6 min read
The model is not the product
Everyone debates which model is smartest. Almost nobody talks about the ninety other things standing between a prompt and a shipped feature.
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AI19 posts
AI
Working notes on building with AI — models as tools, not magic: workflows, agent ceilings, evaluation, and the unglamorous systems that actually ship.
Written by Nzubechukwu Cyprian — Pharmacy student at UNN, full-stack developer and AI builder.
5 min read
My AI workflow is boring, and that's the point
No autonomous agents rewriting my codebase. No prompt wizards. Just a disciplined loop between a draft, a review, and a test suite.
6 min read
Prompting is just requirements gathering with worse vocabulary
The teams that get good results from AI aren't better at talking to models. They're better at saying what they want.
7 min read
I gave an AI agent shell access. Here's what I learned.
A week of running an autonomous coding agent on a real project taught me where the ceiling is — and it isn't intelligence.
7 min read
RAG is a data-cleaning problem wearing a trench coat
Most retrieval-augmented systems fail because of chunking, duplication, and stale documents — not because of embeddings.
6 min read
What AI can't do for a solo developer
AI writes code fast. It still can't decide what's worth building, notice the bug you haven't hit yet, or care about your users.
5 min read
Evaluating AI output like a code reviewer, not a fan
A practical checklist for reviewing machine-written code: correctness first, then edge cases, then the quiet sins.
6 min read
Building with AI on a slow connection
AI-assisted development from Nigeria means timeouts, retries, and offline-first habits. Here's the setup that survives bad networks.
6 min read
AI is changing how beginners learn to code — badly
The path from confusion to competence used to run through being stuck. AI removes the being stuck, and with it the learning.
5 min read
The quiet rise of small, boring, useful AI
The most valuable AI systems I use aren't impressive. They classify, extract, and route — and they save hours a week.
6 min read
AI-generated code has a maintenance problem nobody prices in
Generating code got cheap. Owning it didn't. Every line still has to be understood by someone, eventually, at 2am.
6 min read
Testing AI features is different — here's my setup
You can't assert on a model's exact output. You can assert on structure, invariants, and failure behaviour. That's where tests go.
5 min read
Choosing models: a decision tree for people with budgets
The flagship model is rarely the right default. A practical way to pick: capability floor, then cost, then latency.
5 min read
The context window is a design constraint, not a spec sheet number
Bigger windows changed what's possible — and quietly changed how systems should be designed. Retrieved beats stuffed.
6 min read
What building AI products in Nigeria actually looks like
Payment rails, power, and latency shape every AI product decision here. Constraints don't stop building — they shape what gets built.
4 min read
Why I keep a prompt journal
A plain file of every prompt that worked, every one that failed, and why. The cheapest AI skill you can build.
6 min read
AI hallucination: a field guide from someone who ships
Hallucinations aren't random. They cluster in predictable places: numbers, citations, specifics, and anything you'd verify twice.
5 min read
The best AI feature is the one users don't notice
Generation demos impress. Inference that quietly fills gaps — autocomplete, defaults, cleanup — is what users keep.
7 min read
From notebook to production: the gap nobody blogs about
The demo works. Production is a different country. Version drift, cold starts, monitoring, and the user who types nothing you expected.