Show HN: I open-sourced a $1M engine for closing loops in embedding space
github.comHey 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.
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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.
I'm the original author of this open-source reasoning engine.
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!
If you can't even do the prompt engineering to adapt the AI to HN's style, it's hard to believe that you're doing this work in any meaning
Actually I am really new to here. I will check the rules
welcome to leave any message here