I open-sourced a $1M engine for closing loops in embedding space
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…
What it does
In the maker’s words, at launch
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.
Does the same job
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We kept shipping “simple” LLM features that were fluent-but-wrong. After too many postmortems we wrote down the failure patterns and added a small reasoning layer in front of the model. It’s model-agnostic, sits beside your existing stack, and you can implement it from a single PDF (MIT). What’s inside the PDF A problem map of 16 failure modes we kept hitting in real systems (OCR/layout drift, table-to-question mismatches, embedding≠meaning, pre-deploy collapse, etc.). Four lightweight gates you can add today: Knowledge-boundary canaries (empty/adversarial/known-fact probes).…
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