Understanding Volatility Surface Term Structure: Concepts, Math, and Pitfalls

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A technical educational piece by Shakti Tiwari breaks down volatility surface term structure from first principles, focusing on structural concepts rather than live market data. The article warns that half-implemented models often appear correct in demos but fail in production, emphasizing the gap between theoretical design and actual code behavior. It outlines a three-part mechanism — observation, decision, and cost — noting that most tutorials omit the third element, which accounts for realistic fill models, fee schedules, and tax rules. The author stresses that look-ahead bias and data leakage are common consequences of skipping proper timestamping and past-only data rules. The core takeaway is that testable, clearly defined system contracts and realistic cost modeling separate a working architecture from a wishful one.

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Essential Docker Commands Every Developer Should Know for Daily Use

A practical guide to core Docker commands covers daily essentials such as docker run, exec, logs, ps, and build for working with real containers beyond basic examples. Key flags for docker run include -it for interactive shell access, --rm to auto-remove stopped containers, -e for environment variables, and -v for bind-mounting host directories. The docker ps and docker inspect commands help monitor running containers, while docker stats enables real-time CPU and memory tracking without entering a container. Docker logs, particularly with the -f flag, is highlighted as the go-to first step when diagnosing misbehaving containers, since applications are expected to write output to stdout or stderr. The docker exec command allows developers to run commands or open a shell inside an already-running container, making it useful for debugging in production or staging environments without restarting services.

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ProgrammingDEV Community ·

AI Product Builder: The Hybrid PM-Developer Role Gaining Traction in 2025

A new job role called 'AI Product Builder' is emerging in tech hiring, describing a single professional who handles both product management and software development using AI coding tools. The role is made possible by advances in AI code assistants, better context and prompt management, and automated quality checks that reduce the traditional idea-to-deployment cycle. However, its success depends heavily on the maturity of the existing codebase, including documentation quality, extensibility, and automated testing coverage. Organisations with well-structured, documented codebases are better positioned to let such hybrid roles thrive, while those with younger or messier code should limit the scope of tasks assigned to these individuals. Demand for the role is expected to grow, but companies must invest in the right engineering foundations to make it viable.

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ProgrammingDEV Community ·

How a React 19 Kanban Board Was Built for Real-Time, Offline-First Collaboration

A developer has detailed the architecture behind an enterprise-grade, real-time Kanban board built using React 19, Vite, TypeScript, and Tailwind CSS. The application uses client-side virtualization via TanStack Virtual to render thousands of DOM nodes while sustaining 60fps during drag-and-drop interactions. It features an offline-first Progressive Web App setup powered by a Service Worker that keeps the board fully functional without an internet connection. A custom undo/redo state machine built into Zustand handles optimistic UI updates and automatically rolls back failed network requests. The project achieves 99.12% test coverage through Vitest and is positioned as a foundation for future AI-powered, enterprise task management tooling.

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ProgrammingDEV Community ·

681 AI Sessions Tracked: Repeated File Edits, Not Heavy Use, Drain Your Budget

A developer tracked 681 AI work sessions across 41 projects over four months, from April to August 2026, to understand where subscription costs actually go. The analysis found that 90% of requests cost very little, while just 15% of requests — those where the AI edits the same file three or more times — account for 41% of total output. When the AI loops on a problem without progress, it generates six times more text than a normal request while rarely improving on its second attempt. A key behavioral signal is the phrase 'let me try something else,' which the author identifies as the point where the AI stops reasoning and begins cycling through repeated solutions. The author recommends setting a two-attempt limit per problem in the AI's instruction file and notes that re-explaining project context costs time but not significant money.

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