Even though I’m a much better Python than JavaScript developer, with AI assistance, I’ve been writing a lot of JavaScript code recently. AI-assisted coding, including vibe coding, is making specific programming languages less important, even though learning one is still helpful to make sure you understand the key concepts. This is helping many developers write code in languages we’re not familiar with, which lets us get code working in many more contexts!

My background is in machine learning engineering and back-end development, but AI-assisted coding is making it easy for me to build front-end systems (the part of a website or app that users interact with) using JavaScript (JS) or TypeScript (TS), languages that I am weak in. Generative AI is making syntax less important, so we can all simultaneously be Python, JS, TS, C++, Java, and even Cobol developers. Perhaps one day, instead of being “Python developers" or “C++ developers,” many more of us will just be “developers”!

But understanding the concepts behind different languages is still important. That’s why learning at least one language like Python still offers a great foundation for prompting LLMs to generate code in Python and other languages. If you move from one programming language to another that carries out similar tasks but with different syntax — say, from JS to TS, or C++ to Java, or Rust to Go — once you’ve learned the first set of concepts, you’ll know a lot of the concepts needed to prompt an LLM to code in the second language. (Although TensorFlow and PyTorch are not programming languages, learning the concepts of deep learning behind TensorFlow will also make it much easier to get an LLM to write PyTorch code for you, and vice versa!) In addition, you’ll be able to understand much of the generated code (perhaps with a little LLM assistance).

Different programming languages reflect different views of how to organize computation, and understanding the concepts is still important. For example, someone who does not understand arrays, dictionaries, caches, and memory will be less effective at getting an LLM to write code in most languages.

Similarly, a Python developer who moves toward doing more front-end programming with JS would benefit from learning the concepts behind front-end systems. For example, if you want an LLM to build a front end using the React framework, it will benefit you to understand how React breaks front ends into reusable UI components, and how it updates the DOM data structure that determines what web pages look like. This lets you prompt the LLM much more precisely, and helps you understand how to fix issues if something goes wrong. Similarly, if you want an LLM to help you write code in CUDA or ROCm, it helps to understand how GPUs organize compute and memory.

Just as people who are fluent in multiple human languages can communicate more easily with other people, LLMs are making it easier for developers to build systems in multiple contexts. If you haven’t already done so, I encourage you to try having an LLM write some code in a language you’d like to learn but perhaps haven’t yet gotten around to, and see if it helps you get some new applications to work. - Andrew Ng

I'm a programmer but 😆

Today’s programmer toolkit:
💻 ChatGPT for code
📚 ChatGPT for documentation
🛠 ChatGPT for debugging
✍️ ChatGPT for writing emails (just in case)

I’m not against using ChatGPT — you should use it!
But if you become a full-time ChatGPT programmer, one day when the Wi-Fi goes down, you'll be like:

⚠️ “Error 404: Human brain not found.”

Use ChatGPT as your 🧠 co-pilot, not your 🛫 autopilot.

Because when you outsource your thinking... you’re also outsourcing your future. 😃

Treat AI like a room full of consultants. Ask all. Compare answers. Decide smart.

Yes for :


✅ Parallel thinking – Treat AI like a cluster of consultants. Ask all, compare, decide.

✅ Fail-fast mentality – Run 5 Python scripts. See what breaks first. Learn faster.

✅ Code quality hunting – Don’t stop at “it works.” Make it elegant.

✅ Curiosity-driven development – Different AIs = different perspectives.

✅ Tool agnosticism – No brand loyalty. Just results.

No for :

❌ Overengineering everything – Managing 5 tabs when one good prompt could do.

❌ Burnout workflow – Acting like a machine to test machines. Remember your limits.

❌ Decision paralysis – Too many options = no action.

❌ Not learning – Picking the best AI answer without understanding = stagnation.

❌ Just shipping AI code blindly – You might ship vulnerabilities, not features.

Guide to Building an AI Agent

1️⃣ 𝗖𝗵𝗼𝗼𝘀𝗲 𝘁𝗵𝗲 𝗥𝗶𝗴𝗵𝘁 𝗟𝗟𝗠
Not all LLMs are equal. Pick one that:
- Excels in reasoning benchmarks
- Supports chain-of-thought (CoT) prompting
- Delivers consistent responses

📌 Tip: Experiment with models & fine-tune prompts to enhance reasoning.

2️⃣ 𝗗𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁’𝘀 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 𝗟𝗼𝗴𝗶𝗰
Your agent needs a strategy:
- Tool Use: Call tools when needed; otherwise, respond directly.
- Basic Reflection: Generate, critique, and refine responses.
- ReAct: Plan, execute, observe, and iterate.
- Plan-then-Execute: Outline all steps first, then execute.

📌 Choosing the right approach improves reasoning & reliability.

3️⃣ 𝗗𝗲𝗳𝗶𝗻𝗲 𝗖𝗼𝗿𝗲 𝗜𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻𝘀 & 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀
Set operational rules:
- How to handle unclear queries? (Ask clarifying questions)
- When to use external tools?
- Formatting rules? (Markdown, JSON, etc.)
- Interaction style?

📌 Clear system prompts shape agent behavior.

4️⃣ 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗮 𝗠𝗲𝗺𝗼𝗿𝘆 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆
LLMs forget past interactions. Memory strategies:
- Sliding Window: Retain recent turns, discard old ones.
- Summarized Memory: Condense key points for recall.
- Long-Term Memory: Store user preferences for personalization.

📌 Example: A financial AI recalls risk tolerance from past chats.

5️⃣ 𝗘𝗾𝘂𝗶𝗽 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁 𝘄𝗶𝘁𝗵 𝗧𝗼𝗼𝗹𝘀 & 𝗔𝗣𝗜𝘀
Extend capabilities with external tools:
- Name: Clear, intuitive (e.g., "StockPriceRetriever")
- Description: What does it do?
- Schemas: Define input/output formats
- Error Handling: How to manage failures?

📌 Example: A support AI retrieves order details via CRM API.

6️⃣ 𝗗𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗔𝗴𝗲𝗻𝘁’𝘀 𝗥𝗼𝗹𝗲 & 𝗞𝗲𝘆 𝗧𝗮𝘀𝗸𝘀
Narrowly defined agents perform better. Clarify:
- Mission: (e.g., "I analyze datasets for insights.")
- Key Tasks: (Summarizing, visualizing, analyzing)
- Limitations: ("I don’t offer legal advice.")

📌 Example: A financial AI focuses on finance, not general knowledge.

7️⃣ 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 𝗥𝗮𝘄 𝗟𝗟𝗠 𝗢𝘂𝘁𝗽𝘂𝘁𝘀
Post-process responses for structure & accuracy:
- Convert AI output to structured formats (JSON, tables)
- Validate correctness before user delivery
- Ensure correct tool execution

📌 Example: A financial AI converts extracted data into JSON.

8️⃣ 𝗦𝗰𝗮𝗹𝗶𝗻𝗴 𝘁𝗼 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 (𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱)
For complex workflows:
- Info Sharing: What context is passed between agents?
- Error Handling: What if one agent fails?
- State Management: How to pause/resume tasks?

📌 Example:
1️⃣ One agent fetches data
2️⃣ Another summarizes
3️⃣ A third generates a report

Master the fundamentals, experiment, and refine and.. now go build something amazing!

Happy agenting! 🤖
https://whatsapp.com/channel/0029Va8iIT7KbYMOIWdNVu2Q

🔍 𝐄𝐱𝐩𝐥𝐨𝐫𝐢𝐧𝐠 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐟𝐞𝐬𝐬𝐢𝐨𝐧𝐬 𝐢𝐧 𝐭𝐡𝐞 𝐈𝐓 𝐈𝐧𝐝𝐮𝐬𝐭𝐫𝐲 🔍

This visual breakdown offers a fantastic comparison of key data roles:

💚 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 – The backbone of any data-driven team. They build robust data pipelines, manage infrastructure, and ensure data is accessible and reliable. Strong in deployment, ML-Ops, and working closely with Data Scientists.

💜 𝐌𝐋 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 – These experts bridge software engineering and data science. They focus on building and deploying machine learning models at scale, emphasizing ML Ops, experimentation, and data analysis.

❤️ 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭 – The creative problem solvers. They blend statistical analysis, machine learning, and storytelling to uncover insights and predict future trends. Skilled in experimentation, ML modeling, and storytelling.

💛 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 – Masters of interpretation. They translate data into actionable insights for business decision-making. Their strengths lie in reporting, business insights, and visualization.

Model building is but one facet of a data scientist's role 🪦

The core has always been translating business requirements into data problems, effective cross-functional collaboration, and delivering actionable insights that solve real-world challenges.

The emergence of GenAI, LLMs, sophisticated coding co-pilots, and platforms like MCPs is undoubtedly shifting our workflows and capabilities.

However, the pivotal question remains unchanged: Is your work generating meaningful impact?

Technological stacks evolve; the analytical rigor and problem-centric approach are what sustain a data scientist's value.

Circle of Life IN LLM's
This is what you are going to see 😀

Top 7 Python Frameworks for AI Agents

1. LangChain
2. Microsoft AutoGen
3. CrewAI
4. Haystack by Deepset
5. Hugging Face SmolAgents
6. LangGraph
7. OpenAI Agents Python

🚀 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐯𝐬 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 𝐯𝐬 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭 – 𝐖𝐡𝐚𝐭’𝐬 𝐭𝐡𝐞 𝐃𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞?

Confused between these three trending roles in the data world? 🤔
Here's a simple breakdown:

👷‍♂️ 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫
🔹 Builds and manages data pipelines
🔹 Designs cloud architectures
🛠 Tools: AWS, Apache Spark, Databricks, SQL, Python, Airflow

📊 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭
🔹 Analyzes data for insights
🔹 Creates dashboards and reports
🔹 Supports data-driven decisions
📈 Tools: Excel, Power BI, Tableau, SQL

🧠 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭
🔹 Builds machine learning models
🔹 Predicts trends from data
🔹 Performs statistical analysis
🧪 Tools: Python, TensorFlow, Scikit-learn, Keras

Whether you're building the infrastructure, uncovering insights, or predicting the future — each role is essential in turning raw data into real-world impact. 🌍

This will be bigger than the iPhone.🚀

OpenAI is aiming to add $1 trillion in value with a device most people will hate. Sam Altman plans to produce 100 million AI companions that know everything about your life.

Always listening.
Always watching.
Always learning.

What we know:
OpenAI just acquired Jony Ive's company (iPhone designer)→ Launch in 2027→Worn around your neck→No screen, just cameras/mics→Connects to phone/computer

Goal: Reduce phone addiction by giving AI total access.

Future of computing or privacy nightmare?

Remember Google Glass? Privacy backlash killed it. This makes Glass look friendly.

The iPhone was also doubted at first. Nobody wants to browse the web on their phone. Physical keyboards are better. It’s too expensive.

Whoever nails AI hardware will own the next decade.

Two scenarios:
1️⃣Privacy fears kill adoption.
2️⃣Becomes as essential as the iPhone.

Every moment becomes AI training data. OpenAI rules the world.

My bet? First version flops. Third version? 500 million pockets.

Stanford packed 1.5 hours with everything you need to know about LLMs

Here are 5 lessons that stood out from the lecture:

1/ Architecture ≠ Everything
→ Transformers aren’t the bottleneck anymore.
→ In practice, data quality, evaluation design, and system efficiency drive real gains.

2/ Tokenizers Are Underrated
→ A single tokenization choice can break performance on math, code, or logic.
→ Most models can't generalize numerically because 327 might be one token, while 328 is split.

3/ Scaling Laws Guide Everything
→ More data + bigger models = better loss. But it's predictable.
→ You can estimate how much performance you’ll gain before you even train.

4/ Post-training = The Real Upgrade
→ SFT teaches the model how to behave like an assistant.
→ RLHF and DPO tune what it says and how it says it.

5/ Training is 90% Logistics
→ The web is dirty. Deduplication, PII filtering, and domain weighting are massive jobs.
→ Good data isn’t scraped, it’s curated, reweighted, and post-processed for weeks.

What a crazy week in AI 🤯

Here’s EVERYTHING you need to know, AI NEWS of week:

OpenAI o3-Pro :- OpenAI launches o3-Pro, their most capable reasoning model yet, now available to ChatGPT Pro and Team users. Replaces o1-Pro with enhanced performance in science, education, and programming, scoring 64% win rate vs o3 on human evaluations.

Google AI Extract:- Google unveils “Extract”, an AI assistant that transforms handwritten planning documents into digital data in minutes. Built with Google DeepMind's Gemini model, it processes 100 records daily vs the current 1-2 hours per document manually.

Krea 1 Image Model:- Krea launches their first flagship image model with state-of-the-art photorealism and superior aesthetic control. Features 1.5K native resolution output, supports custom training, and offers free daily generations with no signup required.

Midjourney AI Video:- Midjourney is putting the finishing touches on their V1 Video Model, it'll be launching soon.

Topaz Video Upscaler It’s the first-ever creative upscaler for video. It lets users upscale AI-generated videos to crisp 4K resolution while enhancing quality and finer details.

Dia AI-first web browser The Browser Company launches Dia, an AI-first browser with a built-in assistant directly in the address bar. It can summarize articles, write emails, and even browse websites on your behalf.

Mistral Reasoning Models They are Europe’s first reasoning models, with Small (24B parameters) open-source and Medium for enterprise. Unique feature: can reason natively in multiple languages including English, French, Spanish, and Arabic.

Scouts Web Monitor Agents They are always-on AI agents that monitor the web for anything you care about. Simply tell them what to track and they deploy across dozens of sites, running in the cloud 24/7.

SkyReels Open-Source AI Video It’s the world's first open-source infinite-length video generation model using AutoRegressive Diffusion-Forcing architecture.

Join GenAI: https://whatsapp.com/channel/0029VayIXpnKLaHhzg4Cvp12/123

💥 BOOM, Google just dropped the Gemini CLI 🔥🔥🔥

And honestly… It’s kind of magical. I pointed it to a folder full of images, pedestrians, retail shelves, conveyor belts, and it gave me a clean, 1-line summary for each one.

Then I dropped in a YouTube URL, and it summarized that too. Straight from the terminal. No additional dependencies or configuration. If you're working in hashtag#computervision, building datasets, or just juggling lots of media files, this can be a serious time-saver, especially with its reasoning capabilities.

Best thing, it's completely free, with 60 model requests per minute and 1,000 model requests per day with your Google account. 😊
🔗 Gemini CLI: https://github.com/google-gemini/gemini-cli

🐍 𝐏𝐲𝐭𝐡𝐨𝐧 𝐅𝐨𝐫 𝐄𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠 – 𝐎𝐧𝐞 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞, 𝐄𝐧𝐝𝐥𝐞𝐬𝐬 𝐏𝐨𝐬𝐬𝐢𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬! 💡

🔹 Pandas – For Data Manipulation & Analysis Turn messy data into structured insights.

🔹 Scikit-Learn – For ML
From regression to classification, scikit-learn helps you build and train ML models

🔹 TensorFlow – For DL & NN Ideal for building and training large-scale AI models – image recognition, NLP, or custom neural networks.

🔹 Matplotlib – For Basic Data Visualization Create plots, graphs & charts to understand trends and patterns in your data.

🔹 Seaborn – For Advanced Statistical Visualizations On top of matplotlib, Seaborn adds beauty and simplicity to heatmaps, violin plots, pair plots, and more.

🔹 Flask – For Web Development
Build lightweight web apps and APIs fast.

🔹 Pygame – For Game Development
Create 2D games with graphics, sound, and input handling.

🔹 Kivy – For Mobile App Development
Write once, run anywhere.

One guy. No funding. $80M Ai start-up exit. 🤯

Israeli coder Maor Shlomo sells six-month AI startup Base44 to Wix for $80M in landmark all-cash deal.

Maor Shlomo sold his six‑month‑old, self‑funded startup to Wix for $80 million in cash. This deal is a striking example of how AI and “vibe coding” are empowering individuals and not just big teams to build the next generation of software. Base44 lets anyone create full-featured web and mobile apps simply by describing what they want, no code, no developer team required.

And in just 6 months, Base44 hit:

• 300,000+ users
• $3.5M ARR
• $189K monthly profit
• Partnerships with eToro & Similarweb
• Acquired by Wix for ~$80M

And here’s the kicker:

→ Fully bootstrapped
→ $0 VC funding
→ A team you could count on 2 hands.

This is power of Ai proving that even a solo entrepreneur can make a massive impact in the tech world in record time. For aspiring founders, this story is an inspiring reminder that with the right idea & execution, success can come swiftly.

🔍 C vs Python – Two Roads, One Destination
This image speaks volumes — without a single word.

👨‍💻 On the left: The C developer — focused, intense, hands-on.

🧘‍♂️ On the right: The Python programmer — relaxed, efficient, just as powerful.

🎯 Both are heading toward the same goal, but the approach couldn’t be more different.

💡 What does this tell us?
C is about control.
You manage everything — memory, pointers, performance. It’s like driving a manual car: powerful, but demanding precision.

Python is about simplicity.
It abstracts the complexity, letting you focus on logic and solutions. Fast to learn, easy to use, and incredibly expressive.

🧠 Lessons beyond code:
• There’s no single “right” way — just different strengths.
• Simplicity isn't laziness — it can be a smart shortcut.
• Choose tools that match you — your mindset, project, and goals.

🚀 Whether you're building with the granularity of C or the elegance of Python — the end result is what counts.

Some of the resources that I found to be useful for learning more about Evaluation of AI agents

🔸 Evaluating AI Agents – A DeepLearning.AI short course (in collaboration with Arize AI).
Teaches you how to build an agent, observe its step-by-step process, and evaluate both individual components (like tool selection and router logic) and end-to-end performance in development and production. https://www.deeplearning.ai/short-courses/evaluating-ai-agents

🔸 Mastering AI Agents – eBook by Pratik Bhavsar and the Galileo team.
A guide on agentic frameworks: how to choose them, evaluate performance, identify failure points, and deploy reliable agents at scale. Don't miss their blog posts too!https://galileo.ai/mastering-agents-ebook

🔸 LLM Agent Evaluation – A blog post by Confident AI.
Provides a deep dive into evaluating agents, including tool usage, multi-step reasoning, and workflow-level metrics, using their DeepEval framework. https://www.confident-ai.com/blog/llm-agent-evaluation

🔸 A Field Guide to Rapidly Improving AI Products – Blog post by Hamel Husain.
Covers practical techniques—error analysis, data-driven experimentation, observability tools—to iterate and optimize AI agents effectively. https://hamel.dev/blog/posts/field-guide/

Join Our WhatsApp Channel for more resources: https://whatsapp.com/channel/0029Va8iIT7KbYMOIWdNVu2Q

🚨 Meta’s $300M AI Talent War: OpenAI, Apple & the Future of Superintelligence

It’s no longer just about building models.
It’s about buying minds.

Mark Zuckerberg is going all-in on Meta’s Superintelligence Lab, and the strategy is crystal clear:💰Poach the best AI talent in the world—at any cost.

📊 The Numbers Shaking Up Silicon Valley:
✅ Compensation packages reaching $300M (including equity)
✅ Researchers from OpenAI, Apple, DeepMind, Anthropic already signed
✅ Ruoming Pang, Apple’s Head of Foundation Models, reportedly offered $10M–$15M/year
✅ Meta insiders say 20+ top researchers have quietly joined in recent months
✅ Entire AI teams are getting reverse-acqui-hired

🔥 Sam Altman called it “distasteful”
♟ Others say: Zuck’s playing chess while others are still playing checkers.

But this isn’t just about poaching.
It’s about reshaping the future of AI itself.


🧠 Who’s Jumping Ship & for What 💰
Ruoming Pang – Apple’s AI chief (Foundation Models) → Meta
Yuanzhi Li – OpenAI → Meta
Anton Bakhtin – Anthropic → Meta
Lucas Beyer, Alexander Kolesnikov, Xiaohua Zhai – OpenAI Zurich → Meta
Shengjia Zhao, Jiahui Yu, Shuchao Bi, Hongyu Ren – OpenAI → Meta
Trapit Bansal, Huiwen Chang, Ji Lin, Joel Pobar, Jack Rae – OpenAI/DeepMind/Google → Meta


📈 The Wild Numbers
💰 Up to $300M in total comp over 4 years
💸 Some offers hitting $100M+ in year one alone
👨‍💻 At least 10 mega-offers to OpenAI researchers
💼 Average Meta engineer (E7 level): $850K–$1.5M/year

🎯 Meta aims to dominate open-source AI (LLaMA), frontier models, and superintelligence—by buying time through talent.

And in today’s AI arms race...
Talent is the new compute.

While the world debates AGI safety and regulation,
Meta is quietly building a dream team—one 9-figure offer at a time.

You watch AI tutorial and you think you learned something.

You build an AI project and you actually learn something.