
OptimaQ
Predict addiction risk.- Help patients recover.
What it does
OptimaQ uses dual Q-learning to predict opioid addiction risk and simulate treatment plans. Analyze patient data, track recovery states, and visualize outcomes — all open-source. Built for researchers and behavioral health innovation.
Does a similar job
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- AFAI for researching personal health issues2024 · agenthost.ai · ▲38
I have some chronic medical conditions, and spend a lot of time asking about different drugs and supplements. Wanted to create a resource that others might get value from. It's trained to always provide citations, and to urge people to see their doctor before making major decisions. Open for feedback.
- OSOpen-source model and scorecard for measuring hallucinations in LLMs2023 · vectara.com · ▲65
Hi all! This morning, we released a new Apache 2.0 licensed model on HuggingFace for detecting hallucinations in retrieval augmented generation (RAG) systems. What we've found is that even when given a "simple" instruction like "summarize the following news article," every LLM that's available hallucinates to some extent, making up details that never existed in the source article -- and some of them quite a bit. As a RAG provider and proponents of ethical AI, we want to see LLMs get better at this. We've published an open source model, a blog more thoroughly describing our methodology (and…

SMILES Toxicity Predictor (Using RNN ML)2025 · ▲7Toxicity Drug (Molecule) Predictor Using SMILES String
Optio: AI Decision MakerJul 2026 · apps.apple.com · ▲21Optio 2.0:AI decision-making with video export
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Source: Product Hunt launch ↗
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Hi everyone, I built PyXL — a hardware processor that executes a custom assembly generated from Python programs, without using a traditional interpreter or virtual machine. It compiles Python -> CPython Bytecode -> Instruction set designed for direct hardware execution. I’m sharing an early benchmark: a GPIO test where PyXL achieves a 480ns round-trip toggle — compared to 14-25 micro seconds on a MicroPython Pyboard - even though PyXL runs at a lower clock (100MHz vs. 168MHz). The design is stack-based, fully pipelined, and preserves Python's dynamic typing without static type restrictions.…
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