55 items
AIBAs need to persuade difficult stakeholders and pass competitive interviews. AI can act as a stakeholder simulator, mock interviewer, and devil's advocate for you 24/7. A guide to prompt templates, practice scenarios, and how to evaluate simulation quality for real improvement.
Duy Tran12 min
AIBAs working with AI in Fintech must understand AML/KYC regulations. BA in Healthcare needs to know HIPAA and clinical workflows. BA in eCommerce focuses on personalization and fraud. A guide to domain-specific skills, regulations, and AI use cases for each industry.
Duy Tran15 min
AIBAs aiming for Senior AI BA or transitioning to AI PM need a substantive portfolio — not just a list of tools. A guide to structuring AI project case studies, choosing which artifacts to showcase, and how to tell your story on LinkedIn and your CV.
Duy Tran12 min
AIBAs need dashboards to prove AI features deliver value, monitor health after go-live, and report to stakeholders. A guide to building dashboards with Looker Studio, Power BI, and Metabase — focused on business metrics and AI quality metrics.
Duy Tran12 min
AIAI features have a completely different risk profile from regular features: model drift, data poisoning, hallucination cascades, and bias amplification. BAs need a proper Risk Register, an incident response plan, and a post-mortem template specifically designed for AI incidents.
Duy Tran13 min
AIBAs spend too many hours updating tickets, creating sub-tasks, and manually following up on status. Jira Automation and Azure DevOps Rules can handle most of that. A practical guide to the most important automation rules for BA in AI projects.
Duy Tran12 min
AIAI stories are harder to estimate than regular features because they depend on data readiness, model iterations, and experiment uncertainty. A guide to adapted Planning Poker for AI work, 3-point estimation, spike stories, and how to communicate uncertainty to stakeholders.
Duy Tran11 min
AIBacklog refinement consumes more BA hours than anything else — yet it's also where AI can help the most: duplicate detection, story splitting, AC suggestion, and dependency mapping. A practical guide to integrating AI into your refinement workflow without losing control.
Duy Tran11 min
AIBA should understand AI costs well enough to estimate budgets, negotiate with stakeholders, and make make-or-buy decisions. This article explains token pricing, latency cost, cloud AI vs self-hosted options, and practical FinOps practices without requiring DevOps expertise.
Duy Tran12 min
AIWhen AI produces wrong results, who is responsible? Who decides safety thresholds? When escalation is needed, who do we go through? RACI matrix helps BA clearly define roles, responsibilities, and decision rights for all AI-related actions — from prompt changes to production releases.
Duy Tran9 min
AIHuman-in-the-loop is not just "adding a confirm button". BA must design escalation thresholds, routing rules, SLA for agent review, and feedback loops. A complete HITL design guide with decision matrix and escalation flow templates.
Duy Tran13 min
AIBA should not evaluate AI by intuition like "the output looks okay". You need a clear protocol: evaluation criteria, scoring rubric, blind test methodology, and a go/no-go framework. A full guide from test set design to sign-off decisions.
Duy Tran14 min
AIBA do not need to design pretty UI, but they do need wireframes clear enough for teams to understand and flow diagrams accurate enough so developers do not ask again. Practical guidance on using Figma and Draw.io for BA, especially for AI features with fallback paths, confidence display, and human override.
Duy Tran11 min
AIMany teams launch AI features then don't know if they succeeded. This guide teaches BA to build evaluation framework before launch — define business KPIs + technical KPIs + experience KPIs, 30/60/90-day review schedule, and use metrics to decide next steps.
Duy Tran9 min
AIBA do not need to code to perform prompt testing. Red-teaming is a critical BA skill when working with AI features: finding edge cases, jailbreak attempts, bias, and unwanted output before release. Practical guidance with test case templates.
Duy Tran13 min
AIUAT for AI features isn't like traditional UAT — you test not just business logic but AI output quality, edge cases, bias, and whether users actually trust the AI. Complete guide from UAT plan, business readiness checklist to go/no-go decision framework for BA.
Duy Tran10 min
AIBA use Confluence or Notion not just to store documents, but to create a single source of truth for the whole team. A guide to structuring spaces, BRD/FRD templates, linking requirements with Jira tickets, and managing an assumption log in AI projects.
Duy Tran11 min
AIBA doesn't need to know fine-tuning or embeddings — but needs to write good prompts for daily work and to specify AI features. This guide teaches the RPCF framework: Role, Purpose, Context, Format — how BA designs reproducible, controlled prompts.
Duy Tran10 min
AIStrategy Analysis helps BA understand organizational context before writing requirements. This article shows how to apply SWOT, PESTLE, Impact Mapping, and Value Stream Mapping for strategic analysis, especially when organizations are implementing AI features.
Duy Tran13 min
AIBA Planning is not just filling in scope in a template. In AI projects, the BA plan must include iterative checkpoints, assumption tracking for data/model behavior, and escalation paths when AI feature output drifts from requirements. A practical guide with a BA Monitoring Framework.
Duy Tran12 min
AIWhen AI participates in business processes, traditional UML/BPMN diagrams lack ways to represent AI actors, fallback paths, and human-in-the-loop. This guide teaches BA to diagram AI-assisted flows correctly — with happy path, error path, confidence threshold, and escalation to human.
Duy Tran12 min
AIBAs don't need to code AI, but they need to understand enough to write correct requirements and work effectively with the technical team. LLM, RAG, hallucination, confidence scores, and guardrails explained in business language — with real-world examples.
Duy Tran15 min
AITraditional Elicitation techniques consume many hours of note-taking and synthesis. This guide teaches BA how to use AI to auto-summarize interviews, automatically cluster insights, detect requirement gaps, and create action items — maintaining quality while saving 60% of processing time.
Duy Tran10 min
AIA clear breakdown of BA, Product Owner, Product Manager, and AI Engineer roles in a modern product team. Who writes acceptance criteria? Who decides the roadmap? Who's accountable when an AI feature goes wrong? A practical guide for BAs looking to position themselves correctly in the age of AI.
Duy Tran12 min
AIAnthropic launched Claude Opus 4.7 on April 16, 2026 — the latest flagship AI model with exceptional programming capabilities, 3x higher vision resolution, a new xhigh effort level, and leading agentic performance. A comprehensive review covering benchmarks, real-world feedback, pricing, and a migration guide from Opus 4.6.
Duy Tran20 min
AIA detailed guide to the complete NVIDIA DLI ecosystem for Generative AI and LLMs — from Diffusion Models, RAG Agents, and Agentic AI to Transformer NLP. Analysis of exam content, assessment difficulty, sample questions, exam tips, and a learning roadmap from beginner to professional.
Duy Tran30 min
AIAnthropic has published a 245-page System Card for Claude Mythos Preview — the most powerful AI model ever trained, but NOT released publicly due to its ability to autonomously find zero-days. A detailed analysis of its cyber capabilities, alignment, model welfare, and the remarkable stories from inside.
Duy Tran18 min
AIA comprehensive review of MiniMax — the Chinese AI startup with the world's most complete multimodal ecosystem. From M2.7 (text/code on par with Opus 4.6), Hailuo 2.3 (video), Speech 2.6, to Music 2.6. Analyzes models, products, API, pricing, compares with OpenAI, Google, and Anthropic, and includes a quick-start guide.
Duy Tran25 min
AIA comprehensive guide to becoming an AI Solution Architect — from technical foundations, end-to-end AI system design skills, cloud architecture, and MLOps, to stakeholder communication soft skills. Includes role comparisons, salary benchmarks, required certifications, and common career mistakes.
Duy Tran30 min
AIA week of surprising developments: Microsoft admits Copilot is "for entertainment only" in its terms of service, Japan commits $6.3 billion to Physical AI targeting a 30% global market share, Cognichip raises $60M to let AI design chips, and Anthropic accidentally takes down 8,100 GitHub repositories.
Duy Tran10 min
AIA particularly eventful week in AI: Microsoft simultaneously launches 3 MAI foundational models (speech, voice, image); H Company's Holo3 achieves 78.85% SoTA on the OSWorld computer use benchmark; Anthropic spends $400M acquiring biotech startup Coefficient Bio and locks OpenClaw out of Claude Code subscription plans.
Duy Tran10 min
AIGoogle DeepMind launches Gemma 4 — a family of open models ranking
Duy Tran15 min
AIOllama 0.19 integrates Apple's MLX backend — delivering 93% faster decode speed and 57% faster prefill on M5. A comprehensive technical analysis of unified memory architecture, real-world benchmarks across M1 to M5, and a step-by-step setup guide to maximize your Apple Silicon's potential.
Duy Tran18 min
AI & Machine LearningAn in-depth analysis of AI applications in healthcare — from CNN medical imaging that surpasses specialist physicians, NLP for EHR analysis, drug discovery compressed from 12 years to months, to personalized genomics treatment. Includes real case studies, technical challenges, ethical issues, and a deployment roadmap for Vietnam.
Duy Tran35 min
ProgrammingDetailed analysis of TypeScript source code extracted from the npm bundle of Claude Code v2.1.89 --- revealing the Buddy virtual pet system with rarity RPG released on April 1, 2026, UltraPlan multi-agent architecture, Bridge remote session system, anti-canary obfuscation and dozens of hidden features that have not been documented.
Duy Tran25 min
BackendDetailed instructions for building a complete AI Agent Platform with xClaw — TypeScript monorepo supporting Multi-LLM, RAG Pipeline, Workflow Engine, 13 Domain Packs, Multi-tenant RBAC, MCP Protocol and 8 Chat Channels. From Dual-Database architecture to deploying Docker production.
Duy Tran45 min
Comprehensive course on AI Agent Engineering — from Python & ML foundation, NLP & LLMs (LLaMA, Mistral, Qwen, Phi), RAG with Vector DB (FAISS, Milvus, Pinecone), to building AI Agent systems with LangChain, LlamaIndex, CrewAI, LangGraph. Practice Fine-tuning, Prompt Engineering, Tool Calling, Multi-Agent. Deploy production with FastAPI, Docker, Microservices, MLOps, CI/CD on AWS/Azure/GCP. Fully meets AI/ML Engineer recruitment requirements at leading technology companies.
Comprehensive study roadmap for NVIDIA DLI Generative AI courses — from Diffusion Models, RAG Agents, Agentic AI to LLM Evaluation & Fine-tuning. 10 in-depth lessons with hands-on code, coding assessment practice, and sample questions closely matching the real exam.
A comprehensive study guide for the AWS Certified AI Practitioner (AIF-C01) exam. Covers all 5 domains: AI/ML Fundamentals, Generative AI, Foundation Models, Responsible AI, Security & Governance. 12 in-depth lessons with English practice exams.
Machine Learning roadmap for beginners starts from zero, following an easy-to-understand method: intuition first, code later, just enough math. The course goes from environment setup, first model, proper evaluation, overfitting/data leakage prevention, to common models (Linear, Logistic, Tree, XGBoost), unsupervised, time series, and production deployment. Each cluster of lessons has mini-projects, real-life challenges and a clear output checklist.
Comprehensive course on Vibe Coding with GitHub Copilot — from basic concepts to advanced techniques. Learn how to leverage AI Agent, Inline Suggestions, Custom Instructions, MCP Servers, Copilot CLI, and Copilot Coding Agent to accelerate software development. Includes Prompt Engineering for code, security best practices, handling technical debt, building full-stack apps, and professional Vibe Coding processes for production. Updated according to GitHub Copilot 2026 with Agent Mode, Plan Agent, Cloud Agent and the latest features.
Comprehensive course on AI in Healthcare — from Medical Imaging with CNN, NLP for medical records, Drug Discovery with GNN, to implementing HIPAA/FDA compliant AI. Practice with Python, PyTorch, Hugging Face, and standard medical datasets such as MIMIC, CheXpert, PubMed.
Comprehensive course on Generative AI for images and video — from GAN, VAE platforms to Stable Diffusion, DALL-E, Midjourney API. Practice image generation, inpainting, style transfer, video generation, and build Generative AI pipeline production-ready with Python, Hugging Face Diffusers, and ComfyUI.
Comprehensive course on Recommendation Systems — from Collaborative Filtering, Content-based, Matrix Factorization to Deep Learning RecSys with Two-Tower, Graph Neural Networks, Sequence Models. Hands-on with Python, PyTorch, LightFM, and deploy production-ready recommendation engine.
Comprehensive course on Multimodal AI — from Vision-Language Models (CLIP, LLaVA), Visual Question Answering, Image Captioning, to Document AI, Video Understanding. Practice with Python, PyTorch, Hugging Face Transformers, and state-of-the-art models like GPT-4V, Gemini, LLaVA.
Comprehensive course on Natural Language Processing (NLP) — from tokenization platforms, word embeddings, to Transformer architecture, BERT, GPT. Practice text classification, NER, sentiment analysis, machine translation, question answering and build a production-ready NLP pipeline with Python, Hugging Face, and spaCy.
Comprehensive Reinforcement Learning course — from MDP, Q-Learning to Deep RL platforms with DQN, Policy Gradient, PPO, SAC. Practice game AI, robotics simulation, RLHF for LLM, and deploy RL agents production-ready with Python, Gymnasium, Stable-Baselines3.
Comprehensive AI for Speech & Audio course — from audio signal processing, Speech Recognition (ASR) with Whisper, Text-to-Speech (TTS) with VITS, Voice Cloning, Speaker Verification, to Music AI. Practice with Python, PyTorch, Hugging Face, librosa, and state-of-the-art models.
A comprehensive course on Time Series AI — from Statistical Methods (ARIMA, ETS), Machine Learning (XGBoost, LightGBM), to Deep Learning (LSTM, Transformer, TimesFM). Application of forecasting, anomaly detection, classification. Practice with Python, statsmodels, scikit-learn, PyTorch, and specialized frameworks.
In-depth series on Fashion Design & Print-on-Demand (POD) system architecture: domain analysis, AI-powered design studio (Stable Diffusion, ControlNet, CLIP), product catalog & multi-channel e-commerce, order orchestration & fulfillment, supplier network routing, print production pipeline, AI recommendation & trend forecasting, data platform & ML pipeline, Kubernetes infrastructure, security & IP protection, Printful/Printify/Gelato case studies. From design ideas to products in customers' hands.
The series builds an entire AI system for a Fashion & Print-on-Demand platform — from AI Design Generation (Stable Diffusion, ControlNet), AI Editing in natural language, Personalization System, Virtual Try-On with Computer Vision, to Print File Optimization and AI Product Generation. Each article is an independent AI module that can be deployed in production.
In-depth course on MLOps and LLMOps — the art of bringing AI models from prototype to production safely and effectively. From experiment tracking, CI/CD for ML, to LLM observability, cost optimization, guardrails, and compliance. Highest paying skills in AI.
Comprehensive course on Prompt Engineering — from writing basic prompts to Chain-of-Thoughts, Tree-of-Thoughts, Multimodal Prompting and building a Prompt Library for production. The most essential skill in the AI age, immediately applicable to ChatGPT, Claude, Gemini.
Comprehensive course on Fine-tuning Large Language Models — when to fine-tune, data preparation, fine-tuning on Google Gemini/Vertex AI, OpenAI, and open-source (LoRA/QLoRA). Compare Fine-tuning vs RAG, model evaluation methods, and production deployment. Calculate actual costs.
Comprehensive course on Artificial Intelligence and Large Language Models — from Neural Networks platform, Transformer architecture, to Fine-tuning, RAG, AI Agents and production deployment. Learn hands-on with Python, PyTorch, and LLM APIs.