<?xml version="1.0" encoding="utf-8"?><feed xmlns="http://www.w3.org/2005/Atom" ><generator uri="https://jekyllrb.com/" version="3.10.0">Jekyll</generator><link href="https://adredes-weslee.github.io/feed.xml" rel="self" type="application/atom+xml" /><link href="https://adredes-weslee.github.io/" rel="alternate" type="text/html" /><updated>2026-03-26T15:23:13+00:00</updated><id>https://adredes-weslee.github.io/feed.xml</id><title type="html">Wes Lee</title><subtitle>Public portfolio of Wes Lee, a Singapore-based AI engineer building LLM systems, evaluation platforms, and decision-support products.</subtitle><author><name>Wes Lee</name><email>weslee.qb@gmail.com</email></author><entry><title type="html">Building Operational Workforce Risk Intelligence from Public Signals</title><link href="https://adredes-weslee.github.io/ai-ops/workforce-intelligence/public-signals/2026/03/24/building-operational-workforce-risk-intelligence-from-public-signals.html" rel="alternate" type="text/html" title="Building Operational Workforce Risk Intelligence from Public Signals" /><published>2026-03-24T07:00:00+00:00</published><updated>2026-03-24T07:00:00+00:00</updated><id>https://adredes-weslee.github.io/ai-ops/workforce-intelligence/public-signals/2026/03/24/building-operational-workforce-risk-intelligence-from-public-signals</id><author><name>Wes Lee</name></author><category term="ai-ops" /><category term="workforce-intelligence" /><category term="public-signals" /><category term="nextjs" /><category term="fastapi" /><category term="postgres" /><category term="langfuse" /><category term="incident-pipeline" /><category term="retrenchment" /><category term="forecasting" /><category term="dashboards" /><summary type="html"><![CDATA[Introduction: Public signals are only useful once they become incidents]]></summary></entry><entry><title type="html">Designing Creator AI as a Backend-First Content Generation Platform</title><link href="https://adredes-weslee.github.io/ai-platforms/edtech/orchestration/2026/03/24/designing-creator-ai-as-a-backend-first-content-generation-platform.html" rel="alternate" type="text/html" title="Designing Creator AI as a Backend-First Content Generation Platform" /><published>2026-03-24T06:00:00+00:00</published><updated>2026-03-24T06:00:00+00:00</updated><id>https://adredes-weslee.github.io/ai-platforms/edtech/orchestration/2026/03/24/designing-creator-ai-as-a-backend-first-content-generation-platform</id><author><name>Wes Lee</name></author><category term="ai-platforms" /><category term="edtech" /><category term="orchestration" /><category term="fastapi" /><category term="orchestration" /><category term="retrieval" /><category term="evaluation" /><category term="workflows" /><category term="azure" /><category term="governance" /><category term="bff" /><summary type="html"><![CDATA[Introduction: generation quality is not the whole product]]></summary></entry><entry><title type="html">Building SlideBench to Evaluate AI-Generated Slides</title><link href="https://adredes-weslee.github.io/evaluation-systems/multimodal/edtech/2026/03/24/building-slidebench-to-evaluate-ai-generated-slides.html" rel="alternate" type="text/html" title="Building SlideBench to Evaluate AI-Generated Slides" /><published>2026-03-24T05:00:00+00:00</published><updated>2026-03-24T05:00:00+00:00</updated><id>https://adredes-weslee.github.io/evaluation-systems/multimodal/edtech/2026/03/24/building-slidebench-to-evaluate-ai-generated-slides</id><author><name>Wes Lee</name></author><category term="evaluation-systems" /><category term="multimodal" /><category term="edtech" /><category term="fastapi" /><category term="react" /><category term="faiss" /><category term="llm-judge" /><category term="provenance" /><category term="benchmarking" /><category term="python-pptx" /><summary type="html"><![CDATA[Introduction: generation tools need an evaluation layer of their own]]></summary></entry><entry><title type="html">Building Service-Oriented Document Intelligence</title><link href="https://adredes-weslee.github.io/ai/rag/document-intelligence/2026/03/24/building-service-oriented-document-intelligence.html" rel="alternate" type="text/html" title="Building Service-Oriented Document Intelligence" /><published>2026-03-24T01:00:00+00:00</published><updated>2026-03-24T01:00:00+00:00</updated><id>https://adredes-weslee.github.io/ai/rag/document-intelligence/2026/03/24/building-service-oriented-document-intelligence</id><author><name>Wes Lee</name></author><category term="ai" /><category term="rag" /><category term="document-intelligence" /><category term="fastapi" /><category term="streamlit" /><category term="faiss" /><category term="redis" /><category term="langfuse" /><category term="hybrid-retrieval" /><category term="reranking" /><category term="multilingual" /><category term="system-design" /><summary type="html"><![CDATA[Introduction: Why this needed more than a single QA app]]></summary></entry><entry><title type="html">Designing Human-in-the-Loop Inventory Planning</title><link href="https://adredes-weslee.github.io/ai-ops/forecasting/operations/2026/03/23/human-in-the-loop-inventory-planning.html" rel="alternate" type="text/html" title="Designing Human-in-the-Loop Inventory Planning" /><published>2026-03-23T01:00:00+00:00</published><updated>2026-03-23T01:00:00+00:00</updated><id>https://adredes-weslee.github.io/ai-ops/forecasting/operations/2026/03/23/human-in-the-loop-inventory-planning</id><author><name>Wes Lee</name></author><category term="ai-ops" /><category term="forecasting" /><category term="operations" /><category term="fastapi" /><category term="streamlit" /><category term="inventory-planning" /><category term="prophet" /><category term="xgboost" /><category term="gmroi" /><category term="approvals" /><category term="n8n" /><category term="docker" /><category term="observability" /><summary type="html"><![CDATA[Introduction: Inventory systems fail when they optimize for automation instead of trust]]></summary></entry><entry><title type="html">Building Leakage-Safe Graph ML for Illicit Transaction Detection</title><link href="https://adredes-weslee.github.io/graph-ml/fintech/fraud-detection/2026/03/22/building-leakage-safe-graph-ml-for-illicit-transaction-detection.html" rel="alternate" type="text/html" title="Building Leakage-Safe Graph ML for Illicit Transaction Detection" /><published>2026-03-22T01:00:00+00:00</published><updated>2026-03-22T01:00:00+00:00</updated><id>https://adredes-weslee.github.io/graph-ml/fintech/fraud-detection/2026/03/22/building-leakage-safe-graph-ml-for-illicit-transaction-detection</id><author><name>Wes Lee</name></author><category term="graph-ml" /><category term="fintech" /><category term="fraud-detection" /><category term="pytorch-geometric" /><category term="xgboost" /><category term="logistic-regression" /><category term="graphsage" /><category term="gat" /><category term="calibration" /><category term="precision-at-k" /><category term="temporal-splits" /><category term="robustness" /><summary type="html"><![CDATA[Introduction: Fraud modeling gets overstated when evaluation is weak]]></summary></entry><entry><title type="html">Building a Production AI Robo-Advisor: TabPFN Foundation Models + Dynamic Investment Objectives</title><link href="https://adredes-weslee.github.io/ai/finance/foundation-models/reinforcement-learning/2025/06/24/robo-advisor-risk-profiling-portfolio-optimization.html" rel="alternate" type="text/html" title="Building a Production AI Robo-Advisor: TabPFN Foundation Models + Dynamic Investment Objectives" /><published>2025-06-24T02:00:00+00:00</published><updated>2025-06-24T02:00:00+00:00</updated><id>https://adredes-weslee.github.io/ai/finance/foundation-models/reinforcement-learning/2025/06/24/robo-advisor-risk-profiling-portfolio-optimization</id><author><name>Wes Lee</name></author><category term="ai" /><category term="finance" /><category term="foundation-models" /><category term="reinforcement-learning" /><category term="tabpfn" /><category term="foundation-models" /><category term="robo-advisor" /><category term="portfolio-optimization" /><category term="pytorch" /><category term="streamlit" /><category term="production-ml" /><category term="multi-objective-rl" /><category term="market-regime-detection" /><summary type="html"><![CDATA[Introduction: From Traditional ML to Foundation Model Intelligence]]></summary></entry><entry><title type="html">Building a Production Dengue Forecasting Platform: From Research Notebook to Policy Dashboard</title><link href="https://adredes-weslee.github.io/epidemiology/forecasting/health-economics/2025/06/18/forecasting-dengue-cases-and-cost-benefit-analysis.html" rel="alternate" type="text/html" title="Building a Production Dengue Forecasting Platform: From Research Notebook to Policy Dashboard" /><published>2025-06-18T02:00:00+00:00</published><updated>2025-06-18T02:00:00+00:00</updated><id>https://adredes-weslee.github.io/epidemiology/forecasting/health-economics/2025/06/18/forecasting-dengue-cases-and-cost-benefit-analysis</id><author><name>Wes Lee</name></author><category term="epidemiology" /><category term="forecasting" /><category term="health-economics" /><category term="prophet" /><category term="streamlit" /><category term="time-series" /><category term="production-ml" /><category term="health-analytics" /><category term="cost-benefit-analysis" /><category term="singapore" /><category term="dengue" /><category term="public-health" /><summary type="html"><![CDATA[Introduction: Transforming Research into Operational Intelligence]]></summary></entry><entry><title type="html">DSPy Prompt Optimization: A Scientific Approach to Automotive Intelligence</title><link href="https://adredes-weslee.github.io/ai/nlp/dspy/2025/06/13/dspy-prompt-optimization-automotive-intelligence.html" rel="alternate" type="text/html" title="DSPy Prompt Optimization: A Scientific Approach to Automotive Intelligence" /><published>2025-06-13T01:30:00+00:00</published><updated>2025-06-13T01:30:00+00:00</updated><id>https://adredes-weslee.github.io/ai/nlp/dspy/2025/06/13/dspy-prompt-optimization-automotive-intelligence</id><author><name>Wes Lee</name></author><category term="ai" /><category term="nlp" /><category term="dspy" /><category term="dspy" /><category term="prompt-optimization" /><category term="llms" /><category term="structured-extraction" /><category term="ollama" /><category term="langfuse" /><category term="automotive" /><category term="nhtsa" /><category term="meta-optimization" /><category term="reasoning-fields" /><summary type="html"><![CDATA[Introduction: From Prompt Engineering to Prompt Science]]></summary></entry><entry><title type="html">Developing ML Trading Strategies: From Rule-Based Systems to Reinforcement Learning</title><link href="https://adredes-weslee.github.io/ai/finance/machine-learning/reinforcement-learning/2025/05/12/ml-trading-strategist-comparing-learning-approaches.html" rel="alternate" type="text/html" title="Developing ML Trading Strategies: From Rule-Based Systems to Reinforcement Learning" /><published>2025-05-12T01:30:00+00:00</published><updated>2025-05-12T01:30:00+00:00</updated><id>https://adredes-weslee.github.io/ai/finance/machine-learning/reinforcement-learning/2025/05/12/ml-trading-strategist-comparing-learning-approaches</id><author><name>Wes Lee</name></author><category term="ai" /><category term="finance" /><category term="machine-learning" /><category term="reinforcement-learning" /><category term="algorithmic-trading" /><category term="decision-trees" /><category term="q-learning" /><category term="technical-analysis" /><category term="backtesting" /><category term="python" /><category term="data-science" /><summary type="html"><![CDATA[Introduction to Algorithmic Trading]]></summary></entry><entry><title type="html">Decoding Wall Street: How We Engineered an NLP System for Financial Disclosures</title><link href="https://adredes-weslee.github.io/nlp/finance/machine-learning/data-science/2025/05/09/nlp-earnings-report-analysis.html" rel="alternate" type="text/html" title="Decoding Wall Street: How We Engineered an NLP System for Financial Disclosures" /><published>2025-05-09T01:45:00+00:00</published><updated>2025-05-09T01:45:00+00:00</updated><id>https://adredes-weslee.github.io/nlp/finance/machine-learning/data-science/2025/05/09/nlp-earnings-report-analysis</id><author><name>Wes Lee</name></author><category term="nlp" /><category term="finance" /><category term="machine-learning" /><category term="data-science" /><category term="nlp" /><category term="finance" /><category term="machine-learning" /><category term="data-science" /><category term="text-analysis" /><category term="python" /><summary type="html"><![CDATA[The Challenge: Unlocking Insights from Financial Texts]]></summary></entry><entry><title type="html">Building a Production YouTube Sentiment Analysis Platform: From 114K Comments to Real-Time Intelligence</title><link href="https://adredes-weslee.github.io/nlp/machine-learning/transformers/2024/12/15/building-youtube-comment-sentiment-analyzer.html" rel="alternate" type="text/html" title="Building a Production YouTube Sentiment Analysis Platform: From 114K Comments to Real-Time Intelligence" /><published>2024-12-15T02:00:00+00:00</published><updated>2024-12-15T02:00:00+00:00</updated><id>https://adredes-weslee.github.io/nlp/machine-learning/transformers/2024/12/15/building-youtube-comment-sentiment-analyzer</id><author><name>Wes Lee</name></author><category term="nlp" /><category term="machine-learning" /><category term="transformers" /><category term="youtube" /><category term="streamlit" /><category term="huggingface" /><category term="roberta" /><category term="distilbert" /><category term="production-ml" /><category term="vader" /><category term="plotly" /><category term="pytorch" /><summary type="html"><![CDATA[Introduction: From Research to Production-Ready Sentiment Intelligence]]></summary></entry><entry><title type="html">A Deep Dive into Enterprise RAG: Design, Implementation, and Lessons Learned</title><link href="https://adredes-weslee.github.io/ai/nlp/rag/2024/10/29/building-effective-rag-systems.html" rel="alternate" type="text/html" title="A Deep Dive into Enterprise RAG: Design, Implementation, and Lessons Learned" /><published>2024-10-29T06:33:46+00:00</published><updated>2024-10-29T06:33:46+00:00</updated><id>https://adredes-weslee.github.io/ai/nlp/rag/2024/10/29/building-effective-rag-systems</id><author><name>Wes Lee</name></author><category term="ai" /><category term="nlp" /><category term="rag" /><category term="llms" /><category term="retrieval-augmented-generation" /><category term="vector-databases" /><category term="langchain" /><category term="python" /><category term="system-design" /><category term="mLOps" /><summary type="html"><![CDATA[Introduction: The RAG Revolution in Enterprise]]></summary></entry><entry><title type="html">Unlocking Revenue: A Technical Walkthrough of Customer Segmentation and Price Optimization for CS Tay</title><link href="https://adredes-weslee.github.io/data-science/pricing-strategy/business-analytics/commercial-strategy/2024/08/15/customer-segmentation-price-optimization.html" rel="alternate" type="text/html" title="Unlocking Revenue: A Technical Walkthrough of Customer Segmentation and Price Optimization for CS Tay" /><published>2024-08-15T02:30:00+00:00</published><updated>2024-08-15T02:30:00+00:00</updated><id>https://adredes-weslee.github.io/data-science/pricing-strategy/business-analytics/commercial-strategy/2024/08/15/customer-segmentation-price-optimization</id><author><name>Wes Lee</name></author><category term="data-science" /><category term="pricing-strategy" /><category term="business-analytics" /><category term="commercial-strategy" /><category term="rfm-analysis" /><category term="k-means-clustering" /><category term="price-elasticity" /><category term="revenue-optimization" /><category term="python" /><category term="pandas" /><category term="scikit-learn" /><category term="gurobi" /><category term="capstone-project" /><summary type="html"><![CDATA[Introduction: A Data Science Journey in Commercial Strategy]]></summary></entry><entry><title type="html">Building an HDB Resale Price Predictor: A Technical Deep Dive into Feature Engineering and Regression</title><link href="https://adredes-weslee.github.io/data-science/machine-learning/real-estate/2023/06/18/predicting-hdb-resale-prices.html" rel="alternate" type="text/html" title="Building an HDB Resale Price Predictor: A Technical Deep Dive into Feature Engineering and Regression" /><published>2023-06-18T06:45:00+00:00</published><updated>2023-06-18T06:45:00+00:00</updated><id>https://adredes-weslee.github.io/data-science/machine-learning/real-estate/2023/06/18/predicting-hdb-resale-prices</id><author><name>Wes Lee</name></author><category term="data-science" /><category term="machine-learning" /><category term="real-estate" /><category term="housing" /><category term="singapore" /><category term="regression" /><category term="feature-engineering" /><category term="price-prediction" /><category term="python" /><category term="scikit-learn" /><category term="pandas" /><summary type="html"><![CDATA[Introduction: Decoding Singapore’s Unique Housing Market with Data]]></summary></entry><entry><title type="html">Decoding Heat Stress: A Data Scientist’s Guide to Wet-Bulb Temperature Analysis</title><link href="https://adredes-weslee.github.io/data-science/climate/public-health/2023/05/15/predicting-heat-stress-with-wet-bulb-temperature.html" rel="alternate" type="text/html" title="Decoding Heat Stress: A Data Scientist’s Guide to Wet-Bulb Temperature Analysis" /><published>2023-05-15T02:30:00+00:00</published><updated>2023-05-15T02:30:00+00:00</updated><id>https://adredes-weslee.github.io/data-science/climate/public-health/2023/05/15/predicting-heat-stress-with-wet-bulb-temperature</id><author><name>Wes Lee</name></author><category term="data-science" /><category term="climate" /><category term="public-health" /><category term="wet-bulb-temperature" /><category term="climate-change" /><category term="regression-analysis" /><category term="python" /><category term="pandas" /><category term="scikit-learn" /><category term="data-integration" /><category term="time-series" /><summary type="html"><![CDATA[The Hidden Danger of Heat Stress: A Data Perspective]]></summary></entry></feed>