Hardware Acceleration for Vector DBs: Beyond CPU Constraints
Vector search has hit a physical wall. Explore why CPU-bound indexing fails at scale and how FPGAs and custom ASICs are redefining the database layer.
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.
LiteRT-LM Deep Dive: Engineering LLM Inference for the Edge
How Google's LiteRT-LM framework handles session cloning and KV-cache management to run models like Gemini Nano natively on-device without exploding your memory.
· StrategyThe ROI of Edge AI: Shifting Inference from Cloud to Prosumer Hardware
The economic case for deploying local LLMs to eliminate API costs and latency. Why relying entirely on cloud inference is a massive tax on your margins.
The 2026 Enterprise Stack: Integrating Hardware, Agents, and Strategy
The 2026 Enterprise AI Stack: a reference architecture linking hardware, inference engines, agentic orchestration, and governance into one vertically integrated system.
The Human-as-Orchestrator: Evolving HITL
Designing systems where humans provide strategic intent and override at checkpoints.
Scaling Vector Databases for High-Throughput Text Clustering
Analyzing the bottleneck of bulk clustering and using exact-match caching to reduce index compute load.