Zahra Arman @ZatronSearch
Stop choosing between smart search and private data github.com/zahraarmantech… Joined May 2026-
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AI agents fail in prod not just from bad reasoning — but from **stale state**. StaleTrace gives you real incident forensics so you can actually fix the root cause. Demo: youtu.be/2rOHWLebefg @hwchase17 @joaomdmoura @SigGravitas
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Semantic search leaks. Your embeddings sit in a database as plain vectors. Anyone who can see them can group them by topic and tell what your documents are about — without reading a word. The usual fixes are bad bargains: lose search quality, or use homomorphic encryption and run 100x slower. So I built a third way. ZATRON turns each embedding into a modular barcode. The search still works. The shape of your data disappears. The original vector is never rebuilt. 98% of normal quality on 626K passages. Then I tried to break my own work — a neural network, 80,000 labeled examples, trained to recover what the barcodes hide. It learned nothing. The same attack tore through ordinary embeddings almost perfectly. pip install zatron. If you can crack it, I want to know.
I trained a neural network to break my own encrypted search. Known-plaintext attacker, 80,000 labeled pairs, 3-layer MLP. On unprotected embeddings → AUC 0.999 (total break) On ZATRON barcodes → AUC 0.505 (coin flip) Same attack. Same data. It learned nothing. pip install zatron dev.to/zahraarmantech…
Zahra Arman retweeted
I tried to hide semantic meaning from embeddings without breaking search. Result: 98% retrieval quality preserved. Embedding structure completely hidden. Raw embeddings → topics visible, clustered ZATRON protected → random noise, no structure Search → identical results on both sides Live demo: huggingface.co/spaces/zahraar… Code: github.com/zahraarmantech…
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