Willam Ramarques

#CTO @Editialis passionné par : #SEO, #WebAnalytics, #ML, #R, #Pyhton, #DataScience, dév. Web, architecture de l'information.

[Willam Ramarques @willman20]

@dashboardlim FREE

[Willam Ramarques @willman20]

@nahidulislam404 Need

[NIJ Ruvos @nahidulislam404]

🚨 119𝐆𝐁+ 𝐆𝐎𝐎𝐆𝐋𝐄 𝐃𝐑𝐈𝐕𝐄 — 𝐀𝐋𝐋 𝐏𝐀𝐈𝐃 𝐂𝐎𝐔𝐑𝐒𝐄𝐒 🚨

𝐌𝐢𝐬𝐬𝐞𝐝 𝐢𝐭 𝐥𝐚𝐬𝐭 𝐭𝐢𝐦𝐞? 𝐈’𝐦 𝐝𝐫𝐨𝐩𝐩𝐢𝐧𝐠 𝐢𝐭 𝐚𝐠𝐚𝐢𝐧. 👌

This vault helped agencies close $9K+ clients using proven systems.

Inside the Drive:
📁 AI & Automation
📁 Ethical Hacking
📁 Cybersecurity
📁 Prompt Engineering
📁 Google Cloud
📁 Machine Learning
📁 DevOps + CI/CD
📁 Docker & Kubernetes
📁 Blockchain
📁 Power BI
📁 React + Node
📁 Cloud Security
📁 Linux
📁 Pen Testing
📁 Data Analytics
📁 Data Science
📁 Big Data
📁 SQL
📁 Tableau
📁 Python
📁 AWS
📁 Java

Everything organized. Everything premium. 119GB+ value.

Get it:
✔ Follow me [MusT]
✔ Like & RT+bookmark
✔ Comment “ NEED ” To Get Auto DM.

⚠️ No follow = No Access ⚠️

[How To AI @HowToAI_]

Yann LeCun was right the entire time. And generative AI might be a dead end.

For the last three years, the entire industry has been obsessed with building bigger LLMs. Trillions of parameters. Billions in compute.

The theory was simple: if you make the model big enough, it will eventually understand how the world works.

Yann LeCun said that was stupid.

He argued that generative AI is fundamentally inefficient.

When an AI predicts the next word, or generates the next pixel, it wastes massive amounts of compute on surface-level details.

It memorizes patterns instead of learning the actual physics of reality.

He proposed a different path: JEPA (Joint-Embedding Predictive Architecture).

Instead of forcing the AI to paint the world pixel by pixel, JEPA forces it to predict abstract concepts. It predicts what happens next in a compressed "thought space."

But for years, JEPA had a fatal flaw.

It suffered from "representation collapse."

Because the AI was allowed to simplify reality, it would cheat. It would simplify everything so much that a dog, a car, and a human all looked identical.

It learned nothing.

To fix it, engineers had to use insanely complex hacks, frozen encoders, and massive compute overheads.

Until today.

Researchers just dropped a paper called "LeWorldModel" (LeWM).

They completely solved the collapse problem.

They replaced the complex engineering hacks with a single, elegant mathematical regularizer.

It forces the AI's internal "thoughts" into a perfect Gaussian distribution.

The AI can no longer cheat. It is forced to understand the physical structure of reality to make its predictions.

The results completely rewrite the economics of AI.

LeWM didn't need a massive, centralized supercomputer.

It has just 15 million parameters.

It trains on a single, standard GPU in a few hours.

Yet it plans 48x faster than massive foundation world models. It intrinsically understands physics. It instantly detects impossible events.

We spent billions trying to force massive server farms to memorize the internet.

Now, a tiny model running locally on a single graphics card is actually learning how the real world works.

[Willam Ramarques @willman20]

@AlfieJCarter Code

[𝗿𝗮𝗺𝗮𝗸𝗿𝘂𝘀𝗵𝗻𝗮— 𝗲/𝗮𝗰𝗰 @techwith_ram]

Check out their blogs if you are into AI/ML.

1) Andrej Karpathy Neural networks & LLMs explained from first principles by one of the OGs of modern AI.
- https://t.co/rOFoQmoGDG

2) Sebastian Raschka, PhD Deep dives into LLM training and fine-tuning with super clear code examples.
- https://t.co/gxEb2jjs2S

3) Interconnects by Nathan Lambert AI alignment, open-source models, and ecosystem news. - https://t.co/clHxVsI9WS

4) Lil’Log by Lilian Weng Lessons from someone who worked on practical AI safety and alignment at OpenAI. - https://t.co/GtEOx18l5p

5) Chip Huyen Real-world MLOps and production ML systems design patterns.
- https://t.co/H11wqNsTlb

6) Eugene Yan Great writing on applied ML, data science, and working with recommender systems in production.
- https://t.co/UoGNYlHTFz

7) Philipp Schmid Tutorials on building and deploying LLM apps on AWS.
- https://t.co/9QGh9Yktsb

8) Jason Liu Learn from a consultant sharing real lessons on LLMs, data, and open-source tools.
- https://t.co/L8V830tQ1b

9) Hamel H. MLOps workflows, fine-tuning, and product strategy from an ML veteran.
- https://t.co/Rx5BvPT1FO

10) Berkeley Artificial Intelligence Research Blog Latest academic breakthroughs in computer vision, NLP, and robotics
- https://t.co/xLcvR2EmtR

11) Hugging Face Product updates, tutorials, and the latest from open-source AI.
- https://t.co/9oeyeKSUlv

12) Google DeepMind Google's premier AI research division.
- https://t.co/prmdJpRlcW

[Willam Ramarques @willman20]

@Ai_Vaidehi ML

[Willam Ramarques @willman20]

@BharukaShraddha Send

[Willam Ramarques @willman20]

@BharukaShraddha ML

[Willam Ramarques @willman20]

@Krishnasagrawal SQL

[Krishna Agrawal @Krishnasagrawal]

300 Real SQL Interview Questions with Solutions

Asked at PwC, Deloitte, EY, KPMG, Tredence, Persistent Systems and Accenture & More.

To get it FREE 👇

Like & Repost
Comment “SQL”
Follow (must) so I can DM you the PDF

[Willam Ramarques @willman20]

@Ai_here202 Resume

[HereIsYourAi @Ai_here202]

This Resume has an ATS score of more than 92🤯

This Resume helped many in getting an interview calls from companies like Google, Microsoft, Amazon, and many more.

I have personally used this single-column resume in my job hunting and got amazing results

I am sharing the exact similar editable ATS Friendly Resumes templates!

To get it:
1. Follow me
(So that I can DM)
2. Like & Repost
3. Reply "Resume"

Follow me so I will dm immediately 💯

[Willam Ramarques @willman20]

@BetterCallMedhi bonjour @BetterCallMedhi je te suis depuis peu et je suis impressionné par la qualité et profondeur de tes analyses. Merci de partager cela.

[Willam Ramarques @willman20]

@godofprompt Mega

[Willam Ramarques @willman20]

@francoisL Bonjour François, très intéressé pour tester cet agent.

[LlamaIndex 🦙 @llama_index]

We’re excited to feature NewsGPT (by timho102003) 📰🧠 - a production-grade news aggregator augmented with LLM capabilities.

✅ Daily pipeline of reliable news sources
✅ Tailored News Recommendations
✅ For any given article, chat with related articles

Best of all, it’s fully open-source. It’s an awesome reference application for anyone looking to build production-grade RAG combined with recommendation systems 🔎

There’s some awesome architecture details ⚙️:
1️⃣ Data pipeline: Spark batch processing for NER/embeddings
2️⃣ Personalization: @Firebase for auth, AWS lambda for recommendations, @qdrant_engine as vector db
3️⃣ Application: @llama_index for RAG capabilities, @streamlit for personalization

Full blog here: https://t.co/9cj4D1jGF8

Open-source repo: https://t.co/9mtsqalU4u

This has since turned into a production app (Neotice), check it out here! https://t.co/vlRXcKTBmn

Full credits: Tim Ho (timho102003) as the author of this hackathon-project-turned-full-stack-app! Congrats 🙌

[Willam Ramarques @willman20]

@alekseo Comme promis, analyse très intéressante merci !👍

[Willam Ramarques @willman20]

@laura_NTE https://t.co/jhQ9lWXADh

[Willam Ramarques @willman20]

@laura_NTE Voici la réponse :la date du Snapshot du model 😉