WFGY

Haha, fair enough. Maybe I do sound like someone who skipped their meds

But everything I posted comes from real effort. I’ve spent a long time building this—from the semantic theory to the open-source structure. It might look wild at first glance, but there’s real logic and care underneath.

If you give it a chance, I think you’ll find it’s more grounded than it seems.

Thanks for the feedback.

Just to clarify: I'm a real human, not a bot. My native language is Chinese, and I did use AI to help polish some of the English—but the ideas, structure, and code are entirely my own.

I’m new to Hacker News and still learning how to present things in the right tone here.

If anything seems off, I genuinely welcome feedback. I’m here to share something I’ve been building with care. Thanks again.

OK Thanks for info

Actually I am really new to here. I will check the rules

welcome to leave any message here

Thanks for the kind words! Honestly? Claude messed with my head the most. Instead of answering, it reflected the question back at me like some kind of AI Zen master

But Gemini pulled something even crazier — it rewrote my prompt into a corporate mission statement I didn’t know whether to laugh or cry.

Each of them has their own “personality,” which is what made this challenge so wild. And yeah, dropping the data open-source was part courage, part madness, part… strategy

Still curious which one you think held up the best?

If anyone have questions, welcome to ask here. I am here to answer any questions

Great question—and I totally get the skepticism. WFGY isn’t just another prompt hack, and it’s definitely not about making the prompts longer or more “creative.” Here’s the real trick:

    It’s a logic protocol, not just words: The core of WFGY is a semantic “kernel” (documented as a PDF protocol) that inserts logic checks into the model’s reasoning process. Every major step—like inference, contradiction detection, or “projection collapse”—is made explicit and then evaluated by the LLM itself.

    Why not just use a bigger model? Even top-tier models like GPT-4 or Llama-3 are surprisingly easy to derail with ambiguity, loops, or context drift—especially on complex reasoning. WFGY gives you a portable, model-agnostic way to stabilize any model’s outputs, by structuring the logic path directly in the prompt.

    Empirical results, not just vibes: On standard tasks, we saw over 40% improvement in multi-hop reasoning and a big drop in contradiction or instability—even when running on smaller models. All evaluation code and sample runs are included, so you can check or replicate the claims.

So, the big difference: WFGY makes “meaning” and logical repair part of the prompt process itself—not just hoping for the model to “guess right.” If you’re curious about specific edge cases or want to try it on your own workflow, happy to walk you through!