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Field-tested articles, implementation guides, interactive tools, and small apps by Harish Kumar.
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Artificial Intelligence
Prompt techniques can improve individual outputs, but enterprise AI adoption depends on verification, workflow redesign, critical thinking, risk controls, and domain judgment. Here is a practical capability model for engineering leaders.
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Artificial Intelligence
Prompt techniques can improve individual outputs, but enterprise AI adoption depends on verification, workflow redesign, critical thinking, risk controls, and domain judgment. Here is a practical capability model for engineering leaders.
Read article
Software Architecture
Excessive synchronous service calls can make independently deployed microservices fail and scale as one system. Learn how to identify runtime coupling, contain cascading failures, and incrementally introduce events, local read models, bulkheads, and sagas.
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Software Architecture
A practical architecture for modernizing legacy financial systems with domain APIs, anti-corruption layers, asynchronous messaging, adapters, and stable contracts—without leaking mainframe records, protocols, or release constraints to consumers.
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Article
REST is a design philosophy, not a religion. In financial services, APIs have to survive legacy systems, long-running workflows, regulatory controls, distributed transactions, audit requirements, retries, failures, and decades of technology. Perfect REST may look elegant on a whiteboard, but practical enterprise API design is about consistency, security, resilience, clear domain semantics, and knowing when a deliberate compromise is better than architectural purity.
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Article
AI readiness is not just about models, platforms, data, or governance. It is also about whether people feel safe experimenting, understand how their roles may change, trust the purpose behind AI adoption, and have the skills to use AI responsibly. Real transformation happens when technology readiness and people readiness evolve together.
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Artificial Intelligence
Japan has the engineering talent and infrastructure to lead in enterprise AI. The harder challenge is organizational: shortening the path from experiment to production while managing risk, imperfect data, approvals, and continuous learning.
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Architecture
An engineer with a good agentic setup can now stand up in a fortnight what used to take a quarter. The demo is real. But "we can build it faster" and "we should build it" are different claims, and the gap is where a lot of money is currently being set on fire. This post works through the five-year TCO arithmetic, a nine-gate decision tree, and the new failure mode nobody has priced yet: owning a system nobody had time to understand.
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Next.js
Build a secure Firebase authentication boundary for Next.js App Router using verified ID tokens, HTTP-only session cookies, Node.js route handlers, and server-side authorization.
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Artificial Intelligence
A practical guide to the context, tools, permissions, state, validation, security, evaluation, and governance that turn AI models into dependable engineering agents.
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Architecture
A reference architecture for AI-assisted, human-governed life insurance claims on Azure.
Read articleIntegration
Writing an API that works well and also easily for developers to implement is a must-have skill for any architect, designer, or team who is creating an API. It will matter much less if the API is being used for internal or external users because a well-written API will open many gates to excel at the intention of creating an API. There is a long debate going on on the internet about the best ways to design the APIs, and it is one of the most discussed and sometimes fought topics among people. Th
Read articleExcessive synchronous service calls can make independently deployed microservices fail and scale as one system. Learn how to identify runtime coupling, contain cascading failures, and incrementally introduce events, local read models, bulkheads, and sagas.
A practical architecture for modernizing legacy financial systems with domain APIs, anti-corruption layers, asynchronous messaging, adapters, and stable contracts—without leaking mainframe records, protocols, or release constraints to consumers.
A practical framework for proving enterprise AI value through counterfactual baselines, balanced operational metrics, total cost, risk-adjusted returns, and realized business outcomes.
REST is a design philosophy, not a religion. In financial services, APIs have to survive legacy systems, long-running workflows, regulatory controls, distributed transactions, audit requirements, retries, failures, and decades of technology. Perfect REST may look elegant on a whiteboard, but practical enterprise API design is about consistency, security, resilience, clear domain semantics, and knowing when a deliberate compromise is better than architectural purity.
AI readiness is not just about models, platforms, data, or governance. It is also about whether people feel safe experimenting, understand how their roles may change, trust the purpose behind AI adoption, and have the skills to use AI responsibly. Real transformation happens when technology readiness and people readiness evolve together.
Japan has the engineering talent and infrastructure to lead in enterprise AI. The harder challenge is organizational: shortening the path from experiment to production while managing risk, imperfect data, approvals, and continuous learning.
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