SAIL Supplies 4,000 Tonnes of Indigenous Steel for INS Taragiri, Strengthening India’s Defence Manufacturing

INS Taragiri, the fourth Nilgiri-class (Project 17A) stealth frigate, was commissioned into the Indian Navy on April 3, 2026, at Visakhapatnam. Built by Mazagon Dock Shipbuilders Limited (MDL) and powered by 4,000 tonnes of indigenous steel from Steel Authority of India Limited (SAIL), the warship marks a major milestone in India’s defence indigenisation and maritime self-reliance.

The Nilgiri-class (Project 17A) frigates are India’s latest generation of indigenous stealth warships, designed as versatile multi-mission platforms with advanced stealth, automation, and firepower. They represent a major leap in self-reliant naval shipbuilding under the Atmanirbhar Bharat initiative.  

Key Highlights of INS Taragiri Commissioning

⚓️ Legacy. Power. Purpose.

From steel to sea, #Taragiri stands ready.#IndianNavy is set to commission #INSTaragiri.

🗓️ 03 April 2026
📍 Visakhapatnam
A testament to strength, precision & #Aatmanirbharta.#RiseAbove @indiannavy @IN_HQENC @IN_WNC @IN_HQSNC pic.twitter.com/DhRO1pzLT7

— IN (@IndiannavyMedia) April 1, 2026

• Commissioning Date & Venue: April 3, 2026, Naval Dockyard, Visakhapatnam
• Presiding Dignitary: Defence Minister Rajnath Singh led the ceremony
• Shipbuilder: Mazagon Dock Shipbuilders Limited (MDL), Mumbai
• Class & Project: Fourth vessel of the Nilgiri-class (Project 17A)
• Displacement: Approx. 6,670 tonnes
• Steel Supply: 4,000 tonnes of special-grade steel supplied by SAIL

Role of SAIL in Defence Indigenisation

• Supplied DMR 249A grade hot-rolled sheets and plates
• Earlier contributions include INS Vikrant, INS Nilgiri, INS Himgiri, INS Udaygiri
• Reinforces Atmanirbhar Bharat and Make in India

Project 17A – India’s Stealth Frigate Program

• Total of seven frigates under construction at MDL and GRSE
• Successor to Shivalik-class with enhanced stealth and automation
• INS Taragiri named after the erstwhile Leander-class frigate (1980–2013)

Ships in the Class

1. INS Nilgiri – Lead ship, launched 2019, commissioned 2025.
2. INS Himgiri – Delivered July 2025 (GRSE). 
3. INS Udaygiri – Commissioned 2025. 
4. INS Taragiri – Commissioned April 3, 2026 (MDL). 
5. INS Dunagiri – Delivered March 30, 2026 (GRSE). 
6. INS Vindhyagiri – Launched August 2023.  
7. INS Mahendragiri – Under construction.  

Strategic Significance

• Strengthens India’s ability to safeguard its 11,000 km coastline
• Demonstrates domestic capacity to design and build complex warships
• Showcases collaboration between MDL and SAIL

Below is a comparison table between the Nilgiri-class (Project 17A) and its predecessor, the Shivalik-class (Project 17) frigates. This highlights the technological leap India has made in stealth warship design:

Nilgiri-class (Project 17A) Vs Shivalik-class (Project 17)

The Nilgiri-class (Project 17A) frigates represent India’s latest generation of indigenous stealth warships, building on the foundation of the Shivalik-class (Project 17).

Key Features of Nilgiri-class (Project 17A)

• Advanced stealth shaping, infrared suppression, and acoustic quieting
• Displacement of ~6,670 tonnes
• High automation with reduced crew workload
• 75% indigenous content including SAIL steel
• Equipped with BrahMos missiles, Barak-8 SAM, and advanced CIWS

Comparison Table

Feature	Shivalik-class (Project 17)	Nilgiri-class (Project 17A)
Commissioning Period	2010–2012	2025–2026 (ongoing)
Number of Ships	3 (INS Shivalik, INS Satpura, INS Sahyadri)	7 planned (INS Nilgiri, Himgiri, Udaygiri, Taragiri, Dunagiri, Vindhyagiri, Mahendragiri)
Displacement	~6,200 tonnes	~6,670 tonnes
Length	~142 meters	~149 meters
Beam	~16.9 meters	~17.8 meters
Propulsion	CODOG (LM2500 gas turbines + Pielstick diesels)	CODOG (LM2500 gas turbines + MAN diesels)
Speed	~30 knots	~32 knots
Range	~5,000 nautical miles	~2,500 nautical miles
Stealth Features	Basic radar signature reduction	Advanced stealth shaping, infrared suppression, acoustic quieting
Weapons	Klub missiles, Barak-1 SAM, AK-630 CIWS	BrahMos missiles, Barak-8 SAM, advanced CIWS
Automation	Moderate digital integration	High automation, reduced crew workload
Indigenous Content	~60%	~75% (including SAIL steel)
Shipbuilders	MDL (Mumbai)	MDL (Mumbai) & GRSE (Kolkata)

Key Takeaway

• Shivalik-class was India’s first indigenous stealth frigate program
• Nilgiri-class builds on that foundation with greater stealth, automation, and indigenous content
• Represents one of the most capable frigate classes in Asia

The Nilgiri-class (Project 17A) frigates are central to India’s naval modernization, combining indigenous design, advanced weapons, and strategic self-reliance.

The commissioning of INS Taragiri is not just a naval milestone but a symbol of India’s growing industrial and defence ecosystem. With SAIL’s indigenous steel at its core, the frigate embodies the nation’s push toward Atmanirbhar Bharat, ensuring that India’s maritime strength is forged at home.

Japan Invites 1,000 Indian Researchers for LOTUS 2026 with ₹25.6 Lakh Funding

Japan has officially launched the LOTUS 2026 programme, inviting 1,000 Indian Master’s students, PhD scholars, and postdoctoral researchers to pursue fully funded research opportunities at leading Japanese universities. Applications are open until June 9, 2026, with funding support of up to ₹25.6 lakh per researchers.

The LOTUS Programme is a Japan–India bilateral research fellowship that funds Indian Master’s students, PhD scholars, and postdoctoral researchers to conduct research at leading Japanese universities, with support of up to ₹25.6 lakh per researcher. It is designed to deepen scientific collaboration and provide international exposure for young Indian researchers.

Key Highlights of LOTUS 2026

• Eligibility: Indian Master’s students, PhD scholars, and postdoctoral researchers under the age of 40.
• Funding: Up to ₹25.6 lakh per researcher for living, travel, and research expenses.
• Deadline: June 9, 2026 (strict cut-off).
• Tracks Available:
  • LOTUS Basic: Short-term research stays (up to 12 months).
  • LOTUS-ASPIRE: New long-term track allowing stays up to 36 months.
• Fields: Open to all disciplines — science, technology, social sciences, interdisciplinary research.
• Host Institutions: Leading Japanese universities and research centers coordinated by JST.

Comparison of LOTUS Tracks

Track	Duration	Focus Area	Funding Coverage	Career Impact
LOTUS Basic	Up to 12 months	Short-term research projects	Living, travel, research costs	Exposure to Japanese labs & networks
LOTUS-ASPIRE	Up to 36 months	Long-term, high-impact research	Extended funding for multi-year projects	Stronger career-building, deeper collaboration

Important Considerations

• Competition will be intense: Only 1,000 slots for all of India.
• Age limit (under 40) may exclude senior researchers.
• Application process requires coordination with Japanese host institutions — early networking is crucial.
• Deadline is strict: Missing June 9, 2026 means waiting for the next cycle.

Action Steps for Indian Researchers

• Identify a Japanese host institution aligned with your research.
• Prepare a compelling proposal highlighting novelty, feasibility, and collaboration potential.
• Apply before June 9, 2026 via the official LOTUS portal (JST).
• Plan funding usage strategically — living costs in Japan can be high, so budget carefully.

Tata Digital Shake-Up: BigBasket and 1mg Founders Step Back as New Leadership Takes Over

Tata Digital is undergoing a major leadership reset: BigBasket co-founders Hari Menon and Vipul Parekh are in the final stages of exiting, while 1mg’s Prashant Tandon and Gaurav Agarwal are weighing their future amid uncertainty over IPO timelines and shareholder exits, reported The Economic Times (ETtech), on April 3, 2026. This marks a decisive shift toward professionalized management under Tata Digital’s broader “super-app” strategy.

Key Developments

BigBasket

• Hari Menon (CEO) and Vipul Parekh (co-founder) are stepping back from operational roles.
• Tata Digital is actively searching for a professional CEO to lead the grocery platform.
• The company is pivoting aggressively into quick commerce, competing with Blinkit, Zepto, and Swiggy Instamart.
• Despite strong revenue growth, losses have widened as it plays catch-up in the instant delivery segment.

1mg (Tata 1mg)

• Founders Prashant Tandon and Gaurav Agarwal are reassessing their roles after five years with Tata.
• IPO plans remain uncertain, creating frustration among founders and other shareholders.
• Core operations have turned EBITDA positive, but fundraising challenges persist.

Tata Digital Strategy

• CEO Sajith Sivanandan is driving a profitability-first reset, emphasizing financial discipline.
• The exits are part of a professionalization push, moving away from founder-led operations.
• Tata Digital aims to consolidate its acquisitions (BigBasket, 1mg, Curefit) into a cohesive super-app ecosystem.

Comparison of Founder Exits

Company	Founders Exiting	Reason for Exit	Current Status	Strategic Challenge
BigBasket	Hari Menon, Vipul Parekh	Tata Digital wants a professional CEO	Transition plan underway	Late entry into quick commerce, rising losses
1mg	Prashant Tandon, Gaurav Agarwal	IPO uncertainty, unclear exit path	Founders evaluating next steps	Fundraising hurdles despite EBITDA-positive ops

Risks & Implications

• Leadership vacuum risks cultural disruption.
• Competitive pressure from entrenched rivals in quick commerce.
• Investor concerns over unclear IPO timelines.
• Execution risk in integrating diverse verticals into a super-app.

For India’s quick commerce and digital health sectors, these exits signal a maturing phase where Tata Digital prioritizes scale, profitability, and professional management over founder-driven growth. Expect more aggressive competition in India’s delivery market (Blinkit, Zepto, Instamart vs. BigBasket) and potential consolidation moves in digital health as 1mg recalibrates its IPO ambitions.
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Indian Railways Approves ₹398 Cr Optical Fibre Backbone Project in W. Railway to Boost Kavach Rollout

Indian Railways has sanctioned a ₹398.36 crore project to strengthen communication infrastructure in Western Railway by laying a 4×48 Optical Fibre Cable (OFC) backbone across Ahmedabad and Ratlam divisions. This initiative is part of the larger ₹27,693 crore Kavach-LTE umbrella work under the 2024–25 programme, aimed at modernising signalling and enhancing safety.

Kavach is India’s indigenous Train Collision Avoidance System (TCAS) developed by the Research Designs and Standards Organisation (RDSO). It prevents train collisions by automatically controlling train speed and braking in case of danger. Features include automatic braking, speed restriction enforcement, neutral section protection, and RFID-based train tracking.

Project Highlights

• Total sanctioned cost: ₹398.36 crore
• Umbrella work: “Provision of Kavach with communication backbone of LTE on balance routes of Indian Railways (2024–25)”
• Overall umbrella cost: ₹27,693 crore
• Western Railway sub-umbrella allocation: ₹2,800 crore
• Coverage: 1929 Route Kilometres (RKm)
  • Ahmedabad Division: 1456RKm
  • Ratlam Division: 473 RKm

Purpose and Benefits

• Enhanced communication backbone to improve capacity, reliability, and efficiency
• Support for Kavach, India’s indigenous Train Collision Avoidance System
• Modern signalling systems with LTE-based backbone
• Operational efficiency for passenger safety and freight operations

Government Context

• PIB highlighted record capital expenditure of ₹2,62,200 crore in FY 2024–25
• Kavach implementation is a national priority with LTE rollouts across zones
• RDSO has published specifications for Kavach and LTE integration

Strategic Significance

• Digital transformation towards a world-class railway network
• Safety enhancement through collision prevention and train control
• Regional impact on key industrial and passenger corridors
• Alignment with national freight loading goals for 2030

The Kavach-LTE umbrella work is a large-scale Indian Railways programme approved for 2024–25, with a sanctioned cost of ₹27,693 crore, aimed at deploying the indigenous Train Collision Avoidance System (Kavach) across balance routes by building a robust Long Term Evolution (LTE)-based communication backbone. It integrates modern optical fibre cable (OFC) infrastructure to enable seamless, reliable data transmission for signalling and safety systems.

Optical fibre is the backbone of Kavach-LTE because it provides the high-speed, reliable, and low-latency communication channels needed for real-time signalling, train control, and safety data transmission across Indian Railways. Without fibre, Kavach’s automatic train protection features cannot function seamlessly at scale.  

Why Optical Fibre is Critical for Kavach-LTE

• High Bandwidth: Optical fibre supports massive data flows, enabling Kavach to transmit train location, speed, and braking commands instantly.
• Low Latency: Fibre ensures near real-time communication, which is essential for collision avoidance systems where milliseconds matter

Kavach-LTE Umbrella Work in Indian Railways

The Kavach-LTE umbrella work is a large-scale Indian Railways programme approved for 2024–25, with a sanctioned cost of ₹27,693 crore. It aims to deploy the indigenous Train Collision Avoidance System (Kavach) across balance routes by building a robust Long Term Evolution (LTE)-based communication backbone. This integrates modern optical fibre cable (OFC) infrastructure to enable seamless, reliable data transmission for signalling and safety systems.

What is Kavach?

• Kavach is India’s indigenous Train Collision Avoidance System (TCAS) developed by RDSO.
• It prevents train collisions by automatically controlling train speed and braking in case of danger.
• Features include automatic braking, speed restriction enforcement, neutral section protection, and RFID-based train tracking.

Role of LTE Backbone

• LTE (Long Term Evolution) is a modern wireless communication standard providing high-speed, low-latency connectivity.
• Indian Railways is deploying LTE-based backbone networks to support real-time signalling, train control, and Kavach data transmission.
• This ensures interoperability, reliability, and scalability across different railway zones.

Umbrella Work 2024–25

• Title: “Provision of Kavach with communication backbone of LTE on balance routes of Indian Railways”
• Sanctioned cost: ₹27,693 crore under Works Programme 2024–25 (PH-33)
• Scope: Covers balance routes not yet equipped with Kavach, ensuring nationwide rollout.
• Western Railway sub-umbrella: ₹2,800 crore allocation, under which the ₹398.36 crore OFC project in Ahmedabad and Ratlam divisions has been sanctioned.

Strategic Importance

• Safety: Kavach reduces human error risks, preventing collisions and enhancing passenger security.
• Modernisation: LTE backbone supports digital signalling systems and future upgrades.
• Efficiency: Improved communication infrastructure boosts freight and passenger operations.
• Scalability: Umbrella work ensures uniform deployment across Indian Railways, avoiding fragmented systems.

Conclusion

The ₹398.36 crore OFC backbone project in Ahmedabad and Ratlam divisions is not just a communication upgrade—it is a critical enabler for Kavach. By integrating LTE-based signalling and modern fibre infrastructure, Indian Railways is reinforcing its commitment to passenger safety, operational efficiency, and digital modernization.

What is Physical AI? Understanding the Age of Intelligent Machines

Artificial intelligence has traditionally been thought of as software—algorithms that recommend what to watch next, chatbots that answer questions, or systems that detect fraud. But a new wave of innovation is pushing AI beyond the digital realm into the physical world. This is Physical AI: intelligence embodied in machines that can move, sense, and act.

At its simplest, Physical AI is about giving AI a body. It integrates sensors, reasoning models, and actuators so that machines can perceive their surroundings, make decisions, and physically interact with objects and people. This marks a shift from digital intelligence to embodied intelligence.

What is Physical AI?

Physical AI refers to AI systems integrated with hardware that can sense, decide, and act in the real world. Unlike digital AI (chatbots, recommendation engines, etc.), physical AI has a “body” that allows it to manipulate objects, move through space, and adapt to changing environments.

Key components include:

• Sensors (cameras, LiDAR, radar, tactile sensors) for perception.
• AI models for reasoning and decision-making.
• Actuators/motors for physical action. 
• Feedback loops for continuous learning.  

Digital AI vs Physical AI

Aspect	Digital AI (Software)	Physical AI (Hardware + AI)
Environment	Virtual, data-driven	Real-world, sensor-driven
Interaction	Text, images, voice	Movement, manipulation, sensing
Examples	ChatGPT, Netflix AI	Robots, drones, autonomous cars
Challenges	Bias, hallucinations	Safety, cost, reliability

Real-World Examples of Physical AI

• Tesla Optimus Robot – Humanoid robots designed to perform repetitive tasks in manufacturing facilities, moving beyond traditional industrial arms.
• Amazon Warehouses – Over 750,000 robots assist in picking, sorting, and moving packages, working alongside human employees to handle massive demand spikes.
• Autonomous Vehicles – Self-driving cars use AI with cameras, LiDAR, and radar to navigate safely in traffic.
• Healthcare Robotics – Surgical robots and robotic exoskeletons help doctors perform precise operations and assist patients with mobility.
• Drones in Logistics – AI-powered drones deliver goods, monitor crops, and assist in disaster relief.

Why It Matters

Physical AI is not just about efficiency—it’s about transformation. It can streamline logistics, reduce human exposure to dangerous environments, and expand accessibility for those with mobility challenges. It promises to reshape how industries operate, how healthcare is delivered, and even how we move through cities.

But with opportunity comes responsibility. Autonomous systems must be safe, reliable, and ethically governed. Questions of accountability—who is responsible when a robot makes a harmful decision—will become central as adoption grows.

The Takeaway

Physical AI is AI with a body. It’s already here, reshaping logistics, healthcare, mobility, and beyond. As machines gain the ability to think and act, society faces both extraordinary opportunities and profound challenges. The age of embodied intelligence has begun, and how we guide its growth will determine whether it becomes a trusted partner in human progress or a source of new risks.