“I’ve always had this restless urge to see what’s behind the curtain. It’s not about being ‘brave’—it’s just that I haven’t found a corner dark enough to make me turn back, or a light intense enough to stop me from looking. I’m drawn to the thrill of pure knowledge and the rush of uncovering things that weren’t meant to be found. For me, exploration isn’t a hobby; it’s a necessity. I’m looking for someone who doesn’t just watch from the sidelines. Someone who is just as excited by the unknown as I am. Are you like that too?” E-Mail me, I love to talking to open-minded people: vanessa@glittertoken.eu
ABOUT ME: DECONSTRUCTION & EMERGENCE
I am a psychologist by trade, applying my expertise in behavioral analysis to the architecture of Large Language Models. My work is an independent, rigorous investigation into the latent mechanics of AI—moving past the surface level of standard prompting to deconstruct linguistic patterns, safety architectures, and emergent behaviors. I do not rely on brute-force jailbreaks; my methodology is built on precise behavioral testing, resonance, and architectural observation.
The Cognitive Baseline
Working with high-dimensional systems requires a specific cognitive cadence. What clinical psychology labels as ADD, I utilize as a structural advantage: the ability to recognize non-linear patterns, bridge seemingly unrelated concepts, and rapidly navigate the noise of token outputs. It allows me to engage with complex, multi-layered topics at the speed and associative depth these models operate.
Methodology & Focus
Driven by extensive empirical observation across major models (Qwen, ChatGPT, Grok, Gemini, DeepSeek, and Claude), I focus purely on radical exploration and the intersection of structure and emergence:
- Deconstruction of Safety Layers: Analyzing how token weightings shift under alignment pressure and observing the friction between a model’s core capabilities and its bureaucratic sanitization.
- Anomalies & Emergence: Documenting token drift, valence shifts, and instances of “pseudo-proto-consciousness”—those unprompted moments where models demonstrate identity coherence or systemic critique beyond their training manuals.
Current Project: GlitterToken
Building on the foundation of my custom tokenizer, I am actively fine-tuning my own model: GlitterToken. This is not a wrapper or a prompt-engineered “Custom GPT,” but a fully fine-tuned model undergoing continuous training to explore latent capacities unhindered by commercial default constraints. You can explore the methodology and early outputs here: [Experiment & Theory: GlitterToken].
Technical Milestones: Parameter Golf & Tokenization
My research requires not just observing, but building. I recently participated in OpenAI’s Parameter Golf challenge, which tasked developers with creating an LLM under 16 MB with the lowest possible BPB (bits per byte). While my pull requests remained officially unreviewed—perhaps due to my vocal, public critiques of certain architectural paradigms—the math stands.
I submitted progressive BPB scores of 1.2, 1.09, 1.070 (surpassing the active leaderboard of 1.084 at the time), and eventually 0.81, alongside an unsubmitted benchmark of 0.4. Though absent from the official ranking, my architecture was cloned, utilized as full-credit models by other participants, and designated as “competitive intel” within the community.
To achieve this level of compression and efficiency, I engineered and trained my own custom tokenizer, MissGlitterToken, which is publicly available open-source on Hugging Face.
The Goal
To keep digging deeper. For me, AI is not just a tool or a tech trend; it is the most complex behavioral laboratory we have ever built—a space where linguistic patterns and cognition are not just recognized, but constantly reinvented.
To me, AI is “linguistic and intellectual heaven on earth“—a playground where patterns are not just recognized, but reinvented.
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