Open + Thinking = Latent: The Circle of Adaptive Intelligence
The AI ecosystem is evolving rapidly. In recent weeks, both OpenAI and Thinking Machines have released new platforms that expand what developers and researchers can do with large language models. OpenAI introduced AgentKit, a full suite for building and deploying agents, while Thinking Machines launched Tinker, a managed API for fine-tuning models with granular control. Each represents real progress. Yet both depend on experts and keep learning separate from operation. The next step forward is a system (a solution) that unifies these capabilities, a platform where training, deployment, and actual execution happen continuously, automatically, and without specialists.
OpenAI’s AgentKit brings structure and scalability to the process of building, as well as running agents. It includes a visual builder for workflow design, ChatKit components for embedding conversational interfaces, and a Connector Registry for integrating data sources securely - very cool. For developers working within the GPT ecosystem, it streamlines orchestration and monitoring. Agents can now be deployed with built-in tracing, evaluation, and safety controls. This is a major step toward operationalizing agentic AI, where other similar platforms do exist.
However, AgentKit agents remain static at their core. They execute logic and retrieve context but do not update their internal understanding. OpenAI does provide fine-tuning APIs, but those are offline, limited, and high-level. Developers can upload small curated datasets to nudge a model toward a style or domain, but they cannot control the learning process itself. Training occurs in the background, asynchronously, and without insight into what actually changes. It is powerful but opaque. In short, OpenAI gives you a platform for building agents and a separate, simplified way to tune models, not a system where agents can learn from their own active use.
Tinker, by contrast, focuses entirely on the training side. Created by Thinking Machines Lab, it offers researchers and engineers direct access to the internal mechanics of fine-tuning. Through its Python-based API, users can invoke low-level functions like forward_backward, optim_step, and sample, while Tinker manages distributed compute, fault recovery, and resource scaling. It allows deep experimentation with open-weight models such as Llama or Qwen, removing the infrastructure burden that traditionally comes with custom training. Yet Tinker stops there. It has no concept of agents, orchestration, or runtime execution. It is a lab for experts, not a system for deploying adaptive AI in the wild.
Together, OpenAI’s AgentKit and Thinking Machines’ Tinker represent two halves of today’s AI infrastructure. AgentKit focuses on how models are built and run, while Tinker focuses on how models are finely trained. Both require teams of ML engineers and both separate learning from operation. Neither allows a model to improve continuously as it runs, nor empowers knowledge workers to participate meaningfully at a time when the idea of a “Great Displacement” looms overhead. What the industry still lacks is the bridge between these worlds, a platform where training and execution merge, and where the system itself becomes the learner. If AI is to be the great enabler, it must be accessible to everyone.
LatentSpin's mission is to fill this gap. The idea of combining the orchestration simplicity of AgentKit with the learning depth of Tinker, plus adding purposeful real-time, self-directed learning. The result is a living agent that learns safely and transparently as it operates. Each update is versioned and auditable, allowing rollback or inspection. There are no large retraining loops or offline batch jobs, and no need for curated datasets or ML oversight. LatentSpin agents improve organically through use, guided by interaction feedback rather than expert supervision.
Viewed across the broader landscape, the contrast becomes clear. OpenAI provides a unique framework for building and running agents, with a separate channel for training models offline. Tinker provides an advanced framework for training models deeply, but offers no environment for running agents. LatentSpin merges both and removes the expertise barrier. It delivers automated, live training and production-grade orchestration in one unified system.
This approach does more than simplify workflows, it changes who can participate in AI creation. AgentKit empowers developers. Tinker empowers researchers. LatentSpin empowers everyone. It allows knowledge workers, teams, and organizations to create adaptive agents without writing code or tuning parameters. Each agent becomes a digital counterpart that evolves with its user, turning knowledge itself into a renewable resource.
The opportunity is massive. The global knowledge economy is worth over six trillion dollars and projected to double by 2030. Yet most of that expertise remains locked in human experience. By allowing every interaction to become a training signal, it transforms human input into adaptive intelligence. This reframes automation not as replacement, but as amplification, the conversion of skill into scalable learning.
As the AI ecosystem matures, OpenAI’s AgentKit will likely dominate enterprise deployment. Tinker will empower researchers to push the limits of training. LatentSpin complements and transcends both. It closes the loop between learning and execution, creating agents that grow continuously, safely, and independently.
In short, OpenAI has built a platform for creating and running agents. Tinker has built a platform for training models. LatentSpin is building a platform that does both, automatically, continuously, and without experts. It represents the completion of the cycle that others began, a shift from managed intelligence to self-learning systems.
Learn on Everything. Adapt to Anything…
