TCP fast open

Posted on by lechuck park

The image compares two TCP connection establishment methods:

  1. TCP 3-Way Handshaking (Traditional Method):
  • Shows a standard connection process with three steps:
    1. SYN (Synchronize) packet sent
    2. SYN + ACK (Synchronize + Acknowledge) packet returned
    3. ACK (Acknowledge) packet sent back
  • This happens every time a new TCP connection is established
  • Requires a full round-trip time (RTT) for connection setup
  1. TCP Fast Open:
  • Introduces a “Cookie” mechanism to optimize connection establishment
  • First connection follows the traditional 3-way handshake
  • Subsequent connections can use the stored cookie to reduce connection time
  • Benefits:
    • Reduces Round-Trip Time (1-RTT)
    • Better optimization for multiple connections
  • Requirements for TCP Fast Open:
    • Cookie security must be implemented
    • Both server and client must support the method
    • Intermediate network equipment must support the technique

The blue arrows in the TCP Fast Open diagram represent the cookie exchange and optimized connection process, highlighting the key difference from the traditional method.

With Claude

Experience Selling

Posted on by lechuck park

Experience Selling: Transforming Domain Expertise into Intellectual Capital

Paradigm Shift in Knowledge Economy

Core Value Proposition

  • Transforming specialized domain experience into structured digital data
  • Converting tacit knowledge into explicit, scalable intellectual assets

AI-Powered Knowledge Transformation

  • Digitalization of expert experiences
  • Large Language Model (LLM) training on domain-specific datasets
  • Creating replicable decision-making models from individual expertise

Key Message: In the AI era, experience is no longer a limited personal resource but a dynamic, expandable intellectual asset that can be transformed, shared, and monetized globally.

Cooling Works & Metrics

Posted on by lechuck park

Data Center Cooling System Overview

Cooling System Operation Flow

  1. Cooling Tower: Produces cooling water by releasing heat to the outside environment. This stage involves dissipating heat into the atmosphere.
  2. Chiller: Absorbs heat from the cooling water to produce chilled water. The condenser plays a crucial role in this process.
  3. Air Handling Unit: Uses chilled water to cool air, creating cooling air for the server room.
  4. Server Room: The cooled air is ultimately supplied to the server room to remove heat from IT equipment.

Key Control and Conversion Equipment

  • Pump: Regulates the pressure and speed of cooling and chilled water to maintain appropriate flow rates throughout the system.
  • Header: Handles the distribution and collection of cooling and chilled water, ensuring uniform distribution across the system.
  • Heat Exchanger/Condenser: Performs heat exchange processes at various stages, with the condenser playing a particularly important role in the chiller.
  • Fan: Circulates cooling air to the server room.

Core Measurement Metrics

  • Temperature: Monitors the temperature of cooling water, chilled water, and air at each stage to evaluate system efficiency.
  • Water Flow Rate: Measures the amount of cooling and chilled water circulating in the system to ensure adequate cooling capacity.
  • Supply/Return Temperature Differential: Measures the temperature difference before and after passing through each component to assess heat exchange efficiency.
  • Power Usage: Monitors the power consumption of pumps, chillers, fans, and other equipment to manage energy efficiency.

These metrics are monitored in detail by pump and condenser to optimize the overall performance of the cooling system and improve energy efficiency.

With Claude

infinite Gap

Posted on by lechuck park

This illustration visually represents the philosophical exploration of the relationship between humans and technology, particularly AI. It emphasizes how technological advancements may narrow the gap between humans and machines, yet a fundamental difference will always persist. The concept of “reducing infinity” is depicted, showing that while AI can become more human-like, it can never be entirely the same. Ultimately, the image highlights that despite technological evolution, human judgment remains irreplaceable in final decision-making.

With ChatGPT

New Coding

Posted on by lechuck park

The image titled “New Coding” illustrates the historical evolution of programming languages and the emerging paradigm of AI-assisted coding.

On the left side, it shows the progression of programming languages:

  • “Bytecode” (represented by binary numbers: 0110, 1001, 1010)
  • “Assembly” (shown with a gear and conveyor belt icon)
  • “C/C++” (displayed with the C++ logo)
  • “Python” (illustrated with the Python logo)

Below these languages is text reading “Workload for understanding computers” with a blue gradient arrow, indicating how these programming approaches have strengthened our understanding of computers through their evolution.

The bottom section labeled “Using AI with LLM” shows a human profile communicating with an AI chip/processor, suggesting that AI can now code through natural language based on this historical programming experience and data.

On the right side, a large purple arrow points toward the future concepts:

  • “New Coding As you think”
  • “With AI” (in purple text)

The overall message of the diagram is that programming has evolved from low-level languages to high-level ones, and now we’re entering a new era where AI enables coding directly through human thought, speech, and logical reasoning – representing a fundamental shift in how we create software.

With Claude

Mixture of Experts

Posted on by lechuck park

This image depicts a conceptual diagram of the “MOE (Mixture of Expert)” system, effectively illustrating the similarities between human expert collaboration structures and AI model MoE architectures.

The key points of the diagram are:

  1. The upper section shows a traditional human expert collaboration model:
    • A user presents a complex problem (“Please analyze the problem now”)
    • An intermediary agent distributes this to appropriate experts (A, B, C Experts)
    • Each expert analyzes the problem and provides solutions from their specialized domain
  2. The lower section demonstrates how this same structure is implemented in the AI world:
    • When a user’s question or command is input
    • The LLM Foundation Expert Model processes it
    • The Routing Expert Model distributes tasks to appropriate specialized models (A, B, C Expert Models)

This diagram emphasizes that human expert systems and AI MoE architectures are fundamentally similar. The approach of utilizing multiple experts’ knowledge to solve complex problems has been used in human settings for a long time, and the AI MoE structure applies this human-centered collaborative model to AI systems. The core message of this diagram is that AI models are essentially performing the roles that human experts would traditionally fulfill.

This perspective suggests that mimicking human problem-solving approaches can be effective in AI system design.

With Claude