Practical skills. Real use cases. No hype.
The Artificial Super Intelligence Society is a private community for curious minds learning to apply AI in work, products and startups. It is for learners, engineers, researchers, founders and professionals, from first questions to production systems.
What happens inside
Three habits keep the society useful. Members read closely, build things, and tell each other what actually happened.
Stay informed
Explore meaningful AI developments, research and tools.
Learn by doing
Build practical skills through projects, experiments and honest feedback.
Share experience
Bring questions, prototypes, useful resources and lessons from failure.
What we work on
Six areas, all of them applied. A topic earns its place by being useful at work, in a product or in a startup.
- AI fundamentals
- How models learn, where they fail, and how to reason about both.
- Generative AI
- Prompting, fine-tuning and evaluation for text, image and code models.
- Intelligent agents
- Tool use, planning loops, memory and multi-agent orchestration.
- RAG
- Retrieval pipelines that ground answers in your own documents.
- Automation
- Workflows that remove repetitive work and keep human judgment in the loop.
- Responsible deployment
- Security, privacy, cost and governance, settled before anything ships.
We challenge AI, wrestle with its limitations, and occasionally admit, “You win this round.” Then we try again, token budget permitting.
How we operate
A short set of norms, kept by the members themselves.
Stay relevant
Keep discussions practical.
Keep noise low
No spam or unsolicited sales pitches.
Share with context
Explain what you learned.
Be respectful
Welcome questions and protect privacy.
Value evidence
Credit sources and acknowledge limitations.
Quality over quantity
No pressure to post every day.
Self-governed through common sense and shared responsibility.
Founded by REI
Ruksh E Ibaadat
Senior AI Engineer and Enterprise Agentic AI Architect
REI builds production-grade LLM infrastructure, multi-agent systems, RAG ecosystems, healthcare AI platforms and planetary AI research systems. The society is where that practice is shared, questioned and improved.
Engineering intelligence. Building impact.
Agentic AI architecture
- Agent harness engineering
- Deep agents
- LangGraph
- Multi-agent orchestration
- MCP and A2A
- Durable execution
- Context and memory engineering
LLM infrastructure and RAG
- Agentic RAG
- GraphRAG
- Hybrid retrieval
- Semantic reranking
- FastAPI
- PostgreSQL
- pgvector
- Multi-model orchestration
Model engineering
- PyTorch
- Hugging Face Transformers
- PEFT
- LoRA
- QLoRA
- vLLM
- Model routing
- Quantization
- Batching and caching
Evaluation and LLMOps
- MLflow
- LangSmith
- OpenTelemetry
- Inspect AI
- Regression evaluation
- Tracing
- Cost controls
- Agent reliability and observability
Security and governance
- Agent identities
- Scoped permissions
- Sandboxing
- gVisor
- Access control
- Audit trails
- Approval workflows
- Permission-aware memory
Automation and deployment
- Docker
- Kubernetes
- GitHub Actions
- Temporal
- n8n
- GitOps
- Event-driven automation
- Multimodal and ambient AI systems
Membership by request
Introduce yourself briefly and say what you want to learn, build or contribute. The admin reads every request.
Your request goes to REI on WhatsApp or by email. This page prepares it, and you press send.
Your details are used only to review your request.