e^(-x^2)

Lost in translation

[Yi Ma @YiMaTweets]

For intelligence, compression is not the goal. It is a means to an end. The true goal is to gain information that helps reduce uncertainty, which is entirely measurable. For any particular data of interest, to obtain a most informative representation of its distribution (also called memory or knowledge), an intelligent system tries to learn the most effective and efficient compressing operations (say layers of a network). This is what I have been saying: We learn to compress, and we compress to learn! This is precisely the main theme of our new open book.

[Gary Marcus @GaryMarcus]

Claude Code is not AGI, but it is the single biggest advance in AI since the LLM.

But the thing is, Claude Code is NOT a pure LLM. And it’s not pure deep learning. Not even close.

And that changes everything.

The source code leak proves it. Tucked away at its center is a 3,167 line kernel called print.ts.

print.ts is a pattern matching. And pattern matching is supposed to be the *strength* of LLMs.

But Anthropic figured out that if you really need to get your patterns right, you can’t trust a pure LLM. They are too probabilistic. And too erratic.

Instead, the way Anthropic built that kernel is straight out of classical symbolic AI. For example, it is in large part a big IF-THEN conditional, with 486 branch points and 12 levels of nesting — all inside a deterministic, symbolic loop that the real godfathers of AI, people like John McCarthy and Marvin Minsky and Herb Simon, would have instantly recognized.*

Putting things differently, Anthropic, when push came to shove, went exactly where I long said the field needed to go (and where @geoffreyhinton said we didn’t need to go): to Neurosymbolic AI.

That’s right, the biggest advance since the LLM was neurosymbolic. AlphaFold, AlphaEvolve, AlphaProof, and AlphaGeometry are all neurosymbolic, too; so is Code Interpreter; when you are calling code, you are asking symbolic AI do an important part of the work.

Claude Code isn’t better because of scaling.

It’s better because Anthropic accepted the importance of using classical AI techniques alongside neural networks — precisely marriage I have long advocated.

It’s *massive* vindication for me (go see my 2019 debate with Bengio for context, or to my 2001 book, The Algebraic Mind), but it still ain’t perfect, or even close.

What we really need to do to get trustworthy AI rather than the current unpredictable “jagged” mess, is to go in the knowledge-, reasoning-, and world-model driven direction I laid out in 2020, in an article called the Next Decade in AI, in which neurosymbolic AI is just the *starting point* in a longer journey.*

Read that article if you want to know what else we need to do next.

The first part has already come to pass. In time, other three will, too.

Meanwhile, the implications for the allocation of capital are pretty massive: smartly adding in bits of symbolic AI can do a lot more than scaling alone, and even Anthropic as now discovered (though they won’t say) scaling is no longer the essence of innovation.

The paradigm has changed.

—
*Claude Code is plainly neurosymbolic but the code part is a mess; as Ernie Davis and I argued in Rebooting AI in 2019, we also need major advances in software engineering. But that’s a story for another day.

[Farza 🇵🇰🇺🇸 @FarzaTV]

This is Farzapedia.

I had an LLM take 2,500 entries from my diary, Apple Notes, and some iMessage convos to create a personal Wikipedia for me.

It made 400 detailed articles for my friends, my startups, research areas, and even my favorite animes and their impact on me complete with backlinks.

But, this Wiki was not built for me! I built it for my agent!

The structure of the wiki files and how it's all backlinked is very easily crawlable by any agent + makes it a truly useful knowledge base.

I can spin up Claude Code on the wiki and starting at index.md (a catalog of all my articles) the agent does a really good job at drilling into the specific pages on my wiki it needs context on when I have a query.

For example, when trying to cook up a new landing page I may ask:

"I'm trying to design this landing page for a new idea I have. Please look into the images and films that inspired me recently and give me ideas for new copy and aesthetics".

In my diary I kept track of everything from: learnings, people, inspo, interesting links, images.

So the agent reads my wiki and pulls up my "Philosophy" articles from notes on a Studio Ghibli documentary, "Competitor" articles with YC companies whose landing pages I screenshotted, and pics of 1970s Beatles merch I saved years ago. And it delivers a great answer.

I built a similar system to this a year ago with RAG but it was ass.

A knowledge base that lets an agent find what it needs via a file system it actually understands just works better.

The most magical thing now is as I add new things to my wiki (articles, images of inspo, meeting notes) the system will likely update 2-3 different articles where it feels that context belongs, or, just creates a new article.

It's like this super genius librarian for your brain that's always filing stuff for your perfectly and also let's you easily query the knowledge for tasks useful to you (ex. design, product, writing, etc) and it never gets tired.

I might spend next week productizing this, if that's of interest to you DM me + tell me your usecase!

[Thariq @trq212]

We just released Claude Code channels, which allows you to control your Claude Code session through select MCPs, starting with Telegram and Discord.

Use this to message Claude Code directly from your phone.

[Peter Tong @TongPetersb]

Train Beyond Language. We bet on the visual world as the critical next step alongside and beyond language modeling. So, we studied building foundation models from scratch with vision.
We share our exploration: visual representations, data, world modeling, architecture, and scaling behavior! [1/9]

[Nate.Google @Nate_Google_]

probably the most valuable use of your time in 2026

[Min Choi @minchoi]

Adobe and UPenn researchers just announced tttLRM (CVPR 2026)

This AI turns a set of photos into high-quality 3D Gaussian Splats and can keep refining the 3D as you add more views

6 wild examples:

[Over the Reality 🌐 @OVRtheReality]

Currently, demand for high quality Visual AI data is growing faster than anyone expected. 📈

Every week, more companies reach out with one need: high resolution, multi view, world-scale datasets for their Visual AI models.

Last month, we published a new 1,000 3D Maps Dataset on @huggingface under CC-BY-NC. A small gift to the global AI community.

Since that release, interest exploded.

To date, we have:
↳ Multiple AI startups requesting access to our database
↳ Ongoing negotiations with several well known Visual AI companies
↳ Active conversations with companies ranked among the most valuable globally

Right now, our growing dataset includes:
↳ 178,000 mapped locations
↳ 89.1 million images
↳ 802 TB of real world data

There is a clear trend in this new AI cycle.

Every company is searching for high quality, real world visual data at scale. And this is exactly what we are building.

And 2026 will be a breakout year. What we’ve shared so far is only the beginning.

Roadmap 2026 coming soon.

[Evan Luthra @EvanLuthra]

holy sh*t!!!!!🤯

google just decided to kill thousands of startups

[Sam Altman @sama]

The companies that succeed in the future are going to make very heavy use of AI. People will manage teams of agents to do very complex things.

Today we are launching Frontier, a new platform to enable these companies.

[Mistral AI @MistralAI]

Introducing Voxtral Transcribe 2, next-gen speech-to-text models by @MistralAI.
State-of-the-art transcription, speaker diarization, sub-200ms real-time latency.
Details in 🧵

[Peter Yang @petergyang]

Here's my new tutorial on how to set up Molt (formerly Clawd), the most impressive AI product I've used since Claude Code.

Molt is an AI assistant that you can text at any time to manage emails, edit documents, push code, and so much more.

In this tutorial, I cover:

→ My top 5 use cases for Molt 🦞
→ How to set up Molt safely (important!)
→ How to install Molt step-by-step

📌 Watch now: https://t.co/L43NBbKCtu

[e^(-x^2) @mr_notrobot]

@QalaatAlMudiq Thank you, while I don’t think the source is very reliable or dependable, I do appreciate your journalism and transparency and engagement, and I’m now a fan of your feed. Keep it up 🙌

[e^(-x^2) @mr_notrobot]

@QalaatAlMudiq Is there a link to a video or any trusted source to prove it? I just want to know the reliability of the source. Thank you

[e^(-x^2) @mr_notrobot]

@QalaatAlMudiq Hello, what is the source for this information that proves he was executed by Druze militias for treason?

[AK @_akhaliq]

Transformer^2

Self-adaptive LLMs

[e^(-x^2) @mr_notrobot]

Extending the hand of unity to all Syrians
يداً بيد

[e^(-x^2) @mr_notrobot]

@farahplzstop 🫵

[Ozymandias𓆏 @0zmnds]

Adolphe Appia

[Ozymandias𓆏 @0zmnds]

Jean-Pierre Ugarte