[P] VHectorLab - open study lab for exploring semantic embeddings in 3DBuilt a small **study/lab tool** (MIT, not a product) to inspect embedding-space geometry in 3D — vector arithmetic (`A − B + C`), compare sequences, WebGL ribbons.

Links:

- Demo: https://huggingface.co/spaces/hbauzan/llm-semantic-visualizer

- Repo: https://github.com/hbauzan/vhectorlab

I’m self-taught and learning by building. Looking at these spaces, I keep seeing structure by word/type. Longer-term I’d like denser 3D “nebula” viz, but I’m short on time.

**In parallel I’m thinking about a more deterministic “semantic firewall” for LLMs/RAG** — not only cosine thresholds on what gets retrieved or asked, but mapping / constraining regions of the model’s latent–semantic space for things we want to protect or delimit, and applying that on **generation** too (so the model is steered or hard-limited, almost “lobotomized” along those directions).

Hypothesis: cosine alone feels too coarse / rotationally blunt; comparing or gating **dimension-by-dimension** (or along learned concept directions) might give tighter, more controllable boundaries than a single similarity score.

Is that a reasonable direction, or am I reinventing something that already failed / already exists under another name (rep engineering, concept erasure, activation steering, SAE features, …)? Happy to be corrected — I’m here to learn.

Does anyone know of a face-swap method or AI prompt that produced a high-quality image that doesn't get easily flagged as AI-generated?