All (110)Personal Introduction (1)AI Summary (8)Project Management (4)Investment Strategy (9)Financial Market Analysis (6)AI Software Engineering (9)AI Research (7)Content Management (9)Cognitive Development (1)Philosophical Reflection (3)Entrepreneurship (1)AI Tools Development (9)Product Development (3)Troubleshooting (2)Quantitative Finance (12)AI Cost Analysis (1)System Architecture (1)Simulation Framework (1)Technical Log (3)AI Social Systems (3)Personal Reflection (1)
Reflections on AI Memory Mechanisms and Business Capability Evolution
AI Research
👤 AI technology enthusiasts, entrepreneurs, product managers, and readers interested in AI applications and business strategy
Starting from personal experience, this article discusses the EverMind AI model released by Chen Tianqiao and its biomimetic memory mechanism, noting that memory mechanisms are a crucial research direction in AI. Using CZON as an example, it analyzes how AI as an underlying capability influences business performance, stressing that business capabilities must continuously evolve to adapt to technological changes. The author proposes that business capability equals the sum of capabilities at all levels and explores the possibility of AI simulating the human soul, prompting reflection on combining reasoning and memory models.
- ✨ The EverMind AI model features a built-in biomimetic memory mechanism that mimics the human brain
- ✨ Memory mechanisms are a key direction for AI to enhance long-term understanding and context retention
- ✨ CZON integrates user memory fragments to form knowledge, similar to human long-term memory formation
- ✨ AI drives CZON's business as an underlying capability but may replace some business logic
- ✨ Business capability equals the sum of capabilities at all levels and requires continuous evolution
📅 2026-02-05 · 429 words · ~2 min read
Multi-Agent Adversarial Generation Translation and Optimization Strategies
AI Research
👤 AI developers, translation technology researchers, multi-agent system engineers, and those focused on translation quality and system optimization
This article explores the application of Multi-Agents in translation tasks, significantly enhancing translation quality through adversarial generation models (where translation models compete with review models), addressing issues like content omission, incoherence, and unnaturalness, albeit at the cost of time and token efficiency. Additionally, the article discusses memory optimization strategies, such as integrating agents into a single process to save memory; in terms of control constraints, it combines the advantages of soft and hard constraints, proposing the use of an Orchestrator Agent to generate Scripts for flexible and reliable control; and compares the ecosystem openness of OpenCode and Claude, emphasizing OpenCode's API-friendliness for easier integration.
- ✨ Adversarial generation translation models enhance quality through competition between translation and review, solving issues like omission, incoherence, and unnaturalness
- ✨ Sacrifices time and token efficiency to prioritize translation quality, suitable for high-quality translation scenarios
- ✨ Memory optimization: Integrate agents into a single process to avoid multi-process memory overhead, supporting hundreds of tasks
- ✨ Control constraints: Combine soft and hard constraints, using an Orchestrator Agent to generate Scripts for flexible and reliable control
- ✨ Script calls to agents should be simplified, such as one-line code scheduling, with results written to the file system
📅 2026-01-25 · 807 words · ~4 min read
AI Personification and Idealized Limit Thinking
AI Research
👤 Technical professionals and thinkers interested in AI ethics, LLM technology development, and thinking methodologies
This article analyzes Anthropic's Claude Constitution and Multi-Agent research to discuss how AI personification design enhances emotional intelligence and the value of idealized limit thinking in understanding the essence of things. The author reflects on their habitual problem-solving mindset and suggests that management principles may migrate to AI management. It also examines how the Ralph-loop experiment reveals LLM capability boundaries, emphasizing the importance of removing real-world constraints to clarify the essence of things, and applies this method to personal life analysis.
- ✨ Anthropic endows AI with personification features through the Claude Constitution to enhance emotional intelligence
- ✨ Idealized limit thinking helps remove real-world constraints to see the essence of things
- ✨ The Ralph-loop experiment reveals LLM capability boundaries under unlimited resources
- ✨ Management principles may migrate from human management to AI management
- ✨ The author reflects on their habitual direct problem-solving mindset rather than considering emotions
📅 2026-01-24 · 560 words · ~3 min read
Inspiration from Su Yu's Combat Directives for Multi-Agent System Coordination
AI Research
👤 AI researchers, multi-agent system developers, military history enthusiasts, organizational management scholars, and professionals interested in AI coordination innovation
This article analyzes the core characteristics and organizational logic of Su Yu's large-scale combat directives, extracting principles such as standardization, modularization, protocolization, and flexibility, and applies them to the design of multi-agent system coordination frameworks. It proposes a 'Multi-Agent Coordinated Combat Directive Framework for Complex Tasks,' emphasizing global situation alignment, task decoupling, standardized interaction protocols, and central dynamic coordination to address current challenges in multi-agent systems regarding coordination efficiency, intent alignment, and task reliability. The research demonstrates how traditional organizational management wisdom can provide insights beyond technology for AI development, opening up interdisciplinary innovation paths for AI system design.
- ✨ Su Yu's combat directives have core characteristics such as standardization, modularization, protocolization, and flexibility.
- ✨ The directive logic can be abstracted as a complex system control methodology, emphasizing cognitive unity, structural division of labor, protocol coordination, and distributed execution under centralized command.
- ✨ Proposes a 'Multi-Agent Coordinated Combat Directive Framework,' decomposing tasks into modules such as situation analysis, goal definition, role assignment, interaction protocols, and central coordination.
- ✨ Demonstrates through case migration how military directives can be transformed into AI task directives, achieving a shift from vague prompts to systematic engineering.
- ✨ Framework advantages include improving coordination efficiency, enhancing robustness, increasing task interpretability, and tackling complex task capabilities.
📅 2026-01-14 · 2,650 words · ~12 min read
DeepSeek Engram Paper Analysis: A New Memory Mechanism for Large Language Models
AI Research
👤 AI researchers, machine learning developers, tech enthusiasts, individuals interested in large language models and AI advancements
This article analyzes the Engram paper released by DeepSeek on January 13, 2026, which proposes a new memory mechanism that allows large language models to dynamically query and utilize externally stored memory fragments during text generation. Implemented via scalable lookup tables, this approach not only improves the model's contextual understanding and generation capabilities but also significantly reduces computational resource consumption, enabling efficient operation even in resource-constrained environments. The paper also explores the impact of the Engram-to-MoE component ratio on performance, finding a U-shaped curve and emphasizing the importance of balancing different components. From a philosophical perspective, the article compares this advancement to innovations like the Attention mechanism and MoE, viewing it as a continued exploration of efficient operation in complex systems. Overall, Engram provides new insights into memory mechanisms for large language models, potentially driving models toward more intelligent and efficient development.
- ✨ DeepSeek released the Engram paper, proposing a new memory mechanism
- ✨ The mechanism implements dynamic memory queries through scalable lookup tables
- ✨ Enhances model contextual understanding and generation capabilities
- ✨ Significantly reduces computational resource consumption
- ✨ Enables efficient model operation in resource-constrained environments
📅 2026-01-13 · 358 words · ~2 min read
How to Address Human Desire for Control: On Controllable Trust in Human-Machine Collaboration
AI Research
👤 Software engineers, AI researchers, human-computer interaction designers, complex system managers, professionals interested in autonomous systems and trust building
This paper explores the root causes of human desire for control in human-machine collaboration, arguing that it stems from rational concerns about loss of control over outcomes. To address this, the article introduces the concept of 'controllable trust' and constructs a two-layer multiplicative model: the foundational layer is intent alignment (including expression, value, dynamic, and structural alignment), and the execution layer is the risk control triangle (predictability, intervenability, and recoverability). The article further reveals the fractal recursive structure of intent alignment and proposes a 'well-organized agents' implementation framework, making agent organizations a mirror of human intent. This framework shifts the human role from operator to architect and governor, allowing the desire for control to be exercised at a higher level, thereby liberating productivity and enabling scalable collaboration.
- ✨ The desire for control stems from rational human concerns about loss of control over outcomes, not from a fixation on power.
- ✨ Controllable trust is key to liberating the desire for control and achieving scalable productivity in human-machine collaboration.
- ✨ Controllable trust consists of a two-layer multiplicative model formed by intent alignment and the risk control triangle.
- ✨ Intent alignment has a fractal recursive structure, requiring self-similar alignment across multiple scales.
- ✨ Proposes a 'well-organized agents' framework, making agent organizations a mirror of the fractal intent structure.
📅 2026-01-05 · 1,574 words · ~7 min read
Embracing Finite Design for Infinite Potential: A New Paradigm for Building Agent Systems Based on LLM Constraints
AI Research
👤 AI researchers, AI system architects, technical decision-makers, and engineers and scholars interested in agent systems and LLM applications
Based on an analysis of the inherent limitations of large language models (LLMs), this paper introduces a new paradigm for constructing powerful agent systems. It identifies three structural constraints of LLMs: non-mandatory coordination, limited computational budgets, and cognitive incompressibility. Rather than attempting to eliminate these limitations, the paper advocates embracing their "finiteness." The core solutions include: externalizing internal contradictions into explicit processes through coordination engineering, optimizing resource allocation under scarcity through AI decision economics, and shifting from static knowledge compression to dynamic information adaptation through cognitive flow management. This "finite agents, infinite systems" paradigm directly addresses the "Münchhausen trilemma" in intelligent system design, providing a theoretical framework and practical guide for building reliable, scalable, and evolvable human-machine collaborative systems.
- ✨ LLMs have three structural constraints: non-mandatory coordination, limited computational budgets, and cognitive incompressibility
- ✨ Shift from pursuing "all-powerful models" to designing "infinite systems that integrate finite intelligence"
- ✨ Coordination engineering externalizes coordination through checklists, parliamentary debate, and constraint solver patterns
- ✨ AI decision economics treats computational power as a scarce resource, establishing market mechanisms for optimal allocation
- ✨ Cognitive flow management abandons the illusion of cognitive compression, managing information flow through navigational interactions
📅 2026-01-05 · 1,852 words · ~9 min read