· Strategy
The End of "Tooling": Re-engineering Workflows
Adding AI to existing processes fails; ROI requires embedding AI into the core workflow.
From the Front Lines of Tech
Sharing strategic insights and lessons from over 20 years of building scalable systems, leading high-performing teams, and navigating complex technology shifts.
KV Cache Quantization: Fitting Larger Context Windows on Single GPUs
The bottleneck for long-context agents is memory, not compute. Learn how to implement FP8 or INT8 KV caching to double your context length and survive inference at scale.
Context Bloat: Implementing Progressive Discovery in Agent Memory
Using progressive discovery and smart tool-search to keep agents lean. Learn how to prevent context window overflow and infinite reasoning loops in multi-agent systems.
Multi-Cloud GPU Arbitrage: Routing Workloads Between Hyperscalers and Neoclouds
Don't lock into one vendor. Learn how to use an abstraction layer to route training and inference workloads to the cheapest available capacity across hyperscalers and neoclouds.
Semantic Caching at Scale: Vector Embeddings for 5x Latency Reduction
Moving beyond exact-match caching for repetitive zero-shot inference workloads. Learn how to architect semantic caching to slash latency and compute costs.
· StrategyPortfolio-Based Budgeting for AI Initiatives
Moving away from siloed project funding based on projected margin impact. Discover how to transition from project-based to portfolio-based AI funding to optimize ROI and survive the pilot phase.