Healthtech, Product Management & tech frontiers

 Q1: with ai systems being capable of creating software, what's the advice for coders for next 5 to 10 years for a good career ? Ans 1:  The software development landscape is shifting rapidly, but there's still tremendous opportunity for human developers who adapt strategically. Here are the key areas I'd focus on: Move up the stack to judgment and architecture . AI excels at implementing well-defined specifications but struggles with ambiguous requirements, making strategic decisions, and understanding what should be built. Skills in system design, technical leadership, product thinking, and translating messy business needs into technical solutions will become more valuable, not less. Develop deep domain expertise . The combination of coding ability plus deep knowledge in healthcare, finance, climate tech, manufacturing, or other complex domains is powerful. AI can generate code, but it can't replace someone who understands both the technical and domain-specific nuanc...

Process based on established methods and tools commonly used for transferring work items (like AMS tickets) from Azure DevOps to ServiceNow. This includes handling historical data such as the last 6 months. Many organizations achieve this through APIs, built-in integrations, or third-party tools to ensure data fidelity. If you're looking for someone with hands-on experience, I recommend checking ServiceNow's community forums servicenow.com +2  or LinkedIn groups focused on DevOps and ITSM migrations, where users often share case studies. Tools like OpsHub, Exalate, or ZigiOps also have support teams that can guide you through custom setups marketplace.visualstudio.com +2 .Steps for MigrationThe process typically involves exporting data from Azure DevOps, mapping it to ServiceNow's structure (e.g., incidents, change requests, or custom tables), and importing it. You can use ServiceNow's native Azure DevOps integration for ongoing sync, but for a one-time historical t...

  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...