5 articles by Shahzaib Awan
Beyond the Context Window: Why AI Agents Need Memory That Works
AI agents need memory that helps them remember, revise, and resume work. Explore the research behind persistent memory and how custom datasets and environments can reveal whether agents use past experience wisely when conditions change.
The Experience Layer of AGI: How Real-World Tasks Shape More General AI
The path toward more general AI depends on the experiences agents learn from. Explore how diverse tasks, custom environments, and domain expertise support adaptation and transfer - and how Deaimer contributes through datasets and executable workflows.
From Coding Agents to Scientific Agents: Why Domain Expertise Matters
Working code is only the beginning of scientific AI. Explore why domain knowledge, data semantics, numerical judgment, and custom learning environments matter when developing agents for real research workflows.
Termetheus and the Foundations of Environment Based AI Learning
Explore how Termetheus turns custom terminal tasks into executable learning environments for AI agents, supporting post-training, domain-specific skills, and evaluation on unseen problems.
ColdStart and the Science of Reliable Autonomous Recovery
ColdStart examines whether AI agents can recover unfamiliar broken systems while preserving data, security, and persistent behavior—bringing reliable recovery into focus as a measurable capability.
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