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  • AlphaFold 3 predicts structures for proteins, DNA, and RNA, and more.

AlphaFold 3 predicts structures for proteins, DNA, and RNA, and more.

PLUS: Huge prompts outperform fine-tuning


Good morning, human brains, and welcome back to your daily munch of AI news.

Here’s what’s on the menu today:

  • AlphaFold folds proteins like I do under pressure 🧬🚀

    Google DeepMind released Alphafold 3.

  • Massive prompts outperform fine-tuning 💵 👨‍💼

    A study shows that thousands of examples improve AI performance.

Sponsor Bot Eat Brain (Now 50% off) 🤯


Fold me tender… Fold me sweet. 🧬🚀

It’s the follow-up to AlphaFold 2, which advanced the field of protein folding and drug discovery.

AlphaFold’s favorite movie…? Amino Royale 😂 🧬

What does it do?

It predicts 3D protein structures from amino acid sequences. It’s predicts antibodies twice as accurately as AlphaFold 2. It also outperforms specialized physics-based systems in predicting small molecule interactions with proteins.

So this one is better?

Yes. Alphafold 2 only predicted the 3D structures of proteins. AlphaFold 3 predicts 3D structures of proteins, ligands, ions, DNA, and RNA, and more.

So, what?

Alphafold 2 led to the discovery of new proteins which advanced heart mapping, antimicrobial resistance modeling, extinct bird egg identification, and more.

Alphafold 3 promises to predict the structures of not only proteins, but many biological elements. It has the potential to revolutionize pharmaceutical research and expand the boundaries of biological understanding.

What does AlphaFold drive…? An El Amino. 🛻


Plus, Google DeepMind created a free, fully hosted web app.

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Oversharing is an asset? 💵 👨‍💼

It shows that massive prompts can outperform fine-tuning in AI models. Using hundreds or thousands of examples drastically improves their results.

Verbal diarrhea: the new secret to AI success! 💩🔥

What does that mean?

The study shows that as text prompts grow longer, the performance impact of individual examples or their order diminishes, making prompts more robust.

So, it’s better than fine-tuning?

Fine-tuning requires more data and has a higher one-time cost, but ICL requires more computing power due to extensive prompts.

Does it work for every AI model?

Nope. ICL only works with models that can handle long context windows. Fine-tuning is the better strategy with smaller models.

In AI land, the incessant rambler reigns supreme… 💪


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Now on to the Daily Munch. 🤗


Think Pieces 🧠

The U.S. government invested $285 million to develop “digital twins.“ They simulate the behavior of physical AI chips.

An open-source code library for robotics? It’s called LeRobot and Hugging Face just launched it.

Startup News 💰

OpenAI and Stack Overflow announced a partnership. They will integrate vetted tech content into OpenAI’s models to increase their accuracy and reliability.

OpenAI is reportedly launching a search engine. A web developer unearthed details on “SearchGPT,” that offers text search, image search, widgets, and more.

Google launched Google Threat Intelligence. It’s a cybersecurity tool that combines Mandiant and VirusTotal in its Gemini AI model.

Research 👨‍🔬

Clover — enhances parallel decoding with sequential knowledge, surpassing Medusa in efficiency and accuracy.

Extend Llama-3’s Context — lengthen Llama-3-8B-Instruct's context length from 8K to 80K with QLoRA fine-tuning, training samples, GPT-4, and more.

SAGS — (Structure-Aware 3D Gaussian Splatting) encodes scene geometry using graph neural networks to improve quality and efficiency.



OpenAI started as a nonprofit to “benefit humanity” and turned into a for-profit Microsoft subsidiary. It’s faces more than 8 lawsuits from newspapers that claim it unlawfully scraped their content to train AI models. Yesterday, it published a blog to “deepen the public conversation” about how AI models should behave.


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Until next time 🤖😋🧠 

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