Axix Owl
AI-powered B2B lead generation and prospecting
What it does
Axix Owl is an AI-powered B2B lead generation platform built to help businesses discover, enrich, and qualify prospects faster. Instead of relying on disconnected tools for company research, contact discovery, and prospecting, Axix Owl brings the lead-generation workflow into one platform. It is designed for sales teams, agencies, founders, and B2B teams that want to reduce manual prospect research and build more focused, actionable sales pipelines.
Axix OWL is a B2B intelligence and data enrichment platform with registry data, company profiles, and API access — cloud B2B intelligence for sales teams.
We send a 6-digit code to stop bots. Verify your email before starting the free trial. Instant email with login credentials. No credit card needed. Extra credits raise your monthly limit and bill — +$ 29.39 per 1,000 credits added. Extra credits raise your monthly limit and bill — +$ 23.52 per 1,000 credits added. AxixOwl unifies global registry data into one searchable platform — verified business contact data, company officer records (public filings), and a developer REST API. ~1.6M companies across 12 jurisdictions. No ZoomInfo contract required. Data is sourced from official government and licensed commercial company registries for legitimate business-to-business use, in accordance with…from owl.axixtechnologies.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.
AI · 16d ago · simedw.com
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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, August 2026
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Life & fun · 10d ago · louisabraham.github.io


- SA
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…
Life & fun · Aug 2026 · toneyalexander.github.io


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.
AI · 16d ago · simedw.com