Vantable
Collect, organize, and act on customer feedback.
What it does
Turn customer feedback into better product decisions. Vantable helps product teams collect, organize, and prioritize customer feedback in one place, making it easier to spot trends, understand what customers actually want, and decide what to build next. Built for teams that want a simple, lightweight way to turn feedback into action.
Centralize, cluster, and prioritize product feedback from Slack, Intercom, Linear, G2, Reddit, and every channel your customers use.
Stop losing feedback in Slack threads, review sites, and support inboxes. Vantable collects it automatically, clusters it into themes, and turns the important parts into ticket-ready specs. Auto-ingest feedback from every channel — internal tools, public reviews, sales notes. AI clusters duplicates, detects sentiment and urgency, surfaces emerging themes. Link mentions to Linear or Jira, prioritize with weighted scoring, and draft replies to reporters. Dense, filterable views over every feedback record. Customizable fields, AI-enriched columns, automations that route signal to the right human — all in a Clay-style canvas built for product teams. Start on Starter free for 7 days. No card.…from vantable.io
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StoriesOnBoard Feedback Management2021 · ▲92Holistic product management powered by customer feedback
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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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