README.md

---
title: 🧠🌱SynapTree🌳
emoji: 🌳🧠🌱
colorFrom: indigo
colorTo: blue
sdk: streamlit
sdk_version: 1.42.1
app_file: app.py
pinned: true
license: mit
short_description: AI Knowledge Tree Builder AI
---

AIKnowledgeTreeBuilder is designed with the following tenets: 
1. Portability - Universal access via any device & link sharing 
2. Speed of Build - Rapid deployments (< 2min to production) 
3. Linkiness - Programmatic access to major AI knowledge sources 
4. Abstractive - Core stays lean by isolating high-maintenance components 
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