Eric Seufert

Amazon blocking Muse is a gift to Meta https://t.co/Yze5XmUeUD

Amazon blocking Muse is a gift to Meta "As I write this, Muse has been the #1 Top Downloaded app in the US App Store for four consecutive days. And while it’s true that Meta goes to extraordinary lengths to protect consumers’ data in Muse, my sense is that few consumers are aware of those protections (I made the same point about Apple years ago). So if consumers possess a deep-seated mistrust of Meta’s stewardship over their data, they are not expressing it with their install behavior." https://t.co/l67tiajr7H

Agentic commerce is a mirage (part 4) "Amazon has blocked Muse, Meta’s new personal agent, from accessing its platform. Amazon and Walmart are motivated by very precise, very concrete direct and indirect financial incentives to resist surrendering the customer relationship to third-party shopping agents. Incentives matter: they are authoritative, and they are dominant. “New interaction paradigm” doesn’t, by default, override prevailing incentives even when it’s superior to the status quo; in the case of third-party agentic shopping, that new interaction paradigm is not superior to the status quo for retailers." https://t.co/eLprF8Q6iJ

Muse has been the #1 most-downloaded app in the US App Store since Friday. Meta appears to be supporting Muse with the same degree of cross-promotional push it gave Threads. Threads is still #18 in the US App Store and rolled out ads globally in January. https://t.co/9fFpzK3J1I

You don't need to be a doomer. There's an alternative argument available. https://t.co/J7fuYe3jxz

Meta's Muse is the #1 Top Downloaded app in the US App Store. https://t.co/bgVdiTlXvU

Can bottom-up clustering improve performance in generative retrieval? A new paper from researchers at PayPal explores whether creating semantic IDs (SIDs) through a bottom-up clustering process can better capture local relationships between similar items while also ensuring that each item receives a unique identifier. They implement a bottom-up approach that contrasts with that employed by Google's TIGER: instead of running RQ-VAE to encode item embeddings through successive rounds of residual quantization over K codebooks, they start by building local item neighborhoods, then iteratively construct the catalog hierarchy more coarsely. The initial clustering of catalog-item embeddings uses MiniBatch K-means, with each item receiving a unique, cluster-level integer label as its final SID token. The cluster centroids are then grouped into broader clusters, weighted by item count, and so on (note that the main text describes this merging stage as agglomerative clustering, although the Appendix specifies MiniBatch K-means). This method really just replaces the tokenizer of a generative retrieval system; it doesn't dictate the method used for subsequent retrieval (eg., through a Transformer sequence model). The authors found that their bottom-up approach improved Recall@10 (rewards whether the relevant item appeared) and NDCG@10 (also rewards item placement) on both the Amazon Beauty and a proprietary dataset, although it performed slightly worse / the same on Amazon Sports & Outdoors. I wouldn't consider the paper conclusive, but it does suggest that building SIDs around local item similarity can improve recommendation performance. One somewhat pedantic nitpick: the paper presents RQ-VAE as the diametric opposite of the implemented approach, which it isn't: RQ-VAE clusters *residual vectors*, it doesn't create clusters within a cluster, even though progressively longer SID sequences define increasingly fine-grained groups. Whereas this method does cluster existing clusters. So the distinction isn't *quite* top-down versus bottom-up. Link in comments. It's a short paper at just six pages and a fairly easy read.

Meta’s AI opportunity, whatever the pace "Squint hard enough, and a platform becomes visible: cross-device agentic tools running atop Meta’s owned models, with an interaction surface area that includes WhatsApp, used by at least 2BN people every day. If 'platform' in the digital context is characterized by being resistant to intermediation, or at least appreciably fortified against it, then this connected network of tools and services qualifies." https://t.co/6Efm93hI5Y

Meta’s AI opportunity, whatever the pace "Squint hard enough, and a platform becomes visible: cross-device agentic tools running atop Meta’s owned models, with an interaction surface area that includes WhatsApp, used by at least 2BN people every day. If 'platform' in the digital context is characterized by being resistant to intermediation, or at least appreciably fortified against it, then this connected network of tools and services qualifies." https://t.co/6Efm93hI5Y

Meta is cross-promoting Muse, its new agent app, from Facebook. This is the same approach Meta used to grow Threads, which reached 500MM MAU in June, less than three years after launch. Meta introduced ads to Threads at roughly 400MM MAU. https://t.co/cXq9NlnAYU