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 EU AI Act Omnibus deferred Annex III credit scoring compliance to December 2027 — but GPAI transparency enforcement goes live August 2, 2026. Banks using foundation models in production workflows have obligations they're not prepared for.
Standard RAG retrieves documents and lets the LLM generate field names freely — which means it will invent fields your schema never had. AK-RAG (Attribute Knowledge RAG) indexes your governed attribute catalog instead, and the LLM can only emit attribute_ids that actually exist. Here's the architecture and why it matters for regulated-industry AI.
A Pennsylvania bank employee used an unauthorized AI tool on customer data — no hacker, no breach, no operational disruption — and the bank still filed an SEC Form 8-K. The first shadow AI disclosure is a blueprint for what your regulators are about to ask.
The CFPB eliminated disparate impact from ECOA on April 22 — but AI credit scoring teams that interpret this as clearance to drop bias testing are about to learn the hard way that fair lending risk lives in at least five other regulatory regimes they haven't fully inventoried.
ARPA-H is selecting teams this month to build the first FDA-authorized agentic clinical AI — a system that writes prescriptions, adjusts medications, and acts as a 24/7 cardiovascular care member. FDA has no published guidance for agentic AI validation, no clearance precedent, and no framework that covers autonomous clinical agents.
FinCEN's NPRM comment period just closed — and its effectiveness-based AML standard implicitly rewards AI adoption. The problem: banks rushing to deploy AI for compliance credit have no model governance framework, and OCC examiners are already asking about it in every exam.
SpaceX's $60B acquisition of Cursor ends the era of multi-model, model-neutral AI coding platforms — and every enterprise team that built agentic CI/CD workflows in Cursor just inherited a vendor lock-in risk they didn't plan for.
OpenAI replays 1.3M anonymized production conversations with candidate models before release — catching reward hacking and behavior shifts that adversarial evals miss entirely.
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.
The EU AI Act Omnibus deferred Annex III credit scoring compliance to December 2027 — but GPAI transparency enforcement goes live August 2, 2026. Banks using foundation models in production workflows have obligations they're not prepared for.
Standard RAG retrieves documents and lets the LLM generate field names freely — which means it will invent fields your schema never had. AK-RAG (Attribute Knowledge RAG) indexes your governed attribute catalog instead, and the LLM can only emit attribute_ids that actually exist. Here's the architecture and why it matters for regulated-industry AI.
A Pennsylvania bank employee used an unauthorized AI tool on customer data — no hacker, no breach, no operational disruption — and the bank still filed an SEC Form 8-K. The first shadow AI disclosure is a blueprint for what your regulators are about to ask.
The CFPB eliminated disparate impact from ECOA on April 22 — but AI credit scoring teams that interpret this as clearance to drop bias testing are about to learn the hard way that fair lending risk lives in at least five other regulatory regimes they haven't fully inventoried.
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!
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