Aishvarya Singh

🚀 Google has turned Gemini into a free SAT prep tool — giving students practice tests and instant feedback. Reports suggest Indian competitive exams like IIT-JEE and NEET may be next. 📊 India’s test-prep market is projected to reach $2.6B by 2033 (IMARC) — and the disruption potential is massive. While AI-powered prep tools expand access for students, they also challenge a system where textbook publishers and coaching centres have long monetised standardised exams. 👨‍👩‍👧 As a parent navigating this shift firsthand, here’s what I’ve learned: Before GenAI, we bought stacks of books and practice papers to help our child stay competitive. Today, we use tools like ChatGPT to generate custom question papers, quizzes, and learning flows — moving from static content to dynamic, agent-driven learning. 🔍 Key shifts I’m seeing: 📚 The model of buying, using, and reselling practice workbooks is fading. Even digitised archives feel outdated in an AI-native world. 📸 Tools like Google Lens now provide step-by-step solutions, making corrections more productive than answer keys ever were. 🧠 Schools still reward recall, while entrance exams reward application and problem-solving. AI-based simulations may soon replace traditional “mock tests.” 🛠️ With GenAI, trust isn’t automatic. Prompting skills + verification are now essential learning tools. 🖨️ Print is still hard. Converting AI output into clean PDFs, especially for complex numerical content, remains a real challenge. ⏳ Long learning loops hit limits — after extended back-and-forth, even smart systems can see output quality degrade. ⚖️ The bigger paradox: Families can spend up to ₹25 lakh on education, tuition, and gadgets for children aged 14–17 (Economic Times). Yet only 32.4% of schools have functional computers, and just 24.4% have smart classrooms (UDISE). 🌍 AI is often called a disruptor, but in education, it may be the great equaliser — unlocking legal access to high-quality prep and personalised learning at scale. It can help us move from digitisation to inference transformation. What should schools actually look like in an AI-native world? 🤔 Do share your thoughts and experiences! In my next post, I’ll explore where education goes from here. #AIinEducation #EdTech #TestPrep #Gemini #ChatGPT #FutureOfLearning #DigitalTransformation

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I've been following the context graphs debate with great interest. Jaya Gupta and Ashu Garg's "trillion-dollar" thesis, Prukalpa ⚡'s perspective on heterogeneity, Arvind Jain connecting it to transactive memory systems. These are important ideas. And the problem they're highlighting is real. Enterprises desperately need better ways to capture how decisions get made. Having spent years trying to build exactly these systems, I want to share what I've learned about why this is so hard. Lesson 1: You Need to Own the Interface At Nutanix, I worked on Intentful APIs for cloud infrastructure. The idea was to capture not just what changed, but why. We learned something fundamental: this only works if you own the entire interface. Kubernetes can do this because everything flows through its control plane. But enterprise environments are messier. States get mutated directly in underlying layers. Someone SSHs into a box. A quick database fix happens at 2am before a board meeting. This isn't bad behavior. It's reality. But it means any context graph is working with incomplete information. Lesson 2: Decisions Bottom Out at Human Judgment At Kognitos, I built exception handling systems which captured "decision traces". Decision-making is deeply recursive. Ask why a discount was approved. Then why that exception exists. Keep asking why. Eventually, you arrive at: "Because someone with experience made a judgment call." That's not a failure of documentation. That's how organizations work. Decisions happen under time pressure, with incomplete information, based on a team's evolving understanding. Lesson 3: The "How" is Often Private Recently at SoraNovaAI, I built knowledge base systems for a venture capital firm. What I discovered is that people often keep the "how" of their decision-making private. Not out of malice, but because it's genuinely their edge. A senior investor's pattern recognition. A sales leader's intuition about which deals to push. These are competitive advantages tied to personal goals. Context graphs assume everyone wants their decision-making captured. The reality is more nuanced. I believe in the vision of better decision capture. The pain is real. Organizations lose institutional knowledge constantly, decisions get relitigated, context disappears when people leave. But different people always see different parts of the same decision. That's not a bug. It's how organizations work. What I've learned: - Design for incomplete context, not perfect capture - Focus on domains where incentives align with transparency - Accept that the last mile is human judgment - Celebrate progress over perfection The real world is messy. The opportunity is in building systems that embrace the mess and still make things better. I'd love to hear from others who've navigated this. What's worked? What hasn't? #contextgraphs #enterpriseai #aiagents

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When you join a new org or switch teams in the same, it is quite an unsettling and borderline anxious experience. Here are a few things that I did to ramp up faster. 1. I consciously made an effort to remain unblocked 2. I read a ton of code and its history, sometimes even unrelated 3. I asked questions about the past, the present, and the future 4. I took up relatively mundane tasks - cross-team work, tests, and docs 5. I extended a helping hand in whatever capacity I could 6. I proactively met skip-level leaders and managers to understand the vision Some companies do have a culture of pairing up with someone existing in the team, but even if you do not get a mentor, it is important that you still navigate the situation and ramp up as quickly as you can. The above list is not exhaustive, by any means, and hence you can always add things that you find helpful in your context. but the lowest common denominator is to show extreme intent and interest to get output and drive outcomes. Hope this helps.

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Regulatory uncertainty surrounding the H-1B visa program is creating a significant talent crunch for U.S. companies, particularly for Indian-led AI startups. According to a recent Linkedin News report, the current climate is causing H-1B holders to hesitate leaving stable Big Tech jobs for startups, fearing instability. This is even affecting lateral hires, who are reportedly backing out of offers. In response, startups are adapting their hiring strategies: 🔹 Prioritizing recruitment of U.S. citizens, green card holders, and F-1 student visa holders. 🔹 Shifting to a bifurcated model: hiring go-to-market roles in the U.S. while sourcing tech talent directly from India. A key point of clarification from USCIS on the new $100,000 H-1B fee: ✅ Applies to: Only new H-1B petitions for workers currently outside the U.S. who do not have a valid visa. ❌ Does NOT apply to: Status changes (e.g., F-1 to H-1B), amendments, or extensions for individuals already within the U.S. This distinction may benefit Indian students currently in the U.S. on F-1 visas, as they are exempt from this fee. Overall, these policy shifts are reshaping how U.S. companies approach international hiring and visa sponsorship. #H1B #USTech #Startups #TalentAcquisition #Immigration #HiringTrends #AI #India

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I was home in Bihar recently when my dad showed me a stack of old yellowed manuscripts. They were the result of a research project by Prof. Dr. Harishchandra Satyarthi, a senior historian at B.R. Ambedkar Bihar University and a father figure for our family. The work is titled "MORSAND VILLAGE - A BROKEN CHAIN OF HISTORY: A MICRO-STUDY (From the 17th Century to 1947 A.D.)." Prof. Satyarthi spent 20 years (1960s to late 70s) documenting the history of our village, Morsand. His primary academic research focused on the social and cultural history of ancient India, specifically the evolution of labor systems and epigraphy in the post Maurya period. He applied that same scholarly lens to his ancestral village, using land registration records, the Darbhanga Raj archives, and oral traditions to map out its evolution. He was looking at a single village through the eyes of a specialist in ancient social stratification. The 149 pages are a mix of typewritten English and handwritten Hindi. My dad had tried to get these transcribed for years, but the fading ink and bilingual complexity made manual transcription difficult. I used a relatively simple pipeline to automate the process: 1. Scanned the pages at high resolution. 2. Processed the raw text through Google Cloud Vision OCR. 3. Used Claude to cross-reference the output against the original scans to correct hallucinations and translate the Hindi passages in context. The pipeline handled the "unreadable" pages in about 60–80 seconds each. It was a good reminder that while there is a lot of noise around these tools, they can make the job of recovering and preserving what we already have a whole lot easier. The manuscript itself is a granular look at a complex, toiling society. It maps out migration genealogies, feudal labor systems, and the evolution of the village over centuries: 1. Ancestral Origins: Our founding families in 1664 weren't farmers; they were tax-exempt military grantees who spent their time riding horses. Farming only came later as forests were cleared. 2. Ancient Roots: Evidence of a community called the Kasars living on the site as far back as 500 BCE—2,000 years before the village was formally founded. 3. The Battle of Plassey: Records of members of the village's Dusadh community fighting alongside Robert Clive (British officer and administrator who established the military supremacy of the East India Company in India) in 1757. 4. Social Governance: An annual General Assembly where every person had the right to speak, alongside the use of a ritual coin called a "chaur" to compel confessions. I’ve hosted the transcriptions side-by-side with the original scans here for those who might be interested in reading more: http://www.morsand.com

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Is the $100,000 H1B Fee a Turning Point for India? The news about the $100,000 H1B visa fee has shocked many in India. For a lot of talented professionals, the dream of working in the U.S. suddenly looks much harder. But maybe this change is not just a problem. Maybe it's a chance to reset. Here's what could happen: More Talent Staying in India: If hiring from India becomes too expensive for U.S. companies, they may create more roles here. That means more opportunities without leaving home. Growth of Indian Innovation: Cities like Bangalore, Hyderabad, and Pune could see even more startups and innovation as talent chooses to build here instead of going abroad. Better Education & Jobs at Home: If fewer people go to the U.S., demand for strong universities and high-quality jobs in India will grow. This pressure can make our system stronger. In the short term, this feels like a setback. But in the long run, it could help India rise as a global talent hub. Sometimes, roadblocks abroad spark transformation at home. We'll need to watch closely to see how this all plays out for India's future talent and economy.

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H-1B visa fees jump to $100,000—SaaS & AI startups across India and the US are forced to overhaul hiring, fortifying local hubs and reshaping the global talent race. Full ISR story: https://lnkd.in/ecz_J_4e #H1B #AI #SaaS #TechHiring #IndianStartups #ISRNews

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One question, while everyone is trying to solve the pipelines, when the actual issue is the node. In this case, using technology to track better than making a behaviour shift. #DontMind

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We don't need that much focus on building LLMs, There are far more optimal things happening instead of just diving into a money-burning race. People don't realize it, but India has the largest surplus of software engineering talent. For the first time, the risk of starting something and the resources required to start have gone down so much so that people can finally build. In recent days, I have seen significantly more Indians, yes, us, building something new than anybody else in the world. It's happening.

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H-1B visa fee hike of $100,000 – boon or bane for India? The recent announcement of a $100K H-1B visa fee is being discussed as a game-changer. Many say it will “turbocharge India’s innovation,” as more talent may choose to stay back instead of moving abroad. But let’s pause for a moment. 👉 The talent is already in India. Many of us chose to build careers here. 👉 This will not automatically translate into India’s growth unless we create the right ecosystem. What needs to happen next: 👉 The real game-changer is the role of the Government. 1️⃣ Invest in IT Infrastructure – It’s time we stop depending only on foreign tech giants. India can and should build its own world-class IT companies. 2️⃣ Budget for Technology & R&D – Dedicated government funding in AI, semiconductors, biotech, and next-gen IT will accelerate innovation. 3️⃣ Tech Cities with Civic Sense – Create hubs where IT professionals can thrive: quality housing, good schools, world-class healthcare, transport, safety, and civic sense. A true environment for innovators. 4️⃣ Policy Push for Startups – Ease of doing business, reduced compliance burden, and strong IP protection can unleash homegrown products at global scale. Only then will India truly convert this moment into an opportunity. Otherwise, the fee is just another global barrier with no benefit. 💡 The real question is: Will we seize this as a turning point to build a self-sustaining IT ecosystem? I’d love to hear your thoughts – do you see this as a blessing or a challenge for India? #H1B #Innovation #IndiaTech #ViksitBharat #FutureOfWork

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Time to stop spreading incorrect inferences of Trump's new order regarding H1B visas. It is important to cut through the confusion with facts, not speculation. ✅ The $100,000 annual fee applies to H1B entries (new and current holders), not current holders already in the US ✅ Current H1B holders can remain, but face restrictions if they travel and re-enter Displaced Talent ≠ Automatically harnessed Talent. Singing a celebratory note on how this is "good for India" is premature. Brain drain reversal requires intense, long term work to transform Infrastructure, Policy, Monetary and Innovation ecosystem. Things are changing in India, but there's still a lot of work required. 💡 Talent is Borderless and Fluid. There are many other countries, actively courting tech talent. If the US becomes prohibitively expensive or unwelcoming, talent will migrate elsewhere. ❤️ Spare a thought to families caught mid-travel, professionals with established lives, students planning their futures. Such sudden events create genuine hardship and life-changing disruption. It's good to debate policy merits but we need to maintain empathy for those affected. #H1BVisa #Immigration #TalentMigration #Policy #TechWorkers #GlobalTalent

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