Wavly — Agentic Revenue Operating System
Four AI employees that create, protect & expand revenue.
What it does
Wavly is an AI revenue workforce: Scout, Closer, Keeper, and Grower — four AI employees on a shared Revenue Graph and Revenue Memory that qualify accounts, convert opportunities, defend customer revenue, and expand ARR with governed autonomy and live CRM attribution. Built for RevOps and pipeline teams from $5M ARR.
Agentic Revenue OS: four AI employees on one Revenue Graph and Revenue Memory. Pipeline, Protected, At Risk, Expansion $ with governed autonomy.
Four AI employees on one Revenue Graph. Revenue Memory compounds every win — Pipeline, Protected, At Risk, Expansion $. Scout → Closer → Keeper → Grower → Scout. Each employee writes Revenue Memory — the next one starts smarter. Scout ingests signals across the Revenue Graph, scores DNA match against your best customers, and hands qualified demand to Closer with full committee context — before competitors notice the intent. Pipeline, Protected, At Risk, Expansion — each traced to the AI employee, Memory, and DNA. Scout elevated DNA matches. Closer is converting — pipeline in motion. Keeper ran Memory-backed save missions and defended the customer base. Pre-churn signals with reasoning —…from thewavly.com
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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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