Techtter

𝗛𝗨𝗚𝗘 - 𝗚𝗼𝗼𝗴𝗹𝗲 𝗷𝘂𝘀𝘁 𝗱𝗿𝗼𝗽𝗽𝗲𝗱 𝗳𝗿𝗲𝗲 𝗔𝗜 𝗰𝗼𝘂𝗿𝘀𝗲𝘀 𝗳𝗼𝗿 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿𝘀

No prerequisites or fees required.

➡️ 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝘁𝗼 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 𝗔𝗜

This is an introductory-level micro learning course on how Google implements responsible AI in their products.

lnkd.in/dv77sFqB

➡️ 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝘁𝗼 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗦𝘁𝘂𝗱𝗶𝗼

In this course, you learn what Generative AI Studio is, its features and options, and how to use it by walking through demos of the product.

lnkd.in/dSgvrt8U

➡️ 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝘁𝗼 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜

Learn about Generative AI in a simple, short course.

Discover how to make your own AI apps with Google Tools.

lnkd.in/d98BcDgr

➡️ 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝘁𝗼 𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀

Join this short course to understand big language models (LLMs).

lnkd.in/dhQpVsrj

➡️ 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝘁𝗼 𝗜𝗺𝗮𝗴𝗲 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻

This course introduces diffusion models, a family of machine learning models that recently showed promise in the image generation space.

lnkd.in/dUsHbXA6

➡️ 𝗘𝗻𝗰𝗼𝗱𝗲𝗿-𝗗𝗲𝗰𝗼𝗱𝗲𝗿 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲

Learn about the Encoder-Decoder Architecture for tasks like machine translation and summarization

lnkd.in/d6e7a5_Q

➡️ 𝗔𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻 𝗠𝗲𝗰𝗵𝗮𝗻𝗶𝘀𝗺

This course will introduce you to the attention mechanism, that allows neural networks to focus on specific parts of an input sequence.

lnkd.in/dBwXJtCg

➡️ 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿 𝗠𝗼𝗱𝗲𝗹𝘀 𝗮𝗻𝗱 𝗕𝗘𝗥𝗧 𝗠𝗼𝗱𝗲𝗹

When you complete this course, you can earn the badge displayed here!

lnkd.in/dEY2n6Tw

➡️ 𝗖𝗿𝗲𝗮𝘁𝗲 𝗜𝗺𝗮𝗴𝗲 𝗖𝗮𝗽𝘁𝗶𝗼𝗻𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹𝘀

This course teaches you how to create an image captioning model by using deep learning.

lnkd.in/dh_-4N4r

Follow me for more free resources on AI and AI Agents 👋

1 day ago | [YT] | 3

Techtter

If I had to start learning Agentic AI in 2025,

This is exactly where I’d begin.

Agentic AI is where the most value lies with LLM-based applications.

If you want to build serious, reliable agents that don’t hallucinate their way through a workflow…

You need to start with the right foundation.

Here’s a list of resources that do exactly that

Understand the Fundamentals

1. Google Agents Whitepaper
   lnkd.in/ghcxpVXd
   
2. Google Companion Paper – Advanced Concepts
   lnkd.in/ghcxpVXd
   
3. ReAct: Reasoning + Acting with LLMs
   lnkd.in/gbXxR4Dp
   
4. Agent as a Judge (for evals)
   lnkd.in/gmiUe-nv
   
Learn by Doing –
These Short Courses by Deeplearning . AI Are Fire

AI Agentic Design Patterns with AutoGen
lnkd.in/gE_dMbYa

Multi-AI Agent Systems with crewAI
lnkd.in/gsfXeXH3

AI Agents in LangGraph
lnkd.in/gmgtwpjf

Building Agentic RAG with LlamaIndex
lnkd.in/g9e-kHxF

Github repo with solid implementations + examples

GenAI_Agents by Nir Diamant
lnkd.in/g5HHZrJK

#genai #agenticai #ai -#techtter

1 week ago | [YT] | 5

Techtter

🚀 𝟭𝟱 𝗠𝘂𝘀𝘁-𝗞𝗻𝗼𝘄 𝗚𝗶𝘁𝗛𝘂𝗯 𝗥𝗲𝗽𝗼𝘀𝗶𝘁𝗼𝗿𝗶𝗲𝘀 𝘁𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴

Looking to level up your data engineering skills? Whether you're just starting out or already an expert, these handpicked repositories are packed with powerful tools, frameworks, and best practices to help you build, manage, and optimize data pipelines like a pro

Dive in and take your data engineering game to the next level!

1. data-engineer-handbook : Zach Wilson DataExpert.io
lnkd.in/gvJr8QBW

2. data-engineering-zoomcamp  : Alexey Grigorev DataTalksClub
lnkd.in/gSZxYdKA

3. The Data Engineering Cookbook - Andreas Kretz Learn Data Engineering
lnkd.in/guktWNiA

4. awesome-data-engineering : Igor Barinov
lnkd.in/gTiF-ksK

5. Data Engineer Roadmap : datastack.tv
 lnkd.in/gsQBRivw
 
6. Data Engineering HowTo : Adylzhan Khashtamov
lnkd.in/g8tDmiqE

7. Data Engineering Practice : Daniel Beach
lnkd.in/gsFVqxqP

8. Data Engineering Wiki : Data Engineering
lnkd.in/gVUUfu2Y

9. Pyspark Example Project : Alex Ioannides, PhD
lnkd.in/g-T_ZqXu

10. Awesome Open Source Data Engineering : Gunnar Morling
lnkd.in/gXgWTQ5C

11. Data-Engineer-Nanodegree-Projects-Udacity : Amir KROUDIR
lnkd.in/g9Z8eQ7Y

12. google-data-engineering-coursera : Steven Black
lnkd.in/gF9mMDJU

13. datacamp-Data-Engineer-with-Python-course : Franco Ortiz
lnkd.in/gRb5_5Ep

14. data-engineer-portfolio : Anh Tuan Hoang
lnkd.in/gm3VDFVB

15. Data-Engineering-Project-with-HDFS-and-Kafka : Ahmet Furkan DEMIR
lnkd.in/gkw4wYSC

#dataengineering #aiagents #techtter #learndataengineering

1 week ago | [YT] | 14

Techtter

🚀 [𝗙𝘂𝗹𝗹 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀] 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝘄𝗶𝘁𝗵 𝗣𝘆𝘁𝗵𝗼𝗻 + 𝗔𝗴𝗻𝗼 𝗟𝗶𝗯𝗿𝗮𝗿𝘆 + 𝗩𝗲𝗰𝘁𝗼𝗿 𝗗𝗕 + 𝗗𝗲𝗲𝗽𝗦𝗲𝗲𝗸 / 𝗚𝗲𝗺𝗶𝗻𝗶 𝗙𝗹𝗮𝘀𝗵 𝗟𝗟𝗠𝘀!

In the last few weeks, I've built a series of practical AI Agent projects — designed for real-world automation, not just theory.

Here's what we covered:

✅ 𝗔𝗜 𝗧𝗿𝗮𝘃𝗲𝗹 𝗣𝗹𝗮𝗻𝗻𝗲𝗿 𝗔𝗴𝗲𝗻𝘁 — Finds flights, hotels, and plans your day-to-day itinerary [lnkd.in/d3_9SWTk]
✅ 𝗠𝗲𝗱𝗶𝗰𝗮𝗹 𝗜𝗺𝗮𝗴𝗶𝗻𝗴 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 — Scans MRI, CT, X-Ray with precision for insights [lnkd.in/dTUPXWry]
✅ 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗟𝗮𝘄 𝗙𝗶𝗿𝗺 𝗔𝗜 — Legal research, contract risk detection, and law queries handled by agents [lnkd.in/dMFmWhj4]
✅ 𝗔𝗜 𝗙𝗶𝘁𝗻𝗲𝘀𝘀 𝗖𝗼𝗮𝗰𝗵 𝗔𝗴𝗲𝗻𝘁 — Personalised diet and workout plans based on your goals [lnkd.in/dTwNuetR]
✅ 𝗦𝘁𝗼𝗰𝗸 𝗠𝗮𝗿𝗸𝗲𝘁 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 — Fundamental analysis, news scanning, and smart stock rankings [lnkd.in/dHUXcMVq]
✅ 𝗗𝗲𝗲𝗽𝗦𝗲𝗲𝗸 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 — Local reasoning engine with DeepSeek LLM and vector search [lnkd.in/d7BXR4gQ]

All projects are built using Python, Agno Agentic Library, and advanced LLMs for reasoning and task automation.

🎯 Prerequisites for these projects:

1. 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀: [lnkd.in/dKRsRP_t]
2. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗗𝗲𝘀𝗶𝗴𝗻 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀: [lnkd.in/dKRsRP_t]

Check out the full playlist and build your own AI agent solutions!

💡 These projects are step-by-step, fully code-backed, and ready to implement. Perfect if you’re aiming to automate workflows in law, healthcare, finance, fitness, or travel.

#AI #GenAI #Python #AgenticAI #LLM #DeepSeek #GeminiFlash #Automation #MachineLearning #VectorDB #Agno #AIProjects

2 weeks ago | [YT] | 6

Techtter

If you want to become good at Data Engineering, then learn these use cases:


1. 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐃𝐚𝐭𝐚 𝐋𝐚𝐤𝐞 𝐯𝐬. 𝐃𝐚𝐭𝐚 𝐖𝐚𝐫𝐞𝐡𝐨𝐮𝐬𝐞
🔗lnkd.in/gEpmTyMS

2. 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐌𝐞𝐭𝐚𝐝𝐚𝐭𝐚-𝐃𝐫𝐢𝐯𝐞𝐧 𝐄𝐓𝐋 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐬
🔗 lnkd.in/g2W5t3iq

3. 𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐢𝐧𝐠 𝐌𝐞𝐝𝐚𝐥𝐥𝐢𝐨𝐧 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 𝐢𝐧 𝐃𝐚𝐭𝐚𝐛𝐫𝐢𝐜𝐤𝐬
🔗lnkd.in/gXvicgyt

4. 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐒𝐜𝐚𝐥𝐚𝐛𝐥𝐞 𝐃𝐚𝐭𝐚 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐬 𝐰𝐢𝐭𝐡 𝐀𝐢𝐫𝐟𝐥𝐨𝐰
🔗lnkd.in/gynhyDTP

5. 𝐃𝐞𝐥𝐭𝐚 𝐋𝐚𝐤𝐞: 𝐀𝐂𝐈𝐃 𝐓𝐫𝐚𝐧𝐬𝐚𝐜𝐭𝐢𝐨𝐧𝐬 𝐢𝐧 𝐁𝐢𝐠 𝐃𝐚𝐭𝐚
🔗 lnkd.in/gRk4wmXR

6. 𝐑𝐞𝐚𝐥-𝐓𝐢𝐦𝐞 𝐒𝐭𝐫𝐞𝐚𝐦𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐏𝐲𝐒𝐩𝐚𝐫𝐤
🔗lnkd.in/gHj2pF2V

7. 𝐃𝐚𝐭𝐚 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 𝐂𝐡𝐞𝐜𝐤𝐬 𝐢𝐧 𝐄𝐓𝐋 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐬
🔗lnkd.in/g7jnf-Zk

8. 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐓𝐮𝐧𝐢𝐧𝐠 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐞𝐬 𝐢𝐧 𝐒𝐩𝐚𝐫𝐤
🔗lnkd.in/gn2rFNXC

9. 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐝𝐚𝐭𝐚 𝐩𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 𝐢𝐧 𝐒𝐐𝐋
🔗 lnkd.in/gjrQSEWM

10. 𝐁𝐞𝐬𝐭 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞𝐬 𝐟𝐨𝐫 𝐀𝐳𝐮𝐫𝐞 𝐃𝐚𝐭𝐚 𝐅𝐚𝐜𝐭𝐨𝐫𝐲
🔗 lnkd.in/gragXs6E

#azure #dataengineering #deltalake #datalake #databricks

2 weeks ago | [YT] | 12

Techtter

No matter your background or experience, learning AI is the key to staying relevant in today’s rapidly changing world. And the best time to get in or upskill is now. AI is shaping every industry and AI enhances careers, no matter your role — from marketing to engineering.

𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝘀𝗼𝗺𝗲 𝗿𝗲𝗰𝗲𝗻𝘁 𝗰𝗼𝘂𝗿𝘀𝗲𝘀 𝗜 𝗳𝗼𝘂𝗻𝗱 𝗽𝗮𝗿𝘁𝗶𝗰𝘂𝗹𝗮𝗿 𝘂𝘀𝗲𝗳𝘂𝗹 (𝘆𝗼𝘂 𝗰𝗮𝗻 𝗳𝗶𝗻𝗱 𝗺𝘂𝗰𝗵 𝗺𝗼𝗿𝗲 𝗶𝗻 𝘁𝗵𝗲 𝗹𝗶𝗻𝗸 𝗯𝗲𝗹𝗼𝘄):

1️⃣ 𝗜𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗟𝗮𝗯: 𝗗𝗲𝗺𝗼𝗰𝗿𝗮𝘁𝗶𝘇𝗶𝗻𝗴 𝗔𝗜 𝗠𝗼𝗱𝗲𝗹𝘀 𝗮𝘁 𝗦𝗰𝗮𝗹𝗲
🔗 lnkd.in/dfNqFNkc
→ Learn how to enhance large language models collaboratively and efficiently.

2️⃣ 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝘄𝗶𝘁𝗵 𝗥𝗔𝗚 𝗮𝗻𝗱 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻
🔗 lnkd.in/dEA_GUxh
→ Build cutting-edge AI applications in real-world scenarios.

3️⃣. 𝗜𝗕𝗠 𝘄𝗮𝘁𝘀𝗼𝗻𝘅.𝗮𝗶 𝘄𝗶𝘁𝗵 𝗥𝗔𝗚 𝗮𝗻𝗱 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻
🔗 lnkd.in/dtr6PZRj
→ Make use of our WatsonX platform.

4️⃣. 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲 𝘁𝗵𝗲 𝗕𝗼𝗿𝗶𝗻𝗴 𝗦𝘁𝘂𝗳𝗳 𝗪𝗶𝘁𝗵 𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀
🔗 lnkd.in/df4javRx
→ Increase efficiency by automating boring, redundant tasks.

5️⃣. 𝗦𝗼𝗹𝘃𝗲 𝗥𝗲𝗮𝗹 𝗣𝗿𝗼𝗯𝗹𝗲𝗺𝘀 𝘄𝗶𝘁𝗵 𝗟𝗟𝗠𝘀: 𝗖𝗿𝗲𝗮𝘁𝗲 𝗮 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗦𝘂𝗽𝗽𝗼𝗿𝘁 𝗔𝗽𝗽
🔗 lnkd.in/dDxFcciv
→ Learn by building practical AI solutions.

6️⃣. 𝗜𝗕𝗠 𝘄𝗮𝘁𝘀𝗼𝗻𝘅 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗲 𝗕𝘂𝗶𝗹𝗱 𝗮𝗻 𝗔𝗜 𝗔𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁
🔗 lnkd.in/dTGMbRMZ
→ Build and launch super fast an intelligent virtual assistant with watsonx Orchestrate Assistant builder.

𝗬𝗼𝘂 𝗰𝗮𝗻 𝗮𝗰𝗰𝗲𝘀𝘀 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 𝗮𝗻𝗱 𝘁𝗵𝗲𝗿𝗲 𝗮𝗿𝗲 𝗻𝗼 𝗵𝗼𝗼𝗸𝘀. 𝗜𝗳 𝘆𝗼𝘂 𝗿𝘂𝗻 𝗶𝗻𝘁𝗼 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀 𝘀𝗲𝗻𝗱 𝗺𝗲 𝗮 𝗗𝗠 𝗼𝗿 𝘄𝗿𝗶𝘁𝗲 𝗮 𝗰𝗼𝗺𝗺𝗲𝗻𝘁.

2 weeks ago | [YT] | 8

Techtter

AI agents are the future.

Here’s how you can learn them without spending a cent.

DeepLearning AI has done a great job in making these courses -

1. Multi AI Agent Systems with crewAI
- lnkd.in/dTudrD55

2. Practical Multi AI Agents and Advanced Use Cases with crewAI
- lnkd.in/dQmTTWmK 
 
3. Serverless Agentic Workflows with Amazon Bedrock
- lnkd.in/dENcD795 
 
4. AI Agents in LangGraph
- lnkd.in/dJbGHaV2 
 
5. AI Agentic Design Patterns with AutoGen
- lnkd.in/dzDAA-J4 

#ai #aiagents #aitools #aiengineering #techtter

3 weeks ago | [YT] | 7

Techtter

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3 weeks ago | [YT] | 2

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⏳ 𝗧𝗵𝗲 𝗔𝗜 𝗧𝗮𝗹𝗲𝗻𝘁 𝗥𝗮𝗰𝗲 𝗜𝘀 𝗢𝗻—𝗗𝗼𝗻’𝘁 𝗚𝗲𝘁 𝗟𝗲𝗳𝘁 𝗕𝗲𝗵𝗶𝗻𝗱!
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67% of Mature AI Organizations Are Creating New AI Roles.

87% already have dedicated AI teams, according to a Gartner survey.

A lack of skills and staff is holding many back. Here are a few current & emerging AI roles:

🔹 Chief AI Officer: Leads AI strategy and oversight.

🔹 Head of AI: Directs AI initiatives and teams.

🔹 AI Architect: Designs scalable AI system architecture.

🔹 AI Product Manager: Manages the AI product development lifecycle.

🔹 AI Risk & Governance Specialist: Ensures AI compliance and risk management.

🔹 AI Ethicist: Addresses ethical implications of AI.

🔹 Model Manager: Oversees the AI model lifecycle.

🔹 Model Validator: Verifies AI model accuracy and reliability.

🔹 AI Developer: Builds and implements Gen AI & Agentic systems.

🔹 Model/ML Engineer: Develops and deploys machine learning models.

🔹 AI Application Developer: Integrates AI into business applications.

🔹 AI App Dev Platform Architect: Designs AI development platform infrastructure.

🔹 AI Cybersecurity Analyst: Mitigates AI-specific cybersecurity risks.

🔹 AI Cybersecurity Researcher: Researches AI system vulnerabilities.

🔹 AI Redteam Engineer: Tests AI systems for weaknesses.

🔹 Prompt Engineer: Optimizes prompts for AI models.

🔹 Knowledge Engineer: Translate expertise into a knowledge-based system

🔹 Decision Engineer: Designs AI-driven decision systems.

🔹 AI Process Automation Engineer: Automates processes using AI.

🔹 Data Scientist: Analyzes data for AI insights.

🔹 Data Engineer: Manages data pipelines for AI.

🔹 Analytics Engineer: Builds AI-driven analytics solutions.

🔹 D&A and AI UX Designer: Designs user experiences for AI apps.

This quick list is based on the Gartner report and my general observations.

Future proof your career. Check out Bot Nirvana AI Mastermind: lnkd.in/gFZW3gyt

What are the new roles you are seeing emerge?

#ai #aiagent #careertips #careerguidence #techtter

3 weeks ago | [YT] | 1

Techtter

MCP is the hottest thing under the sun right now.

And you’re still confused about MCP?
Credits: Manthan Patel on LinkedIn

The name sounds techie, but the concept is actually pretty simple to understand.

Think of it like this:

Old Way: Tool Calling

You give your LLM (ie. Claude) a description of when to trigger a 'tool' and what information it needs to collect ie. "Search Query: Trigger this when a user wants to search for information. You need to collect the search terms and filters"

When the LLM detects that someone is searching for information, they'll ask for the details and then send you a structured output with the information so that you can make an API call with it.

It works, but it's a lot of manual setup on your end.

New Way: MCP

Instead of you needing to create this tool call and then make the API call, its already built out for you. ie. Instead of you writing the search query tool call then making the call yourself to the API. Google Search does this for you. They have a bunch of 'tool calls' that they have made that your LLM can access and the code for the API calls already written.

So you can give your LLM access to Google Search's list of tools (MCP Server) and it can determine which one to use. The API call happens automatically (in the MCP server) and returns back the information. While Google Search doesn't have an MCP Server (yet) - I'll dive into one from Github that Anthropic made and explain how it works!

It's like the difference between building your own delivery network vs using DoorDash. Why build it yourself when the experts can handle it?

The real magic of MCP is simplicity. Services like Whatsapp, Zapier, and Notion, just to name a few, could offer their functionality without you needing to mess with API keys or complex integration code.

No more middleware. No more security config. Just direct connections between AI and the services people actually use.

MCP is making AI integrations as simple as clicking "connect."

#mcp #ai #aiagents #aitools #techtter

3 weeks ago | [YT] | 3