First Mentor

There are no words for the silence that remains when a father goes home. 

My dad was the architect of my character—first mentor who didn't just give me directions but taught me how to navigate.  Before the world gave me teachers or mentors, Dad was fine-tuning my life. 

My heart was heavy on 4th March, but it’s full of a man who gave his family everything.

He taught me that work isn't a chore—it’s the 'signature' you leave behind. He mentored in 'Deep Dive Passion' and showed how to love unconditionally beyond materialistic. 

I couldn't give him much back; except for one thing called by Dr. Senthilvel. I carry that name, and your lessons, with me forever. 

After hearing so many stories of your impact this week, I realize that being 'Dr. Senthilvel' isn't just my title—it’s a continuation of your story. I am so proud to follow in your footsteps and aim to inspire others with the same passion. Thank you, Dad.

Block News 26Feb

 

Hot industry news on February 26, 2026, Jack Dorsey argued that the very definition of a "company" has changed. He claims that by embedding AI at the core of their operations, a team of 6,000 can now out-produce the 10,000 they had previously. Few observations are:

Key Details of the Move:

• The Workforce Reset: Headcount is dropping from over 10,000 to just under 6,000.

• The Financial Reaction: Wall Street loved it. Block's stock surged over 20% following the news, as investors prioritized "efficiency" and "profit per employee."

• The AI "Goose": Block has been quietly building internal AI tools (one codenamed "Goose") to automate engineering, customer support, and back-office tasks.

"Most Companies are Late"

The most controversial part of Dorsey’s statement wasn't the layoffs themselves, but his prediction for the rest of the world.

"I don't think we're early to this realization. I think most companies are late. Within the next year, I believe the majority of companies will reach the same conclusion."

If it is right, we are entering a phase where healthy, profitable companies (Block reported a 24% increase in gross profit) will still cut staff simply because the "human-to-output" ratio has been fundamentally disrupted by agentic AI.

What This Means for Tech Workers

This marks a shift from "AI as a tool" to "AI as a replacement." For those in the industry, the message is clear: the most "at-risk" roles are no longer just routine data entry, but middle management and specialized roles where AI "agents" can now draft code, manage projects, and handle customer flows.

How to Stay Relevant:

• Upskill in "Agentic" Workflows: Move from just using AI to orchestrating it.

• Focus on High-Judgment Tasks: AI is great at execution but still struggles with high-stakes strategy and nuanced human empathy.

• The "100 + AI = 1,000" Rule: Dorsey’s new math suggests that the future belongs to the "augmented professional"—someone who can do the work of ten by leveraging a fleet of AI tools.

Reference 

Jack Dorsey's Block Cuts Nearly Half Its Staff in AI Bet This video provides a deep dive into the specific details of the Block layoffs and how the company is using its internal AI tool, Goose, to restructure

OpenAI 110 billion

Yday, OpenAI closed a historic $110 billion funding round, marking it as one of the largest private capital raises in history. This massive injection of capital values the company at approximately $730 billion pre-money (with some estimates placing the post-money valuation as high as $840 billion).

According to the reporting, the round was structured as follows:

Key Investors & Financial Breakdown

• Amazon: Led the round with a reported $50 billion commitment.

• NVIDIA: Contributed roughly $30 billion.

• SoftBank: Contributed roughly $30 billion.

• Microsoft: Notably, OpenAI’s largest early backer did not participate in this specific round, though the partnership remains central.

Strategic "Compute-for-Equity" Structure

The funding is not purely cash; it is described as a "strategic industrialization pact." A significant portion of the $110 billion consists of infrastructure and service commitments:

• Amazon (AWS): OpenAI has committed to consuming ~2 GW of "Trainium" capacity through AWS. The deal deepens the partnership for stateful runtimes on Amazon Bedrock.

• NVIDIA: The investment includes massive hardware and inference capacity commitments to ensure OpenAI’s future compute needs are met.

• SoftBank: Acts as a bridge between capital and scaling global AI infrastructure.

Company Performance Metrics

The TechCrunch report and accompanying internal disclosures highlighted OpenAI's explosive growth:

• Weekly Active Users: Reached 900 million, a jump of 100 million since October 2025.

• Paying Subscribers: Now at 50 million consumer subscribers.

• Codex Growth: Weekly active users for its coding AI have tripled since the start of 2026 to 1.6 million.

• Revenue Projections: OpenAI anticipates $30 billion in revenue for 2026, though it still expects significant losses ($40 billion by 2027) due to massive compute spending.

Context & Impact

This round is viewed as a precursor to an expected OpenAI IPO later in 2026 or early 2027. Analysts describe the deal as a way for OpenAI to break "single-cloud lock-in" by diversifying its compute sources beyond Microsoft, while giving cloud and hardware giants like Amazon and NVIDIA a guaranteed long-term customer and a massive equity stake in the leading AI firm.

Engineering matter in AI

 

As much as I love getting my hands dirty with the latest AI frameworks, I’m also a big believer in intentional design. Here’s the mindset I bring to the table beyond just the code.

We’ve all seen it. You type a prompt, the AI spits out 200 lines of code, and—magically—it runs. It feels like a superpower. This is "Vibe Coding." It’s fast, it’s intoxicating, and it’s a brilliant way to prototype an idea in a weekend.

But as the above image from social media illustrates, there is a massive difference between a structure that looks like a house and one that can actually weather a storm.

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The Allure (and Danger) of the Vibe

In the left panel, we see the "AI Vibe" house. On the surface, it has a roof and walls. But look closer:

• The Foundation: It’s sitting on a pile of loose rocks. In software, this is the lack of a scalable architecture or a proper database schema.

• The Logic: There’s a toilet in the middle of the kitchen. This is what happens when AI generates features without understanding the user flow or business logic.

• The Infrastructure: Plumbing is hanging out in the open, tangled and messy. This is your "spaghetti code"—untested, undocumented, and impossible to maintain.

Vibe coding is great for exploring, but it's dangerous for building. That's why Engineering matters in the age of AI!

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The Engineer-Guided Evolution

The right panel shows the future of our industry: Engineer-Guided AI. Being an engineer in 2026 isn't about writing every semicolon; it’s about being the Architect of the Intelligence. When you guide the AI with an engineering mindset, you provide:

1. Structural Integrity: You define the patterns (MVC, Microservices, Clean Architecture) before the first line is generated.

2. Safety & Security: You don't just ask for a "login page"; you enforce OAuth standards, input validation, and encryption.

3. The Basement (Scalability): You ensure the foundation—the infrastructure and DevOps pipeline—is poured deep enough to support future growth.

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The Bottom Line

AI is the most powerful power tool ever invented, but a power tool in the hands of someone without a blueprint just creates a faster mess.

Don't just code for the vibe. Build for the future. The world doesn't need more "houses" that collapse under pressure; it needs engineers who use AI to build cathedrals.

In AI world, follow your engineering passion to add business/human values

Moltbot

What is Moltbot / OpenClaw? 

 OpenClaw AI (originally known as Clawdbot or Moltbot) is an open-source, autonomous AI agent framework that represents a significant shift from "talking" AI to "acting" AI. Created by developer Peter Steinberger in late 2025, it went viral in early 2026 for its ability to operate a user's computer independently, execute tasks across different apps, and run 24/7.

Unlike a standard chatbot that waits for you to type a prompt, OpenClaw is designed to be a "digital employee" that lives on your hardware and communicates with you through messaging apps like WhatsApp, Telegram, or Discord.

Business Benefits

Technology needs to add value to business.  OpenClaw takes any instruction from any messaging app to execute the backend service seamlessly using AI actions.  It's game changer for the end user accessibility.

Four Core Technologies That Set It Apart

The primary difference lies in the transition from Large Language Models (LLMs)—which focus on generating text—to Agentic AI, which focuses on executing workflows.

1. The "Heartbeat" Mechanism

Most AI is dormant until you click "send." OpenClaw features a "heartbeat"—a background process that allows the AI to trigger itself based on time (cron jobs) or external events (like receiving an urgent email or a GitHub notification). This makes it feel like an assistant that is "always on. 

2. Semantic Snapshots & Computer Use

While early agents used slow, expensive screenshots to "see" a screen, OpenClaw often uses Semantic Snapshots. It parses the "Accessibility Tree" of a website or app (the text-based map used by screen readers) to understand where buttons are. This makes it faster, cheaper, and more accurate at navigating the web for you. 

3. "Soul" and Persistent Memory

OpenClaw uses a dual-memory system: JSONL Transcripts: A literal log of every action and word + Markdown Memory: A MEMORY.md file where it distills "lessons learned" 

4. Model Agnostic

OpenClaw is not a model itself; it is a gateway. You can plug in "brains" from different companies like Claude, GPT, etc. 

Stay tuned for more hands-on publications using Moltbot OpenClaw!

ML to AgenticAI

The landscape of Artificial Intelligence is moving fast, often leaving professionals wondering how all the "buzzwords" actually fit together. To leverage AI effectively, it is essential to understand that the ecosystem isn't a collection of separate tools, but a nested hierarchy of capabilities.

The Layers of Intelligence

1. The Foundation: AI & Machine Learning (ML) At the broadest level, AI encompasses everything from basic chatbots to robotics. Machine Learning sits within it, focusing on systems that learn from data to handle tasks like fraud detection and predictive analytics.

2. The Engine: Neural Networks & Deep Learning Moving deeper, Neural Networks mimic human brain structures to master pattern recognition. This evolves into Deep Learning, the powerhouse behind the Large Language Models (LLMs) and Small Language Models (SLMs) we interact with today.

3. The New Frontier: Generative & Agentic AI While Generative AI creates content, we are now entering the era of Agentic AI. Unlike standard models that wait for a prompt, Agentic AI is designed for autonomy. These systems don't just suggest a solution; they execute it through:

• Autonomous Agents: Operating independently to reach a goal.

• Personalized Automation: Tailoring workflows to specific user needs.

• Adaptive Systems: Learning and adjusting based on real-world feedback.

Why the Distinction Matters

Understanding this ecosystem allows businesses to move beyond simple "chat" interfaces and begin building goal-oriented systems. By transitioning from Generative AI to Agentic AI, organizations can shift from manual task management to intelligent, self-sustaining workflows.

The future isn't just about AI that talks; it’s about AI that acts.

25Years

Today, Hema and I celebrate 25 years together—a true milestone that demands a moment of pause, deep reflection, and joyful celebration. Looks like 10th anniversary was just the other day! https://photos.app.goo.gl/w4LsSzZgbuc4u5mU6  25 Years: A Beautiful Pause for Reflection. 

Over this quarter-century, we’ve moved across countries, faced countless challenges, and created a lifetime of shared memories. The constant that always stands out is this: through every "thick and thin," we have remained fundamentally united, standing side-by-side.

Instead, marriage is an active creation. It’s the continuous process of re-creation, nurturing, growing, lifting, loving, giving, and supporting each other—every single day. I genuinely believe that being a great leader in the world starts right within the four walls of our home.

You, Hema, is the core backend resilient service to enable the stability and success of my frontend visible career, publications and academic life.  I couldn't achieve with alternative - it's only by your sacrifice. 

Our relationship is a dynamic co-creation, and I am still a grateful student, learning how to make our partnership even stronger.

Hema, thank you for being the constant in this incredible ride. I am deeply grateful for everything we have built together, and excited for all that still lies ahead.

❤️ Happy 25th wedding anniversary, my dear. ❤️

ServiceNow Claude

ServiceNow is officially making Anthropic’s Claude the heartbeat of its platform. 

This isn’t just a tech upgrade; it’s an AI takeover. By deploying autonomous agents for clients like Panasonic Avionics, ServiceNow is moving beyond traditional SaaS and becoming the AI workflow backbone for complex, regulated industries.

For the market, the message is loud and clear: ServiceNow is no longer just managing your workflows—it’s automating them at scale

Press release at https://newsroom.servicenow.com/press-releases/details/2026/ServiceNow-and-Anthropic-partner-to-help-customers-build-AI-powered-applications-accelerate-time-to-value-and-apply-trusted-AI-to-critical-industries/default.aspx 

PS: On listening AI paradox recently at industry, proud to be part of my firm, to invest & inspire AI consistently in people/culture for last 14+ months.

AI Paradox

For years, enterprise software was the "safe bet" of Wall Street—predictable, subscription-based, and essential. But today, a "double-whammy" of disappointing outlooks from industry titans has sent the sector into a tailspin, as investors begin to fear that artificial intelligence isn't just a tool for these companies, but a potential replacement for them.

The tremors started with SAP, which saw its shares plunge more than 16%—its biggest daily loss in years—after issuing a 2026 revenue forecast that fell short of analyst expectations. The German giant’s cautious cloud outlook served as a warning light for the entire industry.

Closely following was ServiceNow, which dropped 11%. Despite reporting subscription revenue that actually beat estimates, the results weren't "convincing enough" to drown out the growing narrative that AI disruption might eventually shrink the market for traditional workflow tools.

For the first time in a decade, the "Software as a Service" (SaaS) model is being questioned. As AI agents and autonomous coding tools become more capable, the traditional software giants are racing to reinvent themselves through massive M&A deals and new AI-integrated features. 

20 AI daily tools

 

 Stop Working Harder, Start Working Smarter

We’ve officially moved past the "AI is coming" phase—AI is here, and it’s transforming how we handle everything from research to coding. If you feel like your to-do list is growing faster than you can keep up, it might be time to audit your toolkit.

The infographic below highlights 20 power players in the AI space to shift your workflow from manual to autopilot.

LLM steps

The rapid evolution of Large Language Models (LLMs) has transformed how we interact with technology. From writing code to offering medical insights, these systems feel almost human. But behind the "magic" lies a rigorous, multi-stage lifecycle. 

Based on the expert roadmap provided by Rocky Bhatia, here is a breakdown of how an LLM is built, refined, and maintained.

Stage 1: The Foundation – Data & Architecture

Before a model can "think," it must be fed. This initial phase focuses on gathering the raw materials of intelligence.

Stage 2: Training – Learning to Predict

With the architecture set, the model begins its "education."

Stage 3: Alignment – Fine-Tuning for Quality

A model that knows everything is useless if it isn't helpful or safe. This stage "aligns" the model with human values.

Stage 4: Deployment & Interaction

Once the model is accurate and safe, it is ready for the real world.

Stage 5: Evolution – Performance & Continuous Learning

The journey doesn't end at deployment. Language and information are always changing.

Enterprise AI strategy

 

This image perfectly captures the "AI Paradox" that many enterprise leaders are currently facing. While the board is excited about the shiny "AI Strategy" peak, the engineering teams are deep underwater dealing with the messy reality of technical debt.

Every executive team wants to "move fast" on AI. They see the summit—generative models, automated insights, and transformative growth. But beneath the surface of a sleek AI Strategy lies a massive, cold reality: the technical debt of the last twenty years.

If you try to build a high-speed AI engine on a foundation of "spaghetti" code and siloed data, the iceberg won't just slow you down—it will sink the project.

To move from the "Excited Executive" boat to the "Successful Implementation" submarine, teams need to stop treating AI as a separate project and start treating it as the ultimate reason to clean house.

• Audit Before You Automate: Document your legacy logic before asking an AI to refactor it.

• Data Hygiene is AI Strategy: If your data is messy, your AI will just be "fast and wrong."

• Empower the "Foundations" Team: Give the engineers in the middle boat the resources they need to fix the base. You can't reach the peak if the bottom is crumbling.

2026 Case Studies

Theoretical knowledge of databases and load balancers is a start but seeing how they are applied under extreme pressure is where the real learning happens. These case studies teach you about:

• Scalability: How to grow from 1,000 to 1 billion users.

• Availability: Ensuring services never go down, even during updates.

• Latency: Reducing the milliseconds between a user's click and the system's response.

1. YouTube & MySQL (The Power of Sharding)

Most people assume a giant like YouTube would abandon traditional relational databases for something "fancier." However, YouTube famously scaled MySQL by using Vitess, a database clustering system.

• The Lesson: You don't always need a NoSQL database to scale; horizontal sharding can allow traditional SQL databases to handle massive throughput.

2. Uber’s Geospatial Indexing

Uber processes over 1 million requests per second to match riders with drivers. This isn't just a simple query; it requires calculating distances in real-time.

• The Lesson: Uber uses hexagonal hierarchical spatial indexing (H3). By dividing the world into hexagons, they can quickly calculate "proximity" without taxing the CPU with complex geometry.

3. Meta’s Serverless Scale

Handling 11.5 million serverless function calls per second is an exercise in extreme orchestration. Meta (Facebook) has to manage resource allocation and "cold starts" at a scale most companies will never see.

• The Lesson: Serverless isn't just for small hobby projects; with the right infrastructure, it can power the world's largest social network.

System design is less about finding the "perfect" tool and more about understanding trade-offs. Whether it's choosing between the consistency of Google Docs or the massive throughput of Kafka, these 12 examples provide the blueprint for modern engineering.