Git Design

For any software developer, understanding the Git workflow is fundamental to efficient and collaborative work. This blog post will walk you through the key stages and commands of a typical Git lifecycle, using the provided diagram as a guide.

The Local Repository: Your Personal Workspace

Think of your Local Repo as your personal development environment. It's where you'll make all your changes before sharing them with your team.

• Working Tree: This is your actual project directory. It contains all the files you're currently working on. When you edit a file, the change happens here first.
  
  
• Index / Staging Area: This is a crucial intermediate step. Using the git add command, you select specific changes from your working tree that you want to include in your next commit. This gives you granular control over what gets saved.
  
  
• Local Branch: This is where you store your committed changes. The git commit command takes the changes you've staged and creates a snapshot of your project's history. Each commit is a saved version of your work, complete with a unique ID and a message describing the changes. A common local branch is master or main.
  
  
• Remote-Tracking Ref: This is a local copy of the state of the remote repository. For example, origin/master tracks the master branch on the remote repository named origin. This reference helps Git understand the state of the remote repository relative to your local work.

Syncing with the Remote Repository

Collaboration is at the heart of Git, and the Remote Repo is the central hub where all team members share their code.

Remote Branch: This is a copy of a branch that lives on a remote server, accessible to everyone with the correct permissions.

• git push: After you've committed your changes to your local branch, you use git push to upload them to the remote repository. This makes your work available for others to see and use.
  
  
• git fetch: This command retrieves all the latest changes from the remote repository without integrating them into your local branch. It updates your remote-tracking reference, so you can see what's changed upstream.
  
  
• git pull: This is a combination of git fetch and git merge. It downloads the latest changes from the remote repository and automatically integrates them into your current local branch. This is the most common way to keep your local repository up to date with the team's work.

GPT5 Launch

Today, GPT-5 is officially launched. It is now being rolled out across various platforms, including ChatGPT, the API, and Microsoft products like Microsoft 365 Copilot.

Key details about the launch and the new model:

• Launch Date: The official announcement and rollout began on August 7, 2025. OpenAI teased the launch with a livestream scheduled for that day.

• Unified System: GPT-5 consolidates OpenAI's previous separate models into a single, more capable system. This is intended to simplify the user experience and enhance performance.

• Improved Capabilities: The model is expected to be significantly more powerful than its predecessors, with advancements in areas like logical reasoning, multi-step tasks, and multimodal processing. It is engineered to handle text, images, and other files in a single conversation thread.

• Lower Hallucination Rate: OpenAI claims GPT-5 is designed to be more accurate and provide confidence scores on its outputs, aiming for a lower rate of "hallucinations."

• Versions: The model is available in different variants: GPT-5 (high-end), GPT-5 mini (a smaller, lower-cost version), and GPT-5 nano (a light, API-only version). There is also a GPT-5-chat variant.

• Availability: The rollout is happening in phases, with priority access for paid users (ChatGPT Plus, Team, and Enterprise). Free-tier users are also expected to gain access.

• Sam Altman's Comments: OpenAI CEO Sam Altman has made several comments about the new model's power, even comparing its development to the Manhattan Project and describing a feeling of "uselessness" when testing it.

Mobile to AI era

We are arguably at the very beginning of the AI era, which is poised to subsume and redefine the mobile era, much like the mobile era redefined the PC era.

The AI era signals the decline of this model in three keyways:

1. From GUI to NUI (Natural User Interface)

The primary way of interacting with technology is shifting from tapping on glass to something more intuitive.

• Old: Tapping icons, swiping through menus.
  
  
• New: Speaking or typing a request in natural language. The AI understands intent, context, and ambiguity. The interface becomes a conversation.

2. From Apps to Agents

The siloed nature of apps is becoming a bottleneck. An AI-first world replaces them with intelligent agents.

• Old: To plan a trip, you open Kayak for flights, Airbnb for lodging, and Google Maps for directions. You are the system integrator.
  
  
• New: You tell your AI agent, "Book me a weekend trip to Napa Valley for next month, find a quiet place to stay near a good vineyard, and arrange a car." The agent interacts with the APIs of all the necessary services in the background and presents you with a complete plan.

3. From Device-Centric to Ambient Computing

The smartphone's dominance is challenged as AI becomes embedded in the environment around us.

• Old: Your digital life is centered on the device you pull from your pocket.
  
  
• New: AI is accessible through various endpoints—smart glasses, headphones, cars, home speakers, and yes, still your phone. The "computer" is no longer a specific object but a layer of intelligence that is everywhere. The hardware becomes a simple portal to your personal AI.

Industry leaders are emphasizing this shift in recent times / news.

AI Journey VBlog

 

 

Blog content is created by AI from my first V(ideo)Blog at https://www.youtube.com/playlist?list=PLClRWhkU0HEcZo2jDgscHa0UGcld6v7ZP.

• Evolution of AI in Industry Ganesan Senthilvel outlined the four phases of AI's industrial evolution, beginning in 1943 with neuron research and progressing to machine learning and deep learning. They explained that the rise of social media around 2010 shifted data from structured to unstructured formats, necessitating high-powered GPU computing and neural algorithms to process large volumes of unstructured data.
• AI Concepts and Models Ganesan Senthilvel detailed key AI concepts such as training data, model building, and automatic inference, noting that ML data mining identifies patterns for prediction. They explained regression models are foundational for big data predictions and that recurrent neural networks process sequences of words by feeding results back into the processing layer.
• Generative AI and Recent Innovations Ganesan Senthilvel discussed the recent explosion in AI's popularity, attributing it to generative AI, particularly ChatGPT, which reached one million users in just five days. They highlighted the difference between traditional AI, which analyzes existing information, and generative AI, which produces entirely new content like text, images, or code. They also introduced Retrieval-Augmented Generation (RAG) for trusted information retrieval and Agentic AI for building complex business workflows, in addition to the Model Context Protocol (MCP) framework for universal AI model communication.
• AI System Layers and Learning Approach Ganesan Senthilvel described the five distinct layers of an AI system, starting with the interaction layer as the foundation and moving up through intelligent, engineering, observability, and agent layers, where human and AI interact. They emphasized that hands-on coding and consistent daily learning are crucial for staying current in computer engineering and becoming an engineering leader.

AI system layers

A comprehensive approach to building and managing AI systems can be structured into the following five distinct layers

1. Infrastructure Layer: Core Computational Power

At the base of the stack, the Infrastructure Layer handles the most demanding computational tasks. It is responsible for running complex models, managing large-scale data storage, and ensuring seamless scalability of workloads. A key function of this layer is to provide the power required to host a large language model serving thousands of queries per hour.

Leading Platforms: OpenAI, Hugging Face, Mistral, Anthropic

2. Intelligence Layer: Advanced Cognitive Functions

This layer augments the raw capabilities of AI models by integrating memory, reasoning frameworks, and retrieval mechanisms. It enables the creation of sophisticated systems, such as a RAG-powered financial assistant capable of accessing and referencing specific corporate reports upon request.

Enabling Technologies: LangChain, LlamaIndex, Pinecone

3. Engineering Layer: From Prototype to Production

The Engineering Layer provides the tools and processes necessary to convert experimental AI models into fast, reliable, and scalable products. It bridges the gap between a simple proof-of-concept, like a notebook-based chatbot, and a production-ready application capable of supporting a large user base.

Deployment and Scaling Tools: Lamini, Relevance AI, Modal

4. Observability Layer: Ensuring AI Integrity and Safety

This critical layer is dedicated to monitoring, evaluating, and governing AI behavior. Its primary function is to ensure that AI systems operate in an ethical, accurate, and safe manner. For example, it can be used to identify and flag hallucinations in a medical diagnostic tool before it is used in a real-world clinical setting.

Monitoring and Governance Solutions: WhyLabs, Guardrails AI, Lakera

5. Agent Layer: The Human-AI Interface

The Agent Layer is where users interact with the AI system. This is accomplished through various interfaces, including copilots, virtual assistants, and autonomous agents that perform tasks on behalf of the user. A well-known example is GitHub Copilot, which integrates directly into a developer's workflow to autocomplete code and suggest solutions.

User-Facing Applications: Cursor, GitHub Copilot, Cognition

Warp AI

Warp AI is an intelligent assistant integrated into the Warp terminal; a modern, Rust-based terminal designed for developers and data professionals. 

It aims to streamline workflows, demystify complex commands, and boost productivity by bringing the power of artificial intelligence directly to the command line interface.

Warp AI offers a clear and compelling advantage than Cursor. Its focus on a terminal-native, agent-driven approach to development positions it as a revolutionary tool for the modern developer. 

Ref: https://www.warp.dev/

Amazon Q

Definition

Amazon Q is a powerful generative AI assistant from AWS that's designed to revolutionize how individuals and organizations get work done. It acts as an intelligent, secure, and personalized assistant, leveraging your company's own data and AWS's vast knowledge base to provide insights, accelerate tasks, and boost overall productivity.

Amazon Q Business

Tailored for general enterprise use, it helps employees across various departments streamline tasks, gain insights from company data, generate content, and make faster decisions. It connects securely to over 50 commonly used business tools like Salesforce, Jira, Slack, and Microsoft 365, enabling it to synthesize information and provide tailored assistance.

Amazon Q Developer

 Specifically designed for developers and IT professionals, this variant acts as a highly capable coding assistant. It helps with everything from writing, testing, and deploying code to troubleshooting, performing security scans, modernizing applications, and optimizing AWS resources. It integrates directly into IDEs like Visual Studio Code and IntelliJ IDEA and the AWS Management Console.

Apple Perplexity

Apple is reportedly in discussions with the rapidly ascending AI startup Perplexity AI to integrate its technology into future iPhones, a move that could fundamentally reshape the mobile search and AI assistant landscape. 

Sources close to the matter indicate that Apple is considering Perplexity as a powerful alternative to Google Search and as a replacement for some of ChatGPT's functionalities within its voice assistant, Siri

The tech giant currently receives an estimated $20 billion annually from Google to maintain its search engine as the default on Safari for iOS devices. A decision by Apple to walk away from this lucrative deal would represent a "tectonic shift" in the tech industry

The coming months will be crucial in determining the outcome of these high-stakes negotiations

CEO as AI coder

In a move that has generated considerable buzz across the tech world this week, Google CEO Sundar Pichai revealed he has been personally experimenting with AI-powered coding tools to build a custom webpage. Speaking at the Bloomberg Tech conference in San Francisco, Pichai shared that he has been "vibe coding" in his spare time, a term that describes a more intuitive and conversational approach to software development facilitated by artificial intelligence.

Pichai has been utilizing AI coding assistants like Cursor and Replit for a personal project aimed at creating a webpage that consolidates his preferred sources of information. This hands-on approach allows him to directly experience the capabilities and nuances of the AI tools that are rapidly reshaping the landscape of software engineering.

"Vibe coding," as described in the context of Pichai's endeavors, involves developers using natural language prompts and high-level instructions to guide AI tools in generating code. This method abstracts away much of the traditional, syntax-heavy aspects of programming, enabling a more fluid and conceptual workflow.

His recent foray into "vibe coding" offers a tangible example of his vision for a future where AI and human developers collaborate to build software more efficiently and creatively. This development has been a hot topic of conversation, highlighting a hands-on approach from one of tech's most influential leaders in the burgeoning field of AI-assisted software creation.

Ref: https://www.businessinsider.com/sundar-pichai-google-vibe-coding-software-engineer-ai-cursor-replit-2025-6

Neural Semantic Memory

The paradoxical nature of an AI LLM—being both stateless in conversation and rich in "unmuted" knowledge—is a key aspect of its design. 

While the model may not remember your name from one minute to the next, it retains a vast and intricate understanding of the world, ready to be articulated in response to your every query.

It succinctly captures a fundamental duality at the core of how Large Language Models (LLMs) function

Related neural semantic memory research paper is at https://pmc.ncbi.nlm.nih.gov/articles/PMC3350748/

In layman perspective, frog story semantic is coincides with 3 records but different contexts in our brain like Frog & Toad, Frog & Scorpion, Frog & Princess

AI IDE comparision

 

On doing some research, got to know the above comparison table - which is essential for the current period to AI engineers.

GMeet speech translation

Google CEO Sundar Pichai showcased a significant advancement in communication technology during I/O 20205.with real-time speech translation within Google Meet. 

This new feature, powered by Google's advanced Gemini AI, aims to break down language barriers in virtual meetings.

The system is designed to translate spoken language into another language in real-time, even preserving the speaker's tone and emotion to allow for more natural-sounding conversations.

The introduction of live translation in Google Meet underscores Google's broader strategy of integrating its cutting-edge AI, Gemini, across its suite of products to enhance user experience and productivity.

Google I/O 2025

Today, Google I/O 2025 is live and showcasing a strong emphasis on AI

Gemini takes as a Universal AI Assistant:

• Gemini 2.5 and Deep Think 
• Gemini as a "World Model"
• Project Astra Vision Integrations
• Gemini in Chrome
• Gemini Deep Research and Canvas Upgrades
• Personalized Smart Replies in Gmail
• Agentic Capabilities
• Generative AI for Media and Creativity
• AI in Google Search and Beyond

As an engineer, more focus to developer tools as

• Gemini Code Assist: Google's AI-coding assistant, powered by Gemini 2.5
• Firebase Studio: cloud-based AI workspace simplifies building full-stack AI apps
• Jules: Google's asynchronous coding agent with GitHub.
• Stitch: AI-powered tool generates high-quality UI designs and frontend code.

Ref: https://io.google/2025/ 

Film Passion

I'm proud to share a special moment this week that highlights my elder son Vaishak’s enduring passion for filmmaking.

His short film has recently been selected to be showcased at a film festival in the United States—a recognition that reflects both his creativity and commitment.

Vaishak’s journey began at the age of six when he participated in a short film produced by my corporate social responsibility (CSR) team, focused on raising awareness for hearing-impaired children. Even at that young age, his interest in storytelling through film was evident.

Over the years, he has nurtured this passion while pursuing academic and professional excellence. His milestones include:

1. Delivering a speech at the United Nations headquarters as a high school student from Bangalore

2. Earning a degree from Purdue University

3. Beginning his career at GE Aerospace

As Dr. A.P.J. Abdul Kalam once said, “Dream is not what you see in sleep, dream is something that does not let you sleep.” For Vaishak, passion is the driving force behind every frame. Without it, film is merely footage—empty and forgettable. With it, film becomes art, truth, and a bridge to human connection.

Whether behind the camera or in front of it, his dedication transforms ideas into meaningful stories—evoking emotion, sparking dialogue, and creating timeless impact.

I continue to be inspired by young individuals like him who successfully balance passion and professionalism—something I admire deeply and often struggle to master myself.

After all, passion is the genesis of genius.

AI generated Code

In continuation of my last blogs on Cursor, key question is how much influence to generate code in enterprise world using GenAI technology.

Here's the interesting scorecard by technology industry leaders

• Microsoft CEO Satya Nadella shared that 20% to 30% of Microsoft’s internal code is now generated by AI tools. He added that results vary across languages — AI performs better in Python, while it's less effective in C++.
  
  
• Microsoft CTO Kevin Scott previously said he expects 95% of all code to be AI generated by 2030.
  
  
• Zuckerberg, the Meta CEO said 50% of coding will be done by AI in 2026 during an interview https://www.youtube.com/watch?v=uu4Rkyp8_FA
  
  
• On earnings call last week, CEO Sundar Pichai said AI was generating more than 30% of the company’s code.

As Gandhi said "You must be the change you wish to see in the world"

Openai Windsurf

With Windsurf (formerly Codeium) today's acquisition, OpenAI can corner a lion’s share of AI-driven coding platforms for its OpenAI Codex to compete Cursor & CoPilot

The acquisition of Windsurf appears to be a bold and strategic move by OpenAI. It has the potential to significantly enhance their offerings for developers, provide them with valuable data, and position them as a major player in shaping the future of AI-assisted software development. 

It will be interesting to see how this integration unfolds and how competitors respond in this dynamic space.

OpenAI did indeed have a past (2024) interest in acquiring Cursor. However, due to a combination of Cursor's high valuation and Anysphere's decision to remain independent, the acquisition did not materialize

Cursor vs CoPilot

 

Best fit / use case for both tools

Cursor AI

Ideal for developers seeking accurate, project-specific assistance, deep project modifications, and a more streamlined workflow.

GitHub Copilot:

A good choice for those needing easy integration into existing workflows, broad code knowledge, and enterprise-level security features. 

Key Differences

Scope of Assistance

Cursor AI is tailored to the specific project, understanding its code style and project rules. GitHub Copilot uses a vast knowledge base, allowing it to suggest code from various languages and libraries. 

Code Generation

Cursor AI can directly modify files based on chat-generated code, eliminating the need for manual copy-pasting. GitHub Copilot can generate more complex code snippets and functions based on brief descriptions. 

Integration

Cursor AI integrates directly into the editor, while GitHub Copilot is available as an extension for multiple IDEs. 

User Experience

Cursor AI offers a cleaner UI/UX with a focus on ease of use, while Copilot might have more options and a more complex interface. 

Cursor

Cursor AI is an AI-powered integrated development environment (IDE) built to enhance developer productivity. 

What is Cursor AI?

• AI-Powered IDE: Cursor is a standalone code editor with artificial intelligence deeply integrated into its core functionality.
  
  
• Based on VS Code: It's built as a fork of Visual Studio Code, meaning it has a familiar interface and can use VS Code extensions, themes, and key bindings. This makes the transition easier for existing VS Code users.
  
  
• Proprietary Software: Developed by Anysphere Inc.
  
  
• Cross-Platform: Available for Windows, macOS, and Linux.

Cursor AI appears to be a powerful and well-regarded AI code editor that can significantly enhance developer productivity. Its deep integration with AI, building upon the familiar VS Code foundation, offers a compelling experience for many users. The pricing model may also be a factor for some individuals and teams.

Semantic Kernel Execution

Semantic Kernel is a lightweight, open-source Software Development Kit (SDK) created by Microsoft that enables developers to easily integrate Large Language Models (LLMs) like OpenAI, Azure OpenAI, and Hugging Face into their applications using common programming languages such as C#, Python, and Java.

As illustrated, Semantic Kernel will

1. Select the best AI service to run the prompt.
2. Build the prompt using the provided prompt template.
3. Send the prompt to the AI service.
4. Receive and parse the response.
5. And finally return the response from the LLM to your application.

It acts as an orchestration layer that simplifies the process of building intelligent applications by combining the power of LLMs with traditional programming logic and external resources. It provides a structured and extensible way to create AI-powered features and autonomous agents.

Ironwood

Google's Ironwood TPU (Tensor Processing Unit) is seventh-generation TPU, a custom-designed AI accelerator chip. It was announced on April 9, 2025, at Google Cloud Next 25.  

Ironwood is specifically designed for inference, which is the process of running already trained AI models to make predictions or generate responses. 

Key features include significantly increased compute power, high-bandwidth memory (HBM) capacity and bandwidth, and enhanced inter-chip interconnect (ICI) for efficient scaling.

Ironwood is a key component of Google Cloud's AI Hypercomputer architecture.

Debug-Gym

Sundar Pichai says 25% of Google’s new code comes from AI. Debug-Gym is an open-source environment where AI agents are given sandboxed Docker containers for safety and realistic execution.

Debug-gym provides a structured Python-based environment where AI agents can:

• Access debugging tools through a controlled interface.
• Employ debugging commands similar to Python's pdb (Python Debugger).
• Examine runtime behavior (e.g., variable values, stack frames).
• Refine their approach through active exploration.
• Set breakpoints, navigate code, and create test functions.

In recent AI world, it provides the below benefits:

• Allows for the assessment of AI agent reasoning strategies in complex debugging scenarios.
• Provides a platform to train AI models to learn from interaction histories and structured sequences of debugging actions.
• Offers a way to measure agent capabilities in dynamic code repair.
• Supports the development of agents that can systematically reason through bugs using external tools.

Semantic Kernel

Semantic Kernel is designed to help developers build AI applications by providing a set of tools and libraries that facilitate the integration of large language models (LLMs) into their applications. 

It supports various AI tasks such as text generation, understanding, and transformation. Features are listed as below:

Extensibility: Developers can extend the framework to add custom functionalities or integrate additional AI models.

Interoperability: Semantic Kernel can work with different AI models and services, making it versatile for various applications.

Ease of Use: The framework provides a user-friendly API that simplifies the process of integrating AI capabilities into applications.

Microsoft 50

 

Today, Gates says that the 50th anniversary of Microsoft is “bittersweet,” and that it feels like yesterday when he and Allen “hunched over the PDP-10 in Harvard’s computer lab, writing the code that would become the first product of our new company.” That code, he says, remains “the coolest code I’ve ever written to this day …. I still get a kick out of seeing it, even all these years later.”

Bill Gates writes on his Gates Notes website. “In 1975, Paul Allen and I created Microsoft because we believed in our vision of a computer on every desk and in every home. Five decades later, Microsoft continues to innovate new ways to make life easier and work more productive. Making it 50 years is a huge accomplishment, and we couldn’t have done it without incredible leaders like Steve Ballmer and Satya Nadella, along with the many people who have worked at Microsoft over the years.”

Google Wiz

 

Today, Google has announced its largest acquistion to date, agreeing to purchase cybersecurity firm Wiz for a staggering $32 billion in an all-cash deal.

Wiz, a rapidly growing startup founded in Israel, has quickly become a majoy player in the cloud security speace.  It's AI driven solutions have attracted an ipressive clientele including 50% of Fortune 100 companies.

The timing of this deal is particularly noteworthy.  It comes after a previous attempt by Google to acquire Wize for $23 billion fell through in 2024 due to antitrust concerns.

This acquisition bolsters Google Cloud's security capabilities, positioning it more competitively against industry giants.

Transformational Education

This month, blessed to login Bishop Heber campus as chief guest after 3 decades being post grad student.

Beyond the nostalgic memorable rewinds, wanted to ink about the importance of continuous education.

During the senior leadership team of college board (prior to students session), got two personal questions beyond other topics.

1. What's the materialistic motivation in industry to earn Doctorate?
2. Is MBA required for industry leader role?

In my lens, both are surprising, provoking and powerful queries from industry to institute.

My answers were
1. Zero benefits on hike, promotion, etc; but personally complete transformation on deep learning of the computers between 1992 masters and 2022 PhD
2. Original (silly) drive was to become the alumini of prestigious legacy red bricks CEG, Madras, which got disqualified after my high school. During MBA classes, learnt few formal methods/techniques to shape myself as true leader in the industry.

In tum, both Q&A emphasize my favorite motivational quote - "Education is only way to transform poor in the society".

Microsoft Quantum

A couple reflections on the quantum computing breakthrough we just announced...

Most of us grew up learning there are three main types of matter that matter: solid, liquid, and gas. Today, that changed.

After a nearly 20-year pursuit, we’ve created an entirely new state of matter, unlocked by a new class of materials, topoconductors, that enable a fundamental leap in computing.

It powers Majorana 1, the first quantum processing unit built on a topological core.

We believe this breakthrough will allow us to create a truly meaningful quantum computer not in decades, as some have predicted, but in years.

The qubits created with topoconductors are faster, more reliable, and smaller.

They are 1/100th of a millimeter, meaning we now have a clear path to a million-qubit processor.

Imagine a chip that can fit in the palm of your hand yet is capable of solving problems that even all the computers on Earth today combined could not!

Sometimes researchers have to work on things for decades to make progress possible.

It takes patience and persistence to have big impact in the world.

And I am glad we get the opportunity to do just that at Microsoft.

This is our focus: When productivity rises, economies grow faster, benefiting every sector and every corner of the globe.

It’s not about hyping tech; it’s about building technology that truly serves the world.

Token embedding vector

Token embedding vectors are a way to represent words or sub words as numerical vectors in a high-dimensional space. 

These vectors capture the semantic meaning of the tokens, allowing AI models to understand the relationships between words and their context within a sentence or document.

1. Tokenization: The input text is first broken down into individual units called tokens. These can be words, sub words, or even characters, depending on the specific model and task.
   
   
2. Embedding: Each token is then mapped to a corresponding vector in a high-dimensional space. This mapping is learned by the model during training, where it analyzes vast amounts of text data to understand the relationships between words.
   
   
3. Vector Representation: The resulting vectors are dense and continuous, meaning that each element in the vector is a real number. The position of a token in this vector space reflects its semantic meaning, with similar words having vectors that are closer together.

As a simple given example, the embeddings for our tokens consist of vectors with three elements.

Text Analytics

Text Analytics is Microsoft AI library with the below tasks

• Tokenization: Breaking text into individual words or phrases (tokens).
• Stop Word Removal: Removing common words (e.g., "the," "a," "is") that don't carry much meaning.
• Stemming/Lemmatization: Reducing words to their root form (e.g., "running" to "run"). Stemming is a simpler, rule-based approach, while lemmatization uses dictionaries and is more accurate.
• Part-of-Speech Tagging: Identifying the grammatical role of each word (e.g., noun, verb, adjective).
• Named Entity Recognition (NER): Identifying named entities like people, organizations, and locations.
• Sentiment Analysis: Determining the emotional tone of text (positive, negative, neutral).
• Topic Modeling: Discovering underlying topics in a collection of documents.
• Text Classification: Assigning categories or labels to text.

Insane Journey of DeepSeek

The Insane Story of DeepSeek’s Founder, Liang Wenfeng 

DeepSeek didn’t just build an AI—Liang Wenfeng turned constraints into competitive advantages. 

Here’s how setbacks fueled game-changing innovations.

Implications:

• DeepSeek’s efficiency-first AI models are shifting the competitive landscape.
• Compute scarcity forces companies to rethink AI scaling strategies.
• China’s AI labs are proving that cost-effective models can challenge U.S. AI dominance.
• Nvidia’s stock volatility underscores market sensitivity to compute-efficient AI models.

Looking Ahead

• AI innovation is no longer about who spends the most—it’s about who does more with less.
• Investors and businesses must adapt to a world where efficiency is king.
• The AI race is evolving fast—staying informed isn’t optional.

DeepSeek R1

Hot AI topic of this week "DeepSeek" a Chinese AI startup challenges the dominance of established players like OpenAI, Google and Meta

DeepSeek's emergence as a disruptive force in the AI landscape is undeniable. Its innovative techniques, cost-efficient solutions and optimization strategies have challenged the status quo and forced established players to re-evaluate their approaches. 

While DeepSeek faces challenges, its commitment to open-source collaboration and efficient AI development has the potential to reshape the future of the industry. As the AI race intensifies, DeepSeek's journey will be one to watch closely.

DeepSeek-R1 Release fact sheet at https://api-docs.deepseek.com/news/news250120 

Today, market is getting huge impact due to DeepSeek release.

Cloud Goal

Humbled and honored to share the above cloud goal as 'Multi Cloud Engineer' between 2022 and 2024 with 3:4:3 learning passion

Details are available in the blog https://ganesansenthilvel.blogspot.com/2024/12/multi-cloud-engineer.html

Microsoft CoreAI

Microsoft is creating a new engineering division, led by the former global head of engineering at Facebook (now Meta), that will be responsible for building fundamental AI technologies and tools for the company and its customers.

Jay Parikh, who joined Microsoft in October, will lead the new division, dubbed CoreAI — Platform and Tools, as its executive vice president, according to a memo to employees Yesterday morning from Microsoft CEO Satya Nadella.

Nadella said the new division reflects the next stage of the industry’s AI platform shift. He that 2025 will be a year in which AI models change the fundamental nature of commonly used applications.

“More so than any previous platform shift, every layer of the application stack will be impacted,” the Microsoft CEO wrote. “It’s akin to GUI, internet servers, and cloud-native databases all being introduced into the app stack simultaneously. Thirty years of change is being compressed into three years!”

Parikh will work closely with the leaders of other Microsoft engineering divisions: Scott Guthrie, Microsoft Cloud + AI; Rajesh Jha, Experiences + Devices; Charlie Bell, Microsoft Security; and Mustafa Suleyman, Microsoft Consumer AI; as well as Microsoft CTO Kevin Scott, according to Nadella’s memo.

Nadella described the company’s focus on AI agents as part of the memo.

SeeingAI

Seeing AI app is an artificial intelligence-powered application developed by Microsoft to assist people who are blind or have low vision. It uses the device’s camera to analyze the environment and provide detailed descriptions through audio feedback. The app is available for free on iOS devices and supports multiple features to enhance accessibility and independence.

Key Features of Seeing AI:

1. Short Text: Instantly reads and announces short pieces of text, such as signs or labels.
2. Document: Captures and reads longer documents. The app provides guidance to align the camera properly.
3. Product: Scans barcodes to identify products and speaks the product name. It provides additional information when available.
4. Person: Recognizes faces and provides details about people, such as approximate age, gender, and emotion.
5. Scene: Describes scenes or objects in your surroundings, providing a general idea of what the camera sees.
6. Currency: Identifies different denominations of currency, especially useful for distinguishing between similar-looking bills.
7. Color: Detects and announces colors, helpful for matching clothing or identifying objects.
8. Handwriting: Reads handwritten text, although accuracy may vary depending on handwriting clarity.
9. Light Detection: Emits audible tones to indicate the intensity of light, helping to locate light sources or determine brightness levels.
10. World Channel (Experimental): Allows exploration of a space and detects objects or environments in real-time.

If you’re interested in using Seeing AI, you can download it from the App Store. 

AI based projects

Artificial Intelligence (AI) is transforming software engineering by enhancing productivity, improving code quality, and automating repetitive tasks.

AI enabled development phases are

1. Requirement: Using Natural Language Processing (NLP) AI-powered tools analyze user requirements written in natural language to generate specifications, diagrams, or models.
2. Design: Code Sketching, AI tool to convert high-level design descriptions into skeleton code or UML diagrams.
3. Coding: Code generation, AI-powered tools like GitHub Copilot generate code snippets or entire functions based on descriptions.
4. Testing: Automated Test Case Generation: AI generates test cases based on application behavior, coverage requirements, or specifications.
5. Deployment: Not only automated deployment but also AI anticipates deployment issues by analyzing logs, metrics, and historical data.

Future trends will be 

• AI-Driven Development Platforms: Fully integrated platforms that handle end-to-end software development.
• Cognitive Debugging: Advanced AI that identifies and resolves bugs with minimal human input.
• Meta-Learning: AI models that learn to optimize software engineering tasks over time.

By integrating AI into software engineering, developers can focus on creative, strategic tasks, while AI handles repetitive and complex operations. 2025 is new era of AI - fasten your seat belt!!

git merge vs rebase

What are the differences to use git merge and git rebase commands and their execution model? 

Git Merge 

This creates a new commit 'G' in the main branch. G ties the histories of both main and feature branches. 

Git merge is nondestructive in nature i.e. neither the main nor the feature branch is changed. 

Git Rebase 

It moves the feature branch histories to the head of the main branch. It creates new commits E, F and G for each commit in the feature branch. 

The benefit of rebase is that it has linear commit history.  Rebase can be dangerous if the golden rule of git rebase is not followed. 

ByteByteGo clearly describes the details in the given diagram