The Compute-to-Cashflow Gap
The AI industry is shifting from celebrating large compute budgets to hunting for efficiency. Your competitive advantage is no longer your GPU count, but your cost-per-inference.
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.
AI Quantization and Hardware Co-Design
Explore how quantization and hardware co-design overcome memory bottlenecks, comparing NVIDIA and Google architectures while looking toward the 1-bit future of efficient AI model development.
Debugging NCCL Ring Failures
When standard tools report a healthy cluster, but your training is stalled, the culprit is often a broken ring topology. We decode specific NCCL algorithms and debugging flags.
MCP: The End of the API Wrapper
We analyze the JSON-RPC internals of the Model Context Protocol (MCP) and why the 'Context Exchange' architecture renders traditional integration code obsolete.
Network Jitter: The Silent Killer of Training
In distributed training, the slowest packet determines the speed of the cluster. We benchmark GCP's 'Circuit Switched' Jupiter fabric against AWS's 'Multipath' SRD protocol.
Blackwell's Sparse Attention Engines: The Reality of FP4
FP4 isn't just 'lower precision' - it requires a fundamental rethink of activation outliers. We dive into the bit-level implementation of NVFP4, Micro-Tensor Scaling, and the new Tensor Memory hierarchy.