Update README.md

README.md

@@ -19,3 +19,363 @@ AIKnowledgeTreeBuilder is designed with the following tenets:
 5. Memory - Shareable flows with deep-linked research paths 
 6. Personalized - Rapidly adapts knowledge base to user needs 
 7. Living Brevity - Easily cloneable, self modifies with data and public to shares results.
+
+
+🔧 **Systems, Infrastructure & Low-Level Engineering**
+
+🔧 1. Low-level system integrations compilers Cplusplus
+🔧 2. Linux or embedded systems experience
+🔧 3. Hardware acceleration
+🔧 4. Accelerating ML training inference across AI hardware
+🔧 5. CUDA kernels
+🔧 6. Optimum integration for specialized AI hardware
+🔧 7. Cross-layer performance tuning hardware plus software
+🔧 8. Data-center scale HPC or ML deployment
+🔧 9. GPU accelerator architecture and CUDA kernel optimization
+🔧 10. GPU kernel design and HPC concurrency
+🔧 11. GPU cluster configuration and job scheduling
+🔧 12. HPC provisioning and GPU cluster orchestration
+🔧 13. HPC training pipeline and multi-GPU scheduling
+🔧 14. HPC scheduling and multi-node debugging
+🔧 15. HPC or large-batch evaluations
+🔧 16. Hybrid on-premise and cloud HPC setups
+🔧 17. Large-scale distributed computing and HPC performance
+🔧 18. Low-level HPC code Cplusplus Triton and parallel programming
+🔧 19. Low-level driver optimizations CUDA RDMA etc
+🔧 20. Multi-GPU training and HPC acceleration
+🔧 21. Overseeing HPC infrastructure for RL reasoning tasks
+🔧 22. Performance modeling for large GPU fleets
+🔧 23. Python and low-level matrix operations custom CUDA kernels
+🔧 24. Python Cplusplus tooling for robust model tests
+🔧 25. Stress-testing frontier LLMs and misuse detection
+🔧 26. Building and optimizing distributed backend systems
+🔧 27. Distributed system debugging and optimization
+🔧 28. Distributed system design and MLOps best practices
+🔧 29. High-performance optimization for ML training and inference
+🔧 30. Implementing quantitative models of system throughput
+🔧 31. Load balancing and high-availability design
+🔧 32. Optimizing system performance under heavy ML loads
+🔧 33. Performance optimization for LLM inference
+🔧 34. Python-driven distributed training pipelines
+🔧 35. Throughput and performance optimization
+🔧 36. Cross-team platform innovation and proactive ML based resolution
+🔧 37. Distributed systems design and scalable architectures
+🔧 38. Observability anomaly detection and automated triage AIOps Python Go
+🔧 39. ServiceNow expansions AIOps and AI automation
+🔧 40. User-centric IT workflows and design integration
+
+💻 **Software, Cloud, MLOps & Infrastructure**
+
+💻 1. Python APIs and framework optimizations tokenizers datasets
+💻 2. Python programming
+💻 3. Rust programming
+💻 4. PyTorch and Keras development
+💻 5. TypeScript development
+💻 6. MongoDB integration
+💻 7. Kubernetes orchestration
+💻 8. Building secure and robust developer experiences and APIs
+💻 9. Full-stack development Nodejs Svelte AWS
+💻 10. Javascript TypeScript machine learning libraries transformersjs huggingfacejs
+💻 11. In-browser inference using WebGPU WASM ONNX
+💻 12. Integrating with major cloud platforms AWS GCP Azure
+💻 13. Containerization with Docker and MLOps pipelines
+💻 14. Distributed data processing
+💻 15. Building essential tooling for ML hubs
+💻 16. Cloud infrastructure provisioning Terraform Helm
+💻 17. Coordination of concurrency frameworks Kubernetes etc
+💻 18. Data pipeline tooling Spark Airflow
+💻 19. Deep learning systems performance profiling and tuning
+💻 20. End-to-end MLOps and DevOps practices
+💻 21. GPU-based microservices and DevOps
+💻 22. Infrastructure as Code Terraform Kubernetes
+💻 23. Managing GPU infrastructure at scale K8s orchestration
+💻 24. Model and pipeline parallel strategies
+💻 25. Python and Golang for infrastructure automation
+💻 26. Python-based distributed frameworks Ray Horovod
+💻 27. Reliability and performance scaling of infrastructure
+💻 28. System reliability and SRE best practices
+💻 29. Building observability and debugging tools for crawlers
+💻 30. Building scalable data pipelines for language model training
+💻 31. Cloud infrastructure optimization and integration AWS GCP
+💻 32. Data quality assurance and validation systems
+💻 33. Designing cloud-native architectures for AI services
+💻 34. Ensuring system resilience and scalability
+💻 35. High-availability and scalable system design
+💻 36. Infrastructure design for large-scale ML systems
+💻 37. Integration with ML frameworks
+💻 38. Python and distributed computing frameworks Spark
+💻 39. Python automation and container orchestration Kubernetes
+💻 40. Python for automation and infrastructure monitoring
+💻 41. Python scripting for deployment automation
+💻 42. Scalable system architecture
+💻 43. Enhancing reliability quality and time-to-market through performance optimization
+💻 44. Managing production environments using Azure VSCode Datadog Qualtrics ServiceNow
+💻 45. Building MLOps pipelines for containerizing models with Docker TypeScript Rust MongoDB Svelte TailwindCSS Kubernetes
+
+🤖 **Machine Learning, AI & Model Development**
+
+🤖 1. Performance tuning for Transformers in NLP CV and Speech
+🤖 2. Industrial-level ML for text generation inference
+🤖 3. Optimizing and scaling real-world ML services
+🤖 4. Reliability and performance monitoring for ML systems
+🤖 5. Ablation and training small models for data-quality analysis
+🤖 6. Reducing model size and complexity via quantization
+🤖 7. Neural sparse models and semantic dense retrieval SPLADE BM25
+🤖 8. LLM usage and fine-tuning with chain-of-thought prompting
+🤖 9. Energy efficiency and carbon footprint analysis in ML
+🤖 10. Post-training methods for LLMs RLHF PPO DPO instruction tuning
+🤖 11. Building LLM agents with external tool usage
+🤖 12. Creating LLM agents that control GUIs via screen recordings
+🤖 13. Building web-scale high-quality LLM training datasets
+🤖 14. LLM-based code suggestions in Gradio Playground
+🤖 15. Speech-to-text text-to-speech and speaker diarization
+🤖 16. Abuse detection and ML-based risk scoring
+🤖 17. AI safety and alignment methodologies RLHF reward models
+🤖 18. Building ML-driven products using Python and PyTorch
+🤖 19. Building massive training sets for LLMs
+🤖 20. Developing next-generation AI capabilities
+🤖 21. Collaborative research on AI risk and safety
+🤖 22. Distributed training frameworks for large models
+🤖 23. Experimental large-model prototypes
+🤖 24. Exploratory ML research with LLMs and RL
+🤖 25. Large-scale retrieval optimization RAG etc
+🤖 26. Managing large ML architectures using Transformers
+🤖 27. NLP pipelines using PyTorch and Transformers
+🤖 28. Python-based data pipelines for query handling
+🤖 29. Python-based LLM experimentation
+🤖 30. Transformer-based LLM development and fine-tuning
+🤖 31. Transformer modeling and novel architecture prototyping GPTlike
+🤖 32. Vector databases and semantic search FAISS etc
+🤖 33. Advanced distributed training techniques
+🤖 34. Coordinating experimental design using Python
+🤖 35. Designing experiments to probe LLM inner workings
+🤖 36. Empirical AI research and reinforcement learning experiments
+🤖 37. Leveraging Python for ML experiment pipelines
+🤖 38. Reverse-engineering neural network mechanisms
+🤖 39. Strategic roadmap for safe LLM development
+🤖 40. Transformer-based LLM interpretability and fine-tuning
+🤖 41. AI DL model productization using established frameworks
+🤖 42. Utilizing AI frameworks PyTorch JAX TensorFlow TorchDynamo
+🤖 43. Building AI inference APIs and MLOps solutions with Python
+🤖 44. Developing agentic AI RAG and generative AI solutions LangChain AutoGen
+🤖 45. End-to-end AI lifecycle management and distributed team leadership
+🤖 46. Full-stack AI shipping with parallel and distributed training
+🤖 47. GPU kernel integration with CUDA TensorRT and roadmap alignment
+🤖 48. Large-language model inference and microservices design
+🤖 49. LLM-based enterprise analytics systems
+🤖 50. LLM diffusion-based product development
+🤖 51. LLM alignment and RLHF pipelines for model safety
+🤖 52. Mixed-precision and HPC algorithm development
+🤖 53. Optimizing open-source DL frameworks PyTorch TensorFlow
+🤖 54. Parallel and distributed training architectures and reinforcement learning methods PPO SAC QLearning
+🤖 55. Python development for large-scale MLOps deployment
+🤖 56. Scaling AI inference on hundreds of GPUs
+🤖 57. System design for multi-agent AI workflows
+🤖 58. Developing generative AI solutions with Python Streamlit Gradio and Torch
+🤖 59. Developing Web AI solutions with Javascript TypeScript and HuggingFacejs
+🤖 60. Creating WebML applications for on-device model inference
+🤖 61. Building JSTS libraries for in-browser inference using ONNX and quantization with WebGPU WebNN and WASM
+🤖 62. Driving forward quantization in the open-source ecosystem Accelerate PEFT Diffusers Bitsandbytes AWQ AutoGPTQ
+🤖 63. Designing modern search solutions combining semantic and lexical search dense bi-encoder models SPLADE BM25
+🤖 64. Training neural sparse models with Sentence Transformers integration
+🤖 65. Leveraging chain-of-thought techniques in small models to outperform larger models
+🤖 66. Addressing hardware acceleration and numerical precision challenges for scalable software
+
+📊 **Data Engineering, Analytics & Data Governance**
+
+📊 1. Advanced analytics and forecasting using Python R
+📊 2. Alerting systems and dashboards Grafana etc
+📊 3. Collaboration with data science teams
+📊 4. Data modeling and warehousing
+📊 5. Data storytelling and stakeholder communications
+📊 6. Data warehousing and BI tools Looker etc
+📊 7. Distributed compute frameworks Spark Flink
+📊 8. ETL pipelines using Airflow and Spark
+📊 9. Experiment design and user behavior modeling
+📊 10. Handling large event data Kafka S3
+📊 11. Managing data lakes and warehousing
+📊 12. Python and SQL based data pipelines for finance
+📊 13. Real-time anomaly detection using Python and streaming
+📊 14. Root-cause analysis and incident response
+📊 15. SQL and Python workflows for data visualization
+📊 16. Product analytics and funnel insights
+📊 17. Complex data pipelines and HPC optimization techniques
+📊 18. Large-scale data ingestion transformation and curation
+📊 19. Multi-modal data processing for diverse inputs
+
+🔒 **Security, Compliance & Reliability**
+
+🔒 1. Attack simulations and detection pipelines
+🔒 2. Automation with Python and Bash
+🔒 3. Cross-team incident response orchestration
+🔒 4. IAM solutions AzureAD Okta
+🔒 5. MacOS and iOS endpoint security frameworks
+🔒 6. ML system vulnerability management
+🔒 7. Risk assessment and vulnerability management
+🔒 8. Security audits and penetration testing
+🔒 9. Security best practices for AI products appsec devsecops
+🔒 10. Secure architecture for HPC and ML pipelines
+🔒 11. Security privacy and compliance in data management
+🔒 12. Coordinating with security and compliance teams
+🔒 13. Designing fault-tolerant high-availability LLM serving systems
+🔒 14. Designing resilient and scalable architectures
+🔒 15. Ensuring compliance and secure transactions
+🔒 16. Familiarity with technical operations tools for security
+🔒 17. Managing security processes for AI systems
+🔒 18. Performance tuning for LLM serving systems
+🔒 19. Process optimization and rapid troubleshooting for security
+🔒 20. Python for reliability monitoring and automation
+🔒 21. Python-based monitoring and fault-tolerance solutions
+🔒 22. Risk management and compliance strategies
+🔒 23. Cost optimization and reliability in cloud environments
+🔒 24. Data quality standards and compliance Informatica Collibra Alation
+🔒 25. Enterprise-wide data governance and policies for security
+🔒 26. Hybrid cloud integration for secure operations
+🔒 27. Identity management MFA ActiveDirectory AzureAD SSO ZeroTrust
+🔒 28. Scalable database security MySQL PostgreSQL MongoDB Oracle
+🔒 29. Security and operational excellence in IT and cloud
+
+👥 **Leadership, Management & Collaboration**
+
+👥 1. Coordinating engineering design and research teams
+👥 2. Cross-functional leadership for platform roadmaps
+👥 3. Cross-functional leadership across finance and engineering
+👥 4. Cross-team collaboration and project leadership
+👥 5. Data-driven product management AB testing and analytics
+👥 6. Deep knowledge of AI frameworks and constraints
+👥 7. Driving cross-team alignment on HPC resources
+👥 8. People and team management for data teams
+👥 9. Stakeholder management and vendor oversight
+👥 10. Team-building and product strategy
+👥 11. Team leadership and project delivery
+👥 12. Balancing innovative research with product delivery
+👥 13. Balancing rapid product delivery with AI safety standards
+👥 14. Bridging customer requirements with technical development
+👥 15. Collaboration across diverse technology teams
+👥 16. Coordinating reinforcement learning experiments
+👥 17. Coordinating with security and compliance teams
+👥 18. Cross-functional agile collaboration for ML scalability
+👥 19. Cross-functional team coaching and agile processes
+👥 20. Cross-functional stakeholder management
+👥 21. Cross-regional team alignment
+👥 22. Cross-team collaboration for ML deployment
+👥 23. Data-driven growth strategies for AI products
+👥 24. Data-driven strategy implementation
+👥 25. Detailed project planning and stakeholder coordination
+👥 26. Driving execution of global market entry strategies
+👥 27. Leading high-impact zero-to-one ML development teams
+👥 28. Leading interdisciplinary ML research initiatives
+👥 29. Leading teams building reinforcement learning systems
+👥 30. Leading teams in ML interpretability research
+👥 31. Overseeing Python-driven ML infrastructure
+👥 32. Vendor and cross-team coordination
+👥 33. Facilitating cross-disciplinary innovation
+
+📱 **Full-Stack, UI, Mobile & Product Development**
+
+📱 1. Building internal AI automation tools
+📱 2. CI CD automation and testing frameworks
+📱 3. Cloud-based microservices and REST GraphQL APIs
+📱 4. GraphQL or REST based data fetching
+📱 5. Integrating AI chat features in mobile applications
+📱 6. LLM integration for user support flows
+📱 7. MacOS iOS fleet management and security
+📱 8. MDM solutions and iOS provisioning
+📱 9. Native Android development Kotlin Java
+📱 10. Observability and robust logging tracing
+📱 11. Performance tuning and enhancing user experience for mobile
+📱 12. Python Node backend development for AI features
+📱 13. Rapid prototyping of AI based internal apps
+📱 14. React Nextjs with Python for web services
+📱 15. React TypeScript front-end development
+📱 16. Integrating with GPT and other LLM endpoints
+📱 17. TypeScript React and Python backend development
+📱 18. Zero-touch deployment and patching
+📱 19. Active engagement with open-source communities
+📱 20. API design for scalable LLM interactions
+📱 21. Bridging native mobile frontends with Python backends
+📱 22. Bridging Python based ML models with frontend tooling
+📱 23. Building internal tools to boost productivity in ML teams
+📱 24. Building intuitive UIs integrated with Python backed ML
+📱 25. Building robust developer infrastructure for ML products
+📱 26. Crafting user-centric designs for AI interfaces
+📱 27. Developer tools for prompt engineering and model testing
+📱 28. End-to-end product delivery in software development
+📱 29. Enhancing secure workflows and enterprise integrations
+📱 30. Experimentation and iterative product development
+📱 31. Full-stack development for ML driven products
+📱 32. Integrating robust UIs with backend ML models
+📱 33. Iterative design based on user feedback
+📱 34. Mobile app development incorporating AI features
+📱 35. Optimizing TypeScript Node build systems
+📱 36. Python based API and data pipeline creation
+📱 37. Senior engineering for practical AI and ML solutions
+📱 38. Creating Python and Javascript HTML libraries for ML use cases
+📱 39. Developing specialized software for healthcare ML use cases
+📱 40. Utilizing library frameworks for scalable healthcare solutions
+📱 41. Writing apps using Python Rust CUDA Transformers Keras
+📱 42. Building AI solutions for healthcare with open-source libraries and Azure SaaS
+📱 43. Designing and developing secure robust apps and APIs using Streamlit and Gradio
+📱 44. Expertise with tools like Transformers Diffusers Accelerate PEFT Datasets
+📱 45. Leveraging deep learning frameworks PyTorch XLA and cloud platforms
+
+🎯 **Specialized Domains & Emerging Technologies**
+
+🎯 1. 3D computer vision and neural rendering radiance fields
+🎯 2. Advanced 3D reconstruction techniques Gaussian splatting NERF
+🎯 3. Graphics engines and deep learning for graphics Unreal Unity
+🎯 4. Low-level rendering pipelines DirectX Vulkan DX12
+🎯 5. Performance optimized computer vision algorithms real-time tracking relighting
+🎯 6. Semantic video search and 3D reconstruction services
+🎯 7. Agent frameworks and LLM pipelines LangChain AutoGen
+🎯 8. Concurrency in Cplusplus Python and vector database integration
+🎯 9. Cross-layer performance analysis and debugging techniques
+🎯 10. EDA and transistor-level performance modeling SPICE BSIM STA
+🎯 11. GPU and SoC modeling and SoC architecture SystemC TLM
+🎯 12. Next-generation hardware bringup and system simulation
+🎯 13. Parallel computing fundamentals and performance simulation
+🎯 14. Advanced development for programmable networks SDN SONiC P4
+🎯 15. System design for multi-agent AI workflows
+🎯 16. Advanced AI for self-driving software
+🎯 17. Autonomous vehicle data pipelines and debugging
+🎯 18. Car fleet software updates OTA and telemetry management
+🎯 19. Large-scale multi-sensor data operations and calibration
+🎯 20. Path planning and decision-making in robotics
+🎯 21. Real-time embedded systems for robotics Cplusplus Python
+🎯 22. Sensor fusion and HPC integration for perception systems
+🎯 23. Domain randomization and sim-to-real transfer for reinforcement learning
+🎯 24. GPU accelerated physics simulation Isaac Sim
+🎯 25. Large-scale reinforcement learning methods PPO SAC QLearning
+🎯 26. Policy optimization for robotics at scale
+🎯 27. Reinforcement learning orchestration and simulation based training
+🎯 28. Communication libraries NCCL NVSHMEM UCX
+🎯 29. HPC networking InfiniBand RoCE and distributed GPU programming
+🎯 30. GPU verification architecture techniques TLM SystemC modeling
+🎯 31. Hardware prototyping and verification SDN SONiC P4 programmable hardware
+🎯 32. GPU communications libraries management and performance tuning
+🎯 33. Senior software architecture for data centers EthernetIP design switch OS
+🎯 34. Developing Web AI solutions using Python Streamlit Gradio and Torch
+🎯 35. Developing Web AI solutions with Javascript TypeScript and HuggingFacejs
+🎯 36. Creating WebML applications for on-device model inference
+🎯 37. Building JSTS libraries for in-browser inference using ONNX and quantization with WebGPU WebNN and WASM
+🎯 38. Driving forward quantization in the open-source ecosystem Accelerate PEFT Diffusers Bitsandbytes AWQ AutoGPTQ
+🎯 39. Designing modern search solutions combining semantic and lexical search dense bi-encoder models SPLADE BM25
+🎯 40. Training neural sparse models with Sentence Transformers integration
+🎯 41. Leveraging chain-of-thought techniques in small models to outperform larger models
+🎯 42. Addressing hardware acceleration and numerical precision challenges for scalable software
+
+📢 **Community, Open-Source & Communication**
+
+📢 1. Educating the ML community on accelerating training and inference workloads
+📢 2. Working through strategic collaborations
+📢 3. Contributing documentation and code examples for technical and business audiences
+📢 4. Building and evangelizing demos and strategic partner conversations
+📢 5. Sharing fast Python AI development code samples and demos
+📢 6. Communicating effectively in public speaking and technical education
+📢 7. Engaging on social platforms GitHub LinkedIn Twitter Reddit
+📢 8. Bringing fresh informed ideas while collaborating in a decentralized manner
+📢 9. Writing technical documentation examples and notebooks to demonstrate new features
+📢 10. Writing clear documentation across the product lifecycle
+📢 11. Contributing to open-source libraries Transformers Datasets Accelerate
+📢 12. Communicating via GitHub forums or Slack
+📢 13. Demonstrating creativity to make complex technology accessible