Integrate research workflows with systematic tracking of feature and tool additions to ensure visibility and reproducibility in AI development.

Use an interactive UI as a reference point to iteratively research and develop deep agents, refining capabilities through successive demos.

Emphasize building AI tools around a user-driven iterative cycle to drive practical adoption and refinement of capabilities.

Use the XJS starter template to streamline app development and accelerate approval processes.

Implement deep agents using Langchain to automate complex AI workflows and enhance automation capabilities.

Implement a dedicated code execution layer within your Model Control Plane (MCP) to orchestrate tool connectors and manage runtime workflows for AI ap...Show more

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.

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

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.

Create a side-by-side interface within the same environment to illustrate native app functionalities more clearly during demos.

Outline a function’s requirements in detail before writing any code to ensure clarity and alignment with AI-driven development objectives.

Use structured research prompts to check macro indicators influencing sector earnings calendars and related financial metrics.

Implement asynchronous execution by running a single script against multiple MCP tools in parallel to reduce cumulative wait times.

Distinguish between charting data formats and LLM input requirements by focusing on time spans and stock parameters.

Aggregate stock ticker data over custom time ranges and window sizes to support flexible financial analysis.

Share full descriptions and comparisons of AI implementations to ensure stakeholders evaluate tools with a consistent understanding.

Implement deep agents using Langchain to orchestrate advanced AI workflows across multiple steps and tools.