Whisperer
Your AI copilot for every conversation
What it does
Whisperer is an AI Workspace that brings together your knowledge, meetings, documents, tasks, calendar, communications, and AI in one ecosystem. Think of it as a Swiss Army knife for productivity: every capability shares your context and works together instead of living in isolation. Whisperer helps you capture knowledge, understand information, automate routine work, and turn data into action.
Whisperer — AI-рабочее пространство: знания, встречи, документы, задачи, календарь и AI-модели в единой экосистеме. Старт бесплатно, 60 минут.
Стоимость лида — €12,40, на 9 % ниже плана. Основной прирост дала органика. Без долгой настройки: установите, откройте перед созвоном — остальное Whisperer сделает сам, и всё окажется в вашем пространстве. Десктоп-клиент для macOS и Windows. Один аккаунт — тариф и лимиты подтянутся сами. Откройте Whisperer до созвона: оверлей висит поверх экрана и не попадает в трансляцию. Подсказывает ответ, синхронно переводит речь и ведёт стенограмму. После созвона — заметки, задачи и файлы в кабинете. Каждый инструмент читает и пишет один и тот же контекст — вводить заново ничего не нужно. Пишет звонок — микрофон и системный звук — и расшифровывает на лету: спикеры, таймкоды. Или импортируйте готовую…from whisperer.one
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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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Hello HN! I found that picking out plausible but diverse skin tones for my digital art and game development projects was kind of difficult, and I got curious about if there was a way to define a color space that made it easy. I've built a color picker and procedural generation algorithm based on the space as well as a bunch of other fun js features and demos throughout the page that use the equations. If you find it interesting, I have lots of explanations of how I built it and what properties the space has. The methodology might be a bit shaky, but hopefully the result is as helpful for…
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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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