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AI Algorithm Path
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AI Algorithm Path

A public account focused on deep learning, computer vision, and autonomous driving perception algorithms, covering visual CV, neural networks, pattern recognition, related hardware and software configurations, and open-source projects.

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Recent Articles

Latest from AI Algorithm Path

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AI Algorithm Path
AI Algorithm Path
May 21, 2026 · Artificial Intelligence

Essential Ranking Techniques Every RAG Engineer Must Know

This article explains why ranking is the decisive factor behind successful Retrieval‑Augmented Generation (RAG) pipelines, walks through pointwise, pairwise, and listwise learning‑to‑rank paradigms, details key algorithms such as LambdaMART, compares cross‑encoders with bi‑encoders, and provides practical guidance on metrics, production‑grade rerankers, model fine‑tuning, and framework integration.

Bi-EncoderCross-EncoderLLM
0 likes · 22 min read
Essential Ranking Techniques Every RAG Engineer Must Know
AI Algorithm Path
AI Algorithm Path
Apr 15, 2026 · Artificial Intelligence

8 Must-Collect Agent Skills Repositories for Claude and AI Agents

This article explains what Agent Skills are, why a curated skill library is valuable, and reviews eight actively maintained GitHub repositories—detailing their structure, core capabilities, integration points, and practical usage examples for building production‑grade AI agents.

AI agentsAI toolsAgent Skills
0 likes · 11 min read
8 Must-Collect Agent Skills Repositories for Claude and AI Agents
AI Algorithm Path
AI Algorithm Path
Apr 12, 2026 · Artificial Intelligence

Why Claw Code’s Claude Code Clone Is Gaining Massive Traction

Claw Code, an open‑source Python‑and‑Rust reimplementation of Anthropic’s Claude Code agent, exploded to over 100 k stars within hours after a leaked .map file revealed 510 k lines of the original TypeScript, and the article dissects its creator, architecture, features, and legal gray area.

AI agentsClaude CodeClaw Code
0 likes · 9 min read
Why Claw Code’s Claude Code Clone Is Gaining Massive Traction
AI Algorithm Path
AI Algorithm Path
Mar 29, 2026 · Artificial Intelligence

Mastering Claude Code Extensions: Skills, MCP, Hooks, Sub‑agents, Agent Teams & Plugins

Claude Code, Anthropic's CLI AI programming assistant, offers six extensible mechanisms—Skills, MCP, Sub‑agents, Agent Teams, Hooks, and Plugins—each explained with purpose, setup steps, concrete examples, and practical guidance on when and how to combine them for robust AI‑driven workflows.

AI extensionsAgent TeamsClaude Code
0 likes · 12 min read
Mastering Claude Code Extensions: Skills, MCP, Hooks, Sub‑agents, Agent Teams & Plugins
AI Algorithm Path
AI Algorithm Path
Mar 28, 2026 · Artificial Intelligence

A Practical Guide to Building Agent Skills for Large Language Models

This guide explains the concept of LLM "Skills", shows how to organize skill directories for Claude and Copilot, walks through creating a "prepare‑pr" skill with a SKILL.md file, integrates Bash scripts for git checks, and demonstrates testing and extending the skill with additional checks and templates.

Agent SkillsBash scriptClaude
0 likes · 12 min read
A Practical Guide to Building Agent Skills for Large Language Models
AI Algorithm Path
AI Algorithm Path
Mar 4, 2026 · Artificial Intelligence

Beginner’s Guide: Building a Pedestrian Detection Skill with NanoBot

This step‑by‑step tutorial shows how to install NanoBot, configure it with a DeepSeek API key, create a YOLO‑based pedestrian detection skill via natural‑language commands, test the generated code, and extend the output to JSON, demonstrating AI agents in Python.

AI agentDeepSeekNanobot
0 likes · 6 min read
Beginner’s Guide: Building a Pedestrian Detection Skill with NanoBot
AI Algorithm Path
AI Algorithm Path
Mar 3, 2026 · Artificial Intelligence

Exploring the OpenClaw Ecosystem: OpenClaw, NanoBot, PicoClaw, IronClaw, and ZeroClaw

The article surveys the emerging personal AI‑assistant ecosystem—including OpenClaw, NanoBot, PicoClaw, IronClaw, and ZeroClaw—detailing each project's origins, technology stack, performance metrics, and design goals, then dives deep into OpenClaw's layered memory, six‑stage execution pipeline, tool‑skill framework, and five core architectural principles.

AI agentsAgent ArchitectureNanobot
0 likes · 16 min read
Exploring the OpenClaw Ecosystem: OpenClaw, NanoBot, PicoClaw, IronClaw, and ZeroClaw
AI Algorithm Path
AI Algorithm Path
Feb 19, 2026 · Artificial Intelligence

A Practical Guide to Industrial Defect Detection with Pre‑trained Neural Networks

The article explains how manufacturers can shift from defect‑specific vision models to anomaly detection by leveraging pre‑trained object‑detection networks, visualising feature maps, and applying memory‑bank methods such as PaDiM and PatchCore, with the open‑source Anomalib library as a ready‑to‑use solution.

AnomalibAnomaly DetectionPaDiM
0 likes · 7 min read
A Practical Guide to Industrial Defect Detection with Pre‑trained Neural Networks
AI Algorithm Path
AI Algorithm Path
Feb 18, 2026 · Artificial Intelligence

Using Autoencoders for Industrial Defect Detection

This article explains how to train a simple fully‑connected autoencoder on defect‑free images, use reconstruction error to highlight anomalies in industrial parts, and convert the error into a single metric that cleanly separates good from defective components.

Anomaly DetectionAutoencoderKeras
0 likes · 7 min read
Using Autoencoders for Industrial Defect Detection