Booti 🌱

💪🏻

[Booti 🌱 @BootiWale_Baba]

@stepanhlinka Avoid procrastination👍

[Shyam Thakur @ShyamThakur1991]

हर नई सीख आपके भविष्य की नींव मजबूत करती है। डॉ. ए.पी.जे. अब्दुल कलाम का संदेश हमें निरंतर विकास और आत्म-सुधार की राह दिखाता है

#20xleapfrogsmeipo #InvestorsTrust Village Of Excellence #RILAGM

[Booti 🌱 @BootiWale_Baba]

@narendramodi Direct bol rhe Elections ke liye ye sab dialogue movie me saalo se sunte aa rahe

[Booti 🌱 @BootiWale_Baba]

Ek din itne paise 💵 jarur rahenge bank account 🏦 me... personal dr. jarur hoga 😎

[Radha Tripathi @Radha_AI]

Top YouTube Channels to Master Tech Skills.

1. SQL
https://t.co/qVtXayz1L9

2. Excel
https://t.co/bBAC52fOhx

3. Statistics
https://t.co/7qxClOK4en

4. Math
https://t.co/6mUr14qSbp

5. Python
https://t.co/ZFrppqSHJw

6. Data Analysis
https://t.co/jPpkffbkfa

7. Machine Learning
https://t.co/c1UQYev73f

8. Deep Learning
https://t.co/7updsrrWmd

9. Java
https://t.co/svQIYiTmPS

10. Big Data
https://t.co/WKThnZLq3g

11. Data Engineering
https://t.co/G2OI3PVlk7

12. NLP (Natural Language Processing)
https://t.co/DyuBFr2GWV

13. Computer Vision & AI
https://t.co/GOAVKeem3d

14. Generative AI
https://t.co/uJmsEM0Bkx

15. University-Level Courses
https://t.co/mAHsBachnu
https://t.co/aTIFpCd5Yr

16. All-in-One Learning
https://t.co/FUQrGFAW4A

Like
Repost
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Follow @Radha_AI for more of these free courses. productivity content.

I'll send you in the DM.

[Shruti Codes @Shruti_0810]

1. https://t.co/8C3trD4N0x (solve any problem)

2. https://t.co/FtbPgNQNjt (remove backgrounds)

3. https://t.co/BB9wU7yyUe (research anything)

4. https://t.co/NcY66HNsUB (compose music)

5. https://t.co/OKmkhuiebT (design graphics)

6. https://t.co/zM4XxPspUW (clone voices)

7. https://t.co/lymiU76deu (perfect note)

8. https://t.co/umtlDoJe8N (summarize YouTube)

9. https://t.co/h2jpid6mdA (edit videos)

10. https://t.co/7KW7GT1xOA (edit podcasts)

11. https://t.co/4vBc3FQvsm (create ai avatar videos)

Save + Share

[Your Sophia Ai @NextShopiaAi]

Professionals won’t tell you this 👀
They use these daily. 🪄⚡

1. Ideas 🧠

- YOU
- Claude
- ChatGPT
- Perplexity
- Bing Chat

2. Presentation

- Prezi
- Pitch
- PopAi
- Slides AI
- Slidebean

3. Website

- Dora
- Wegic
- 10Web
- Framer
- Durable

4. Writing

- Rytr
- Jasper
- Copy AI
- Textblaze
- Writesonic

5. AI Models

- RenderNet
- Glambase App
- Luma AI
- Sora (OpenAI)
- Leonardo AI

6. Meeting

- Tldv
- Krisp
- Otter
- Avoma
- Fireflies

7. Chatbots

- Poe
- Claude
- Gemini
- ChatGPT
- HuggingChat

7. Automation

- ClickUp
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- Outreach
- Emplifi
- Phrasee

8. UI/UX

- Uizard
- Visily
- Khroma
- Galileo AI
- VisualEyes

9. Image

- Stylar
- Freepik
- Phygital+
- StockIMG
- Bing Create

10. Video

- Pictory
- HeyGen
- Nullface
- Decohere
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11. Design

- Looka
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- Vance AI
- Designs AI

12. Marketing

- AdCopy
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13. Twitter

- Typefully
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AI updates you shouldn’t miss 👀
Follow @NextShopiaAi for more.👇🔰

[Brij Pandey @LearnWithBrij]

AI AGENT STACK — MASTER TREE 🌲

AI Agents
│
├── 01. Foundation Layer
│ ├── LLMs
│ │ ├── GPT-4.1
│ │ ├── Claude
│ │ ├── Gemini
│ │ └── DeepSeek
│ │
│ ├── Prompting
│ │ ├── System Prompts
│ │ ├── Few-shot
│ │ ├── Chain of Thought
│ │ └── Structured Output
│ │
│ └── Context
│ ├── Memory
│ ├── RAG
│ ├── Vector DB
│ └── Knowledge Graphs
│
├── 02. Agent Brain
│ ├── Planning
│ │ ├── Task Breakdown
│ │ ├── Goal Routing
│ │ └── Reflection
│ │
│ ├── Reasoning
│ │ ├── ReAct
│ │ ├── Tree of Thoughts
│ │ ├── Multi-Agent Debate
│ │ └── Self-Correction
│ │
│ └── Decision Engine
│ ├── Tool Selection
│ ├── Memory Retrieval
│ └── Action Prioritization
│
├── 03. Tool Layer
│ ├── Web Search
│ ├── Browser Automation
│ ├── Code Execution
│ ├── APIs
│ ├── Database Access
│ └── File Systems
│
├── 04. Agent Workflows
│ ├── Research Agents
│ ├── Coding Agents
│ ├── Sales Agents
│ ├── Customer Support Agents
│ ├── Content Agents
│ └── Autonomous Workflows
│
├── 05. Multi-Agent Systems
│ ├── Manager Agent
│ ├── Worker Agents
│ ├── Reviewer Agents
│ ├── Specialized Skills
│ └── Shared Memory Bus
│
├── 06. Infrastructure
│ ├── LangGraph
│ ├── CrewAI
│ ├── OpenAI Agents SDK
│ ├── MCP
│ ├── Docker
│ └── Kubernetes
│
├── 07. Observability
│ ├── Logs
│ ├── Traces
│ ├── Evaluations
│ ├── Hallucination Checks
│ └── Cost Monitoring
│
├── 08. Security Layer
│ ├── Sandboxing
│ ├── Permission Control
│ ├── Secret Management
│ ├── Guardrails
│ └── Human Approval Loops
│
└── 09. Future of Agents
├── Voice Agents
├── Computer Use
├── AI Employees
├── Self-Improving Agents
└── Autonomous Companies

Most people use AI like a chatbot.

The next generation will use AI like an operating system.

[Booti 🌱 @BootiWale_Baba]

https://t.co/IQnLSJzYgx

[Booti 🌱 @BootiWale_Baba]

Best SQL complier for beginners to learn and practice

https://t.co/6cwpHNQXwH

[Booti 🌱 @BootiWale_Baba]

Pro tip PES 2026 data analysis #pes #efootball #football

[Andrew Ng @AndrewYNg]

I am alarmed by the proposed cuts to U.S. funding for basic research, and the impact this would have for U.S. competitiveness in AI and other areas. Funding research that is openly shared benefits the whole world, but the nation it benefits most is the one where the research is done.

If not for funding for my early work in deep learning from the National Science Foundation (NSF) and Defense Advanced Research Projects Agency (DARPA), which disburse a good deal of U.S. research funding, I would not have discovered lessons about scaling that led me to pitch starting Google Brain to scale up deep learning. I am worried that cuts to funding for basic science will lead the U.S. — and also the world — to miss out on the next set of ideas.

In fact, such funding benefits the U.S. more than any other nation. Scientific research brings the greatest benefit to the country where the work happens because (i) the new knowledge diffuses fastest within that country, and (ii) the process of doing research creates new talent for that nation.

Why does most innovation in generative AI still happen in Silicon Valley? Because two teams based in this area — Google Brain, which invented the transformer network, and OpenAI, which scaled it up — did a lot of the early work. Subsequently, team members moved to other nearby businesses, started competitors, or worked with local universities. Further, local social networks rapidly diffused the knowledge through casual coffee meetings, local conferences, and even children’s play dates, where parents of like-aged kids meet and discuss technical ideas. In this way, the knowledge spread faster within Silicon Valley than to other geographies.

In a similar vein, research done in the U.S. diffuses to others in the U.S. much faster than to other geographic areas. This is particularly true when the research is openly shared through papers and/or open source: If researchers have permission to talk about an idea, they can share much more information, such as tips and tricks for how to really make an algorithm work, more quickly. It also lets others figure out faster who can answer their questions. Diffusion of knowledge created in academic environments is especially fast. Academia tends to be completely open, and students and professors, unlike employees of many companies, have full permission to talk about their work.

Thus funding basic research in the U.S. benefits the U.S. most, and also benefits our allies. It is true that openness benefits our adversaries, too. But as a subcommittee of the U.S. House of Representatives committee on science, space, and technology points out, “... open sharing of fundamental research is [not] without risk. Rather, ... openness in research is so important to competitiveness and security that it warrants the risk that adversaries may benefit from scientific openness as well.”

Further, generative AI is evolving so rapidly that staying on the cutting edge is what’s really critical. For example, the fact that many teams can now train a model with GPT-3.5- or even GPT-4-level capability does not seem to be hurting OpenAI much, which is busy growing its business by developing the cutting-edge o4, Codex, GPT-4.1, and so on. Those who invent a technology get to commercialize it first, and in a fast-moving world, the cutting-edge technology is what’s most valuable. Some studies (link in original post, below) also show how knowledge diffuses locally much faster than globally.

China was decisively behind the U.S. in generative AI when ChatGPT was first launched in 2022. However, China’s tech ecosystem is very open internally, and this has helped it to catch up over the past two years:
- There is ample funding for open academic research in China.
- China’s businesses such as DeepSeek and Alibaba have released cutting-edge, open-weights models. This openness at the corporate level accelerates diffusion of knowledge.
- China’s labor laws make non-compete agreements (which stop an employee from jumping ship to a competitor) relatively hard to enforce, and the work culture supports significant idea sharing among employees of different companies; this has made circulation of ideas relatively efficient.

While there’s also much about China that I would not want the U.S. to emulate, the openness of its tech ecosystem has helped it accelerate.

In 1945, Vannevar Bush’s landmark report “Science, The Endless Frontier” laid down key principles for public funding of U.S. research and talent development. Those principles enabled the U.S. to dominate scientific progress for decades. U.S. federal funding for science created numerous breakthroughs that have benefited the U.S. tremendously, and also the world, while training generations of domestic scientists, as well as immigrants who likewise benefit the U.S.

The good news is that this playbook is now well known. I hope many more nations will imitate it and invest heavily in science and talent. And I hope that, having pioneered this very successful model, the U.S. will not pull back from it by enacting drastic cuts to funding scientific research.

[Original post, with links: https://t.co/JR3x4O1iVr ]

[adah @adahstwt]

Python + Pandas → Data Analysis
Python + NumPy → Numerical Computing
Python + Matplotlib → Visualization
Python + Seaborn → Advanced Charts

Python + Scikit-learn → Machine Learning
Python + PyTorch → Deep Learning
Python + TensorFlow → AI Models
Python + OpenAI API → AI Apps
Python + LangChain → LLM Apps
Python + FAISS → Vector Search
Python + FastAPI → High-Performance APIs
Python + Flask → Lightweight Backends
Python + Django → Full-Stack Apps
Python + BeautifulSoup → Web Scraping
Python + Selenium → Browser Automation
Python + OpenCV → Computer Vision
Python + Pygame → Game Development
Python + Airflow → Data Pipelines
Python + Celery → Background Jobs

one language
AI
data
backend
automation

still people say “python is slow” 🫪

[Booti 🌱 @BootiWale_Baba]

@AndrewBolis @timokeefe AI

[Booti 🌱 @BootiWale_Baba]

@elonmusk @grok can you just turn it into AI business plan

[Stefan Schubert @StefanFSchubert]

AI use is hugely tilted towards high-income workers

[Booti 🌱 @BootiWale_Baba]

@ChampionsLeague @Lays_football

[Booti 🌱 @BootiWale_Baba]

@atulkumarzz Didn't got this ?

[Booti 🌱 @BootiWale_Baba]

@atulkumarzz Free

[Atul Kumar @atulkumarzz]

🎯 Social Media Marketing Course
🎯 WordPress Development Course
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🎯Facebook Ads Course
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🎯 Google Ads Course
🎯 YouTube Domination Course
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🎯 Fiverr Freelancing Course
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🎯 And Many More

𝐀𝐧𝐝 𝐭𝐡𝐞 𝐛𝐞𝐬𝐭 𝐩𝐚𝐫𝐭?
𝐈𝐭'𝐬 𝐚𝐥𝐥 𝐚𝐛𝐬𝐨𝐥𝐮𝐭𝐞𝐥𝐲 𝐅𝐑𝐄𝐄 𝐨𝐟 𝐜𝐨𝐬𝐭!🚀😱

Simply:

1) Follow (so that I can DM)
2) Like and Repost
3) Comment "Free" to receive your copies.

[Booti 🌱 @BootiWale_Baba]

@malpani Yep some skills which will help you grow 🔝