SKU: 12638269743

Dream Theater - Lost Not Forgotten Archives: Falling Into Infinity Demos, 1996 – 1997 (2CD Digipak)

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Dream Theater - Lost Not Forgotten Archives: Falling Into Infinity Demos, 1996 – 1997 (2CD Digipak)Format: 2 x CDCatalogue No.: 19658705512Barcode: 196587055127Release Date: 13 May 2022Genre: Prog Rock Art Rock Spanning 3 LPs, the original demos from the recording sessions of one of Dream Theaters most esteemed albums, Falling Into Infinity. These 16 tracks, recorded throughout 1996 and 1997 as demos for what would ultimately become 1997s Falling Into Infinity studio release, spotlight some of the bands most highly regarded works from that era.

Dream Theater - Lost Not Forgotten Archives: Falling Into Infinity Demos, 1996 1997 (2CD Digipak)
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SKU: 12638269743

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4.6 ★★★★★
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D
Verified Purchase
David Escobar
Carnegie, US
★★★★★ 1
Nothing new
Format: Audiobook
There nothing new in this book you will defiantly find this content in any leadership book
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on November 16, 2019
F
Verified Purchase
Filipe Fernandes
Louisville, US
★★★★★ 5
Great book
Format: Paperback
Love the fact you put examples in python and javascript. Great book.
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Reviewed in the United States on June 10, 2025
E
Verified Purchase
Eddwin Paz
Louisville, US
★★★★★ 5
proper documentation from langchain
Format: Paperback
Liked the book. But Still missing Human in the loop.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on April 19, 2025
B
Verified Purchase
B. Black
Port Orchard, US
★★★★★ 3
Already outdated
Format: Paperback
Concepts are sound but the code in this book is already obsolete
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on August 5, 2025
J
Joe Faith
Fort Morgan, US
★★★★★ 5
Unlocking Practical AI: A Developer’s Guide to Building with LLMs and LangChain
Format: Paperback
If you're a developer eager to move beyond LLM experimentation and build robust, context-aware AI applications, this book offers both inspiration and practical guidance. The authors open with a clear passion for the transformative potential of large language models (LLMs) and LangChain, framing these technologies as not just enhancements to the developer’s toolkit, but as gateways to new kinds of “thing-building” superpowers. This sense of possibility is grounded in step-by-step instruction, making the book approachable for those with Python or JavaScript backgrounds who may be new to the world of production-grade AI agents. What stands out is the book’s careful scaffolding: starting with foundational concepts like prompt-based programming and progressing to advanced capabilities such as retrieval-augmented generation, agent planning, and tool integration. Each stage is contextualized with real-world use cases, like customizing chatbots to interact with your own documents, personalizing user experiences through memory, and deploying to production with reliability and security in mind. The focus on chain-of-thought reasoning and LangGraph’s agent architecture demonstrates the authors’ awareness of the current state of AI, where context and planning are just as important as raw language ability.
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Reviewed in the United States on July 17, 2025

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