Bob

phd on machine learning, data mining

[Matthew Berman @MatthewBerman]

2m views in 4 hours for a financial services agent for building pitch decks is insane

[Bob @wangyangtao]

@Saboo_Shubham_ 👍👍👍

[Bob @wangyangtao]

@wanerfu @grok 整理好发给我

[Md Ismail Šojal 🕷️ @0x0SojalSec]

This Google Collab notebooks to implement every Machine Learning Algorithm from scratch🤯

- https://t.co/ngbtQvCQos

and this is free

[Min Choi @minchoi]

Ok Claude Code is insane.

People can't stop building and getting productivity superpowers.

10 wild examples. Bookmark this👇

[Katyayani Shukla @aibytekat]

Instead of watching a 2-hour movie, watch this masterclass on Claude Code

[nader dabit @dabit3]

https://t.co/PqGEcSahx8

[Hasan Toor @hasantoxr]

10 GitHub repositories that will teach you more practical AI engineering than most paid courses.

1. Hands-On Large Language Models

Complete code from the book, with notebooks covering LLM basics, training, and fine-tuning.
👉 https://t.co/vOaLqPDp99

2. AI Agents for Beginners (Microsoft)

A free, structured 11-lesson course to get started with AI agents the right way.
👉 https://t.co/2b2WktBvnr

3. GenAI Agents

Clear tutorials and implementations of generative AI agent techniques, from basic to advanced.
👉 https://t.co/GeoXexJQff

4. Made With ML

One of the best resources for building production-grade ML systems end to end.
👉 https://t.co/X1onmkUV3A

5. Prompt Engineering Guide

A massive collection of guides, papers, notebooks, and resources on prompt engineering.
👉 https://t.co/fxuI566Njy

6. Hands-On AI Engineering

Curated examples of AI-powered applications and agentic systems using LLMs.
👉 https://t.co/ft6ODna8iz

7. Awesome Generative AI Guide

A one-stop repo for GenAI research updates, notebooks, interview prep, and more.
👉 https://t.co/nnDwR5VSja

8. Designing Machine Learning Systems (Resources)

Summaries and references for one of the most important ML systems books out there.
👉 https://t.co/tqQKWZkN6h

9. Machine Learning for Beginners (Microsoft)

Beginner-friendly ML curriculum with practical examples and exercises.
👉 https://t.co/TJYVur1D5I

10. LLM Course

A hands-on, end-to-end course on building, evaluating, and deploying LLM applications.
👉 https://t.co/XnFRMBIAQ7

[Ben Tossell @bentossell]

https://t.co/Ref8GgkIR5

[Christopher Manning @chrmanning]

This paper looks interesting too.

Maybe 2026 will be the Year of the Improved Residual Connection.

[Charly Wargnier @DataChaz]

MIT and Oxford released their $2,500 agentic AI curriculum on GitHub at no cost.

15,000 people already paid for it.

Now it's on GitHub!

It covers patterns, orchestration, memory, coordination, and deployment.
A strong roadmap to production ready systems.

Repo in 🧵 ↓

[Akshay 🚀 @akshay_pachaar]

In case you missed it.

Yesterday, I published all my work on AI engineering in the form of a FREE 380+ pages pdf.

It covers everything to set you up for a career in AI engineering.

Check this out 👇

[Vaishnavi @_vmlops]

Machine Learning Interview Questions Repo
https://t.co/1XBuR9c2Im

[isadebk @IsAdeibukun]

@DBVolkov https://t.co/VyfdiQDsio

[Shubham Saboo @Saboo_Shubham_]

In case you missed it, the archive is live now for all 25-days of agents: https://t.co/9tV2BoO30T

Repost to share with your friends and follow @Saboo_Shubham_ for more such updates going into 2026.

[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 ♻️

[Priyanka Vergadia @pvergadia]

Which AI Agent framework should you choose? LangGraph, CrewAI, AutoGen, or MetaGPT?

I created this "AI Agent Frameworks Cheatsheet" to help you decide based on your specific use case.

Here is how I see the ecosystem right now:
1️⃣ LangGraph (For the Control & Precision)
If you need a stateful, multi-agent system where you have absolute control over the flow, this is your go-to. It treats workflows as cyclic graphs.
Why I love it: It solves the "looping" problem in agentic workflows by giving you granular control over state and human-in-the-loop interactions.
Best for: Complex enterprise systems with dynamic data sharing.

2️⃣ CrewAI (For Role-Based Collaboration)
CrewAI is brilliant because it mimics a human team. You define roles (Researcher, Writer, Analyst), and the framework handles the "management" aspect.
Why I love it: It’s incredibly intuitive for process-driven tasks. It excels at collaborative workflows where one agent’s output is another’s input.
Best for: Content pipelines, market research, and multi-step business logic.

3️⃣ Microsoft Agent Framework (For Conversational Reasoning)
AutoGen (part of the Microsoft ecosystem) is the pioneer of agent-to-agent conversation. It’s highly flexible and allows agents to "talk" through problems.
Why I love it: It’s great for iterative tasks. One agent can write code, another can execute/test it, and they can keep talking until the bug is fixed.
Best for: Interactive assistants and collaborative problem-solving.

4️⃣ MetaGPT (For Software Dev Automation)
MetaGPT takes a unique approach by incorporating Standard Operating Procedures (SOPs). It’s essentially a "Startup-in-a-box."
Why I love it: It doesn't just write code; it generates the Product Requirement Document (PRD), design docs, and the full repository structure.
Best for: Product builders looking for end-to-end software automation.

The Quick Summary:
🛠 LangGraph = Control & State
👥 CrewAI = Processes & Roles
💬 Microsoft/AutoGen = Reasoning & Dialogue
🚀 MetaGPT = Software Lifecycle

I’d love to know: Which of these are you currently building with? Are there any other frameworks I should include in my next update?**👇

#AIAgents #GenerativeAI #LangGraph #CrewAI #AutoGen #MetaGPT

Follow me for more visual guides on the AI and Cloud ecosystem! ☁️✨

Find more resources at https://t.co/SnBk6yixHO

[Tech with Mak @techNmak]

The full journey of modern AI engineering, from start to finish.