#data preprocessing
175 related articles
·3 min
Complete RAG System Development Guide: Data Preprocessing, Vector Retrieval, and Output Optimization in Practice
A complete guide to building RAG systems: covering data preprocessing, vector databases, embedding models, hybrid search, re-ranking, and advanced topics like Graph RAG and multimodal RAG.
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·3 min
PyTorch Image Classification in Practice: Complete Pipeline from Data Preprocessing to Model Training
A PyTorch flower classification project covering the full image classification pipeline: data preprocessing, transforms augmentation, ResNet pretrained models, and Resize strategies with reusable template code.
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·4 min
11-Node LangGraph in Practice: Complete Architecture Breakdown of a Compliance-Grade RAG Agent
Deep dive into an 11-node Agentic RAG agent built with LangGraph, featuring 6-way intelligent routing, hallucination guards, PII masking, circuit breakers, and zero-cost deployment.
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·4 min
A Guide to Full-Stack Machine Learning Project Repository Structure: From Chaos to Convention
A detailed guide to organizing full-stack ML project repositories, covering directory structure design, data-code separation, and externalized configuration to help ML developers move from experimental code to production-grade engineering standards.
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·4 min
Full-Stack Machine Learning Project Repository Structure Guide: From Chaos to Convention
A detailed guide to organizing full-stack ML project repositories, covering directory structure design, data-code separation, and configuration externalization to help ML developers move from experimental code to production-grade engineering.
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·3 min
From Demo to Production: The Real Challenges of Deploying LLM Systems
Deep analysis of deploying LLM systems from prototype to production: a real-world AI incident investigation assistant case revealing critical engineering challenges beyond the model.
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·3 min
From Demo to Production: The Real Challenges of Deploying LLM Systems
Deep analysis of deploying LLM systems from prototype to production: a real-world AI incident investigation assistant case study revealing key engineering challenges beyond the model.
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·3 min
Ctrlb-decompose: Denoise Logs Before Feeding Them to LLMs — Save Tokens, Improve Quality
Ctrlb-decompose is an open-source log denoising tool that strips redundant noise via templatization and clustering before sending logs to LLMs, reducing Token costs and improving AI analysis quality.
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·3 min
Ctrlb-decompose: Denoise Logs Before Feeding Them to LLMs — Save Tokens, Improve Quality
Ctrlb-decompose is an open-source log denoising tool that strips redundant noise from logs before sending them to LLMs, reducing Token costs and improving AI analysis quality for AIOps and observability.
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·2 min
book-to-skill: An Open-Source Tool That Converts Technical Book PDFs into Claude Code Skills
book-to-skill is an open-source GitHub project with over 10K stars that converts technical book PDFs into Claude Code Skills, enabling AI coding assistants to directly leverage book knowledge.
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·4 min
LangChain Guardrails Explained: Building Safe and Controllable AI Agents
A detailed guide to LangChain Guardrails covering layered ecosystem architecture, middleware implementation, deterministic and model-driven protection for building production-grade secure AI Agents.
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·3 min
Monocular RGB Human Mesh Reconstruction: Real-Time Motion Capture at 18 FPS with Rust + CUDA
Deep dive into a real-time 3D human mesh reconstruction project using a single RGB camera, built with Rust, Candle, and CUDA, achieving 55ms/frame on RTX 5080. Exploring its architecture, Metal porting plans, and applications in VTuber, AR/VR, and sports analysis.
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·3 min
Monocular RGB Human Mesh Reconstruction: Real-Time Motion Capture at 18 FPS with Rust + CUDA
A deep dive into a real-time 3D human mesh reconstruction project using a single RGB camera, built with Rust, Candle, and CUDA, achieving 55ms/frame on an RTX 5080.
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·3 min
AI Not Performing Well? Start by Getting These Simple but Often Overlooked Things Right
When your AI system underperforms, the problem is often not the model or algorithm — it's basic work like data cleaning, prompt writing, and evaluation that hasn't been done right.
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·3 min
AI Not Working Well? Start by Getting These Simple but Often Overlooked Things Right
When AI systems underperform, the problem often isn't the model or algorithm — it's that basics like data cleaning, prompt writing, and evaluation aren't done right. Learn the simple fixes that matter most.
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·3 min
The Open-Weights Model Debate: Balancing Safety and Openness
An in-depth analysis of the open-weights model debate: public release brings transparency and innovation, but raises safety and misuse risks. Exploring tiered release, red-teaming, and governance challenges.
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·3 min
The Open-Weights Model Debate: Balancing Safety and Openness
An in-depth analysis of the open-weights model debate: publicly releasing model weights enables transparency and innovation but raises safety risks. Explores tiered release, red-teaming, and the industry dynamics behind open AI governance.
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·3 min
claude-video: An Open-Source Tool That Teaches Claude to "See" Videos
claude-video is a trending open-source tool that enables Claude to analyze videos via frame extraction and audio transcription. Learn how it works and its use cases.
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·3 min
Simplified Technical English (STE): A Cross-Disciplinary Approach to Optimizing LLM Prompts
Explore how ASD-STE100 Simplified Technical English from aviation can be applied to LLM prompt engineering. STE's disambiguation principles—controlled vocabulary, short sentences, active voice—can improve LLM output accuracy and consistency.
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·3 min
Can MVTec Merlic Handle Robot Pick-and-Place Tasks? A Comparative Selection Analysis
Analysis of MVTec Merlic's capabilities for robot pick-and-place tasks, comparing it with Halcon for hand-eye calibration and 3D pose estimation, with practical selection guidance.
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