Use AI tools to analyze project data and recommend actionable next steps, streamlining decision-making processes in development workflows.

Leverage AI to automatically capture screenshots of competitor product pages and analyze market landscapes to identify gaps and opportunities.

Build an AI-driven 'use list products' feature that automatically aggregates and curates relevant tools or items based on user context and needs.

When components hang off the main system loosely, proactively map and manage these dependencies to ensure maintainability and ease of updates.

Emphasizing the importance of a Model Control Protocol (MCP) to standardize interactions with AI models and ensure consistent integration in projects.

Use Playwright to automate simple end-to-end testing workflows including programmatic cancellation of tests, enabling faster feedback loops.

AI tools can refine planning steps by soliciting user input iteratively to clarify objectives and maintain alignment with high-level goals.

Emphasizing citations for AI outputs may be more acceptable to users than exposing raw code, balancing transparency and simplicity.

Develop a tool that automatically validates and audits data transformations in AI workflows, offering transparency to users and developers.

TypeScript is used for developing code demonstrations but requires further learning to handle hidden code in AI tool demos efficiently.

MCP server is used for option contract pricing, but requires transparency in implementation to ensure accurate financial computations.

During use of the MCP server, hidden code abstractions led to incorrect option contract pricing until manual inspection revealed the miscalculation.

Users prefer not to see background code execution, so abstracting code behind clean interfaces can improve user experience.

Emphasize implementing reliable data manipulation and validation methods within data transformation workflows to ensure correct outputs in AI systems.

MCP’s Code Mode converts provided context and tool definitions into ready-to-run TypeScript code snippets for development workflows.

Expect code generated in MCP’s code mode to vary slightly across sessions unless the exact same prompt and notes are used.

Leverage AI models like Claude to generate code that fetches data without explicit prompts, effectively automating data-extraction scripts.

Conceal implementation details in demos to keep focus on core features and improve user experience during presentations.

Maintain and rehydrate explicit context across ChatGPT sessions to ensure consistent responses and reproducible outputs.

Provide the LLM with a list of available tools in its context so it can generate executable TypeScript code for those tools automatically.