First empirical demonstration of activation-level sandbagging detection. Linear probes achieve 90-96% accuracy across Mistral, Gemma, and Qwen models. Key finding - sandbagging representations are model-specific, and steering can reduce sandbagging by 20%.
I tested activation steering on 4 agent behaviors across 3 models. The results surprised me.
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?
A hands-on exploration of writing custom GPU kernels with OpenAI Triton, going from PyTorch's 11% bandwidth utilization to 88% on RMSNorm.
A deep dive into implementing speculative decoding from scratch, with benchmarks on GPT-2 and extensions to diffusion models.
A deep dive into implementing speculative decoding from scratch, with benchmarks on GPT-2 and extensions to diffusion models.
Dive into the world of autonomous AI agents with practical implementations, code examples, and real-world scenarios. Learn how to build intelligent systems with advanced memory management, dynamic prompt evolution, and sophisticated monitoring capabilities in telecom customer service.
Explore a detailed technical implementation of a multi-agent system for retail banking credit assessment. Learn about agent architecture, distributed systems patterns, error handling, compliance requirements, and performance optimization through actual code examples and system diagrams. Ideal for software architects and engineers building scalable financial systems.
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.