A practical engineering guide to building reliable AI agents — tool use, ReAct loops, structured output, memory patterns, multi-agent systems, and the failure modes nobody warns you about.
Ml
-
Building AI Agents That Actually Work -
Fine-Tuning LLMs on Your Own Hardware LoRA and QLoRA explained, unsloth for efficient fine-tuning, dataset preparation, evaluation, merging adapters, and when fine-tuning actually beats RAG — a practical guide to training language models on consumer and prosumer hardware.
-
Mojo: Python Syntax, Metal-Level Performance A deep technical guide to Mojo — Modular's MLIR-based language that combines Python syntax with C/Rust-level performance for AI and ML workloads. Covers the type system, SIMD intrinsics, memory ownership, metaprogramming, Python interop, and an honest assessment of where Mojo stands today.
-
GPU Infrastructure for ML: CUDA, MIG, Kubernetes Device Plugins, and Cost-Efficient Training A practical guide to GPU infrastructure for machine learning — CUDA fundamentals, NVIDIA MIG for multi-tenancy, sharing GPUs in Kubernetes with device plugins and time-slicing, building cost-efficient training clusters, and monitoring GPU utilization.