A Realistic Roadmap to Start an AI Career in 2026
How to learn AI in 2026 through real, usable projects The post A Realistic Roadmap to Start an AI Career in 2026 appeared first on Towards Data Science.
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BondingAI Acquires GenAItechLab, Add Core Team Members
BondingAI acquisition of GenAItechLab.com was recently completed, including all the IP related to the xLLM technology, the material published on MLtechniques and the most recent technology pertaining to deep neural networks watermarking. GenAItechLab was founded in 2024 by Vincent Granville, a world-class leader and well-known scientist building innovative and efficient AI solutions from scratch, hallucination-free, without Blackbox or GPU, yet delivering better results faster. Doctor Granville is now Chief AI Architect, co-founder and investor at BondingAI. In the […]
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The AI Bubble Will Pop — And Why That Doesn’t Matter
How history’s biggest tech bubble explains where AI is headed next The post The AI Bubble Will Pop — And Why That Doesn’t Matter appeared first on Towards Data Science.
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Amazon adds delivery tracking, last-minute adds, gift ideas to Alexa+
Amazon upgrades Alexa+ with new shopping tools, turning Echo screens into hubs for orders, deals, and gift buying.
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Finding Meaningful Work in the Age of Vibe Coding
Vibe coding has devalued coding. Is there any meaningful work still left for us?
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Google’s first AI glasses expected next year
Google will compete with Meta with its own line of AI-powered smart glasses.
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Differences between transformer-based AI and the new generation of AI models
I frequently refer to OpenAI and the likes as LLM 1.0, by contrast to our xLLM architecture that I present as LLM 2.0. Over time, I received a lot of questions. Here I address the main differentiators. First, xLLM is a no-Blackbox, secure, auditable, double-distilled agentic LLM/RAG for trustworthy Enterprise AI, using 10,000 fewer (multi-)tokens, no vector database but Python-native, fast nested hashes in its original version, and no transformer to generate the structured output to a prompt. […]
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