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Search 188 articles, tutorials, videos, tools, and apps.
Field-tested articles, implementation guides, interactive tools, and small apps by Harish Kumar.
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Architecture
A practical reference architecture for building one scalable platform that supports consumer products, enterprise services, AI workloads, and sensitive genetic data—without over-engineering the organization or its infrastructure.
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Backend Engineering
A practical guide to evolving production schemas safely with expand-and-contract migrations, compatibility releases, controlled backfills, observability, and rollback plans.
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Engineering Leadership
AI can make individual tasks faster without improving organizational performance. Learn where saved time actually goes, why higher workloads can suppress adoption, and how leaders can turn productivity gains into faster flow, better quality, resilience, and innovation.
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Observability
A practical guide to deploying Datadog for a Spring Boot service on Kubernetes, including infrastructure metrics, APM, logs, custom metrics, dashboards, alerts, cost controls, and common implementation mistakes.
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Software Architecture
A practical guide to choosing between synchronous REST APIs and event-driven integration for financial systems, covering commands, queries, workflows, consistency, fan-out, reliability, and operational trade-offs.
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API Engineering
A practical enterprise strategy for evolving APIs through compatibility rules, automated change detection, visible deprecations, measured migration windows, and lifecycle governance.
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Kubernetes
A practical guide to Kubernetes pod termination, covering readiness propagation, connection draining, in-flight requests, background workers, shutdown budgets, and the race conditions behind failed deployments.
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AI Engineering
Strong AI governance does not require routing every experiment through a review board. Learn how risk tiers, automated controls, approved safe paths, and targeted human review can protect an enterprise without stopping delivery.
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Artificial Intelligence
AI proofs of concept are easy to start and difficult to operationalize. Learn why experimentation rarely compounds into enterprise transformation—and how product ownership, shared platforms, evaluation, governance, and operating metrics turn isolated demos into repeatable business outcomes.
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AI Engineering
A practical architecture for instrumenting IDE assistants and coding agents to detect policy drift, evaluate engineering outcomes, and enforce security and governance controls without collecting source code or prompts.
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Frontend Engineering
A practical comparison of Playwright and Cypress for enterprise single-page applications, covering architecture, parallel execution, flake control, debugging, and governance requirements.
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AI Engineering
A practical threat model and defense-in-depth guide for reducing prompt injection risk in AI pair-programming workflows while preserving human control over code, tools, and production changes.
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Engineering
A practical architecture for adding LLM-assisted review to GitHub pull requests while controlling permissions, prompt injection, cost, comment noise, and developer latency.
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Next.js
A practical guide to browser, CDN, and server-side caching for Next.js API routes and Route Handlers, including safe headers, invalidation patterns, and production tradeoffs.
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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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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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Artificial Intelligence
A practical framework for proving enterprise AI value through counterfactual baselines, balanced operational metrics, total cost, risk-adjusted returns, and realized business outcomes.
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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.
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Artificial Intelligence
Semantic Contract give AI and software systems a shared, machine-readable model of entities, relationships, rules, and meaning. Learn how they enable semantic reasoning, enterprise knowledge graphs, grounded RAG, and safer multi-agent workflows.
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Artificial Intelligence
A practical guide to building a local AI inference server, from a single RTX 3060 or 3090 to a 96 GB multi-GPU system, with Ollama, LM Studio, Hugging Face models, and an OpenAI-compatible vLLM API.
Read articleArchitecture
A practical reference architecture for building one scalable platform that supports consumer products, enterprise services, AI workloads, and sensitive genetic data—without over-engineering the organization or its infrastructure.
Read articleA practical guide to evolving production schemas safely with expand-and-contract migrations, compatibility releases, controlled backfills, observability, and rollback plans.
AI can make individual tasks faster without improving organizational performance. Learn where saved time actually goes, why higher workloads can suppress adoption, and how leaders can turn productivity gains into faster flow, better quality, resilience, and innovation.
A practical guide to deploying Datadog for a Spring Boot service on Kubernetes, including infrastructure metrics, APM, logs, custom metrics, dashboards, alerts, cost controls, and common implementation mistakes.
A practical guide to choosing between synchronous REST APIs and event-driven integration for financial systems, covering commands, queries, workflows, consistency, fan-out, reliability, and operational trade-offs.
A practical enterprise strategy for evolving APIs through compatibility rules, automated change detection, visible deprecations, measured migration windows, and lifecycle governance.
A practical guide to Kubernetes pod termination, covering readiness propagation, connection draining, in-flight requests, background workers, shutdown budgets, and the race conditions behind failed deployments.
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