Product

From 50 algorithms to 2,000+. From 200 developers to 650+. How embed built enterprise-scale personalization infrastructure that's now powering the Base app and ready for 2026.

Linear Boost is a new scoring mode in the embed console that lets you design exactly how your feed ranks content. By choosing the signals you trust like interests, social proof, and content type and assigning simple weights, you get a transparent, customizable 0–1 score for every item. This gives feed builders fine‑grained control over what surfaces first in their user's feeds, and lays the foundation for more advanced ML‑powered ranking tools to come.

❜embed users can now control feed freshness, quality, and fairness by ordering candidate pools at the candidate generation step, shaping which items are pre-sorted before final ranking. This new feature allows feeds to be ordered by criteria such as chronological, popular, trending, or random, giving creators more flexibility to balance discovery, engagement, and content quality.

Discover how “Zora filters” help you surface better coins from the Zora ecosystem—fast. This guide shows which fields to combine (price, volume, market cap, momentum, holders), starter thresholds for liquidity and quality, and guardrails to avoid over‑filtering. You’ll also get success metrics for freshness, diversity, and conversion, plus a live example via Superfeeds.ai’s “Zora Coins on Farcaster” feed.

Measure feed health with DAU, impressions, post engagement %, user engagement %, unique authors, and unique posts. Learn how to interpret these metrics together to diagnose issues and iterate confidently.

Prediction markets are still gaining momentum and onboarding a new cohorts of users. Whether these users are traders or simply consuming predictions as content, prediction market apps face a number of challenges like thin liquidity and poor discovery. These can can be solved with recommendation algorithms. Our new prediction market models help apps show a broader set of more relevant markets to their users.

In this post we break down how mastering feed mixing gives you full control over the experience that you want to create for your users. By coupling multiple feeds together you can create an exciting, diverse and balanced experience.

This post breaks down our approach to fighting spam in open protocols. It exposes the limits of legacy batch analysis, and introduces a new AI-powered approach that scores content and users instantly—making feeds more authentic, flexible, and resilient for both builders and communities.

Our ranking algorithms just got an upgrade and understand image and video content in depth. As a result you'll obtain smarter and better recommendations.

Inside your social feeds: AI algorithms decide what you see by analyzing your behavior and demographic data. While these models excel at predicting engagement (guessing what you'll click with 90% accuracy after just 5-10 interactions), they've evolved beyond simple recommendations to maximize platform profits—often promoting viral, polarizing content over meaningful connections. Web3 social platforms offer a solution through transparency, user control, and bridging algorithms that foster understanding across diverse perspectives.

The 'mbd manifesto: Accelerating AI algorithmic transparency and choice by building personalized recommendations that return value to data owners. Starting with Web3 social feeds, this platform enables developers to implement cutting-edge content personalization in minutes while ensuring users maintain control through natural language customization, clear feedback mechanisms, and fair rewards for data contributions—sowing seeds for a community-owned AI revolution.