Healthtech, Product Management & tech frontiers

  Track A: Language-first approach. Start by mastering a general-purpose language such as Python. This builds core programming logic and problem-solving skills. Once comfortable, the learner can branch into adjacent domains—web development (Django/Flask), data analysis, automation, or basic machine learning. For example, a Python-first learner can move into backend APIs, data pipelines, or scripting roles across startups, healthcare IT, fintech, and enterprise automation. This track offers flexibility and faster entry into diverse roles. Track B: Full-stack-first approach. Begin with full-stack development, such as the MEAN stack (MongoDB, Express, Angular, Node.js). This provides an end-to-end understanding of building real-world applications—frontend, backend, databases, and deployment. After this, the learner should systematically study Data Structures and Algorithms (DSA) : arrays, stacks, queues, linked lists, trees, and searching/sorting algorithms. For example, a full-sta...

 In a post on X that has sparked widespread debate, Karpathy confessed: “I’ve never felt this much behind as a programmer. The profession is being dramatically refactored as the bits contributed by the programmer are increasingly sparse and between.” Earlier, programmers wrote most of the software themselves. They planned the code, wrote every line, and fixed problems manually. Now, AI tools are doing many of these tasks. Because of this, the amount of code written directly by humans is reducing. Ref https://www.financialexpress.com/life/technology-teslas-former-ai-chief-andrej-karpathy-warns-software-engineers-says-never-felt-this-far-behind-as-a-programmer-4092061/ Based on the article about Andrej Karpathy's warning and the broader trends in AI-driven software development, programmers should focus on developing a blend of **technical, cognitive, and strategic skills** to stay relevant. The era of simply writing lines of code is evolving into one where programmers act as **archit...

  The Perils of Short-Term Forecasting: Lessons Across Industries Forecasting is as much an art as it is a science. Yet history shows that analysts often fall into the trap of extrapolating recent performance, ignoring longer cycles of valuation, regulation, or technological change. This tendency to focus narrowly on short-term data has led to some of the most notable misjudgments across finance, healthcare, technology, and beyond. Stock Market Missteps In 1929, economist Irving Fisher famously declared that stock prices had reached a “permanently high plateau.” His optimism, rooted in the roaring 1920s bull market, overlooked the decade-long buildup of speculative debt and overvaluation. The result was the Great Depression crash. Similar errors resurfaced during the dot-com bubble of the late 1990s. Analysts such as James Glassman predicted the Dow would soar to 36,000, extrapolating short-term tech stock surges while ignoring unsustainable valuations and lack of profits. The...

The recent trouble with the Airbus A320 — caused by a critical software bug — shows why rigorous testing is non-negotiable in complex systems. The “A320 bug” — linked to the aircraft’s ELAC (elevator/aileron control) software — was triggered by intense solar radiation that can corrupt flight-control data. As a result, dozens of international carriers were forced to ground or recall thousands of jets: it’s arguably the largest aircraft-fleet recall in aviation history. Had the faulty version of the software been subjected to more exhaustive testing — including scenarios such as solar-radiation-induced data corruption — the vulnerability might have been caught before commercial deployment. A robust test suite would have included “edge cases” (rare but plausible events) to stress-test the code under extreme conditions. The root cause: under periods of “intense solar radiation” (solar flares / charged particles), the electronic data processed by ELAC software can get corrupted — a bit-f...

# **The AI Decade: A Sobering Assessment of Three Futures (2025–2035)** *Why the next ten years will determine whether artificial intelligence delivers a productivity revolution, triggers a catastrophic bust, or—most likely—muddles through in messy, uneven waves* --- ## **Introduction: The Trillion-Dollar Question** Global capital markets are currently prosecuting the largest coordinated technology bet in human history. Between 2023 and 2025, corporations and governments committed over $500 billion to AI infrastructure, foundation models, and deployment—a figure projected to exceed $1 trillion cumulatively by 2028. This spending dwarfs the scale of previous technology build-outs, including the dotcom era and the initial cloud computing wave. Yet unlike past cycles, this investment is proceeding with a troubling combination of certainty and opacity. Corporate leaders speak with conviction about AI's transformative potential while simultaneously acknowledging—often in the same breath...

🔹 Definition P-value in statistics tells us how extreme our observed data is, assuming the null hypothesis (H₀) is true. The difference between one-tailed and two-tailed p-values depends on what kind of difference or effect we are testing for. 🔸 1. Two-Tailed Test You use a two-tailed test when you’re testing for any significant difference — either greater than or less than a certain value. ✅ Example: Suppose the average blood pressure (BP) in a population is 120 mmHg. You want to test whether a new drug changes BP (it could increase or decrease). Null hypothesis (H₀): μ = 120 Alternative hypothesis (H₁): μ ≠ 120 If your sample mean = 126, and the calculated p-value = 0.04 (two-tailed) , then this means there’s a 4% chance of observing such an extreme difference (either direction) if the true mean were really 120. If α = 0.05, since 0.04 < 0.05 → Reject H₀. Conclusion: The drug significantly changes BP (either up or down). 🔸 2. One-Taile...