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.
Community Embeds
Shorts
A short-post stream for links, demos, videos, and quick discussion threads.
Post a URL, add a short description, and let the community discuss it.
Add Embed Discussion
Loading sign-in state…
SuperML.dev repo is now available! » AgenticAI on GitHub
Community Embeds
Shorts
A short-post stream for links, demos, videos, and quick discussion threads.
Recent shorts and discussion threads from the community.
Recent Community Embeds
No shorts yet. Share the first link and start the discussion.
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 →
Showcase
Discover & Showcase AI Products
Browse production-ready AI/ML tools curated for enterprise architects — or list your own for free.
Explore tools →
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 →
SuperML Showcase
Discover. Compare. Ship.
A curated directory of production-ready AI & ML tools — vetted for enterprise builders. List your product free and reach architects making real infrastructure decisions.
Get the badge for your product
Featured on
SuperML Showcase
superml.dev/tools
Free listing · Instant visibility · Do-follow backlink
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.
FDA Wants to Credential AI Like a Doctor. The Infrastructure Doesn't Exist.
FDA's August 2026 discussion paper proposes a competency-based framework for generative AI medical devices — two risk axes, ten benchmark elements, five clinical confirmation rungs. The comment window closes October 19. Most manufacturers haven't mapped their products to it yet.
Step-Level Agent Monitoring: Halting Bad Runs at 1ms
Post-hoc log review catches agent failures after the damage is done, and a guardian agent on every step doubles your cost. A streaming trajectory monitor offers a third design, and the access regime you have determines how much it can see.
The EU AI Act High-Risk Deferral Is a Trap, Not a Reprieve
The Digital Omnibus pushed the EU AI Act's Annex III high-risk deadline from August 2026 to December 2027 — exactly one conformity assessment cycle. Banks that treated the delay as breathing room are already behind.
Compound AI Failures Live at the Seams, Not in the Model
An analysis of 150 production incidents finds that compound AI systems break at component boundaries, and half of those failures are silent. Here is how to place circuit breakers, quality gates, and typed interfaces where the damage actually starts.
Eval-Gated Agent Releases: Passing Evals Is Not a Gate
Most teams treat a green eval run as permission to ship an agent change. The data says that's the wrong contract. Here is how to build risk-tiered release gates where evals narrow the review queue instead of replacing it.
Fail-Plausible: When Agents Turn Errors Into Fiction
A production agent that crashes loudly is a solved problem. One that converts an error into a fluent, believable answer is not. Here is the failure class, why tests miss it, and the architecture that contains it.
Your LLM Gateway Budget Cap Is Advisory, Not Enforced
A max_budget setting on your LLM gateway is a promise, not a control. Here is why budget enforcement silently fails in multi-replica agent deployments, and the reserve-then-settle architecture that actually holds.
Agent Eval Costs: Stop Runs You Can Already Predict
Full-pass agent benchmarks now cost hundreds to thousands of dollars per run. Early outcome prediction halts runs whose result is already evident, and the architecture around it decides whether your rankings survive.
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.