AI Lab: Personal AI Experiment Infrastructure and LLM Agent Research Notes Repository

akrisanov/ai-lab is a personal research repository focused on AI infrastructure, inference optimization, Kubernetes GPU management, and LLM Agent experiments. Its core value does not lie in providing directly reusable products, but in recording and sharing the author's exploration journey in the AI infrastructure field, covering full-stack technical practices from underlying GPU resource management to upper-layer LLM Agent applications. It is a valuable reference for understanding the evolution path of modern AI system architectures. The project is more like an open log of a technical laboratory, containing experiment notes, configuration examples, code snippets, and pitfall records, providing real and valuable technical insights for researchers and engineers with a foundation.

Unlike many open-source projects that pursue 'out-of-the-box' usability, ai-lab is more like an open log of a technical laboratory. Its target audience needs to have the following foundations: familiarity with Linux system operations and Shell script writing, understanding of basic Docker and Kubernetes concepts, proficiency in Python programming and experience with PyTorch/TensorFlow, and comprehension of Transformer architecture and basic LLM principles. The best beneficiaries include AI platform engineers, MLOps engineers, technical architects, and senior developers; it may not be suitable for pure business developers, AI beginners, or product managers.

The project includes multiple core modules:

1. k8s-gpu-basics: Kubernetes GPU management fundamentals, covering key topics such as NVIDIA Device Plugin deployment, GPU resource quota configuration, multi-GPU node scheduling, memory sharing and isolation (MIG technology), monitoring, and alerts.
2. agents-week: Weekly experiments on Agent technology, including ReAct mode implementation, tool calling frameworks (Function Calling, MCP), memory mechanism design, multi-Agent collaboration, and Agent evaluation methods.
3. ai-agents-and-applications: A collection of Agent application cases, covering practical scenarios such as code assistant Agents, data analysis Agents, operation and maintenance automation Agents, content creation Agents, and research assistant Agents.

The uniqueness of ai-lab lies in its focus on the infrastructure layer, involving inference optimization technologies (quantization, pruning, distillation, speculative decoding), service deployment architectures (comparison and selection of vLLM, TensorRT-LLM, TGI), cost control strategies (Spot instance utilization, auto-scaling, model routing), and observability construction (distributed tracing, performance profiling, error analysis). The author adopts an experiment-driven methodology: propose hypotheses → design experiments → collect data → analyze results → precipitate knowledge. This scientific exploration method is worth learning from.

The suggested learning path is divided into four stages:

1. Infrastructure Foundation (1-2 weeks): Start with the k8s-gpu-basics module, build a minimally usable GPU K8s cluster, and understand GPU resource abstraction and scheduling, CUDA environment configuration, and Pod-level GPU allocation limits.
2. Inference Optimization Practice (2-3 weeks): Deploy mainstream inference engines (e.g., vLLM), conduct performance benchmark tests, and experiment with the impact of different optimization technologies on latency and throughput.
3. Agent Architecture Exploration (3-4 weeks): Read the agents-week and ai-agents-and-applications modules, implement a simple ReAct Agent, and add tool calling capabilities.
4. End-to-End Project Practice (ongoing): Choose a specific application scenario (e.g., intelligent customer service, code review assistant) to build a complete solution.

Comparison between ai-lab and similar projects:

Dimension	ai-lab	Typical Tutorial Projects	Production-Grade Frameworks
Goal	Knowledge Precipitation & Sharing	Introductory Teaching	Enterprise Deployment
Depth	Mid-to-Advanced	Beginner	Mid-to-Advanced
Completeness	Note Snippets	Complete Examples	Productized Solutions
Update Frequency	Irregular	Regular Maintenance	Continuous Iteration
Community Support	Personal Project	Active Community	Commercial Support

ai-lab is positioned between tutorials and production frameworks, suitable as a 'second classroom' for engineers' personal technical growth.

Potential improvement directions for the project include:

1. Add a structured index (README document) to provide module navigation and prerequisite knowledge explanations;
2. Provide Docker Compose or Nix configurations to lower the threshold for experiment reproduction;
3. Clarify the software versions (K8s, CUDA, models, etc.) corresponding to each module;
4. Provide comparable performance data for key experiments;
5. Share more 'unsuccessful' experiment cases to help readers avoid pitfalls.

akrisanov/ai-lab is a sincere and valuable technical sharing project. It may not help you quickly build AI applications, but it allows you to deeply understand the underlying principles and engineering practices of AI systems. In today's era of rapid AI technology iteration, such open sharing of personal knowledge bases has positive significance for the technical progress of the community.