VELS
Voice-enabled learning simulations for learning and training
What it does
VELS is an AI voice-based learning and training simulations for mastering professional skills. Practice interviews, pitches, and presentations in a safe, personalized environment. Get real-time feedback, tailor scenarios, and boost your confidence
Does the same job
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Hello! I’m So Tokumaru ( https://tokumaru.work ), high school student from Japan working on ways to leverage large language models (LLMs) for K-12 programming education. Recently, I built a project called "TutoriaLLM," a self-hosted application designed to support educators and students through a multi-modal LLM that offers guided tutorials. TutoriaLLM integrates voice (voice-to-voice) and text-based AI interactions into Blockly, a block editor widely known for its use in Scratch. It enables server-side code execution and allows students to test their code directly in Minecraft…
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I built a system that lets LLMs automatically learn and improve problem-solving strategies over time, inspired by Andrej Karpathy's idea of a "third paradigm" for LLM learning. The basic idea: instead of using static system prompts, the LLM builds up a database of strategies that actually work for different problem types. When you give it a new problem, it selects the most relevant strategies, applies them, then evaluates how well they worked and refines them. For example, after seeing enough word problems, it learned this strategy: 1) Read carefully and identify unknowns, 2) Define…
- APA personalised AI tutor with < 1s voice responses2024 · educationbot.cerebrium.ai · ▲72
TLDR: We created a personalised Andrej Karpathy tutor that can response to questions about his Youtube videos in sub 1 second responses (voice-to-voice). We do this using a voice enabled RAG agent. See later in the post for demo link, Github Repo and blog write up. A few weeks ago we released the worlds fastest voice bot, achieving 500ms voice-to-voice response times, including a 200ms delay waiting for a user to stop speaking. After reaching the front page of HN, we thought about how we could take this a step further based on feedback we were getting from the community. Many companies were…
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I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
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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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Launched alongside, September 2024
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