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🔄 In a Training Loop

Mateusz Piesiak

Mati83moni
3 14 6
dipankarsarkar's profile picture
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AI & ML interests

Hybrid LLMs, MoE, Mamba, multilingual NLP, biomedical reasoning, efficient pretraining

Recent Activity

published a dataset 4 days ago
Mati83moni/MoE-Titan-Vault
reacted to theirpost with 👍 5 days ago
🔍 Watermark Finder — find, mark, and strip hidden text watermarks. Open source, MIT. Detects six distinct covert channels with exact character offsets, not guesses: Unicode tag payloads, variation-selector encoding, zero-width binary/markers, homoglyph substitution, bidi controls, and repetition stamps. Also verifies C2PA content credentials (Adobe, Leica, Google-signed manifests) — and keeps "integrity" (did the bytes change?) strictly separate from "trust" (is the signer who they claim?), because a forged certificate verifies perfectly too. It scores writing register (how much prose reads like unedited assistant output) with visible uncertainty ranges and per-feature contributions — and refuses a verdict below ~15 words rather than fake precision. That score measures register, not authorship, and the README says so louder than I'm saying it here. Two deploy modes: a hosted engine (FastAPI on a HF Space) behind a Cloudflare Worker, or a fully client-side build compiled to WebAssembly via Pyodide — the document never leaves the browser tab in that mode. 304 tests across four suites, real CI on every push, no accounts (anonymous HMAC-token workspace). Live: https://watermark-finder.pages.dev/ Source: https://github.com/Mati83mon/Watermark_Finder
reacted to theirpost with ❤️ 5 days ago
🔍 Watermark Finder — find, mark, and strip hidden text watermarks. Open source, MIT. Detects six distinct covert channels with exact character offsets, not guesses: Unicode tag payloads, variation-selector encoding, zero-width binary/markers, homoglyph substitution, bidi controls, and repetition stamps. Also verifies C2PA content credentials (Adobe, Leica, Google-signed manifests) — and keeps "integrity" (did the bytes change?) strictly separate from "trust" (is the signer who they claim?), because a forged certificate verifies perfectly too. It scores writing register (how much prose reads like unedited assistant output) with visible uncertainty ranges and per-feature contributions — and refuses a verdict below ~15 words rather than fake precision. That score measures register, not authorship, and the README says so louder than I'm saying it here. Two deploy modes: a hosted engine (FastAPI on a HF Space) behind a Cloudflare Worker, or a fully client-side build compiled to WebAssembly via Pyodide — the document never leaves the browser tab in that mode. 304 tests across four suites, real CI on every push, no accounts (anonymous HMAC-token workspace). Live: https://watermark-finder.pages.dev/ Source: https://github.com/Mati83mon/Watermark_Finder
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