Show HN: Are You in the Weights?
New tool lets you search your digital footprint across frontier LLMs—and see how well they know you.
Why it matters
As training data becomes a competitive moat, individuals want visibility into what traces they've left in model weights. This consumer-facing tool taps into growing awareness of data privacy in the LLM era and highlights a gap in transparency around model training datasets.
The key facts
10 to knowProduct queries frontier and small models in parallel to check individual recognition
Clusters responses to measure strength of recognition across different models
Addresses shift in web traffic moving into LLM interactions rather than traditional web
Built by designer + technical co-founder in past few weeks
Launched on HN with 68 points and 36 comments
Tool queries frontier and small models in parallel to check personal recognition
Clusters responses to measure recognition strength across different models
Addresses emerging awareness of training data leakage into LLM weights
Early-stage product (launched within past few weeks)
Published on Hacker News (community signal but limited mainstream validation)
Go to the source
Hacker Newsintheweights.com
Publisher excerpt: With more traffic moving off-web and into LLMs, I got curious about what traces we leave "in the weights". My design partner and I built a site in the past few weeks that checks recognition across frontier and small models. It queries many of them in parallel, clusters the responses, and tells you…
