Even identical AI models can behave differently depending on the service provider, impacting application performance and integration.

Early-stage AI tools should be judged with reasonable expectations as they are brand new and subject to rapid improvement.

Adopt an “Agent harness” pattern that orchestrates multiple AI agents in the background to enable modular, scalable workflows.

Compare AI model performance in dedicated agent-only environments versus coding-centric IDE integrations to identify optimal use cases and capabilitie...Show more

Use the same codebase and prompt to compare AI model behavior across providers (e.g., v0 Gemini 3 vs new environment) by measuring tool call success r...Show more

Leverage Theory Ventures’ survey on open versus closed-source AI models to inform product strategy and community sentiment analysis.

Robo Flow offers a block-based visual interface for AI-driven vision applications, built on Meta’s SAM segmentation model for rapid image analysis.

Olama Turbo is a rapidly evolving open-source model hosting platform, adding top-performing OSS models and even closed-source Gemini 3 for flexible ex...Show more

Anti Gravity provides a single unified surface to orchestrate multiple AI sessions, streamlining development workflows.

Upgrading to Olama Turbo highlights how quickly open-source AI platforms can release new models, driving reconsideration of paid services like Max.

Use Robo Flow's block-based interface with Meta's SAM model to rapidly prototype and deploy computer vision applications with minimal code.

Just because a technology is trending doesn't guarantee its value—always dive deeper into both the AI models and their full stack before committing.

Leveraging multiple AI agents in a pipeline by having them operate on top of each other enables complex, modular workflows for advanced task orchestra...Show more

Tom admits his excitement over models that can run locally might bias his judgment on overall AI progress and tool selection.

Combine local, on-device models for inference-heavy tasks with cloud-hosted models for specialized or larger-scale workloads to optimize performance a...Show more

Running capable models like Llama 3.2 or Kimi 2 locally can eliminate inference cloud costs, unlocking more affordable AI-driven research tools.

No single model is best for every use case—differences in behavior between Claude, GPT, and Kimi mean choice should align with specific tasks.

Although not exactly GPT-5, Kimi 2 delivers a 'frontier model' vibe for general research tasks and deep agent workflows.

Llama 3.2 represented the best decently sized local model before the recent wave of performant on-device inference-capable models.

Minimax was Tom’s go-to model before switching to Kimi 2, serving as an interim solution via cloud for general-purpose tasks.