SpaceApe.eth ⟠

Believer in the future of France. #defi

[Naval @naval]

The future of freedom depends on zero-knowledge proofs.

[Jim Fan @DrJimFan]

It took my brain a while to parse what's going on in this video. We are so obsessed with "human-level" robotics that we forget it is just an artificial ceiling. Why don't we make a new species superhuman from day one? Boston Dynamics has once again reinvented itself. Gradually, then suddenly.

[Elon Musk @elonmusk]

We should send rockets not at each other, but rather to the stars

[Yann LeCun @ylecun]

🥁 Llama3 is out 🥁
8B and 70B models available today.
8k context length.
Trained with 15 trillion tokens on a custom-built 24k GPU cluster.
Great performance on various benchmarks, with Llam3-8B doing better than Llama2-70B in some cases.
More versions are coming over the next few months.

https://t.co/EkU9aIHdZE

[SpaceApe.eth ⟠ @0x_ape]

@NickDiFabio1 Book

[Arthur Hayes @CryptoHayes]

$ENA is fucking ripping and the Koreans aren’t even involved yet. When that kimchi hits … oh baby.

Yachtzee 😘😘😘

[dotta 📎 @dotta]

What is Forgotten Runes?

Within this vast world of characters, a TV Show, Video Games, Airdrops, and tokenomics it can be hard to know where to start.

Let me take on on an adventure and show you why the greatest franchise of all time will come from Wizard NFTs

🧵

[Brian Roemmele @BrianRoemmele]

Left: Open Source No Internet LLaVA AI.

Right: OpenAI ChatGPT-4 turbo.

Choose.

[Matt Loszak @MattLoszak]

For every ten likes, I’ll make this person increasingly realize how incredible nuclear power is

[Alec Stapp @AlecStapp]

Still laughing about the time a computer scientist who had his bike stolen tried to explain binary search to a cop

[LlamaIndex 🦙 @llama_index]

We’re excited to introduce RAGs v2 - build, customize, and use multiple ChatGPTs over your data, all with natural language 💬

A huge upgrade vs. the initial launch:
💫 Easily create multiple RAG pipelines and save them
💫 Easily swap between and customize each one (e.g. over different data, or w/ different system prompts)
💫 Delete unused RAG pipelines
💫 (dev quality) added much-needed linting/CI

Check out the video 🎥 for details. It’s super easy to setup and use. Some additional features:
🧠 Supports a lot of LLMs both for building RAG and within each RAG pipeline
🌐 Supports loading load files or web pages.

Check out our repo here: https://t.co/838BDVOEbA

[SpaceApe.eth ⟠ @0x_ape]

@levelsio React native is a basket case don’t do it. I have a 1.5mb swift app that does the same as a 1.1gb react-native app. If you need to reach into camera apis or do anything more than show forms and images you will regret it immediately.

[SpaceApe.eth ⟠ @0x_ape]

@levelsio @runwayml This is awesome! What is the moving rgb logo in the corner?

[Charlie Holtz @charlieholtz]

David Attenborough is now narrating my life

Here's a GPT-4-vision + @elevenlabs python script so you can star in your own Planet Earth:

[SkalskiP @skalskip92]

RIP image annotation companies

Fully automated image labeling with GroundingDINO + SAM + OpenAI Vision API

code: https://t.co/g4RHUw2AqE

[Teknium 🪽 @Teknium]

OpenHermes 2 on Mistral 7B got a 43% on Human Eval @ Pass 1. Wow - Beats Code-Llama 34B and nearly gets to 3.5-turbo

[andi (twocents.com) @Nexuist]

We spend months thinking about sending JSON to web pages while some guy makes $280,000 a year adding a button to a VB6 form used by a shipyard that processes 500 million tons of cargo every year

[Carlos E. Perez @IntuitMachine]

Let's talk about Vec2Text

This paper introduces a powerful new technique for inverting text embeddings back to their source texts. The method, Vec2Text, demonstrates for the first time the ability to recover full text sequences from state-of-the-art neural text encoders. Through an iterative process of generation and error correction guided by embedding geometry, Vec2Text is able to reconstruct inputs with over 90% accuracy.

The implications of this advance are profound. It challenges the assumption that embeddings anonymize data by distilling texts down to latent representations. In fact, embeddings leak as much private information as raw text. This forces a re-evaluation of how embeddings are handled, shared, and secured. They must be safeguarded with the same stringency as the original textual data.

Beyond privacy concerns, Vec2Text enables myriad applications for improving natural language processing systems. Inversion offers new capabilities for model analysis, data augmentation, search relevance, conditioned generation, and debugging embedding spaces. The technique powerfully demonstrates that embeddings contain enough signal to reconstruct their source texts.

By developing the first method to reliably invert real-world embedding models at scale, this work sparks a new direction in understanding what embeddings contain. The results demand changes in how privacy and security communities view token embeddings. Meanwhile, the novel text generation process at the core of Vec2Text can be harnessed across language applications to advance the field. Both cautionary tale and technical blueprint, this paper highlights the double-edged sword of readable embeddings.

[Javi Lopez ⛩️ @javilopen]

This is insane 🤯

It's like a portal that transforms the real world into an ATARI video game 🕹️

Soon, we will be able to generate from scratch, and play in real time, parallel worlds thanks to Augmented Reality. Are you ready?

[Alberto @taiuti]

What if I told you there's a Neural 3D Reconstruction method that:

* is mesh-based
* 👀 renders at 120FPS 👀
* uses Neural Texture Fields

NeuRas by @Waabi_ai is the new cool kid in town that nobody is paying attention to (yet), imo:

1/3