Labs To Blog
NL-2-SQL Lab
Natural language query to SQL generation with schema-aware reasoning.
Design Production-Grade AI Systems
SuperML helps architects and builders implement enterprise AI architecture with depth: agents, semantic layers, NL-to-SQL, Smart SDLC, and production implementation patterns. Enterprise AI architecture guides, labs, and templates.
SuperML.dev repo is now available! » AgenticAI on GitHub
Start Here
Enterprise AI Architecture, Routed Clearly
SuperML is for architects and builders designing production-grade AI systems. Pick your path: deep guides, hands-on labs, templates, open source projects, newsletter briefings, and topic hubs.
Latest Architecture Guides
Production AI Design Breakdowns
System diagrams, decisions, and implementation depth for enterprise AI builds.
Read guides →
Labs
Run Working AI Labs
Move from architecture notes to executable demos and experiment flows.
Open labs →
Templates
Reusable AI Building Blocks
Implementation-ready templates and practical accelerators for builder workflows.
Browse templates →
GitHub Projects
Open Source Enterprise AI Repos
Explore Smart SDLC, agentic systems, and production-oriented project code.
View GitHub →
Newsletter
Weekly Enterprise AI Signal
Get architecture breakdowns, implementation playbooks, and selected reads.
Subscribe now →
Popular Topics
Agentic AI, NL-to-SQL, Ontology
Explore curated topic hubs built for enterprise AI architecture builders.
Explore topics →
Labs.SuperML.dev
Labs To Blogs: Learn, Then Run It
Every lab maps to a blog so readers can move from architecture insights to hands-on execution in one click.
Labs To Blog
Fraud Detection Lab
Real-time anomaly detection workflows with transparent model behavior.
Labs To Blog
Agentic Fraud Lab
Rule-generation style agentic flows for modern fraud operations.
Latest From SuperML
View all posts »A curated stream of practical tutorials, production AI notes, and opinionated briefings for builders.
The Seven-Model Problem: Enterprise AI Inference Has Left the Lab — and the Control Plane Hasn't Caught Up
F5's 2026 State of Application Strategy Report drops a number that should alarm every platform architect: the average enterprise is now running seven AI models simultaneously in production. The traffic cop that routes between them, governs them, and keeps them from burning your budget? Most enterprises don't have one.
OpenAI and Anthropic Adopted the Palantir Playbook. Now Enterprise Architecture Teams Need a Counter-Move.
When the two largest model labs simultaneously launched forward-deployed engineering ventures backed by Wall Street capital, they didn't just change how AI gets sold — they changed who owns your production AI architecture. Here's what that means for engineering teams trying to stay in control.
SR 26-2 Blew a Hole in Bank AI Governance. Now Every Model Risk Team Has to Fill It.
Federal regulators just rewrote SR 11-7 — and explicitly excluded generative AI and agentic AI from the new framework. Banks are now deploying their most powerful AI systems without a regulatory rulebook, and the internal governance vacuum is the real risk story.
Ontology: The Missing Semantic Layer That Makes Enterprise AI Actually Work
Everyone's deploying AI, but most enterprise systems are reasoning over raw data with no shared understanding of what that data means. Ontologies fix that — and Palantir built an entire $50B platform on this idea. Here's what you need to know.
Five AI Vendors Shipped Agent Registries in One Quarter — That's Not Competition, It's a Production Crisis Signal
When Microsoft, AWS, Google, ServiceNow, and Okta all ship 'agent registries' within weeks of each other, enterprise architects need to read that convergence carefully — because the agent inventory problem is now a compliance deadline, not a backlog item.
CommBank's Fraud Agent Now Writes Its Own Detection Rules — The Architecture Shift Behind a 20% Drop in Fraud Losses
Commonwealth Bank deployed an agentic AI system that doesn't just flag fraud — it generates new detection rules in real-time, then hands them to humans for approval. The 20% fraud loss reduction is real. So is the governance architecture required to make it not a liability.
The Ontology Layer Every Enterprise AI System Needs (But Almost None Have)
Most enterprise AI systems fail in production not because the models are wrong, but because nobody defined what 'customer', 'transaction', or 'risk' means consistently across systems. This is a practical implementation guide for building the semantic layer that makes AI grounded, governed, and production-ready.
When RAG Breaks at Agent Scale: Pinecone Nexus and the Context Compilation Turn in Enterprise AI
Pinecone's pivot from vector database to 'knowledge engine' exposes a structural flaw in how enterprise teams built their RAG stacks — and signals a new architecture layer between raw data and agent runtime that will reshape how production AI systems are designed.
Highlights
Why build with SuperML.dev?
Designed for engineers, researchers, and students who want to build real-world AI projects.
Agentic AI Templates
From LangChain agents to multi-modal planners, learn AI systems hands-on.
ML with Financial Data
Learn stock prediction using technical indicators, news sentiment, and fundamentals.
Fraud Detection Frameworks
XGBoost, Deep Learning & real pipelines for detecting transactional anomalies.
Optimized for Devs
Built using Astro & Tailwind CSS for lightning-fast UX and maintainability.
Fully Open Source
Use, fork, or contribute — your learning and tools should never be locked.
SuperML.dev Community
Join others building together — from weekend hobbyists to professional engineers.
Learn Your First project on Agentic AI and ML by SuperML.dev Today
Step 1: Clone
Fork SuperML.dev starter projects or use the main template. It's all on GitHub.
Step 2: Build & Learn
Follow step-by-step Jupyter notebooks or guides for each AI/ML use case.
🎉 Earn a badge!
✦ New Open Source Framework
Build faster with Smart SDLC
An AI-driven agile framework that brings structured expertise to GitHub Copilot, Claude, or any coding assistant — covering your full development lifecycle with zero runtime dependencies.
👤
5 AI Personas
Product/BA, Architect, Developer, Modernization Lead, Team Lead — each with a distinct role in Copilot.
🔄
Full SDLC Coverage
PRD, architecture, ADRs, epics, user stories, sprint planning — all AI-powered from one framework.
⚡
One Command Setup
npx smart-sdlc init — installs skills, agents, and knowledge base in seconds.
🔌
Native Integrations
JIRA, Confluence, GitHub, GitLab, and Azure DevOps — REST API & MCP Server workflows built in.
FAQ
Frequently Asked
Got questions about SuperML, agentic systems, or how to get started?
Is SuperML open source?
Yes! All projects, templates, and guides are MIT licensed. Fork and build freely.
Can I contribute?
We’d love that. Submit PRs, write blogs, or build templates with us!
Who is this for?
Developers, students, indie hackers, AI hobbyists — anyone with a builder's mindset.
12+
Open Source Projects
2K+
Community Members
3.2K
GitHub Stars
5K+
Weekly Readers
Join the SuperML Ecosystem
Build projects, learn deeply, share your work — and bring Agentic AI to life.