Edelson Amoretti

Conoce a quienes te rodean y sabrás quienes son tus verdaderos camaradas.

[Edelson Amoretti @EdelsonAmoretti]

Ya va siendo hora -dijo la razón- de que busques la respuesta en tu corazón.

[AI Spanish Community @aiscwork]

En lugar de ver Netflix este finde, dedica 1 hora a esto.

Un CURSO COMPLETO de Claude que te enseña a automatizar lo que te roba 3 horas al día.

El lunes lo agradecerás.

[久保帯人＆スタッフ @tite_official]

【FC情報：Wallpaper更新】
7月の会員限定壁紙を本日公開！
※画像はサンプルです
https://t.co/JA4MsKfI2k

PC版はBLEACHの主人公、黒崎一護！

SP版は仁義を尽くす漢、��場鉄左衛門。

久保先生の過去イラストを、壁紙にして #KlubOutside で限定配布中！
〈スタッフ〉

[Param @ParamSiddh]

Best YouTube Channels To Learn AI in 2026 (No BS). Save it.

1. Fundamentals – 3Blue1Brown
2. Deep Learning – Andrej Karpathy
3. AI Research – Yannic Kilcher
4. Practical AI – AssemblyAI
5. LLMs – AI Explained
6. ML Theory – StatQuest
7. Papers Simplified – Two Minute Papers
8. GenAI – Matthew Berman
9. AI Agents – Nicholas Renotte
10. Applied ML – Krish Naik
11. PyTorch – Aladdin Persson
12. Math for ML – Serrano Academy
13. Industry Insights – Lex Fridman
14. Real-world AI – DeepLearningAI

[Edelson Amoretti @EdelsonAmoretti]

@Adamaestr0_ Opus

[Rosa María Palacios @rmapalacios]

En ese momento de la campaña en que tienes que bloquear racistas, histéricos y fraudistas sin importar por quien votaron.
Igual de tóxicos todos.
Aprendan a gestionar sus emociones y después regresen a redes sociales.

[Swati Gupta @hrswatigupta]

Anthropic pays $750,000+ a year for engineers who can build LLM architectures from scratch. Stanford taught the entire thing in 1 hour lecture & released it for free.

Bookmark & watch this today before someone takes it down and read this article below

[Nikki Siapno @NikkiSiapno]

6 API architecture styles every developer should know:

1. REST
↳ https://t.co/GSjtaCU2Y8

2. gRPC
↳ https://t.co/idHY2XQ17I

3. WebSockets
↳ https://t.co/KPYmiV2UdW

4. GraphQL
↳ https://t.co/K96RB7gzmk

5. SOAP
↳ https://t.co/pRqGugnJTk

6. MQTT
↳ https://t.co/pRqGugnJTk

What else would you add?

👋 PS: Want to improve at system design? Download my free System Design Handbook and join 34,000+ engineers who get my free weekly newsletter → https://t.co/LybPLdor9s

🔖 Save for later
♻️ Repost to help others learn system APIs.
➕ Follow me ( Nikki Siapno ) + turn on notifications.

[Aurora Martel @AuroraMar1eL]

🚨 The AI industry just lost 3 years. Trillions spent. Billions burned.
Chasing the wrong idea.

Yann LeCun called it from day one. Nobody listened. Until now.

The bet was simple: make the model big enough and it’ll eventually understand the world.

LeCun said that’s nonsense.

Generative AI is fundamentally inefficient.
When a model predicts the next word or the next pixel, it pours compute into surface-level detail.

It learns patterns, not the underlying physics of reality.

He pushed a different approach: JEPA (Joint-Embedding Predictive Architecture).

Instead of making AI recreate the world pixel by pixel, JEPA makes it predict concepts.
What happens next—not in raw data space, but in a compressed “thought space.”

But for years, JEPA hit a wall.

Representation collapse.

When you let an AI “simplify” reality, it takes the easy way out: it cheats. It compresses the world until a dog, a car, and a human all blur into the same thing.

It doesn’t learn. It collapses.

The fix has been ugly: elaborate hacks, frozen encoders, and mountains of compute just to keep the model honest.

Until now.

A new paper, LeWorldModel (LeWM), claims to solve the collapse problem outright.

No Rube Goldberg engineering. Just one clean mathematical regularizer.

It pins the model’s internal representations to a true Gaussian distribution—so it can’t hide behind shortcuts. If it wants to predict, it has to model the real structure of the world.

And the implications are wild.

LeWM doesn’t need a massive centralized cluster.
It’s only 15 million parameters.
It trains on a single standard GPU in a few hours.

Yet it plans 48× faster than huge foundation world models, shows an intrinsic grasp of physics, and flags impossible events on sight.

We’ve spent billions building server farms to memorize the internet.

Now a small model, running locally on one graphics card, is starting to learn how reality actually works.

[Kirk Borne @KirkDBorne]

New Release! "Machine Learning Foundations, Volume 1: Supervised Learning" - available at https://t.co/1fqHdfrceP

𝗕𝗘𝗡𝗘𝗙𝗜𝗧𝗦:

🟠Master the key concepts of supervised machine learning, including model capacity, the bias-variance tradeoff, generalization, and optimization techniques

🔴Implement the full supervised learning pipeline, from data preprocessing and feature engineering to model selection, training, and evaluation

🟣Understand key learning tasks, including classification, regression, multi-label, and multi-output problems

🔵Implement foundational algorithms from scratch, including linear and logistic regression, decision trees, gradient boosting, and SVMs

🟢Gain hands-on experience with industry-standard tools such as Scikit-Learn, XGBoost, and NLTK

🟡Refine and optimize your models using techniques such as hyperparameter tuning, cross-validation, and calibration

🟠Work with diverse data types, including tabular data, text, and images

🔴Address real-world challenges such as imbalanced datasets, missing data, and high-dimensional inputs

[Vikas Singh @vikas_ai_]

20 Completely FREE Courses for 2026:

1. Machine Learning https://t.co/tNAhs32jyj

2. LLM https://t.co/18tihhCSt5

3. Generative AI https://t.co/A16CfY7s9l

4. Python https://t.co/EAQ4nU4ODB

5. JavaScript https://t.co/adoX5nyTGc

6. SQL https://t.co/zO3WSlaDVF

7. Java https://t.co/MQy1jGnmc3

8. React 19 https://t.co/5MVWSIOfF8

9. React Native https://t.co/Y22Xcz48lj

10. Django https://t.co/TtQyT1xNTP

11. C++ https://t.co/3KBNdRfdeg

12. Rust https://t.co/rLRLSezul7

13. TypeScript https://t.co/JpNtxHMGeU

14. Golang https://t.co/TgnDpeaRWk

15. C# https://t.co/5lVsgcxTMX

16. Kotlin https://t.co/dE6r0Kjs0H

17. Swift https://t.co/9GipSdsMsL

18. PHP https://t.co/mtznCgU4Xu

19. C https://t.co/dHqQOvcRmO

20. DSA https://t.co/4lGLIEXZkW

👇Comment "Course" for more resources.

Repost so others can benefit.

Bookmark for future.

[Sebastian Raschka @rasbt]

https://t.co/7JkJxiUPv6

[Google en español @googleespanol]

💃¿Listo para tu próximo trend de baile en redes sociales?

Entra a Gemini, activa el modo Live, muéstrale tus pasos y dile:

“¿Cómo puedo mejorar esta secuencia de pasos que estoy haciendo?”

Gemini te sugerirá variaciones y mejoras en tiempo real.

[Edelson Amoretti @EdelsonAmoretti]

@arceyul IA

[Anil Ananthaswamy @anilananth]

I dedicated my book WHY MACHINES LEARN to "Teachers everywhere, sung and unsung." That dedication was in part to say thanks to teachers like @Cornell Prof. Kilian Weinberger, whose 39 hour-long lectures on ML, delivered to his class in 2018 (https://t.co/9Ypccb1DpB) were instrumental in my coming to grips with machine learning. Earlier this year I met Kilian in person @SimonsInstitute for the first time and had the joy of giving him a copy of WML.

[Edelson Amoretti @EdelsonAmoretti]

🤣

[Sebastian Raschka @rasbt]

Multi-Head Latent Attention
🔗 https://t.co/6az2lGjFqz

[Edelson Amoretti @EdelsonAmoretti]

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[Krishna Agrawal @Krishnasagrawal]

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[Edelson Amoretti @EdelsonAmoretti]

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[Aman Sahota @AmanSahota1234]

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