ai-systems

Learn to build your Second Brain AI assistant with LLMs, agents, RAG, fine-tuning, LLMOps and AI systems techniques.

A Python package to assess and improve fairness of machine learning models.

A source-grounded course and architectural reference for engineers designing systems that must survive real traffic, partial failure, security review, and changing requirements, spanning enterprise system design, distributed systems, AI systems, cybersecurity, reliability, cloud, HPC, edge, and mission-critical infrastructure.

Practical system design, tools, and hands-on resources for building Gen-AI agents & agentic AI systems.

Official implementation of Meta Prompting for AI Systems (https://arxiv.org/abs/2311.11482)

IdenProf dataset is a collection of images of identifiable professionals. It is been collected to enable the development of AI systems that can serve by identifying people and the nature of their job by simply looking at an image, just like humans can do.

AI Systems as Scientific Instruments for Understanding the Mechanisms of Intelligence

An innovative AI system developed to extract actionable insights by converting natural language into SQL queries via Google's Gemini model and dynamically visualizing the data with Plotly Express, streamlining decision-making across all levels of expertise.

A curated list of AI safety resources: alignment, interpretability, governance, verification, and responsible deployment of frontier AI systems.

Tamper-evident evidence infrastructure for AI agents. Capture, seal, verify, and audit AI decisions in a single portable .epi file.

Saturn accelerates the training of large-scale deep learning models with a novel joint optimization approach.

Source repo for the blog post Stop Stuffing Your System Prompt: Build Scalable Agent Skills in LangGraph

Resources for recent AI systems (deployment concerns, cost and accessibility). -- closed

🧾 Open, hands-on AI education program — from Machine Learning to Generative Intelligence. Weekly lessons in Jupyter Notebooks, ready for Google Colab.🧾

Learn Agentic AI using Dapr Agentic Cloud Ascent (DACA) Design Pattern and Agent-Native Cloud Technologies: OpenAI Agents SDK, Memory, MCP, A2A, Knowledge Graphs, Dapr, Rancher Desktop, and Kubernetes.

Organize your AI agent teams with the Viable System Model. The missing organizational layer for multi-agent systems.

World’s first production-grade compiler and execution platform for AI systems. Turn any AI assistant into a scalable workforce.

A local control plane for Model Context Protocol (MCP) servers — unify stdio and HTTP MCP tools behind a single, secure local endpoint.

Curated intelligence layer for generative and agentic AI, translating research into production systems | AditiKhare.com — AI Product Ecosystem

Prototype for Block, a new LLM-multi instance scheduling Framework