VLMs are Coming for Image Tagging
As AI advances, vision language models (or VLMs, large models capable of interpreting images and text simultaneously) are nearing the point where they can go beyond CLIP and enable highly accurate tagging without fine tuning. While it might seem absurd to run every item in inventory through a prompt, this is now becoming possible even on highly performant models, such as Gemini Flash 2.0, which costs only $0.075 per million input tokens when run in batch mode, or about $1 per 10k images.
New Launch: AI Powered Product Discovery
Showing the right items to the right users at the right time — also known as Product Discovery — means leveraging your unique data to show users personalized content that aligns with your brand. Through talking with dozens of teams, and working with design partners, we saw that the common denominator of great personalization strategies was a flywheel where great product metadata flowed into great experiences across search, recommender systems, marketing, and SEO.
What You’re Getting Wrong About A/B Tests (And How to Fix Them)
A/B tests are a powerful statistical tool, commonly used (or abused) for making decisions about everything from button colors to machine learning models. But, we’ve seen A/B tests frequently used incorrectly. This blog post describes a light framework for planning out A/B tests, influenced by best practices at places like Square and Stitch Fix.
RecSys Metrics Laddering
To optimize on CLTV, stop trying to iterate on CLTV. By setting appropriate goals at different levels of granularity, every team has a metric they can move fast to improve. That’s the fastest route to strategic success, whether improved CLTV, growth, or whatever is right for your business.
From Frustration to Function: How Great Teams Get Great Results from RecSys
To paraphrase Tolstoy: All happy RecSys systems teams are alike, all unhappy RecSys teams are unhappy in their own way. What differentiates a successful team is the ability to experiment with ideas quickly to find the few that truly make a difference. This post lays out common pitfalls and outlines the best practices of successful recommender systems teams.
Announcing our Seed Round
Today, I’m excited to announce our $1.5 million seed investment round led by Bain Capital Ventures with participation from Cadenza Ventures, Y Combinator, and angel investors Brad Klingenberg (ex-Chief Algorithms Officer at Stitch Fix), Patrick Hayes (co-founder of SigOpt), and Dave Aronchick (co-founder of Bacalhau and Expanso).