Blynk Ads
Let AI Create & Optimise Ads in a single prompt
What it does
AI-native performance marketing platform BlynkAds runs your Meta & Google ad campaigns, creates ad creatives, builds landing pages, and optimizes campaigns using closed-loop sales feedback from CRMs. All of this is done from a single prompt in minutes, without needing a performance marketer, video/graphics designer & developer team.
Scale your business with BlynkAI Tech. Generate high-converting landing pages, AI graphics, and precision-targeted campaigns on Meta and Google in minutes. Trusted by 500+ entrepreneurs to automate 95% of ad creation with no marketing experience required.
AI-native platform that creates campaigns, finds target audiences, automatically generates creatives, and even optimizes your existing campaigns. No need to chase performance marketers anymore. In just one prompt, AI suggests audiences, appropriate age, gender, location, and form questions. Didn’t you fire your designer? Blynk Ads generates creatives better than designers, as we have added 1000+ samples. Humans can miss 500+ metrics, but AI won’t. It reads your sales team feedback from the CRM and optimizes automatically or tells you exactly what to do next! Turn campaign performance into simple, actionable insights that help you understand where your money is going, what’s driving results,…from blynkads.com
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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…
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.
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