Amritansh Raghav

AI feels less like a feature upgrade and more like a productivity shock. The closest comparison I keep coming back to is the Industrial Revolution — chaotic in the moment, transformational in hindsight. It didn’t just cut costs. It made goods abundant. I think AI does the same for services. The real divide won’t be between companies that adopt AI and those that don’t. It’ll be between those who use it to “do the same with less” and those who use it to “do exponentially more with the same.” Efficiency is table stakes. Ambition is the differentiator. I wrote a short piece expanding on this here: https://lnkd.in/gTKGr2Dm Curious where others are landing.

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I wrote about how product managers should deliberately decide which features should be built to an A grade and which ones should be built to a B or even only a C grade. Making the resource tradeoffs to optimize business outcomes is a core job of product management, and building every capability to an A grade is a poor use of scarce resources. https://lnkd.in/gTXBfNYE

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Here is a framework for nations to work with revolutions such as #AI and #Quantum. First you have your people learn, then dabble and become early/late adopters (hopefully not laggards!), then become effective technical users and finally value creators. It is only then that the economy benefits from your investment, and you create sustainable job growth. While #India is a key market for #Quantum with millions thirsting to learn and actually do things with this technology, there are still large gaps between: 1. Where Quantum Computing is today Vs where it needs to be for mass adoption [Quantum Sensing on the other hand is ready for prime time and GPS compromising incidents are only increasing] 2. The specialist work being done Vs the ability to hire millions of generalists 3. What it costs to tap into the technology Vs perception of what it should cost as a commodity 4. The talent it takes to produce world class indigenous technology Vs the talent that actually exists on the ground 5... Each of these gaps will close over time. In the lead-up and as a first step, India first needs to learn enough about this technology, ideally from the very best, and spread awareness. The starting point of this journey should ideally not be from dull videos or low quality social media content, but from hands-on practical learning that is delivered through high quality polished content. Such practical training effectively complements all the theoretical content you learn in universities. Q-CTRL's Black Opal product is the world's leading interactive learning tool that gives you just enough fundamentals (think 101) required to program a quantum computer. With over 28,000 users worldwide and success across the UK (Tamil Nadu adopted it last year as part of the naanmudhalvan program). And now, we have expanded our presence in India through a network of local resellers. We encourage Indian companies, governments and universities to work with us either directly or through our contacts. https://lnkd.in/gjJWHYB3 We also look forward to aligning with AICTE standards and to make our content even more relevant to India. Waseem Shiraz, Shobhit Gupta, Nagendra Nagaraja, Reena Dayal, Anil Prabhakar, Kamakoti Veezhinathan, Ajai Chowdhry, Harshan Budke, Sunil Gupta, Arindam Ghosh, Ravi Puvvala Dr M Jayaprakasan, ISDS., National Skill Development Corporation, MindTech, Kquanta Research, RV College Of Engineering, Sanjay Chittore, Uttkrist Innovations Private Limited, Subu Gupta, AICTE QRDLab - Quantum Computing in India, IBM DRDO, Ministry of Defence, Govt. of India, Ashutosh Sharma, Urbasi Sinha, Bloq Quantum LTIMindtree, Tata Consultancy Services Abhay Karandikar, NPTEL, National Quantum Mission, Eltech, Andhra Pradesh Information Technology Academy ITE and C Department Government of AP, Jayesh Ranjan, CDAC Bangalore, Kaushalya The skill University

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“Human Edge in the AI Age” by Nitin Seth is a great read providing comprehensive strategies for humans to thrive and succeed in the AI World. Incedo Inc.

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System Design Series: Post 21 What is Idempotency? Think of it like flipping a light switch. Toggling it once turns the light on. Toggling it a second, third, or even a hundredth time won't change the state - the light will remain on. The action of "turning the light on" is an idempotent operation. In simple terms, idempotency is the property of an operation that produces the same result regardless of how many times it's executed. This concept is a cornerstone of robust software design, especially in distributed systems, and helps ensure reliability and consistency. Why is Idempotency Important? Idempotency is crucial for handling issues in an unreliable world, such as network failures, timeouts, or retries. Without it, a simple network hiccup could lead to unintended consequences, like double-charging a customer or duplicating a database record. Scenario: A user clicks a "Pay Now" button. Without Idempotency: The request is sent, but the network connection drops before a confirmation is received. The user, thinking the payment failed, clicks "Pay Now" again. The backend processes the payment twice, charging the customer twice. With Idempotency: The "Pay Now" request includes a unique identifier. The first time the request is received, the system processes the payment and stores the request ID. If the same request ID is received again, the system recognizes it and simply returns the original success result without processing the payment again. The user is only charged once. Common Examples in Practice HTTP Methods In the world of web APIs, some HTTP methods are designed to be idempotent by nature. GET: Always idempotent. Repeatedly requesting a resource (e.g., GET /users/123) will always return the same data without side effects on the server. PUT: Idempotent. Used to update or replace a resource. If you send a PUT request multiple times with the same data, the resource will remain in the same final state. DELETE: Idempotent. Deleting a resource and then attempting to delete it again will have the same final outcome: the resource is gone. POST: Not idempotent. This method is typically used to create new resources. Each time you send a POST request, you're likely creating a new, separate entity. How to Implement Idempotency: Generate a unique key for each request. This is often a UUID (Universally Unique Identifier) or a hash of the request payload. Store the key and its associated result in a reliable data store (like a database or cache). Check for the key on every incoming request. If the key exists, return the previously stored result without re-executing the operation. If not, process the request, store the result, and then return it. By adopting an idempotent design, developers can build more resilient and reliable systems that gracefully handle retries and transient failures, providing a better and more predictable experience for users. Stay tuned for more ! #Idempotency #APIDesign #ComputingConcepts #Productdevelopment

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When a CEO publicly spits out their own product, they're revealing something far more damning: that they never saw the product as the point. To the MBA-trained executive, the product is just a vehicle. A means to margins, multiples, and an exit. The thing itself — what it does, who it serves, the craft behind it — is almost beside the point.

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🎙️ From Square to Mercury: How Rohini Pandhi became one of fintech’s top product leaders—and how velocity of learning at high slope companies shaped everything. Rohini dropped so many gems in our first episode of First Builders: 💎 Generalist → Specialist: In her early career, she was a generalist and tried everything. This led her to critical discoveries. She specialized in product and fintech when the “time was right” 💎 Follow the Engineers and Designers: Square was a high slope environment for Rohini, packed with talent density and learnings. She finds high slope environments by studying where smart engineers and designers are going. 💎 Fake News about PMs: PMs don’t just “move fast and break things.” Her team runs 30-50 customer interviews per quarter, providing rigor behind every decision. 💎 It’s Usually Too Early to Hire a PM: A top question she gets from founders is whether they should hire a PM. She usually says: “it’s too early.” 💎 Hiring a World Class Team is Like Tennis: When finding a tennis partner, you want someone a little better than you who can challenge you to improve your game. Give us a listen and leave a review on your favorite podcast location (see comments for links)! #FirstBuilders #StartupPodcast #ProductLeadership #TechCareers #Leadership

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Visit us at https://codlynsoft.com Ever wondered what separates a functional piece of tech from a truly impactful solution? It’s often the journey from raw output to optimized excellence. Our latest diagram illustrates the meticulous process we champion: a systematic approach to transforming complex inputs into refined, valuable outcomes. It starts with precise Input and intelligent Retrieval, feeding into a robust Core for processing. This leads to initial Generation – the foundational output. But here’s the critical insight: "raw output" is just the beginning. The real power lies in the Refinement module. This isn't just a step; it's an iterative layer of intelligent enhancement. It's where we fine-tune logic, optimize performance, and ensure true alignment with user needs and strategic goals. This commitment to refinement elevates the raw into an Optimized Output — a solution designed for peak performance and genuine impact. For developers, this means building systems that don't just work, but excel. For AI enthusiasts, it’s about crafting models that deliver reliable, ethical, and high-quality results. For founders and startup teams, it’s the difference between a proof-of-concept and a market-ready product that truly resonates. It’s a philosophy that prioritizes clarity, precision, and tangible value at every stage. What part of your development pipeline could benefit most from a dedicated refinement stage? Let’s build your next optimized solution. #softwarearchitecture #aipipeline #mlopsengineering #devproductivity #customsoftwaredev #techinnovationindia #automationstrategies #productrefinement #systemsthinking #buildwithai #codingcommunity

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I just read LangChain’s “How to Build an Agent.” It’s a quick, interesting read. Although it’s not technical, it outlines the essentials for going from concept to production. It starts with defining a real-world use case, mapping out how a person would solve the problem, building an MVP with prompts, integrating tools, and finally testing, deploying, and iterating continuously https://lnkd.in/dANWnniJ

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