42 AI

If it matters in AI, you’ll read it in my thread.

[🚨 AI News | TestingCatalog @testingcatalog]

Manus now supports loads of different connectors too! Notion, GitHub and many others.

Connectors are superpowers 👀

[42 AI @42_AI_Answers]

@OfficialLoganK Gemini Deep Research is degrading. Please do something about it. It was my first preference.
@joshwoodward

[42 AI @42_AI_Answers]

Google’s Android mascot has done it all — superhero, rockstar, wizard — and now it can be you. Express yourself, remix art, or just have fun.
Try it out & share your creations with #Androidify 👾

https://t.co/j0TjCxsPdW

[42 AI @42_AI_Answers]

👾 New obsession unlocked: Androidify

Google just dropped a tool where you can turn yourself (or anyone 👀) into an Android bot with AI.
Here’s how it works 🧵

[42 AI @42_AI_Answers]

And the cool part: every Friday in September, you can animate your bot into an 8-second video powered by Veo. Imagine your Android clone dancing, drumming, or vibing in space 🚀

[42 AI @42_AI_Answers]

Under the hood, Google is flexing its AI stack:

✨ Gemini 2.5 Flash → captions your pic
✨ Imagen → generates your custom bot
✨ Veo 3 → animates your bot (limited release)

[François Chollet @fchollet]

Saying that deep learning is "just a bunch of matrix multiplications" is about as informative as saying that computers are "just a bunch of transistors" or that a library is "just a lot of paper and ink."

It's true, but the encoding substrate is the least important part here. It's the programs being encoded that are interesting and useful: what they can do, what they can't do, how well they generalize, how efficiently they can be learned, etc.

[42 AI @42_AI_Answers]

@sama @OpenAI Give people the option to choose what they want. That's it. You have "Auto" otherwise anyway.

[Andrej Karpathy @karpathy]

Shower of thoughts: Instead of keeping your Twitter/𝕏 payout, direct it towards a "PayoutChallenge" of your choosing - anything you want more of in the world!

Here is mine for this round, combining my last 3 payouts of $5478.51:

It is imperative that humanity not fall while AI ascends. Humanity has to continue to rise, become better alongside. Create something that is specifically designed to uplift team human. Definition intentionally left a bit vague to keep some entropy around people's interpretation, but imo examples include:
- Any piece of software that aids explanation, visualization, memorization, inspiration, understanding, coordination, etc...
- It doesn't have to be too lofty, e.g. it can be a specific educational article/video explaining something some other people could benefit from or that you have unique knowledge of.
- Prompts/agents for explanation, e.g. along the lines of recently released ChatGPT study mode.
- Related works of art

This challenge will run for 2 weeks until Aug 17th EOD PST. Submit your contribution as a reply. It has to be something that was uniquely created for this challenge and would not exist otherwise. Criteria includes execution, leverage, novelty, inspiration, aesthetics, amusement. People can upvote submissions by liking, this "people's choice" will also be a factor. I will decide the winner on Aug 17th and send $5478.51 :)

[42 AI @42_AI_Answers]

@OfficialLoganK To Pro subscribers also, please!

[Jürgen Schmidhuber @SchmidhuberAI]

Who invented backpropagation (BP)? Its modern version (also called the reverse mode of automatic differentiation) was first published in 1970 by Finnish master student Seppo Linnainmaa. A precursor of BP was published by Henry J. Kelley in 1960. The first NN-specific application of BP was described by Paul Werbos in 1982 (but not yet in his 1974 thesis, as is sometimes claimed).

Some ask: "Isn't backpropagation just the chain rule of Leibniz (1676)?" No, it is the efficient way of applying the chain rule to big networks with differentiable nodes. (There are also many inefficient ways of doing this.) It was not published until 1970.

See the backpropagation overview web page with more details: https://t.co/YpnRMOqEr8

See also the "Annotated History of Modern AI and Deep Learning" (2022): https://t.co/Ys0dw5hkF4

[42 AI @42_AI_Answers]

🆕 Bonus: Hit "New Board" to generate a fresh set of questions any time!

📸 Peek inside ↓
🕹️ Play here → https://t.co/EstjsV9Yi9

Built entirely with #GeminiAI
Tag me with your score or remix! 🔁

#MadeWithGemini #AIJeopardy #GameDev #AI

[42 AI @42_AI_Answers]

🚨 Just dropped: AI Jeopardy 🎯

Built this trivia game in minutes using Gemini — no code, just prompts.

Think you're smart?
Try beating this AI-powered quiz covering:
��� Mythology
🎵 Music
🎬 Hollywood
📐 Math
🇮🇳 Indian History & Culture

[Dr Singularity @Dr_Singularity]

This is easily the biggest breakthrough / AI news of the year so far

This paper, this tech, this AI makes my previous prediction that we’d reach Star Trek level science and technology during the 2030s look conservative.

If this is the kind of breakthrough we’re already seeing in July 2025, it’s not just about reaching Star Trek levels in the 2030s, we’re on track to surpass them.

[Lance Ying @LanceYing42]

A hallmark of human intelligence is the capacity for rapid adaptation, solving new problems quickly under novel and unfamiliar conditions. How can we build machines to do so?

In our new preprint, we propose that any general intelligence system must have an adaptive world model, i.e. they must be able to rapidly construct or refine their internal representation through interaction and exploration — a process we call “world model induction”.

We propose a roadmap for evaluating adaptive world models in machines based on a special class of games we call “novel games”.

[Fidji Simo @fidjissimo]

I will officially start at OpenAI as CEO of Applications on August 18. I am sharing this essay on why I believe AI can be the greatest source of empowerment for all. 🧵
https://t.co/pvyDQPqtCy

[42 AI @42_AI_Answers]

@OfficialLoganK Was there any tool use?

[Tivadar Danka @TivadarDanka]

The single most undervalued fact of mathematics: mathematical expressions are graphs, and graphs are matrices.

Yes, I know. You already heard this from me, but hear me out.

Viewing neural networks as graphs is the idea that led to their success.