
Are you in the Weights?
Find out if you live forever in the brain of the LLMs

The weights are the billions of numbers forming an AI's brain. Type a name and see how strongly the leading AI models recognize it. Are you in the weights?
AI Analysis
Are you in the Weights is a fun web tool where users type a name to see how strongly leading LLMs recognize it, revealing its presence in the AI's 'weights' (training data). Core features include instant recognition scores and visualizations for multiple models. USP: Humorous, gamified insight into whether someone 'lives forever' in AI brains. It addresses curiosity about AI training data opacity and personal vanity regarding fame in the digital age. Overall value proposition: Entertaining peek into LLM knowledge with light educational value on AI internals.
In 2025-2026, AI adoption is mainstream with growing public interest in training data transparency, model biases, and ethical concerns amid regulatory discussions. User demand for interactive, fun AI experiences is rising as LLMs become everyday tools. This aligns perfectly with hype around AI personalization and memorization. Excellent Timing.
Technically feasible using LLM APIs for name probing or precomputed embeddings, with low development and operation costs for a simple web app. Minimal supply chain risks; basic compliance for data privacy needed. High scalability via cloud hosting. Strong team fit for indie AI developers. Overall rating: High.
Main segments: AI enthusiasts, tech professionals, developers, gamers, and curious consumers aged 18-35, concentrated in US, Europe, and East Asia tech communities. TAM for consumer AI tools is vast (~$50B+), SAM for novelty AI apps ~$500M, SOM for this tool likely under $10M initially. Core pain: Curiosity about personal/celebrity presence in opaque LLM training data. Willingness to pay: Moderate for premium insights or ad-free experience.
Competition level: Medium. Direct competitors: 1. Have I Been Trained (haveibeentrained.com), 2. LAION-5B search tools (laion.ai), 3. Various Hugging Face LLM demo spaces, 4. 'Memorization' research demos on GitHub. Advantages: Unique fun 'live forever' framing, simple UI focused on names. Disadvantages: Potentially shallow analysis compared to academic tools, limited to names vs full datasets.
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