Local AI with VS Code: Mastering Private, Offline LLM Development: Run Open-Source Models Securely with Ollama, Continue, Llama.cpp, and Zero-Cloud Extensions – Keep Your Code and Data 100% Private - Tapa blanda
Sinopsis
Local AI with VS Code is the definitive guide for developers, engineers, data scientists, and privacy-conscious creators who want to build intelligent applications without sending a single byte to the cloud. Written by Liora Quillan, this hands-on book shows you how to turn VS Code into a powerful, offline AI development environment using open-source LLMs, privacy-first extensions, and fully local inference engines such as Ollama, Continue, Llama.cpp, GPT4All, and more.
With cloud AI platforms becoming increasingly expensive and restrictive, developers are turning to a new generation of local, open-source large language models for coding, automation, debugging, RAG pipelines, and intelligent agent workflows. This book teaches you exactly how to run these models efficiently, privately, and securely — right on your laptop or workstation.
You’ll learn step-by-step how to:
- Install and optimize VS Code for local AI workflows
- Run advanced models through Ollama, Continue, and Llama.cpp
- Build offline copilots, code assistants, and autonomous developer agents
- Implement local RAG, embeddings, vector search, and document Q&A
- Use zero-cloud extensions to keep your data 100% private
- Create AI-powered automation with Python, Node.js, and shell tools
- Develop security-hardened, air-gapped AI pipelines for enterprise or regulated environments
- Optimize performance on CPU-only systems (no GPU required)
"Sinopsis" puede pertenecer a otra edición de este libro.
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Local AI with VS Code: Mastering Private, Offline LLM Development: Run Open-Source Models Securely with Ollama, Continue, Llama.cpp, and Zero-Cloud Ex
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Local AI with VS Code: Mastering Private, Offline LLM Development: Run Open-Source Models Securely with Ollama, Continue, Llama.cpp, and Zero-Cloud Ex
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Local AI with VS Code (Paperback)
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Paperback. Condición: new. Paperback. Local AI with VS Code is the definitive guide for developers, engineers, data scientists, and privacy-conscious creators who want to build intelligent applications without sending a single byte to the cloud. Written by Liora Quillan, this hands-on book shows you how to turn VS Code into a powerful, offline AI development environment using open-source LLMs, privacy-first extensions, and fully local inference engines such as Ollama, Continue, Llama.cpp, GPT4All, and more.With cloud AI platforms becoming increasingly expensive and restrictive, developers are turning to a new generation of local, open-source large language models for coding, automation, debugging, RAG pipelines, and intelligent agent workflows. This book teaches you exactly how to run these models efficiently, privately, and securely - right on your laptop or workstation. You'll learn step-by-step how to: Install and optimize VS Code for local AI workflowsRun advanced models through Ollama, Continue, and Llama.cppBuild offline copilots, code assistants, and autonomous developer agentsImplement local RAG, embeddings, vector search, and document Q&AUse zero-cloud extensions to keep your data 100% privateCreate AI-powered automation with Python, Node.js, and shell toolsDevelop security-hardened, air-gapped AI pipelines for enterprise or regulated environmentsOptimize performance on CPU-only systems (no GPU required)Whether you're a software engineer, indie hacker, DevOps engineer, ML practitioner, or cybersecurity professional, this book gives you the blueprint to develop private, offline, high-performance AI systems using the tools you already love. No subscriptions. No APIs. No cloud. Your machine, your models, your data. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9798277894347
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Local AI with VS Code: Mastering Private, Offline LLM Development: Run Open-Source Models Securely with Ollama, Continue, Llama.cpp, and Zero-Cloud Ex
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Local AI with VS Code: Mastering Private, Offline LLM Development: Run Open-Source Models Securely with Ollama, Continue, Llama.cpp, and Zero-Cloud Ex
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Local AI with VS Code (Paperback)
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Paperback. Condición: new. Paperback. Local AI with VS Code is the definitive guide for developers, engineers, data scientists, and privacy-conscious creators who want to build intelligent applications without sending a single byte to the cloud. Written by Liora Quillan, this hands-on book shows you how to turn VS Code into a powerful, offline AI development environment using open-source LLMs, privacy-first extensions, and fully local inference engines such as Ollama, Continue, Llama.cpp, GPT4All, and more.With cloud AI platforms becoming increasingly expensive and restrictive, developers are turning to a new generation of local, open-source large language models for coding, automation, debugging, RAG pipelines, and intelligent agent workflows. This book teaches you exactly how to run these models efficiently, privately, and securely - right on your laptop or workstation. You'll learn step-by-step how to: Install and optimize VS Code for local AI workflowsRun advanced models through Ollama, Continue, and Llama.cppBuild offline copilots, code assistants, and autonomous developer agentsImplement local RAG, embeddings, vector search, and document Q&AUse zero-cloud extensions to keep your data 100% privateCreate AI-powered automation with Python, Node.js, and shell toolsDevelop security-hardened, air-gapped AI pipelines for enterprise or regulated environmentsOptimize performance on CPU-only systems (no GPU required)Whether you're a software engineer, indie hacker, DevOps engineer, ML practitioner, or cybersecurity professional, this book gives you the blueprint to develop private, offline, high-performance AI systems using the tools you already love. No subscriptions. No APIs. No cloud. Your machine, your models, your data. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Nº de ref. del artículo: 9798277894347
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