How to Get the Best from Claude Code Teams
In February 2026, Anthropic shipped a C compiler written almost entirely by Claude Code. 16 agents, working in parallel across ~2,000 sessions, produced over 100,000 lines of Rust that compiles the...
Gian Paolo Santopaolo
Stop Buying GPUs for the Wrong Spec: The Training vs Inference Resource Trap
Your RTX 6000 Ada has 91 TFLOPS of FP32 compute. During inference, almost none of it matters. Training and inference stress completely different parts of the GPU. Understanding which bottleneck do...
Planning Pattern for AI Agents: Strategic Reasoning Before Action
Goldman Sachs deployed Claude-powered AI agents to 12,000+ developers and back-office staff, achieving 30% reduction in client onboarding times and saving thousands of manual labor hours weekly. Th...
Building ReAct Agents with Microsoft Agent Framework: From Theory to Production
Bank of America’s AI assistant Erica handles over 3 billion customer interactions annually using the ReAct pattern. When a customer reports suspicious charges, Erica doesn’t just hallucinate an ans...
Securing AI Agents with Zero Trust and Sandboxing: The Production Reality Check
A financial services company deployed an AI agent to process customer support tickets. Within 48 hours, a crafted prompt injection allowed an attacker to extract API keys from the agent’s memory,...
Predict Peak VRAM Before Downloading a Model (Weights + KV Cache + Quantization)
OOM debugging is a waste of time. If a model is on the Hugging Face Hub in Safetensors, you can estimate most of the VRAM it will need before downloading weights — by reading only the metadata hea...
I Benchmarked ResNet vs ViT on 50K Images. They're Nearly Identical.
Everyone wants to throw Transformers at every computer vision problem. The research papers show impressive gains. The hype is real. But I wanted to know: What happens when you actually run the sam...
From PyTorch to Production: Deploy ML Models Locally with ONNX (Zero Infrastructure Cost)
The cloud isn’t always the answer. Sometimes the best deployment strategy is no deployment at all—let the user’s hardware do the work. Train in PyTorch, export to ONNX, run anywhere: browser, deskt...
PyTorch Training Loop: A Production-Ready Template for AI Engineers
You can train almost anything in PyTorch with seven lines of code. The problem? Those seven lines are correct but incomplete for real-world engineering. You need mini-batches, validation, device ha...
LangExtract: Production LLM-Powered Information Extraction with Source Grounding
Extracting structured data from unstructured financial documents is a mess. Traditional NER (Named Entity Recognition) fails on domain-specific terminology. Regex breaks on minor format changes. Cu...