Hawky
AI Agents for Performance Marketing Teams
What it does
Hawky breaks every ad into hook, frames, copy, audio, CTA and audience fit, scores each element against your own winners, and queues the exact edit. It decodes video, image, copy, voiceover, on-screen text and audio in a single pass. Predictive fatigue detection catches decline two to three days earlier than a dashboard and names the element responsible. Brand-aware, not template-aware, and tied to bottom-funnel revenue rather than surface CTR.
Hawky watches every ad in your account, flags the element draining spend, generates the fix, and queues the live swap. Element-level signal, before the spend is wasted.
Hawky watches every ad in your account. It takes each one apart into hook, frames, copy, audio, and CTA, flags the element draining spend, and queues the exact edit. Ask Copilot anything you'd ask an analyst. We gave Hawky's performance agent one goal: a bottom-funnel metric we'd struggled to move. It started testing its own hypotheses, and the impact showed up in the numbers almost immediately. It's not just the quantity I love, it's the quality. I uploaded my brand book once and every ad speaks the language we want to speak. With Hawky's performance agent we finally went deep on the analytics, suggestions, and frequency, and it was unmatched. In a couple of weeks we tested more hypotheses…from hawky.ai
Does the same job
all alternatives →
More ai this month
the category →
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
Astute▲585Automate your B2B brand going viral, with new media creators
AI · 18d ago · company-app.joinastute.com
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…
AI · 26d ago · cactuscompute.com
Launched alongside, August 2026
the whole month →- TL
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