FibroAgent: An Agentic AI Tool for Liver Fibrosis Screening and Clinical Decision Support in Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) - Cureus
FibroAgent: An Agentic AI Tool for Liver Fibrosis Screening and Clinical Decision Support in Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) Cureus
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Syntaqlite Playground
Tool: Syntaqlite Playground Lalit Maganti's syntaqlite is currently being discussed on Hacker News thanks to Eight years of wanting, three months of building with AI , a deep dive into exactly how it was built. This inspired me to revisit a research project I ran when Lalit first released it a couple of weeks ago, where I tried it out and then compiled it to a WebAssembly wheel so it could run in Pyodide in a browser (the library itself uses C and Rust). This new playground loads up the Python library and provides a UI for trying out its different features: formating, parsing into an AST, validating, and tokenizing SQLite SQL queries. Tags: sql , ai-assisted-programming , sqlite , tools , agentic-engineering
Gemma 4 Uncensored (autoresearch results)
Gemma 4 Uncensored — all 4 models, MoE expert abliteration, automated research loop Released uncensored versions of all four Gemma 4 models. bf16 + GGUF for each. Collection : https://huggingface.co/collections/TrevorJS/gemma-4-uncensored-69d2885d6e4fc0581f492698 Code : https://github.com/TrevorS/gemma-4-abliteration Results Model Baseline After KL Div E2B (2.3B) 98% 0.4% 0.346 E4B (4.5B) 99% 0.7% 0.068 26B MoE 98% 0.7% 0.090 31B 100% 3.2% 0.124 Refusal rates from 686 prompts across 4 datasets (JailbreakBench, tulu-harmbench, NousResearch, mlabonne). Manually audited — most flagged refusals are actually the model complying with a disclaimer attached. 26B MoE Standard abliteration only touches dense layers, which gets you from 98% → 29% on the MoE. The remaining refusals are in the expert w
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Real-time AI (audio/video in, voice out) on an M3 Pro with Gemma E2B
Sure you can't do agentic coding with the Gemma 4 E2B, but this model is a game-changer for people learning a new language. Imagine a few years from now that people can run this locally on their phones. They can point their camera at objects and talk about them. And this model is multi-lingual, so people can always fallback to their native language if they want. This is essentially what OpenAI demoed a few years ago. Repo: https://github.com/fikrikarim/parlor submitted by /u/ffinzy [link] [comments]
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