EloSense — Inteligência de conteúdo
AI for Content & Brand Intelligence
What it does
EloSense é o motor emocional que lê o sentimento da sua audiência, mapeia leads quentes e te diz o que postar pra fortalecer a ligação com seus seguidores.
EloSense é o motor emocional que lê o sentimento da sua audiência, mapeia leads quentes e te diz o que postar para fortalecer a ligação com seus seguidores.
Porque a receita não está no like — está no comentário e na DM, e nenhuma ferramenta está lendo. O EloSense é o primeiro sistema de Social Intelligence do mercado: dois motores proprietários, calibrados com seu histórico real, que te entregam o que fazer agora com cada post, cada verba e cada pessoa da audiência. Você posta. Vem 200 likes. Vem 12 comentários. Vem 4 DMs. Toda venda real do Instagram fecha nesses 4 — e você responde entre uma criação e outra, no celular, em ordem de chegada. Métrica do Meta, Metricool, MLabs te mostra alcance. Nenhuma te mostra em quem da audiência focar pra fechar agora . Por isso 3 horas de criação morrem em post no escuro, lead pronto some, verba queima no…from elosense.com.br
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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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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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