ClinSpeakOET
Timed practice, exam-style flow, feedback you can act on.
What it does
I’ve launched ClinSpeak OET on the App Store: a focused tool for OET Speaking preparation through timed, scenario‑based role play and structured feedback—designed for busy healthcare professionals who need repeatable practice between clinical commitments. It is intended as exam preparation support, not a substitute for formal training or clinical advice. I’m keen to connect with educators and leaders supporting international recruits as we continue to refine the experience.
Does the same job
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- 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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Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
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Parallel agents, diff reviewer, and multi-model comparisons
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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…
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