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.

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### 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.

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### 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.

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### 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.