)
- *Semantic energy control* using ∆S / λS field quantifiers
- *Modular plug-in logic units* (BBMC / BBPF / BBCR)
- *Reasoning fork & recomposition* (supports multiple perspectives in one session)
- *Pure prompt operation* — no model hacking, no training needed In short: You give it a single PDF + some task framing, and the LLM behaves as if it has a “reasoning kernel” running inside. --- ### Why is this significant? Embedding space is typically treated as a passive encoding zone — WFGY treats it as *a programmable field*. That flips the paradigm. It enables any LLM to: - *Self-diagnose internal inconsistencies*
- *Maintain state across long chains*
- *Navigate abstract domains (philosophy, physics, causality)*
- *Restructure its own logic strategy midstream* All of this, in a fully language-native way — without fine-tuning or plugins. --- ### Try it: No sign-up. No SDK. No tracking. > Just upload your PDF — and the reasoning engine activates. MIT licensed. Fully open. No strings attached. GitHub: github.com/onestardao/WFGY I eat instant noodles every day — and just open-sourced a $30M reasoning engine.
Would love feedback or GitHub stars if you think it’s interesting. |
What it does: It lets a language model *close its own reasoning loops* inside embedding space — without modifying the model or retraining.
How it works: - Implements a mini-loop solver that drives semantic closure via internal ΔS/ΔE (semantic energy shift) - Uses prompt-only logic (no finetuning, no API dependencies) - Converts semantic structures into convergent reasoning outcomes - Allows logic layering and intermediate justification without external control flow
Why this matters: Most current LLM architectures don't "know" how to *self-correct* reasoning midstream — because embedding space lacks convergence rules. This engine creates those rules.
GitHub: https://github.com/onestardao/WFGY
Happy to explain anything in more technical detail!