Kolz Winback
Every cart gets a countdown. Every phone gets a call.
What it does
Kolz Winback recovers abandoned checkouts with AI phone calls, expiring coupons, and warm handoffs. 70.2% of carts get abandoned, and email recovery plateaus at 6 to 8%. Winback calls within 5 to 15 minutes of abandonment, while intent is still hot. FuseCodes mint a unique one-time coupon the moment the shopper says yes, texted with the cart link and a live countdown. Live Handoff warm-transfers hot buyers to you with a whisper intro. $20 per 100 calls, only answered calls count.
Recover abandoned Shopify checkouts with AI calls, expiring one-time FuseCodes, SMS and email follow-up, and live handoff to your team.
Winback AI calls shoppers within minutes, answers product questions, offers an expiring one-time FuseCode™, and sends them back to checkout. When a buyer needs you, Live Handoff transfers the call to your phone. One real recovery call, start to finish, nothing cut. The agent answers a question, handles the objection about shipping, then offers the expiring code. Every code is unique, one-time, and born with a countdown. Miss the window and it dies, and the shopper knows it. Accepted on a call, it's texted instantly with the cart link. No phone number on the checkout? The FuseCode travels by email instead, so 100% of your abandoned carts get a recovery touch, not just the ones you can call.…from winback.kolz.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 · 17d 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 · 27d 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 · 17d ago · simedw.com