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      • Dagster + NATS Event-Driven Pipeline Design and Implementation
      • Rust + NATS + Dagster AI Factory: OpenAI Proxy, Idempotent Design, SSE Streaming, and Go Migration Record
      • Django 5 Travel Booking Site Generation Test with Qwen3.5-122B-A10B Local Inference
      • Why EPYC 9175F's 512MB L3 Cache Accelerates MoE Inference: Hypothesis Validation with a 1T Model
      • Why Quantization Choice Changes Everything for Hermes-4.3-36B: BF16/FP8/nvfp4 Measured Comparison
      • MiniMax-2.5 229B MoE with IQ5K Quantization on Blackwell GPU: 35 tok/s Generation, 65K Context Validation
      • The Reality of 40B Dense Models: What Running IQuest-Coder-V1-40B on CPU/GPU/Aider Actually Showed
      • MiniMax-2.5 (229B MoE) Expert Offload and Web Generation: IQ5_K to IQ3_S
      • Qwen3.5-397B IQ4_NL Measured: 22.5tok/s Average from 28 Runs, Hybrid Offload Config and 400B-Class MoE Daily Viability
      • Llama-4-Scout-17B-16E Measured: CPU Q6_K 17tok/s vs GPU nvfp4 60tok/s, Cache Strategy and 100K Context Boundary
      • 1T MoE Kimi-K2.5 CPU Inference: Thread Optimization Through Long Context Operations
      • Llama-4-Maverick-17B-128E CPU Inference: Q4_K_M vs Q8_0 Speed-Quality Trade-off Measured
      • Qwen3-Coder-Next 80B in Three Modes: BF16 CPU / IQ4_NL Hybrid / nvfp4 GPU Measured
      • GLM-4.7-Flash IQ5_K Benchmark: CPU vs Hybrid vs Full GPU Performance Comparison
      • Why DeepSeek-V3.2 Appears Slower Than Kimi-K2.5: Prompt Cache Mismatches and TG Bottleneck Analysis
      • code-tree Specification, Design Intent, and Expected Effects — LLM Context Optimization Tool
      • shelpa-mcp: Design Record of a Scrapped Virtual Pipeline
      • shelpa: Design and Lessons from a Scrapped Sandbox MCP
      • Verifying ctree Refactoring Effectiveness — Project Structure Optimization
      • Building code-tree HTML Template and Markdown Scanner — Extending to Document Formats
      • Automatic Path Error Recovery MCP Tool for Local LLMs: Building pathfinder
      • Optimizing pathfinder: Model Selection, Precision Tuning, and History Correlation Validation
      • Qwen3.5-397B Autonomous Code Generation: From Dental Clinic Sites to Django CMS Foundations
      • Bilingual AI Proofreading and Translation Prompt Definitions
      • LTX-2 Video Generation Prompt Engineering: From 36-Scene Horror to Cinematic Continuity Pipelines
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      Software Tools

      Development tools, IDE configurations, MCP integrations, and code analysis utilities.

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      These articles use AI-generated summaries of Obsidian notes originally kept as technical memos.
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      English translations are produced with AI assistance.
      cpu

      code-tree Specification, Design Intent, and Expected Effects — LLM Context Optimization Tool

      code-tree architecture and tool specifications, context compression and token cost reduction …

      terminal

      shelpa-mcp: Design Record of a Scrapped Virtual Pipeline

      Architecture design of MCP-compliant virtual shell server (shelpa-mcp), command routing, pipeline …

      shield

      shelpa: Design and Lessons from a Scrapped Sandbox MCP

      Design philosophy, security implementation, and ultimate abandonment of shelpa — a virtual pipeline …

      sitemap

      Verifying ctree Refactoring Effectiveness — Project Structure Optimization

      Capturing dependency separation and single-responsibility achievement through code-tree refactoring, …

      cpu

      Building code-tree HTML Template and Markdown Scanner — Extending to Document Formats

      Design and implementation of extending code-tree from programming languages to HTML templates …

      tool

      Automatic Path Error Recovery MCP Tool for Local LLMs: Building pathfinder

      Solving path-related errors (ENOENT, etc.) in local LLM environments through deterministic path …

      trending_up

      Optimizing pathfinder: Model Selection, Precision Tuning, and History Correlation Validation

      Development journey of pathfinder optimization: quantization benchmarking (INT8 vs FP16/FP32), …

      build

      Qwen3.5-397B Autonomous Code Generation: From Dental Clinic Sites to Django CMS Foundations

      Two code generation validations using the 400B-class MoE model Qwen3.5-397B. One-shot generation of …

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