Tasbin
@ttasbin • 1,201 subscribers
Creator of Athena-709M
Videos
Today, we’re releasing Athena (mvrko-sim-1), the flagship Large Event Model from markopolo.ai that beats GPT-5.6, Claude Opus 4.8, and Claude Sonnet 5 in OPeRa public benchmark for Shopping behaviour prediction! Athena introduces a completely different approach to understanding digital behavior. Most systems record actions after they happen. Athena models the sequence behind them to predict what a shopper will do next and exactly where they will do it, before the action even takes place. Athena vs. frontier models: We evaluated Athena on the full OPeRA test set alongside GPT-5.6, GPT-4.1, Claude Sonnet 5, and Claude Opus 4.8. Athena achieved 24.5% strict exact match scoring the highest among the five models evaluated. Athena is not a general-purpose language model. It is a Large Event Model built to understand behavior as a connected sequence. It combines the page someone is viewing, the actions that brought them there, and the goal they are trying to complete and then predicts the exact next browser action and target element. That distinction matters. Today’s digital systems largely react after someone clicks, abandons, purchases, or leaves. Athena creates the foundation for systems that can understand what is likely to happen before the outcome. Together with Rubaiyat, and the team at markopolo.ai, we built Athena from years of work on shopper behavior, digital journeys, and behavioral intelligence. Commerce was our first proving ground. Great thing is, we’re now releasing Athena publicly so builders can adapt the model to new datasets, industries, and problems. We’re seeing early usecases in Cyber Security, Gaming, Mobile App Ecosystem, Retail and more! Athena is now live on Hugging Face! We built the foundation for relevance and predictability! Now, let’s see what the world builds on top of it. Links in the first reply.
Tasbin55,854 views • 1 month ago
Today we’re launching markopolo.ai an AI that creates deeply personalized campaigns that run themselves. Convert 30-40% of warm leads vs. the industry standard of 10-15%. People don’t abandon because they don’t want the product. They abandon because the follow-up is generic, late, or in the wrong channel/language. So me, Rubaiyat and our team has built an engine that automatically repairs the leak. Our platform reads each visitor's behavioral signals and generates individual follow-ups across email, SMS, and voice, automatically. No generic workflows. No A/B testing. Just LLM-powered campaigns that understand your customers intent and adapt in real-time. Here’s how modern brands use Markopolo to recover millions in lost intent: 1. Reads real shopper behavior in real time Hesitations, comparisons, timing, language preference, a full behavioral fingerprint for every visitor. 2. Creates unique 1:1 journeys instantly Email → SMS → WhatsApp → AI Voice — whatever that specific shopper responds to. No flows. No templates. 3. Follows up in the right tone, language & moment If a shopper buys in Hindi and researches in English, Markopolo adjusts. If they only respond at 7 PM on WhatsApp, it adapts. 4. Fully autonomous campaigns No workflow builders. No segmentation maintenance. Just automated, deeply personal conversations that bring people back. The result? 30–40% recovery consistently. If markopolo.ai doesn’t beat your current recovery rate within 60 days, we’ll refund you.
Tasbin97,869 views • 9 months ago
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