AI & The Future
Letting AI Speed Up the Search, Not the Judgment
A practical reflection on using AI to broaden investigation while keeping source checking, trade-off decisions, and accountability with people.
Writing
AI & The Future
Making sense of AI — from context engineering to building reliable, production-ready AI products.
Practical, developer-focused writing on artificial intelligence: how to get great results from AI tools, understand how language models actually work, build reliable AI-powered products, and navigate the fast-moving landscape of LLMs, agents, and context engineering. Written for engineers who want to work smarter with AI — not just follow the hype.
AI & The Future
Asking AI to Name Its Assumptions
A practical reflection on making AI-assisted work safer by asking the model to expose assumptions before accepting its answer.
AI & The Future
Treating AI Suggestions as Draft Evidence
A practical reflection on using AI output as a starting point for investigation, not as a finished answer that replaces verification.
AI & The Future
Keeping AI Changes Small Enough to Review
A practical reflection on using AI safely by keeping generated changes narrow, inspectable, and tied to evidence.
AI & The Future
Letting AI Draft the Boring First Pass
A practical reflection on using AI for the plain first draft, while keeping human judgment focused on evidence, shape, and responsibility.
AI & The Future
Asking AI to Show Its Working Context
A practical reflection on asking AI systems to expose their working context so engineers can verify evidence, assumptions, and gaps.
AI & The Future
Letting AI Draft While Humans Keep the Map
A practical reflection on using AI to draft faster while humans keep the system map: constraints, sources, ownership, and verification paths.
AI & The Future
What AI Cannot Know From the Prompt Alone
A practical reflection on why prompts are not enough: AI needs source context, runtime evidence, domain constraints, and human judgment to become useful engineering help.
AI & The Future
Using AI Without Outsourcing Judgment
A practical reflection on using AI as a thinking partner while keeping human judgment anchored in source code, tests, logs, and product context.
AI & The Future
AI Tools for Reading a Codebase
A practical guide to using AI tools while reading a codebase: where summaries help, where they mislead, and how engineers can combine AI assistance with source-level verification.
AI & The Future
From Prototype Demo to Production Behavior
A grounded AI product note on the gap between an impressive prototype demo and the production behavior users can safely depend on.
AI & The Future
Human Feedback Loops for AI Work
A grounded look at AI work habits: why useful AI workflows need human feedback loops, evidence checks, test results, and team memory instead of one-off prompt outputs.
AI & The Future
The Cost of Trusting a Fluent AI Answer
A grounded look at fluent AI answers: why confident language can feel like evidence, where that creates risk, and how teams can keep AI useful by checking claims against sources, tests, and real context.
AI & The Future
Vector Databases Explained
A beginner-friendly explanation of vector databases: how embeddings represent meaning, why similarity search helps AI products, and what teams need to handle around chunking, metadata, freshness, evaluation, and cost.
AI & The Future
AI in Cybersecurity
A practical reflection on AI in cybersecurity: how AI helps detection, triage, secure coding, and incident response while creating new risks around automation, privacy, prompt injection, and false confidence.
AI & The Future
Reducing Hallucinations in LLMs
A practical guide to reducing LLM hallucinations with clearer tasks, bounded context, retrieval, citations, verification, refusals, evaluation sets, and product design that makes uncertainty visible.
AI & The Future
Fine-Tuning vs. Prompting vs. RAG
A practical comparison of prompting, RAG, and fine-tuning for AI products: what each approach changes, when it helps, where it fails, and how teams can choose the smallest reliable intervention.
AI & The Future
Ethical Considerations in AI Products
A practical article on product ethics for AI teams: consent, data boundaries, bias and fairness, human review, explanations, risk controls, and incentives that shape whether AI features remain useful and responsible.
AI & The Future
Retrieval-Augmented Generation (RAG) Basics
A beginner-friendly explanation of Retrieval-Augmented Generation: how documents are split into chunks, turned into embeddings, found with vector search, and used to ground AI answers with citations, evaluation, and clear failure checks.
AI & The Future
The Context Window Bottleneck
A practical look at context window limits as one of the quiet bottlenecks in AI workflows. The article explains why long chats, big codebases, stale summaries, and missing working memory can make AI assistance drift, and how teams can work around the limit without turning every task into a document dump.
AI & The Future
When an AI Answer Needs a Test
A calm explainer on treating AI answers as claims that need proportionate verification. The article shows how engineers can keep responsibility by testing AI output against evidence, context, and real system behavior before acting on it.
AI & The Future
Khi AI chạm vào việc hằng ngày, ý tưởng bắt đầu nở ra
Khi AI đủ thực tế để bước vào những việc nhỏ trong văn phòng, ý tưởng không còn nằm ở các bản demo xa xôi. Chúng xuất hiện ngay trong onboarding, hỗ trợ khách hàng, đối chiếu tài chính, chuẩn bị họp, đọc tin tức, kiểm tra tài liệu và rất nhiều workflow lặp lại khác. Bài viết là một góc nhìn bình tĩnh về cách nhận ra các điểm nghẽn ấy, cải tiến từng chút và giữ con người ở phần phán đoán quan trọng.
AI & The Future
Cognitive Debt in AI-Assisted Development: When Code Grows Faster Than Understanding
AI coding tools can make a team produce code faster than it can understand, review, debug, and safely operate that code. This article explains cognitive debt, how it differs from technical debt and intent debt, why agentic coding makes it more visible, and the practical controls teams can use: smaller batches, better PR rationale, human-owned invariants, disposable prototypes, and AI used to repay understanding instead of only generating more work.
AI & The Future
Beyond the Hype: What LLMs Actually Understand
LLMs can feel like they understand us because they handle patterns, context, and language-shaped reasoning well. This explainer separates useful machine understanding from human understanding so teams can verify, constrain, and use AI with better judgment.
AI & The Future
AI Literacy: From Prompting to Orchestrating Agents
Using AI more often does not automatically mean working at a higher level. This guide maps three levels of AI literacy with a visual ladder, concrete tool examples, workflow keys, and a path toward deeper practical guides for each level.
AI & The Future
Your Fear of AI Is the Size of Your Ambition: Why It’s Time to Aim Dramatically Bigger
Two people can look at the same AI breakthrough and feel opposite things — terror or fuel — and the difference reveals something uncomfortable: our fear of the future is roughly the size of our ambition. If your plan is to keep doing exactly what you do, a machine that does it cheaper is frightening. If your plan is to build something dramatically bigger, it’s the best news you’ve ever gotten. This is a clear, energising case for retiring “don’t boil the ocean,” trading the 1.05x present for the 10x future, and why — through ephemeralization and the Jevons paradox — raising your ambitions tends to create more, not less.
AI & The Future
From SDLC to ADLC: How AI Agents Are Rewriting the Software Development Lifecycle
Software development is going through its biggest shift since the graphical interface. The phases engineers once ran by hand — planning, coding, testing, deployment — are increasingly executed by AI agents working in parallel. This is a clear, visual guide to the move from the classic SDLC to the emerging ADLC (Agentic Development Life Cycle): the two lifecycles drawn side by side, the six concrete shifts happening right now, what the early numbers suggest, what it actually means for an engineer’s day-to-day, and five low-risk best practices to start moving faster — without handing over the judgment that still has to be yours.
AI & The Future
AI Agents, Explained Without the Hype: What They Are, How They Work, and What They Can Do for You
Everyone is suddenly talking about “AI agents” — software that doesn’t just answer your questions but actually goes off and does the work. But what is an agent really, how is it different from the chatbot you already use, and where does it genuinely help versus quietly create a mess? This is a friendly, jargon-light guide for everyone, tech or not: the simple loop every agent runs, the five ingredients that make one, the levels of autonomy from co-pilot to auto-pilot, down-to-earth examples at the office and at home, the failure modes to watch for, and a concrete way to put your first useful agent to work this week.
AI & The Future
From Prompts to Workflows: Designing AI Workflows That Save You Hours Every Week
Most people use AI one question at a time — and leave most of its value on the table. The real leverage isn’t a clever prompt; it’s a repeatable AI workflow: a small chain of steps you design once and reuse forever, turning a recurring two-hour chore into a two-minute review. This is a practical, example-rich guide for everyone, tech or not: the difference between a prompt and a workflow, the simple anatomy every workflow shares, ready-to-steal workflows for the office and for everyday life, the three levels from manual to fully automatic, a six-step way to design your own, and a back-of-the-envelope way to prove the time you’re actually saving.
AI & The Future
The AI Trends Worth Your Attention in 2026 — and How to Turn Them Into Real Value
Every week brings another “this changes everything” AI headline, and it’s exhausting trying to tell the signal from the noise. This is a calm, grounded tour of the AI trends that genuinely matter in 2026 — agentic AI, multimodal, AI baked into the apps you already use, on-device intelligence, and the rise of judgment and verification as the human edge — paired with the part most articles skip: a practical framework for turning any trend into actual value in your work and life. You’ll get a hype-vs-value filter, the value ladder, a simple value equation, a worked example, and a 30-day plan — so you can stop chasing news and start compounding benefit.
AI & The Future
AI Is Rewiring Every Software Role: How Thinking Changes for BA, PO, PM, Dev, QA and Beyond
AI isn't just a faster tool bolted onto how we build software — it's quietly moving the center of gravity of every role. The work that used to define a BA, PO, PM, developer, or QA engineer is being automated from the bottom up, and what remains is a different job built on judgment, taste, and verification. This is a clear-eyed, role-by-role look at what's fading, what's rising, and what to watch out for as teams move from AI-assisted to AI-first — so you can see what may be lost, what's added, and how to stay genuinely valuable in a changed era.
AI & The Future
Context Engineering: The Skill That Separates Great AI Users From Everyone Else
Most AI results are disappointing not because the models are bad, but because the context is thin. A practical guide to context engineering — what lives in the model's window, the five pillars of great context, common anti-patterns, and reusable templates that consistently produce better results.
AI & The Future
Generative AI in Daily Life and Work: A Practical Catalog of What You Can Use Today
Generative AI has quietly moved from headline to household tool — yet most people still use it for little more than the occasional question. This is a hands-on catalog of the most common, genuinely useful applications across work, learning, creativity, and everyday life, organized so you can find at least one thing to try today. No hype, no jargon — just real use cases you can apply immediately, plus the one skill that makes all of them work and the limits you should never ignore.
AI & The Future
Prompt Engineering Is Just Communication
Good prompts are clear collaboration: context, intent, constraints, examples, and feedback. The same habits that help teammates work together also help AI tools produce useful, verifiable answers.
AI & The Future
AI in QA: Automated Test Generation
A practical look at AI-assisted QA and automated test generation: where AI helps with breadth, edge cases, fixtures, and test skeletons, and why human judgment still owns risk, truth, and trust.
AI & The Future
Running Local LLMs for Privacy
A practical explanation of local LLMs for privacy-sensitive work: what improves when prompts and documents stay on owned machines, what quality and operations costs remain, and how teams can adopt local models responsibly.
AI & The Future
AI Agents: The Next Frontier
A practical look at AI agents beyond the hype: what changes when models can use tools, follow workflows, remember context, ask for approval, and produce auditable work, plus where human judgment still matters.
AI & The Future
Data Privacy in the Era of AI
A practical article on data privacy in AI work: how prompts, documents, logs, retrieval, training, and evaluation create new data paths, and how teams can use AI while keeping consent, minimization, access, and retention clear.
AI & The Future
Multimodal AI: Text, Vision, and Audio
A practical introduction to multimodal AI: how text, images, audio, and video change product workflows, what becomes possible, and why evaluation, privacy, accessibility, and human review matter more as inputs become richer.
AI & The Future
AI for Code Refactoring
A practical reflection on using AI for code refactoring: where it helps with mapping, options, tests, and mechanical changes, and why engineers still need boundaries, evidence, and verification before trusting the result.
AI & The Future
Evaluating LLM Performance
A practical guide to evaluating LLM performance with task-specific datasets, rubrics, human review, regression checks, latency, cost, safety, and production feedback instead of relying on impressive demos.