Insights on technology, leadership, and personal growth
Part 1 → 1 · 7 min total
An LLM isn't a brain — it's a frozen next-token predictor. That one fact explains every confidently-wrong answer your AI app ships, and what to build around it.
Part 0 → 14 · 184 min total
Before the mindset. Before the frameworks. Before any of the workflow strategies in this series — you need the right tools configured the right way. Most developers pick the wrong tool, configure it badly, and blame the AI. This is the setup guide nobody wrote.
You wrote the for loop. The AI wrote 800 lines while you were on the phone. The developers who thrive aren't the fastest coders — they're the ones who learned to stop thinking about how and start thinking about what.
You handed everything to the AI. Three months later, you can't debug without it. The fix isn't using AI less — it's using it strategically. Here's the exact framework for knowing which tasks to delegate and which ones to own.
Vague prompt: 40 minutes of refinement cycles. Precise prompt: production-ready code on the first try. The difference isn't the AI — it's the 5-part specification architecture that eliminates every source of ambiguity before the AI writes a single line.
The AI generated payment processing code with a subtle SQL injection vulnerability. The tests passed. The code review passed. It shipped to production. Understanding how AI fails — with complete confidence — is the skill that separates safe developers from dangerous ones.
Two developers. Same AI tool. One fixes the bug in 5 minutes. The other spends 40 getting generic suggestions that miss the root cause. The difference isn't the model — it's the 3 pieces of context that unlock AI debugging. Here's the framework and the 4-step workflow.
AI generates code in seconds. The professional risk is the instinct to review it just as fast. A bug in formatCurrency costs ten minutes. A bug in verifySession costs your company. This is the framework for calibrating review intensity to blast radius.
You asked AI to build a feature. It gave you working code in the wrong architecture. Then you had to throw it away and start over. The fix isn't better prompting — it's a mindset shift: design the blueprint before the AI lays a single brick.
AI can generate technical debt faster than any team of humans. It creates consistent, compounding anti-patterns across an entire codebase at machine speed. The 4 Quality Gates are the systematic checks that catch it before it lands in your main branch.
You've been using a sledgehammer for everything. Using your highest-capability model for formatting fixes wastes money and time. Using a fast lightweight model for security-critical code is negligent. Here's the framework for matching the right AI to every coding task.
One developer on your team uses AI brilliantly. Another ships bugs 40% faster. Without a shared standard, AI amplifies both good and bad engineering practices simultaneously. Here's how to build an AI standard that makes the whole team better.
After 6 months of daily AI coding, a developer sat down with a blank file and no AI running. A task that should have taken 20 minutes took 2 hours. This is the predictable journey every AI developer faces—and how to come out on the right side.
The AI wrote it — that's not an excuse. The moment you hit commit, every line becomes yours. Here's the complete framework for taking full ownership, avoiding the bias and security traps AI reliably creates, and building a career that compounds as AI handles more of everything else.
The AI wrote your tests. They all pass. Your code coverage is 94%. Then production breaks. The problem isn't the AI—it's that most AI-generated tests are hollow. Here's how to write tests that genuinely protect your code.
200,000 lines of code. No documentation. The original developer left 18 months ago. Before AI, this would take weeks of archaeology. With AI, you can understand, document, and safely modify a codebase you've never seen—in days.
Part 1 → 14 · 165 min total
Your brain has 86 billion neurons. ChatGPT has trillions. Today we isolate just one digital brain cell and watch it make a decision — in Python.
AI isn't programmed — it's trained. Four steps, repeated millions of times: guess, measure the mistake, find who's responsible, fix it. Here's exactly how that works.
ChatGPT didn't just learn — it learned in four completely different ways. Discover how Supervised, Unsupervised, Reinforcement, and Self-Supervised learning combine in a secret 3-step pipeline to turn raw text into a helpful, safe, and eloquent AI.
The AI doesn't read your words — it reads numbers. And those numbers aren't equal across languages. Here's what tokens are, why they cost money, and why non-English speakers pay more.
ChatGPT doesn't think its answer in advance — it predicts one token at a time in a recursive loop. Understanding this changes how you design prompts, control costs, and build responsive AI applications.
If AI doesn't read words, how does it know that 'Apple' the fruit is different from 'Apple' the company? The secret is in a 10,000-dimensional galaxy of numbers called Embeddings.
The old internet searched for words. The new internet searches for meaning. Here's the math that killed keyword search — and powers everything from TikTok to RAG.
In 2017, Google researchers published a paper called 'Attention Is All You Need' — and didn't patent it. That decision launched ChatGPT, Claude, Gemini, and the entire modern AI industry. Here is the complete story of the architecture that changed everything.
Every LLM has a context window — a hard limit on how much it can 'see' at once. Understanding what fills it up, why models forget things, and the 'Lost in the Middle' effect will change how you architect every AI feature you build.
ChatGPT confidently recommended a smart glasses model discontinued two years ago. The fix doesn't require retraining anything. RAG — Retrieval Augmented Generation — lets any LLM answer from YOUR data, in real time, without a single GPU.
GPT-3 was brilliant but chaotic — it would answer 'How do I bake a cake?' with a story about a Belgian chocolate factory in 1923. RLHF is the secret process that transformed raw AI intelligence into the helpful assistant you use every day.
The model isn't getting dumber. Your prompts are getting lazier. Learn the 5 techniques that separate vague, frustrating AI interactions from precise, powerful ones — including the exact RAG prompt template used in production systems.
An AI chatbot invented a refund policy that cost a company $650. Lawyers filed AI-generated case citations that didn't exist. AI confidently fabricates because it can't say 'I don't know' — here's the root cause, the four danger tiers, and the five solutions that actually work.
Build responsible AI using Google's proven framework: 7 core principles, 4 hard limits, the issue spotting process, Python bias detection with demographic parity and equalized odds, and GitHub Actions CI/CD gates that block biased models from shipping.
4 articles · newest first
92.6% of developers use AI monthly. 26.9% of production code is now AI-authored. Yet productivity gains have plateaued at 10%. Here's the full picture — the data, the shift in operating model, the risks nobody talks about, and what it actually means to be a software engineer right now.
Every week another headline says AI is coming for developers. But look at who is actually using these tools. Developers aren't running from AI. They're building with it — faster, bigger, alone. Here's the structural reason why.
Twenty years in the industry, and I still found myself caught in the same trap — just in a different city. This is what stepping back actually taught me about AI, expertise, and the only thing that never goes out of date.
Using AI can make you way more productive, but it also comes with a risk. If we're not careful, we might get too used to letting the AI do the thinking and slowly lose our own problem-solving skills.
6 articles · newest first
We built a prompt compressor using only ModernBERT attention scores: no extra LLM calls, no black box, ~45–58% token savings on real prompts (measured, code included). Here's how it works, the complete code, honest benchmarks, and when to use it over LLMLingua-2.
Standard AI gives you a paragraph. Deep Research gives you a structured multi-source report with cited evidence — automatically. Learn the DEEP framework and 7 proven prompt templates that separate generic AI responses from professional research outputs.
I built an AI agent that detects my location, checks the weather, finds national parks, and gives me opinionated hiking recommendations — all running locally with zero cloud APIs. This is the 4-step agentic loop that powers every modern AI agent, and how to build one from scratch.
Learn to build a fully autonomous AI research agent using the Anthropic Messages API with web search via Tavily, PDF downloading, multi-turn memory, and a Streamlit UI. Step-by-step from zero to production.
Three hands-on interpretability techniques for understanding what language models think: attention visualization with circuitsvis, sparse autoencoders for hidden concept discovery, and steering vectors for behavior control—all with runnable code.
A developer would cost $10,000 and take 2 weeks. I built the same production-ready branded infographics tool in 2 hours for $0 using vibe coding — PRD first, then Google AI Studio, screenshots, and one-click deploy.
1 article · newest first
Key strategies and patterns for creating maintainable, performant React applications that scale with your business needs.
Practical lessons and proven frameworks I've used to build high-performing, distributed engineering teams across time zones and cultures.