A deep dive into building distributed LLM evaluation infrastructure that actually scales - architectural decisions, trade-offs, and lessons learned.
A practical framework for evaluating your multi-agent context management strategy. From ad-hoc string concatenation to self-evolving context systems - where does your architecture stand?
Enterprise data platforms face a 100,000x query increase from agentic AI. Introducing Symbiotic Agent-Ready Platforms (SARPs) - the architectural paradigm shift needed to survive the transition to machine intelligence.
AI companies are getting sued over training data, agents operate with no permission framework, and users can't control their AI profiles. I wrote four open standards (LLMConsent) to create a decentralized consent protocol for AI - like HTTP but for data rights, agent permissions, and user sovereignty. This is an RFC, not a product.
A deep dive into implementing speculative decoding from scratch, with benchmarks on GPT-2 and extensions to diffusion models.
Think your data pipelines could do more than just process information? ETLC 2.0 takes data engineering to the next level with Adaptive Context, Contextual Joins, and a scalable Context Store. It's not just about moving data—it's about making it intelligent. Ready to unlock the future of data pipelines? Read on.
Traditional data warehouses are struggling to keep up with modern demands. Enter Dynamic Context Engines (DCEs) - real-time, path-aware platforms that enrich data with context for smarter, faster decisions. Discover why they're the future of data analytics.
Explore how Large Language Models (LLMs) are revolutionizing ETL pipelines. Discover advanced techniques like context-driven transformations, semantic joins, and multimodal integration, redefining data engineering with smarter, adaptive, and intelligent workflows.
Buckle up for a wild ride through 10 mind-blowing data pipeline disasters and their solutions. From ancient code to biased algorithms, this post reveals the chaos and how to conquer it!
Think your AI apps could use a deeper understanding of your data? ETL-C (extract, load, transform, and contextualize) could be the answer. It's about adding context for better decisions. Intrigued? Read on.