Balance pushing AI capabilities with pragmatic release schedules by assessing feature completeness against strategic product timelines

Systematically evaluate open-source models like GPT-5.0 OSS 20B on standard intelligence benchmarks before public release to inform performance gaps a...Show more

Combine frontier (cloud-based) models with local on-device solutions to create flexible AI pipelines that optimize for latency, cost, and privacy

Establish systematic benchmarks to compare model families (e.g., GPT-3.5 vs GPT-4) and choose the right model tier based on performance requirements.

Use Kimi K2 and Minimax models deployed in the cloud as scalable AI workhorses for high-throughput inference and research experiments.

Experiment with Deep Agents paired with Deep UI interfaces to create more natural, AI-driven user interactions in applications.

Use product management tools to outline a detailed three-month build roadmap that aligns team milestones with AI project deliverables.

Implement critique and citation features with hyperlink support to structure review processes and reference external sources within AI-generated conte...Show more

Implement Clerk-based user authentication to manage sessions and permissions for AI agents, ensuring secure multi-user access in your agent platform.

Use Langchain to orchestrate and integrate deep agents into advanced AI workflows, enabling modular, maintainable pipelines for complex tasks.

Integrate a long-term memory layer into deep agents to maintain context across interactions and improve decision-making over extended sessions.

Use Agent Builder to auto-generate AI agents from plain-language descriptions, streamlining the creation and deployment of specialized agents.

Implement a structured process for consulting domain experts during AI tool development to ensure relevance and accuracy.

Leverage consistent prompt structures across Python and JavaScript environments to maintain cross-language AI development efficiency.

Use AI tools as research vehicles by defining clear starting points and iterating on prompts to refine knowledge extraction workflows.

Tom highlights an iterative process of refining AI tools based on UI feedback and testing cycles, underlining the importance of user interfaces in dev...Show more

Tom outlines a context management approach that reads and writes files to track and restore task states, offering a simple but effective method for co...Show more

Tom demonstrates spinning up AI agents reliably and discusses managing flows efficiently even when not sharing full outputs, highlighting scalable orc...Show more

Cam emphasizes showing full outputs in the Agent Builder flow to maintain transparency and facilitate debugging throughout the AI development pipeline...Show more

Tom proposes immediate feedback mechanisms for AI development tests, enabling rapid iteration and improved model performance through on-the-spot gradi...Show more