README.md

metadata

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