Opinionated briefings and high-signal takes from the SuperML viewpoint.
The vector database you choose for your RAG system determines more than retrieval speed — it shapes your architecture's scalability ceiling, operational complexity, cost model, and what filtering capabilities you have at query time. This is the production comparison for teams choosing in 2026.
LangChain and LangGraph solve different problems and the choice between them is not about preference — it's about the shape of your workflow. This is the architecture decision guide: when chains are enough, when you need stateful graphs, and when to use neither.
Most AI architecture guides are written for startups deploying on greenfield infrastructure. Financial services has different constraints: regulatory audit requirements, latency SLAs on core banking integrations, data residency rules, fair lending exposure, and model risk governance. This is the pattern library for AI architects building production systems inside those constraints.
John Boyd designed the OODA loop for fighter pilots making life-or-death decisions in milliseconds with incomplete information. It turns out this is a better mental model for production AI agents than the ReAct loop — especially in high-stakes, time-pressured environments where agents need to fail fast, course-correct, and maintain situational awareness across a multi-step decision horizon.
Fresh tutorials, walkthroughs, and practical AI/ML build notes.
The FSB's 12 sound practices for responsible AI adoption include the most honest regulatory admission yet: human oversight of agentic AI in banking can't scale, so banks need AI to monitor AI.
As autonomous AI agents move from demos to production — scheduling meetings, writing code, executing trades — most enterprises have no governance framework built for systems that act, not just predict. Here's what one looks like.
Most banks have the data to personalize credit card offers at scale. Most can't actually do it in production because their ML architecture wasn't designed for real-time decisioning. Here's what the right stack looks like — and why the wrong one loses to Amazon.
Learn how Thompson Sampling solves the explore-exploit dilemma for real-time personalization. From Bayesian foundations to a full Python implementation for credit card offer ranking, with production deployment patterns.
Feature-Decision-Execution (FDE) is the layered architecture pattern that separates ML prediction from business logic from system action — the pattern that makes production ML systems maintainable, auditable, and safe to iterate on.
Wolters Kluwer's H1 2026 Banking AI Risk Index found 72% of banks lack kill switches or failure reporting for AI models — the minimum viable governance for agentic AI in production financial systems.
A hands-on tutorial on the REA (Resources, Events, Agents) framework applied to banking ontology — from McCarthy's 1982 origins to building a working OWL ontology with Python, RDFLib, SPARQL queries, and AI/ML integration patterns.
SR 11-7 is 15 years old and SR 26-2 explicitly excluded generative AI from its scope. Banks are now governing their most powerful AI systems against a framework that was never designed for them. Here's the practitioner guide to what model risk management actually looks like when you apply it to LLMs, RAG pipelines, and agentic AI.
Most RAG tutorials get you from zero to a working demo in 30 minutes. Production RAG takes 6–12 months to get right, and the problems that sink it are not the ones covered in the tutorial. This is the production engineering guide: chunking strategy, hybrid retrieval, re-ranking, evaluation frameworks, and the operational patterns that keep RAG systems working after launch.
Row-based ML catches individual bad actors but misses coordinated fraud rings. Graph Neural Networks propagate relational context through transaction networks — here's the architecture, the PyTorch Geometric code, and the production gotchas that matter more than model choice.
Microsoft just announced at Build 2026 that GitHub Copilot will replace GPT-4 Turbo with its own homegrown Polaris model in August — and enterprise teams running agentic coding workflows need to treat this as a model substitution event, not a feature upgrade.
Claude Code is now authoring 4% of all public GitHub commits. Opus 4.8's Dynamic Workflows can migrate entire codebases with hundreds of parallel subagents. The governance gap is no longer theoretical.
The FSB's 12 sound practices for responsible AI adoption include the most honest regulatory admission yet: human oversight of agentic AI in banking can't scale, so banks need AI to monitor AI.
As autonomous AI agents move from demos to production — scheduling meetings, writing code, executing trades — most enterprises have no governance framework built for systems that act, not just predict. Here's what one looks like.
Most banks have the data to personalize credit card offers at scale. Most can't actually do it in production because their ML architecture wasn't designed for real-time decisioning. Here's what the right stack looks like — and why the wrong one loses to Amazon.
Feature-Decision-Execution (FDE) is the layered architecture pattern that separates ML prediction from business logic from system action — the pattern that makes production ML systems maintainable, auditable, and safe to iterate on.
Learn how Thompson Sampling solves the explore-exploit dilemma for real-time personalization. From Bayesian foundations to a full Python implementation for credit card offer ranking, with production deployment patterns.
The vector database you choose for your RAG system determines more than retrieval speed — it shapes your architecture's scalability ceiling, operational complexity, cost model, and what filtering capabilities you have at query time. This is the production comparison for teams choosing in 2026.
LangChain and LangGraph solve different problems and the choice between them is not about preference — it's about the shape of your workflow. This is the architecture decision guide: when chains are enough, when you need stateful graphs, and when to use neither.
Most AI architecture guides are written for startups deploying on greenfield infrastructure. Financial services has different constraints: regulatory audit requirements, latency SLAs on core banking integrations, data residency rules, fair lending exposure, and model risk governance. This is the pattern library for AI architects building production systems inside those constraints.
John Boyd designed the OODA loop for fighter pilots making life-or-death decisions in milliseconds with incomplete information. It turns out this is a better mental model for production AI agents than the ReAct loop — especially in high-stakes, time-pressured environments where agents need to fail fast, course-correct, and maintain situational awareness across a multi-step decision horizon.
Hey there! Ready to dive into Machine Learning Stacking A Visual Guide? This friendly guide will walk you through everything step-by-step with easy-to-follow examples. Perfect for beginners and pros alike!
Hey there! Ready to dive into Machine Learning Lifecycle In Python? This friendly guide will walk you through everything step-by-step with easy-to-follow examples. Perfect for beginners and pros alike!
Hey there! Ready to dive into Machine Learning Math And Algorithms Demystified? This friendly guide will walk you through everything step-by-step with easy-to-follow examples. Perfect for beginners and pros alike!
Hey there! Ready to dive into Machine Learning Python Cheat Sheet? This friendly guide will walk you through everything step-by-step with easy-to-follow examples. Perfect for beginners and pros alike!
Hey there! Ready to dive into Deep Learnings Impact On Computer Vision? This friendly guide will walk you through everything step-by-step with easy-to-follow examples. Perfect for beginners and pros alike!
Hey there! Ready to dive into Masked Language Modeling With Python Transformer Architecture? This friendly guide will walk you through everything step-by-step with easy-to-follow examples. Perfect for beginners and pros alike!
Hey there! Ready to dive into Mastering Qlora? This friendly guide will walk you through everything step-by-step with easy-to-follow examples. Perfect for beginners and pros alike!
Hey there! Ready to dive into Limitations Of Lora For Pre Training Large Language Models? This friendly guide will walk you through everything step-by-step with easy-to-follow examples. Perfect for beginners and pros alike!