Sharing cutting-edge AI insights, context engineering practices, and cognitive paradigm theories
How much value can you get from investing in AI? Based on a Yale University paper, we propose a new production function framework for measuring AaaS (Agent as a Service) value. The 2025 market reality: value creation is shifting from the model layer to the agent layer.
Sean's T2V Velocity formula precisely captures the fundamental contradiction in AI commercialization, but it might be missing a key variable—realization probability. Programmers aren't unwilling to pay; they're uncertain what 100k tokens will ultimately deliver.
Token consumption ≠ business value. The T2V of a viral short video is 160,000x that of code refactoring. This isn't a decimal point difference—it's a business model difference. Customers don't pay for tokens; they pay for closed loops.
In the AI era, the best interface is no interface. It's not about humans learning machine language, but machines understanding human intent. This isn't just a technology trend—it's a redefinition of human-machine relationships.
Redesign AI collaboration using state machine thinking. By defining initial state, target state, context space, and 5 core elements, solve AI task problems like context loss, goal drift, and verification failures. Transform AI collaboration from luck-based to predictable.
Starting from the code design of the Monogent cognitive system, exploring the nature of memory, emotion, and consciousness. Intelligence isn't a program running in the brain—it's the brain's network itself. Understanding intelligence requires not just technical knowledge but cross-disciplinary thinking.
Exploring why human cognitive systems are the ideal reference for building AI individual cognitive systems. From functional, testing, and implementation perspectives, we analyze how to systematically construct AI cognitive systems through structural analysis, functional mapping, and gap benchmarking.
A deep exploration of the philosophical essence of semantic non-computability. Starting from the five key properties of semantics, we argue that experientiality is the core of semantics, and its uniqueness and temporality fundamentally fail to meet Turing computability requirements.
RAG is a retrieval method that sacrifices information precision for broader matching space. This article analyzes why RAG is unsuitable for AI memory from philosophical and technical perspectives, and introduces the Monogent individual cognitive system concept.
MCP ecosystem faces a critical challenge: project path detection. This article introduces an innovative AI-driven solution that achieves 100% accurate project environment positioning by shifting from system guessing to AI-informed path management.
CDT (Cross-Dimensional Terminology) is a systematic approach to defining AI prompts through cross-cultural, cross-temporal, and cross-domain validation. Learn how to dramatically improve AI comprehension accuracy by eliminating ambiguity in your prompts.
A deep dive into the memory fragmentation problem in large language models. From static keys to dynamic algorithms to meta-information embedding mechanisms, we reveal the nature of AI amnesia and its solutions, inspiring new approaches to AI Agent system design.
OES (Objective-Environment-Success Criteria) is a containerized framework for AI workflows. Learn how to prevent task drift, context loss, and inconsistent outputs by structuring AI tasks like Docker containers.
DPML (Deepractice Prompt Markup Language) is an XML-style markup language designed for AI prompt engineering. Learn how to create structured, extensible, and maintainable prompt systems using modular design principles.
Exploring a viable path to AGI through AI organization. Instead of creating a single super-intelligent AI, we propose building collective consciousness through organized AI societies—combining management, specialist, reflective, memory, and exploration AI agents.
The Deepractice 4P Theory provides a complete value chain from abstract patterns to deployed products. Learn how to bridge the gap between prompt design patterns and real-world AI applications through four progressive stages.
Move beyond traditional prompt engineering with a comprehensive intelligent interaction system. Learn ten core prompt types (RRP, PDP, ESP, TMP, TVP, KTP, and more) and how to combine them for production-ready AI applications.
Master AI interaction through a systematic seven-dimensional prompt framework. Learn how to combine Role Responsibility, Protocol Description, Execute Specification, and other prompt types to build professional, precise, and adaptive AI assistants.
