SShortSingh.
TopTechnologyAI & MLStartupsProgrammingScienceBusinessFinanceCrypto & Web3World
Back to feed
Programming

Developer details method for turning code review feedback into Cursor AI rules

0
·1 views

A developer has outlined a method for converting human code review comments into actionable rules for the Cursor AI coding assistant. The process involves analyzing comments on merged pull requests and tracking subsequent code changes to identify genuine team patterns. The developer emphasizes that repeated feedback across different pull requests is stronger evidence than multiple comments on a single one. An experiment scanning 15 pull requests yielded only two viable candidate rules, highlighting the need for rigorous evidence. The author recommends testing a small set of reviewed rules on real code to see if they prevent repeated mistakes.

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 ·

Developer overhauls invoice generator after user finds layout bug in real business use

A developer's invoice generator tool contained a layout bug that drew a line through the 'TOTAL DUE' text, discovered only when a user shared a screenshot from their real business. This user feedback prompted a complete visual redesign of the PDF output. The developer implemented several changes, including adding color bands, zebra striping, and proper headers to create a more professional and trustworthy document. The shop owner who reported the issue now uses the updated tool for their actual invoicing needs. The incident highlighted how user testing in real-world scenarios is crucial for moving a tool from merely functional to professionally viable.

0 comments Read more at DEV Community
0
ProgrammingDEV Community ·

Lecturer outlines core mathematical principle behind generative AI models

Professor Prathosh AP's public lecture series explains the foundational principle of generative AI. The method involves using deep neural networks to transform random noise into data that mimics a target distribution. This is achieved by optimizing network parameters to minimize a statistical divergence between the generated and real data. The process is illustrated with an example using Gaussian noise and a simple linear network. The lecture notes that complex data distributions require more flexible, deep networks to approximate effectively.

0 comments Read more at DEV Community
0
ProgrammingDEV Community ·

Developer details Octupuz puzzle app creation, emphasizing extensive pre-code specification

Developer Panth, who leads software at Oizom, built the Octupuz puzzle game app in about a month as a personal project. Before writing code, the project's specification was a detailed 849 KB JSON document outlining 72 screens, 23 components, and animations researched from Instagram. The app presents a social feed similar to Instagram, but every post is an interactive puzzle, with 41 live game types including Queens and Nonogram. It is currently a web-based Progressive Web App accessible at octupuz.in, with plans for a Play Store release. The backend, built on Supabase, is designed to store only social data and game bundle paths, leaving puzzle logic and generation entirely to client-side game code.

0 comments Read more at DEV Community
0
ProgrammingDEV Community ·

Integration bug breaks DeepSeek AI's 'thinking' conversations in GitHub Copilot

A GitHub Copilot integration issue is disrupting multi-turn conversations with DeepSeek AI models. The problem occurs when DeepSeek's 'thinking mode' models require specific conversational context to be maintained. GitHub Copilot's current provider adapter fails to preserve this required data field, causing subsequent requests to fail. This breaks conversation flows and impacts developer productivity when using these AI assistants together.

0 comments Read more at DEV Community
Developer details method for turning code review feedback into Cursor AI rules · ShortSingh