ai-systems-engineering

Andrej Karpathy's 2026 AI Systems Engineering Roadmap — 7 production-grade projects covering Context Engineering, Tool Design, Agent Orchestration, Eval Discipline, Reliability Engineering, MCP Servers & Harness Mindset. From PR review agents to autonomous DevOps incident response.

Sanitized black-box production case covering AI-assisted TradeOps, algorithmic trading systems, MCP integration boundaries and platform reliability.

A hardware-aware, autonomous LLM inference runtime that uses learned policies to optimize model configuration (pruning/quantization) for latency and perplexity

Lead Automation Platform Case Study: sanitized portfolio on event-driven lead capture, webhook verification, idempotent processing, payment verification, CRM automation, and reporting. No production code or secrets.

🧪 Data Science Systems Engineering(v1) — Status: DEPRECATED — production-grade implementation for hybrid cloud/on-premises AI infrastructure, optimizing computational workflows and ML systems across Debian XFCE with GCP integration 🧠⚡🔬

High-level roadmaps, frameworks and resources for evolving from cloud platform engineering to AI systems engineering. Focus on building and managing the infrastructure that enables AI systems to operate effectively, securely, and responsibly.

Build a production-style AI system that ingests logs and metrics, detects anomalies, and uses an LLM to summarize incidents and suggest likely root causes, with observability and reliability in mind.

This is an E-commerce System that is fully controlled by the SNSAI, where every single feature can be controlled by AI system and user can just ask the AI to go for any searching of products, or suggestions for products, select it, doubt clearing about the product, add-on the product, price calc, automatic order system, AI wallet system, and a lot.