
PhaBeta MedML Studio
Build clinical ML models without writing code
What it does
PhaBeta MedML Studio automates your entire machine learning pipeline for healthcare, ensuring fairness, privacy, and clinical trust. Build, evaluate, and deploy models with confidence.
Does the same job
all alternatives →- MSMljar Studio – local AI data analyst that saves analysis as notebooksMay 2026 · mljar.com · ▲73
Hi HN, I’ve been working on mljar-supervised (open-source AutoML for tabular data) for a few years. Recently I built a desktop app around it called MLJAR Studio. The idea is simple: you talk to your data in natural language, the AI generates Python code, executes it locally, and the whole conversation becomes a reproducible notebook (*.ipynb file). So instead of just chatting with data, you end up with something you can inspect, modify, and rerun. What MLJAR Studio does: - Sets up a local Python environment automatically, runs on Mac, Windows, and Linux - Installs missing packages during the…

OrchestraMLJun 2026 · orchestra-ml.vercel.app · ▲82From English prompt to deployed ML model with human approval

- TSTruss – Serve any ML model without boilerplate code2022 · github.com · ▲68
- UGUsing GPT-3 and Whisper to save doctors’ time2023 · ▲117
Hey HN, We're Alex, Martin and Laurent. We previously founded Wit.ai (W14), which we sold to Facebook in 2015. Since 2019, we've been working on Nabla (https://nabla.com), an intelligent assistant for health practitioners. When GPT-3 was released in 2020, we investigated it's usage in a medical context[0], to mixed results. Since then we’ve kept exploring opportunities at the intersection of healthcare and AI, and noticed that doctors spend am awful lot of time on medical documentation (writing clinical notes, updating their EHR, etc.). Today, we're releasing Nabla Copilot, a…
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Parallel agents, diff reviewer, and multi-model comparisons
Dev tools · May 2026 · kilo.ai


- NW
Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
Life & fun · May 2026 · github.com
- FM
Dev tools · May 2026 · github.com