Labs To Blog
NL-2-SQL Lab
Natural language query to SQL generation with schema-aware reasoning.
AI & Machine Learning Simplified for Developers
SuperML.dev empowers developers with project-based learning and production-ready templates for Stock Prediction, Credit Card Fraud Detection, and Agentic AI. 🔥 New guides & tools weekly!
SuperML.dev repo is now available! » AgenticAI on GitHub
Today At SuperML
Curated For Builders, Not Endless Noise
Follow what matters now: practical learning tracks, agentic system patterns, and opinionated AI briefs.
Learning Tracks
Hands-On AI And ML Foundations
Machine learning, deep learning, and structured learning paths for developers.
Browse track →
Agentic + LLM Systems
RAG, Agents, MCP, Prompt Engineering
Build patterns and production notes for real AI apps and agent workflows.
Explore systems →
Opinionated AI Briefs
Signal On Enterprise AI Moves
Weekly takes on infra, governance, regulation, finance, and production AI shifts.
Read briefing →
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.
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.
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.
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.
Cerebras Files for $26.6B IPO With OpenAI as 86% of the Backlog: The Wafer-Scale Tier Just Became an Architecture Decision
Cerebras's S-1 lands with a 750 MW OpenAI inference contract, an $1B circular loan, and 86% revenue concentration in two customers — and quietly forces enterprise AI teams to make a routing decision they've been postponing.
The NL-2-SQL Agent Trap: Why LLMs Need an Ontology Layer to Stop Hallucinating Your Database
Google's BigQuery + Gemini NL2SQL pipeline reveals the core problem every team hits: LLMs generate syntactically valid SQL that is semantically wrong. The fix isn't a better prompt — it's an ontology layer that maps business language to your actual schema. Here's the full architecture, the failure modes, and what to build.
When Your AI Vendor Becomes Your Systems Integrator: The Enterprise Architecture Reckoning Behind the OpenAI-Anthropic PE Playbook
OpenAI's $10B 'Deployment Company' and Anthropic's $1.5B Blackstone-Goldman venture both announced May 4 — and both use the same playbook: embed engineers inside your company, redesign your workflows around their model. Here's what that actually breaks in your AI architecture, governance stack, and vendor strategy.
The 5% Problem: What Datadog's 2026 AI Engineering Data Says About the Production Reliability Crisis Nobody Is Talking About
Datadog's State of AI Engineering 2026 report found 5% of all LLM calls fail in production — and 60% of those failures are caused by rate limits, not model quality. Here's what that number actually means for enterprise AI systems and why finance teams should be alarmed.
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.