TechnicalPerspectives
Decision Intelligence, Explainability, and Guardrails: engineering AI systems that operate in production with traceability, rigorous evaluation, and verifiable controls.
AMI Labs and JEPA: LeCun's Bet on World Models Over LLMs
Yann LeCun confirms AMI Labs with a sought valuation of $5B+. Technical analysis of the JEPA architecture: why predicting in representation space — not generating tokens — may outperform LLMs in causal reasoning and physical planning.
H-Neurons: The Sparse Circuitry Behind LLM Hallucinations
Tsinghua researchers demonstrate that less than 0.1% of an LLM's neurons reliably predict when the model will hallucinate — with generalization across families and scales. Three intervention vectors for production systems.
NVIDIA Physical AI: Cosmos 3, GR00T N2, and the Stack for Robotics in Production
Technical analysis of NVIDIA's Physical AI stack: Cosmos 3, GR00T N1.7/N2, Isaac Lab 3.0, and Newton. What it means for decision systems in critical physical environments and what remains unresolved in the governance layer.
OpenClaw in March 2026: Benefits, Risks, and an Opinionated Review
A balanced reading of OpenClaw as of March 2026: product vision, real strengths, and the security, operational, and complexity risks that come with a local-first agentic platform.
Agentic AI in 2026 and What to Expect in 2027
What is actually working in agentic AI in 2026, what still fails, and why 2027 will likely bring less theater and more serious infrastructure for operating agents under control.
Decision Intelligence: From Prediction to Verifiable Decision
Predictive models don't make decisions. Decision Intelligence closes that gap: evaluation suites, end-to-end traceability, operational guardrails, and feedback loops on real outcomes.
XAI in Production: SHAP, LIME, Attention and When to Use Each
Explainability isn't a post-hoc report—it's a system layer. We compare SHAP, LIME, and attention mechanisms in real contexts and present the Explainability Stack for production systems.
Operational Guardrails for AI: What They Are, Types, and How to Implement Them
A model without guardrails is an operational risk. We describe five guardrail types with reference architectures on Google Cloud Functions, BigQuery, and Vertex AI.
Nested Learning: Hierarchical Architectures for Production Decisions
Beyond the monolithic model: Mixture of Experts, Google Pathways, multi-stage ranking, and hierarchical evaluation patterns for nested production systems with Vertex AI.
MLOps Pipeline Design: From Notebooks to Continuous Production
MLOps pipeline design with CI/CD for models, feature stores, model registry, canary deployments, and automatic rollback on Vertex AI Pipelines.
Model Monitoring: Detecting Drift Before Disaster
How to detect data drift, concept drift, and prediction drift before models silently fail. PSI, KS test, automated alerts, and retraining.
Feature Engineering in Production: From Notebooks to Pipelines
Feature stores, training-serving consistency, online vs offline features, point-in-time correctness, and feature governance on Google Cloud.
RAG in Production: Retrieval-Augmented Generation Architectures for Enterprise
Chunking, embeddings, vector search, retrieval evaluation, hallucination detection, and guardrails for production LLMs with Vertex AI Gemini.
AI for Mining: Decision Systems in Extractive Operations
Predictive maintenance, ore grade prediction, process optimization, and safety systems for critical mining operations with Google Cloud.
AI Governance: Frameworks for Responsible Enterprise Adoption
Model risk management, model inventory, bias testing, fairness metrics, and regulatory compliance. The three lines of defense model applied to AI.
Liquid Foundation Models: Continuous-Time Neural Dynamics for Edge Decision Intelligence
Transformers are intrinsically static after training. Liquid Foundation Models are adaptive systems governed by continuous-time ODEs — designed to operate on the edge with sub-millisecond latency and zero cloud dependency.
Causal Decision Intelligence: Structural Causal Models for Production AI Systems
Predictive ML optimizes P(Y|X). Critical decisions require P(Y|do(X)). The distinction between correlation and intervention is not philosophical — it is the difference between systems that work and systems that silently fail in production.
Cloud for Intelligent Agents in Banking: AWS vs Google Cloud
2025/2026 comparison: AWS Bedrock Agents with Claude 3.5 vs Google Vertex AI Agent Builder with Gemini 2.0. Security, CMF/SBIF compliance, operational latency, and TCO for financial sector CIOs and CTOs.
Pinecone vs Milvus in Production: Architecture, Benchmarks and Trade-offs
Advanced technical analysis of the two leading vector database systems: HNSW vs IVF-PQ algorithms, filtering at scale, dense-sparse hybrid search, Milvus 2.5 sharding, and Pinecone v2 serverless architecture.