SShortSingh.
Back to feed

Scrum Teams Could Benefit From Linter-Style Prevention Over Sprint Reporting

0
·3 views

A software development opinion piece argues that most agile tools function as recording instruments, surfacing problems only after a sprint has already failed, rather than preventing them in real time. The author draws a parallel between code linters — which block invalid actions at the moment they occur — and how Scrum workflow tools could be redesigned to enforce process rules upfront. Key examples include blocking a sprint from starting without a defined goal, preventing backlog items from skipping workflow states, and requiring all Definition of Done criteria before a card reaches Done. The piece outlines three core linter principles — encoding rules in the tool, blocking invalid actions rather than flagging them, and catching failures at the earliest possible point — and maps each to Scrum practice. The central argument is that shifting from reactive dashboards to proactive guardrails reduces the cost of process failures and removes reliance on team discipline alone.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

Log in to join the discussion and vote.

Log in

Related stories

0
ProgrammingDEV Community ·

Google X-Ray Search Can Help Tech Job Seekers Find Roles Beyond LinkedIn

Google X-Ray Search is an advanced search technique that uses Google's search operators to scan specific recruitment platforms like Greenhouse, Lever, Ashby, and Workable for job listings. By combining role titles, experience levels, and locations into a structured query, job seekers can surface targeted vacancies that may not appear on mainstream job boards. The method is particularly useful for early-career professionals in fields such as data science, software engineering, and cybersecurity. Experts advise against applying blindly, recommending that candidates research the company, match their skills to the job description, and tailor each application accordingly. Having technical skills alone is considered insufficient without the ability to find opportunities strategically and present qualifications in a compelling, role-specific way.

0
ProgrammingDEV Community ·

Developer Publishes Step-by-Step Guide to Building a Modular AI Backend with FastAPI

A developer has published a tutorial on DEV Community outlining the first chapter of building ACAI, an Adaptive Cognitive AI Architecture. The guide focuses on establishing a minimal working backend using FastAPI, a Python-based web framework, connected through an orchestrator to a model service layer. To allow testing without external API credentials, the initial implementation uses a mock AI model that returns placeholder responses. The project follows a structured directory layout and relies on tools including Pydantic, Uvicorn, and python-dotenv for configuration and validation. The tutorial is designed as a staged series, with later chapters intended to replace the mock model with real AI provider integrations.

0
ProgrammingDEV Community ·

Developer Seeks SharePoint Online Testers for React Faceted Search SPFx Web Part

A new read-only React faceted search web part has been submitted as a pull request to the PnP SharePoint Framework webparts repository. The component uses SharePoint Search REST API to search the current site and supports features like file type refiners, result counts, and multiple error states. All local checks — including Jest tests, TypeScript builds, ESLint, and production packaging — have passed successfully. However, tenant-level validation on a live SharePoint Online environment has not yet been completed. The developer is requesting community members with SharePoint Online access to test search indexing, refiners, permissions, and edge-case states, and share findings on the pull request.

0
ProgrammingDEV Community ·

Engineer Builds Personal RAG System to Unify Scattered Digital Knowledge

A software engineer has begun developing a Retrieval-Augmented Generation (RAG) system from first principles, aiming to create a unified, searchable knowledge layer across tools like Notion, Slack, Gmail, Logseq, and Markdown files. The project was inspired by frustration with fragmented personal knowledge and tools like NotebookLM that require separate, manually managed workspaces. RAG systems work by retrieving relevant context from a personal datastore and feeding it alongside a user query to a large language model, enabling more accurate and personalized responses. The proposed architecture breaks down into core components including source connectors, an ingestion pipeline, data processing, and knowledge storage optimized for fast retrieval. The engineer is documenting the build process as a multi-part series, with the goal of creating what he describes as a personal Google Search across one's entire digital life.