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Claude Code best practices | Code w/ Claude - YouTube

9 October 2025·415 words·2 mins

Articoli Code Review AI Best Practices

Articoli Interessanti - This article is part of a series.

Part : GitHub - microsoft/VibeVoice: Open-Source Voice AI

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Part : GitHub - DGoettlich/history-llms: Information hub for our project training the largest possible historical language models.

Part : LLMRouter - LLMRouter

Part : Everything as Code: How We Manage Our Company In One Monorepo At Kasava, we've embraced the concept of "everything as code" to streamline our operations and ensure consistency across our projects. This approach allows us to manage our entire company within a single monorepo, providing a unified source of truth for all our configurations, infrastructure, and applications. **Why a Monorepo?** A monorepo offers several advantages: 1. **Unified Configuration**: All our settings, from development environments to production, are stored in one place. This makes it easier to maintain consistency and reduces the risk of configuration drift. 2. **Simplified Dependency Management**: With all our code in one repository, managing dependencies becomes more straightforward. We can easily track which versions of libraries and tools are being used across different projects. 3. **Enhanced Collaboration**: A single repository fosters better collaboration among team members. Everyone has access to the same codebase, making it easier to share knowledge and work together on projects. 4. **Consistent Build and Deployment Processes**: By standardizing our build and deployment processes, we ensure that all our applications follow the same best practices. This leads to more reliable and predictable deployments. **Our Monorepo Structure** Our monorepo is organized into several key directories: - **/config**: Contains all configuration files for various environments, including development, staging, and production. - **/infrastructure**: Houses the infrastructure as code (IaC) scripts for provisioning and managing our cloud resources. - **/apps**: Includes all our applications, both internal tools and customer-facing products. - **/lib**: Stores reusable libraries and modules that can be shared across different projects. - **/scripts**: Contains utility scripts for automating various tasks, such as data migrations and backups. **Tools and Technologies** To manage our monorepo effectively, we use a combination of tools and technologies: - **Version Control**: Git is our primary version control system, and we use GitHub for hosting our repositories. - **Continuous Integration/Continuous Deployment (CI/CD)**: We employ Jenkins for automating our build, test, and deployment processes. - **Infrastructure as Code (IaC)**: Terraform is our tool of choice for managing cloud infrastructure. - **Configuration Management**: Ansible is used for configuring and managing our servers and applications. - **Monitoring and Logging**: We use Prometheus and Grafana for monitoring,

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#### Source

Type: Web Article Original link: https://youtu.be/gv0WHhKelSE Publication date: 2025-10-14

Summary
#

WHAT - This is an educational YouTube tutorial that presents best practices for using Claude Code, an Anthropic AI service. The tutorial was presented by Cal Rueb, a member of the Anthropic AI technical team, during the “Code w/ Claude” event held in San Francisco on May 22, 2025.

WHY - It is relevant for AI business because it provides practical guidelines for optimizing the use of Claude Code, improving the efficiency and quality of the generated code. This can reduce development times and improve software maintainability.

WHO - The main actors are Anthropic AI, the company that develops Claude Code, and Cal Rueb, the tutorial presenter. The primary audience is the developer community that uses or intends to use Claude Code.

WHERE - It positions itself in the market of AI solutions for software development, offering tools for optimizing code generated by artificial intelligence models.

WHEN - The tutorial was presented in 2025, indicating that Claude Code is an established service with an active user base and a support community.

BUSINESS IMPACT:

Opportunities: Adopting the best practices presented can improve the quality of the generated code, reducing development times and enhancing maintainability.
Risks: Ignoring these best practices could lead to low-quality code, increasing maintenance costs and reducing competitiveness.
Integration: The guidelines can be integrated into the existing stack to improve the quality of code generated by other AI tools.

TECHNICAL SUMMARY:

Core technology stack: The tutorial focuses on Claude Code, which likely uses advanced language models to generate code. The programming language mentioned is Go.
Scalability: Best practices can be applied to projects of different sizes, improving the scalability of the generated code.
Technical differentiators: The use of specific guidelines for Claude Code can differentiate the product from other code generation tools, offering a competitive advantage.

Use Cases
#

Private AI Stack: Integration into proprietary pipelines
Client Solutions: Implementation for client projects
Strategic Intelligence: Input for technological roadmap
Competitive Analysis: Monitoring AI ecosystem

Resources
#

Original Links
#

Claude Code best practices | Code w/ Claude - YouTube - Original link

Article recommended and selected by the Human Technology eXcellence team, processed through artificial intelligence (in this case with LLM HTX-EU-Mistral3.1Small) on 2025-10-14 06:39 Original source: https://youtu.be/gv0WHhKelSE

Related Articles
#

Anthropic’s Interactive Prompt Engineering Tutorial - Open Source
Field Notes From Shipping Real Code With Claude - Tech
How to Use Claude Code Subagents to Parallelize Development - AI Agent, AI

Articoli Interessanti - This article is part of a series.

Part : GitHub - microsoft/VibeVoice: Open-Source Voice AI

Part : GitHub - GVCLab/PersonaLive: PersonaLive! : Expressive Portrait Image Animation for Live Streaming

Part : GitHub - NevaMind-AI/memU: Memory infrastructure for large language models and AI agents

Part : GitHub - VibiumDev/vibium: Browser automation for AI agents and humans

Part : GitHub - yichuan-w/LEANN: RAG on Everything with LEANN. Enjoy 97% storage savings while running a fast, accurate, and 100% private RAG application on your personal device.

Part : GitHub - DGoettlich/history-llms: Information hub for our project training the largest possible historical language models.

Part : LLMRouter - LLMRouter

Part : Everything as Code: How We Manage Our Company In One Monorepo At Kasava, we've embraced the concept of "everything as code" to streamline our operations and ensure consistency across our projects. This approach allows us to manage our entire company within a single monorepo, providing a unified source of truth for all our configurations, infrastructure, and applications. **Why a Monorepo?** A monorepo offers several advantages: 1. **Unified Configuration**: All our settings, from development environments to production, are stored in one place. This makes it easier to maintain consistency and reduces the risk of configuration drift. 2. **Simplified Dependency Management**: With all our code in one repository, managing dependencies becomes more straightforward. We can easily track which versions of libraries and tools are being used across different projects. 3. **Enhanced Collaboration**: A single repository fosters better collaboration among team members. Everyone has access to the same codebase, making it easier to share knowledge and work together on projects. 4. **Consistent Build and Deployment Processes**: By standardizing our build and deployment processes, we ensure that all our applications follow the same best practices. This leads to more reliable and predictable deployments. **Our Monorepo Structure** Our monorepo is organized into several key directories: - **/config**: Contains all configuration files for various environments, including development, staging, and production. - **/infrastructure**: Houses the infrastructure as code (IaC) scripts for provisioning and managing our cloud resources. - **/apps**: Includes all our applications, both internal tools and customer-facing products. - **/lib**: Stores reusable libraries and modules that can be shared across different projects. - **/scripts**: Contains utility scripts for automating various tasks, such as data migrations and backups. **Tools and Technologies** To manage our monorepo effectively, we use a combination of tools and technologies: - **Version Control**: Git is our primary version control system, and we use GitHub for hosting our repositories. - **Continuous Integration/Continuous Deployment (CI/CD)**: We employ Jenkins for automating our build, test, and deployment processes. - **Infrastructure as Code (IaC)**: Terraform is our tool of choice for managing cloud infrastructure. - **Configuration Management**: Ansible is used for configuring and managing our servers and applications. - **Monitoring and Logging**: We use Prometheus and Grafana for monitoring,

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Part : Fundamentals of Building Autonomous LLM Agents This paper is based on a seminar technical report from the course Trends in Autonomous Agents: Advances in Architecture and Practice offered at the Technical University of Munich (TUM).

Part : GitHub - Tencent-Hunyuan/HunyuanOCR

Part : Effective harnesses for long-running agents Anthropic

Part : GitHub - pixeltable/pixeltable: Pixeltable — Data Infrastructure providing a declarative, incremental approach for multimodal AI workloads

Part : AI Explained - Stanford Research Paper.pdf - Google Drive

Part : We present Olmo 3, our next family of fully open, leading language models

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Part : GitHub - GibsonAI/Memori: Open-Source Memory Engine for LLMs, AI Agents & Multi-Agent Systems

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Part : Love this framing！ This is exactly what we’re building at Weco: - you write an eval script (your verifier) - Weco iterates on the code to optimize it against that eval Software 1

Part : Supercharge your OCR Pipelines with Open Models

Part : [2511.09030] Solving a Million-Step LLM Task with Zero Errors

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Part : GitHub - rbalestr-lab/lejepa

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Part : Improving frontend design through Skills | Claude

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Part : Context Retrieval for AI Agents across Apps & Databases

Part : said we should delete tokenizers

Part : You Should Write An Agent · The Fly Blog

Part : "🚀 Hello, Kimi K2 Thinking! The Open-Source Thinking Agent Model is here"

Part : Link to the Strix GitHub repo: (don't forget to star 🌟)

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Part : This Claude Code prompt literally turns Claude Code into ultrathink...

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Part : Airbyte: The Leading Data Integration Platform for ETL/ELT Pipelines

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Part : We used DeepSeek OCR to extract every dataset from tables/charts ac...

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Part : Make Any App Searchable for AI Agents

Part : PaddleOCR-VL: Boosting Multilingual Document Parsing via a 0.9B Ultra-Compact Vision-Language Model

Part : Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting

Part : nanochat

Part : ROMA: Recursive Open Meta-Agents

Part : NeuTTS Air

Part : Cua: Open-source infrastructure for Computer-Use Agents

Part : MCP Analytics and Authentication Platform

Part : My trick for getting consistent classification from LLMs

Part : If you're late to the whole "memory in AI agents" topic like me, I recommend investing 43 minutes to watch this video

Part : DeepLearning.AI: Start or Advance Your Career in AI

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Part : AI-Researcher: Autonomous Scientific Innovation

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Part : Introducing Tongyi Deep Research

Part : 💾🎉 copyparty

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Part : ibm-granite/granite-docling-258M · Hugging Face

Part : Google just dropped an ace 64-page guide on building AI Agents

Part : opcode - The Elegant Desktop Companion for Claude Code

Part : NocoDB Cloud

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Part : MemoRAG: Moving Towards Next-Gen RAG Via Memory-Inspired Knowledge Discovery

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Part : PaddleOCR

Part : DeepSite v2 - a Hugging Face Space by enzostvs

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Part : Show HN: CLAVIER-36 – A programming environment for generative music

Part : Small models are the future of agentic ai

Part : Kimi K2: Open Agentic Intelligence

Part : Introducing Qwen3-Max-Preview (Instruct)

Part : Scientific Paper Agent with LangGraph

Part : Anthropic's Interactive Prompt Engineering Tutorial

Part : swiss-ai/Apertus-70B-2509 · Hugging Face

Part : Making a font of my handwriting · Chameth.com

Part : SurfSense

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Part : The LLM Red Teaming Framework

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Part : VibeVoice: A Frontier Open-Source Text-to-Speech Model

Part : [2502.12110] A-MEM: Agentic Memory for LLM Agents

Part : [2504.19413] Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory

Part : Apertus 70B: Truly Open - Swiss LLM by ETH, EPFL and CSCS

Part : HumanLayer

Part : PageIndex: Document Index for Reasoning-based RAG

Part : Deploying DeepSeek on 96 H100 GPUs

Part : Claude Code: A Highly Agentic Coding Assistant - DeepLearning.AI

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Part : browser-use/web-ui

Part : Casper Capital - 100 AI Tools You Can’t Ignore in 2025...

Part : CS294/194-196 Large Language Model Agents | CS 194/294-196 Large Language Model Agents

Part : Show HN: Whispering – Open-source, local-first dictation you can trust

Part : Fallinorg v1.0.0-beta

Part : paperetl

Part : Automatically annotate papers using LLMs

Part : My AI Had Already Fixed the Code Before I Saw It

Part : Llama-Scan: Convert PDFs to Text W Local LLMs

Part : Claudia – Desktop companion for Claude code

Part : Show HN: Fallinorg - Offline Mac app that organizes files by meaning

Part : Focalboard

Part : Elysia: Agentic Framework Powered by Decision Trees

Part : LangExtract

Part : +1 for "context engineering" over "prompt engineering"

Part : The race for LLM cognitive core

Part : [2507.07935] Working with AI: Measuring the Occupational Implications of Generative AI

Part : Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting

Part : Prava - Teaching GPT‑5 to use a computer

Part : InstaVM - Secure Code Execution Platform

Part : Litestar is worth a look

Part : Jobs at Kaizen | Y Combinator

Part : Launch HN: Lucidic (YC W25) – Debug, test, and evaluate AI agents in production

Part : Introducing pay per crawl: Enabling content owners to charge AI crawlers for access

Part : Agentic Design Patterns - Documenti Google

Part : [2507.14447] Routine: A Structural Planning Framework for LLM Agent System in Enterprise

Part : Qwen3-Coder: Agentic coding in the world

Part : FutureHouse Platform

Part : Voxtral | Mistral AI

Part : Research Agent with Gemini 2.5 Pro and LlamaIndex | Gemini API | Google AI for Developers

Part : AI Act, c'è il codice di condotta per un approccio responsabile e facilitato per le Pmi - Cyber Security 360

Part : [2507.06398] Jolting Technologies: Superexponential Acceleration in AI Capabilities and Implications for AGI

Part : MindsDB, an AI Data Solution - MindsDB

Part : Backlog.md – Markdown-native Task Manager and Kanban visualizer for any Git repo

Part : Opencode: AI coding agent, built for the terminal

Part : The new skill in AI is not prompting, it's context engineering

Part : SymbolicAI: A neuro-symbolic perspective on LLMs

Part : Gemini for Google Workspace Prompting Guide 101

Part : Judge Rules Training AI on Copyrighted Works Is Fair Use, Agentic Biology Evolves, and more...

Part : MCP is eating the world—and it's here to stay

Part : How Dataherald Makes Natural Language to SQL Easy "How Dataherald Makes Natural Language to SQL Easy" is already in English.

Part : Field Notes From Shipping Real Code With Claude

Part : Nice - my AI startup school talk is now up!

Part : Nice - my AI startup school talk is now up! Chapters: 0:00 Imo fair to say that software is changing quite fundamentally again

Part : Automated 73% of his remote job using basic automation tools, told his manager everything, and got a promotion

Part : Building Effective AI Agents

Part : How Anthropic Teams Use Claude Code

Part : Snorting the AGI with Claude Code

Part : Nanonets-OCR-s – OCR model that transforms documents into structured markdown

Part : The Illusion of Thinking

Part : Trends – Artificial Intelligence | BOND

Part : Claude Code is My Computer | Peter Steinberger

Part : [2505.24863] AlphaOne: Reasoning Models Thinking Slow and Fast at Test Time

Part : [2505.24864] ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models

Part : My AI Skeptic Friends Are All Nuts · The Fly Blog

Part : Designing Pareto-optimal GenAI workflows with syftr

Part : "BillionMail 📧 An Open-Source MailServer, NewsLetter, Email Marketing Solution for Smarter Campaigns"

Part : Ask HN: What is the best LLM for consumer grade hardware?

Part : [2411.06037] Sufficient Context: A New Lens on Retrieval Augmented Generation Systems

Part : Show HN: Onlook – Open-source, visual-first Cursor for designers

Part : Agent Development Kit (ADK)

Part : Strands Agents

Part : Show HN: AutoThink – Boosts local LLM performance with adaptive reasoning

Part : Introduction - IntelOwl Project Documentation

Part : Show HN: My LLM CLI tool can run tools now, from Python code or plugins

Part : [2505.03335v2] Absolute Zero: Reinforced Self-play Reasoning with Zero Data

Part : Codex’s Robot Dev Team, Grok's Fixation on South Africa, Saudi Arabia’s AI Power Play, and more...

Part : [2502.00032v1] Querying Databases with Function Calling

Part : Come Addestrare un LLM con i Tuoi Dati Personali: Guida Completa con LLaMA 3.2

Part : AI Hedge Fund

Part : Troy Hunt: Have I Been Pwned 2.0 is Now Live!

Part : A Research Preview of Codex

Part : [2505.06120] LLMs Get Lost In Multi-Turn Conversation

Part : Ollama's new engine for multimodal models

Part : Vision Now Available in Llama.cpp

Part : [2505.03335] Absolute Zero: Reinforced Self-play Reasoning with Zero Data

Part : Requests for Startups | Y Combinator

Part : Token & Token Usage | DeepSeek API Docs

Part : Cua is Docker for Computer-Use AI Agents

Part : [2504.07139] Artificial Intelligence Index Report 2025

Part : Gemma 3 QAT Models: Bringing state-of-the-Art AI to consumer GPUs

Part : DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning | Nature

Part : A foundation model to predict and capture human cognition | Nature

Part : Large language models are proficient in solving and creating emotional intelligence tests | Communications Psychology

Part : Everything About Transformers "Everything About Transformers"