tullie

7 ms·

1.
Whatnot acquires Shaped to accelerate AI(techcrunch.com)
3 points by tullie 2mo ago|0 comments
2.
by tullie 8mo ago
Fair enough. We're releasing our first bring-your-own-cloud (BYOC) offering in April. We're working with a big e-commerce platform in Germany that has data sovereignty requirements so totally get the constraint and excited to offe
3.
by tullie 8mo ago
Makes sense. Implementation simplicity was part of the reason we didn't go for this. Currently the language is a transpiler, which maps SELECT and FROM into the retrieval stages, WHERE as post-filter stage, ORDER BY as score stage and
4.
by tullie 8mo ago
I've been loving all these projects that are integrating LLMs/encoding directly into the language. There's so much power there. Someone shared with me these the other day and we're inspired to add more remote LLM calls d
5.
by tullie 8mo ago
This line comes from a specific customer we migrated from Elastic Search, they had 3k lines of query logic, and it was completely unmaintainable. When they moved to Shaped we were able to describe all of their queries into a 30 line ShapedQ
6.
by tullie 8mo ago
You can think of Shaped more like a vector store + feature store + ML inference combined into one service. This bundling is what makes it so easy to get state-of-the art real-time recommendations and search performance. E.g imagine trying t
7.
by tullie 8mo ago
Subprocessors are here: https://docs.shaped.ai/docs/v2/support/security Thanks for the feedback on the privacy policy, let me see if we can get that changed. For what it's worth we don't share perso
8.
by tullie 8mo ago
Makes sense! Agreed on the diversity for agents being a bit contrived here. All of the retrievers do support pre-filtering, you just add the where clause within the retriever function. We're working on more query optimization to make t
9.
by tullie 8mo ago
Would love to chat about it, and talk about dbt integration. There's a few use cases that have come up where this would be really helpful. I'll PM you. 1) So we do actually have a python and typescript API, it's just the cons
10.
Show HN: ShapedQL – A SQL engine for multi-stage ranking and RAG(playground.shaped.ai)
80 points by tullie 8mo ago|23 comments
11.
by tullie 9mo ago
Hey everyone, I’m one of the founders of Shaped. We’ve spent the last few years building relevance infrastructure. Over the time we've noticed a convergence happening: The stack for Search (usually Elastic/OpenSearch) and the stac
12.
by tullie 1y ago
Yeah exactly. I was really worried about reducing the serendipity that HN provides (as it's arguably why I've used it for so long as well) but the configurability allows it so that everyone can tweak their level of personalization
13.
by tullie 1y ago
Yeah I definitely agree. I think the nuance is that personalization doesn't have to completely overrun the current ranking algorithm, it more just slightly boosts known similar content you might like. Should be the best of both worlds
14.
Show HN: A personalized HN feed that learns from your favorites(hn.shaped.ai)
23 points by tullie 1y ago|10 comments
15.
The Vector Bottleneck: Limitations of Embedding-Based Retrieval(shaped.ai)
5 points by tullie 1y ago|0 comments
16.
Shaped (YC W22) Is Hiring(ycombinator.com)
1 points by tullie 1y ago
17.
2025 Netflix Personalization, Recommendations and Search Workshop (PRS)(shaped.ai)
1 points by tullie 1y ago|0 comments
18.
One Embedding to Rule Them All(shaped.ai)
1 points by tullie 1y ago|0 comments
19.
Pinterest Replaced Multiple Models with One Embedding – It Outperformed Them All(shaped.ai)
3 points by tullie 1y ago|0 comments
20.
by tullie 1y ago
The other direction that isn’t explicitly mentioned in this post is the variants of SASRec and Bert4Rec that are still trained on ID-Tokens but showing scaling laws much like LLMs. E.g. Meta’s approach https://arxiv.org/abs&
21.
Jagged Flash Attention Optimization(shaped.ai)
24 points by tullie 2y ago|3 comments
22.
Show HN: Configurable Value Model Interface for Recommendation and Search(play.shaped.ai)
9 points by tullie 2y ago|0 comments
23.
Optimizing for Multiple Objectives in Search and Recommendations(shaped.ai)
10 points by tullie 2y ago|1 comments
24.
Show HN: Shaped – Fine-tuning semantic search on behavioral signal(play.shaped.ai)
13 points by tullie 2y ago|0 comments
25.
by tullie 2y ago
The performance for us was best when we evaluated a couple of options, both in terms of scale and latency. I also like the arrow/dataframe interface. We use arrow everywhere else at Shaped so it was a natural integration.
26.
by tullie 2y ago
LanceDB! We love it.
27.
by tullie 2y ago
Yes when integrating Shaped you connect up the data sources needed to ingest: interactions, items and users. The Shaped interface then allows you to select which exact fields should be used for creating a Shaped model. We provide a full SQL
28.
by tullie 2y ago
Australia represent! Although we're based in NYC we still are a mostly Aus/international team over here, it's great! The biggest change is some of the less sexy stuff, like scale and security. E.g. we're now able to scal
29.
by tullie 2y ago
Thank you! Would love to catch up sometime assuming you're in NYC with the rest of the Pinecone team! Yes by 100M+ users we definitely mean end-users, wasn't intentional to mislead so thanks for flagging -- we'll update.
30.
by tullie 2y ago
Yes play.shaped.ai! We just opened that up in a gateless way for this post. Let me know what you think. I should also mention that these demo models are on our cold-tier so that it doesn't break things, in production there's a big

More ›