🙃

[🙃 @AllWorkNoPlay]

@elonmusk Ues, after a massive dollar devaluation

[🙃 @AllWorkNoPlay]

@MainzBioMed Sending stool right now
💩-->✉️

[a16z @a16z]

World Labs CEO Dr. Fei-Fei Li: "The world is not made of words."

"Language models have given machines an extraordinary command of concepts, vocabulary, and reasoning, but the physical world, virtual or real, runs on a different substrate."

"Where language models learn the statistical structure of text, world models learn the statistical structure of space and time: how light falls on a surface, how a garden looks from an angle no camera has captured, how objects respond to force and follow the laws of physics."

"Language gave machines a way to talk about that world. World models are how machines will finally come to understand, imagine, reason and interact with it."

Full piece: https://t.co/C9qOJg5wuc

[🙃 @AllWorkNoPlay]

@LePapillonBlu2 ❤️ "you're getting discombobulated"

Considering a tattoo now...

[🙃 @AllWorkNoPlay]

@dm_jns @AnnickDeRidder

[Son Luong @sluongng]

Codex just found a “workaround” of not having sudo on my pc…

[🙃 @AllWorkNoPlay]

@NickScheber @teslaeurope TBH road network in Estonia is probably not the most complex one for FSD

[🙃 @AllWorkNoPlay]

@WhiteHouse @VP Europe has separation of powers and rule of law.

[🙃 @AllWorkNoPlay]

@KenRoth

[🙃 @AllWorkNoPlay]

@billykyle @Tesla Animals will learn how to influence cars #signofthetimes

[🙃 @AllWorkNoPlay]

@WhiteHouse Opportunism dressed as strength

[🙃 @AllWorkNoPlay]

@SawyerMerritt Nice gamification

[🙃 @AllWorkNoPlay]

@just_julia007 Moto kopen of jong lief zoeken

[🙃 @AllWorkNoPlay]

@davidfowl About 7

[🙃 @AllWorkNoPlay]

@JDVance @WhiteHouse Consistency is not exactly his middle name

[Milan Jovanović @mjovanovictech]

Not every AI feature needs a dedicated vector database.

If your data already lives in PostgreSQL, adding Pinecone, Qdrant, or Weaviate might be one moving part too many.

For many use cases, `pgvector` is enough.

It gives PostgreSQL native vector storage and similarity search.

That means you can keep your embeddings next to your relational data and still use the things PostgreSQL is already great at:

- joins
- filters
- pagination
- transactions
- indexes

The basic flow is simple:

1. Generate an embedding for your content
2. Store it in a `vector` column
3. Generate an embedding for the search query
4. Order results by cosine distance with `<=>`
5. Return the closest matches

Now your users can search by meaning, not just exact keywords.

A query like “how to secure an API” can return articles about authentication, JWT validation, and authorization, even if those exact words don’t appear.

In this article, I show how to build simple vector search in .NET using PostgreSQL, pgvector, Aspire, Ollama, MEAI, and Dapper.

Read the full article here: https://t.co/XiTjXOz2C4

[𐌁𐌉Ᏽ 𐌕𐌉𐌌𐌉 @OrevaZSN]

Idea: An anonymous “vote to end meeting” button on Teams where if 50% of people press it, the meeting ends immediately.

[🙃 @AllWorkNoPlay]

@FILIPVERTRIEST @aikisteve @AnnickDeRidder @Tesla

[🙃 @AllWorkNoPlay]

@JDVance @WhiteHouse A good starting point is looking into presidential pardons.

[🙃 @AllWorkNoPlay]

@WhiteHouse fixed it for you

THE MOST PRO-TRUMP-FAMILY ADMINISTRATION. 🇺🇸