Philippe Noël
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philippemnoel.bsky.social
Philippe Noël
@philippemnoel.bsky.social
2.3K followers 37 following 170 posts
CEO @paradedb.bsky.social • H'20 • 🇫🇷🇨🇦 https://philippemnoel.posthaven.com
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This morning, I found myself re-reading this article by The Honest Broker on evaluating character. If you haven't read it, I highly recommend you do. It's an incredibly distilled set of principles that has benefited me immensely in life. Intemporal advice.

www.honest-broker.com/p/my-8-best-...
My 8 Best Techniques for Evaluating Character
These methods have helped me enormously—and can save you much heartache and anxiety
www.honest-broker.com
I saw this on LinkedIn and it was just too accurate to not share here. Postgres powers so much of the world's software yet the core team is a couple dozen people. The ecosystem around it is also surprisingly small for how far reaching it is.
Couldn't agree more. This is also what we're seeing at ParadeDB
A trend towards pointing LLM/agents to simple search tools (BM25 keyword search or even grep) rather then giving them super complicated 'thick search stacks' (vector or hybrid search). Idea is the LLM can work like a human & try to understand how to use them softwaredoug.com/blog/2025/09...
Agents turn simple keyword search into compelling search experiences
Agents need tools they understand, like simple keyword search. They can reason about these tools, evaluate the results, refine, and iterate to deliver rather...
softwaredoug.com
Reposted by Philippe Noël
Floor Drees summarized the talk she gave a PGConf EU with CloudNativePG maintainer Gabriele Bartolini last week, titled "They grow up so fast: donating your open source project to a foundation".

In case you're curious about the project's origin story: dev.to/floord/they-... #Postgres #Kubernetes
They grow up so fast: donating your open source project to a foundation (or: the CloudNativePG story)
The first commit to the CloudNativePG project was made in February 2020. Just two years later, EDB...
dev.to
Learning SEO optimization is kinda fun
I'm at PGConf EU in Riga this week. If you're around, come say hi!
All good, I think we're still a step before doing that
I'm interested in hiring an "SEO consultant" to come in, evaluate our SEO posture and make a list of recommendations for what to improve next. Got anyone to recommend?
Reposted by Philippe Noël
Chris @chris.blue · Sep 30
Had a few folks ask why you'd need ACID for search. This @paradedb.bsky.social article from @philippemnoel.bsky.social and company does a decent job making the case.

There are a lot of cases (e.g. fintech) where strong consistency and durability is a big deal for search.
ParadeDB
The Transactional Elasticsearch Alternative Built on Postgres
www.paradedb.com
I just try not to run too many apps at the same time now :(
So annoying. Need the next update to fix this haha
An excellent, to-the-point blog post. One architecture difference to note is that MongoDB Search runs outside of the main MongoDB process, while in ParadeDB it runs inside.

We've revamping our tokenizers and will fix this emoji tokenization issue. Thanks Franck!
Is it just me or the new macOS Liquid GLass Spotlight is kinda laggy... Ugh (On M3 MacBook Air)
A fantastic podcast by our engineer, Ankit Mittal, on how Instacart built search on Postgres, where the future of search infrastructure is going, and why that led him to join us at ParadeDB: www.youtube.com/watch?v=hohp...
Postgres vs. Elasticsearch: Instacart’s Unexpected Winner in High-Stakes Search with Ankit Mittal
YouTube video by The Data Engineering Show - Podcast
www.youtube.com
What is your take on Elasticsearch shipping a SQL interface?
I'm not as familiar with this use case, but I don't see how this couldn't be declared as SQL.
We came up with this categorization internally:

- real-time search (e.g. dashboards, SaaS search boxes)
- observability
- RAG/GenAI
Instacart can know this because they have recommender systems trained on what this user might want.

But I don't think all search use cases need this. I suspect AI-search use cases will to a lesser extent, but that it'll be beneficial for them. That's more likely to be an area we optimize around.
By arbitrary, you mean custom code / ML models? I think these matter a lot in a use case like e-commerce where a lot of domain knowledge and user-specific knowledge is applied. For example, an Instacart user might type "banana" but they want "banana yogurt" rather than "bananas".
I think it's important to define the use cases well. ParadeDB is not focusing on e-commerce, and so we have a lower emphasis on re-ranking. Elastic has good capabilities there since they are so broad they target pretty much all search use cases.