A to-do list that actively completes its own tasks is a popular and powerful application being built on Dreamer. This use case, famously requested by Sam Altman, becomes achievable when a primary agent can orchestrate multiple specialized tools and sub-agents to research, automate, and resolve tasks.
According to Dreamer's CEO, the biggest capability missing from LLMs is "taste." By default, AI-generated applications and UIs are generic and identifiable by the model that created them. It requires extensive human effort in prompt engineering and templating to create delightful, non-generic user experiences.
To ensure AI agents are trustworthy and can work together safely, Dreamer's architecture includes a central "Sidekick" that acts as a kernel. It manages permissions and communication between agents, preventing uncontrolled data access and ensuring actions align with user intent, much like a computer's operating system.
Traditional software development is too costly for short-lived events like conferences or ski trips. AI agent platforms like Dreamer enable non-technical users to quickly build powerful, "episodic" applications that are highly useful for a limited time, solving a major cost-value mismatch for temporary needs.
Dreamer's hiring process now evaluates an engineer's ability to work with and through AI coding agents. Beyond a basic coding screen, the main interview involves a project built using tools like Codex, testing the candidate's skill in prompting, reviewing, and orchestrating AI to be productive.
Dreamer's AI "Sidekick" builds apps using the same command-line interface available to human developers. This forced the team to create excellent documentation and a clear API surface, which not only enables the agent but also significantly improves the developer experience for humans, creating a virtuous cycle.
To accelerate its ecosystem, Dreamer pays developers who publish API integrations ("tools") on its platform based on usage. This revenue-sharing model creates a powerful incentive for the community to build high-quality, diverse tools, which in turn makes the platform more valuable for all agent builders.
David Singleton, CEO of Dreamer, reveals that the complex platform—encompassing an agent studio, tool ecosystem, and OS-like architecture—was built by a core team of only about six people. This highlights the incredible productivity and leverage that small, high-talent-density teams can achieve in the AI era.
Dreamer's CTO chose TypeScript for their agent SDK over Python, his personal favorite. Strong typing provides immediate feedback at compile time, enabling AI coding agents to enter a tight loop of generating code, perceiving errors, and self-correcting—a critical advantage for building reliable software with AI.