eneros

coder, learner, fun lover. be curious, push boundaries. Comfort Zone: A Beautiful Place, But Nothing Ever Grows There.

[eneros @enerosweb]

https://t.co/JH0fzAqiEZ

[Ronin @DeRonin_]

https://t.co/J0ULhSHLLH

[eneros @enerosweb]

@seeedstudio Could it have mini camera?

[eneros @enerosweb]

@tamstar1234 Do you have example workflow file that does that?

[Goldie Ghamari | گلسا قمری 🇮🇷 @gghamari]

IRAN REVOLUTION LIVE WITH IRANIAN HOSTS - DAY 17 - PART 2 https://t.co/m7C8ehtYQ3

[Saheed 🚢🔥 @mrgroowth]

Most people think “learn more” means consuming more stuff.

The truth?

You can change your life faster than you think — if you watch just a handful of talks and act.

Here are 14 powerful TED Talks to shift your mindset in two weeks:

[Dhairya @dkare1009]

Stop wasting hours trying to learn AI. 📘📚

I have already done it for you.

With one list. Zero confusion. And no fluff

📹 Videos:

1. LLM Introduction: https://t.co/Qja4lkPWlY

2. LLMs from Scratch: https://t.co/DAtGeO5if3

3. Agentic AI Overview (Stanford): https://t.co/APcq2oulIY

4. Building and Evaluating Agents: https://t.co/UeCQBskKUS

5. Building Effective Agents: https://t.co/B2tpQHaVoz

6. Building Agents with MCP: https://t.co/CwVBIVUjd0

7. Building an Agent from Scratch: https://t.co/u2jhiZy6UV

8. Philo Agents: https://t.co/lFMIus5CpQ

🗂️ Repos

1. GenAI Agents: https://t.co/yoTno6RBAb

2. Microsoft's AI Agents for Beginners: https://t.co/EGGYhcMq7b

3. Prompt Engineering Guide: https://t.co/fSCoEaFtNf

4. Hands-On Large Language Models: https://t.co/TvpkfJN2sR

5. AI Agents for Beginners: https://t.co/EGGYhcMq7b

6. GenAI Agentshttps://lnkd.in/dEt72MEy

7. Made with ML: https://t.co/cCWWXKh2wW

8. Hands-On AI Engineering:https://t.co/fiLwjmXR8B

9. Awesome Generative AI Guide: https://t.co/MEhtfRlhiu

10. Designing Machine Learning Systems: https://t.co/l21VO4rRBK

11. Machine Learning for Beginners from Microsoft: https://t.co/d3EPcDJWmz

12. LLM Course: https://t.co/xXxETt90eS

🗺️ Guides

1. Google's Agent Whitepaper: https://t.co/rVDu4EyPB5

2. Google's Agent Companion: https://t.co/IWjvSpSE2q

3. Building Effective Agents by Anthropic: https://t.co/0wK5pe5DD6.

4. Claude Code Best Agentic Coding practices: https://t.co/fu7GHgvnAi

5. OpenAI's Practical Guide to Building Agents: https://t.co/sXpo72PxpI

📚Books:

1. Understanding Deep Learning: https://t.co/YRV9Kz78Gy

2. Building an LLM from Scratch: https://t.co/naslph9aCF

3. The LLM Engineering Handbook: https://t.co/BwmUJ6OgHe

4. AI Agents: The Definitive Guide - Nicole Koenigstein: https://t.co/ZIDeOOamnz

5. Building Applications with AI Agents - Michael Albada: https://t.co/409SxePxhA

6. AI Agents with MCP - Kyle Stratis: https://t.co/3k9lFG3ByM

7. AI Engineering: https://t.co/tHfgc3wNKQ

📜 Papers

1. ReAct: https://t.co/8yV9k9RjOK

2. Generative Agents: https://t.co/PpaAbCvWmj.

3. Toolformer: https://t.co/mSfjjT6urU

4. Chain-of-Thought Prompting: https://t.co/uGktDnFBOb.

🧑🏫 Courses:

1. HuggingFace's Agent Course: https://t.co/4MLjHKcWSI

2. MCP with Anthropic: https://t.co/EnUWTrvaK4

3. Building Vector Databases with Pinecone: https://t.co/AmQzrCVweX

4. Vector Databases from Embeddings to Apps: https://t.co/HZbr4UBlw2

5. Agent Memory: https://t.co/TxvrpeBMFj

Repost for your network ♻️

[eneros @enerosweb]

@AndrewBolis YT

[eneros @enerosweb]

@atlasfunded 26

[eneros @enerosweb]

@VividProwess Make X free again

[eneros @enerosweb]

@TaherZariwala Me

[eneros @enerosweb]

@NorthBelle4 @HilzFuld @piersmorgan Good reason for me to start following you 😊

[Tim Denning @Tim_Denning]

I'm 38.

When I was young I worshipped politics, went woke (broke) & believed in the myth of equality.

Then I discovered Thomas Sowell, and he changed my life forever.

12 lessons from America's most controversial & unknown philosopher:

[Santiago @svpino]

I recorded a new YouTube video to teach you how to evaluate a RAG application.

And we'll do it step by step. Starting from scratch.

8 out of 10 people I talk to are evaluating their LLM-powered systems manually. This is wild!

They try a few samples and deploy the system if the answers look good. It reminds me of people testing the UI of an application by just "looking at it" from time to time.

Please, don't do this.

In this video, I'll show you how you can build automated tests for a simple RAG system that answers questions from a website.

It's a 50-minute video. My goal is not to show you the code but to help you understand everything that's happening.

Here is the link to the video:

https://t.co/OAI6sTPWPQ

I'm using @langchain and @giskard_ai to implement the evaluation process. Giskard is an open-source library that will help you with the following:

1. Generate test cases automatically. Each test case consists of a question, a ground-truth answer, and a reference context.

2. It will run every test case and point out problematic topics and RAG components that need improvement.

3. It will show recommendations to improve the system.

It's a great library! Star their repository here: https://t.co/RebHVwqEsZ

Hope you enjoy the video!

[Luca @luca_cloud]

I spent the last 6 months looking at the Kubernetes setups of over 1100 engineering teams. I summarized my learnings in a 30 page report. Packed with 💣💣s, hope people enjoy it. Check it out 👇

[chansung @algo_diver]

wanna learn #MLOps in step-by-step manner? I built a repo to show how to construct ML pipeline for @huggingface vision transformer model with @TensorFlow and #TFX in 5 steps👇🏻👇🏻

+ optionally can be integrated with @GoogleCloudTech and @github Actions.

https://t.co/dpugGhRXWf

[eneros @enerosweb]

I'm excited to speak at KubeCon + CloudNativeCon Europe 2022 https://t.co/bFo9fcOUdT @sched

[eneros @enerosweb]

https://t.co/FuwQLOuM5H

[PyTorch @PyTorch]

This tutorial will examine ways in which you can run your PyTorch profiling sessions in Linux, get all the traces, and visualize results using your regular Windows machine.

Read more here: https://t.co/vwcivdtMZI