30 Articles Shaping the Future of Enterprise AI in 2026

Over several decades, I unlearned everything that I learned in college classes, and built a new discipline from scratch, as much different from traditional AI than it is from standard machine learning, statistics and computer science. Outside academia with a focus on practical applications. Item #2 in the list below is the culmination of this research. In the meanwhile, other tech founders just use what they or their team learned at Stanford or Google Labs, with little twists.

My solutions are adopted by large companies, with revenue solidly building up (and ROI for the customer), offering far less expensive systems while delivering better accuracy much faster in a secure environment. An environment where the client has full control over everything with no call to external APIs. Not even using Blackbox libraries such as PyTorch, Keras or TensorFlow, not even for my own, original deep neural network architecture and my non-DNN alternatives.

The following articles are in reverse chronological order, posted in the last 12 months. It also includes books and live presentations.

1. Differences between transformer-based AI and the new generation of AI models
2. No-Blackbox, Secure, Efficient AI and LLM Solutions
3. Cybersecurity Use Case: AI Agent for Anomaly Detection
4. How to Build and Optimize High-Performance Deep Neural Networks from Scratch
5. xLLM by BondingAI: 5–min Demo
6. BondingAI Acquires GenAItechLab, Add Core Team Members
7. Language Models: A 75-Year Journey That Didn’t Start With Transformers
8. How to Get AI to Deliver Superior ROI, Faster
9. How to design LLMs that don’t need prompt engineering
10. The Rise of Specialized LLMs for Enterprise
11. Watermarking and Forensics for AI Models, Data, and Deep Neural Networks
12. Video: the LLM 2.0 Revolution
13. Stay Ahead of AI Risks – Free Live Session for Tech Leaders
14. Scaling, Optimization & Cost Reduction for LLM/RAG & Enterprise AI
15. Benchmarking xLLM and Specialized Language Models: New Approach & Results
16. 10 Tips to Boost Performance of your AI Models
17. A New Type of Non-Standard High Performance DNN with Remarkable Stability
18. Doing Better with Less: LLM 2.0 for Enterprise
19. What is LLM 2.0?
20. LLMs – Key Concepts Explained in Simple English, with Focus on LLM 2.0
21. 10 Must-Read Articles and Books About Next-Gen AI in 2025
22. Universal Dataset to Test, Enhance and Benchmark AI Algorithms
23. 10 Tips to Design Hallucination-Free RAG/LLM Systems
24. Blueprint: Next-Gen Enterprise RAG & LLM 2.0 – Nvidia PDFs Use Case
25. From 10 Terabytes to Zero Parameter: The LLM 2.0 Revolution
26. LLM 2.0, the New Generation of Large Language Models
27. LLM Deep Contextual Retrieval and Multi-Index Chunking: Nvidia PDFs, Case Study
28. xLLM: New Generation of Large Language Models for Enterprise
29. Visualizing Trading Strategies that Consistently Outperform the Stock Market
30. New Book: Building Disruptive AI & LLM Technology from Scratch
31. Building a Ranking System to Enhance Prompt Results: The New PageRank for RAG/LLM
32. No-Code LLM Fine-Tuning and Debugging in Real Time: Case Study
33. Hyperfast Contextual Custom LLM with Agents, Multitokens, Explainable AI, and Distillation

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