AI didn’t kill coding. It changed what coding looks like. A developer who used to spend 6 hours w...AI didn’t kill coding. It changed what coding looks like. A developer who used to spend 6 hours w...
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AI didn’t kill coding.
It changed what coding looks like.
A developer who used to spend 6 hours writing code can now spend that time thinking about architecture, reviewing AI-generated code, fixing edge cases, and shipping faster.
But there’s a catch.
If one developer can now do the work that previously required 3–4 developers…
What happens to the other roles?
I think the biggest change in IT won’t be “AI replacing developers.”
It will be companies asking:
“If AI makes our developers 3× more productive, do we still need the same-sized team?”
That’s a question the industry will have to answer sooner than we think.
What’s your take?
Eyewear Ecommerce Website Redesign — AI vs. WaxyWeb
A side-by-side design comparison exploring two approaches to an online eyewear store.
The WaxyWeb redesign focuses on the relationship between product presentation, collection browsing, editorial imagery, and the store’s visual identity.
Design considerations:
Eyewear product presentation
Ecommerce layout and hierarchy
Collection and product discovery
Fashion-focused visual direction
Consistent online shopping experience
For fashion, eyewear, and lifestyle brands, the goal is to create a store that presents products clearly while giving the brand a recognisable visual presence.
I built an AI-powered research intelligence platform that helps analysts and business teams query complex datasets through a single conversational interface.
𝐓𝐡𝐞 𝐏𝐫𝐨𝐛𝐥𝐞𝐦
Research teams often work across disconnected databases, reports, spreadsheets, dashboards, and documents. This slows analysis, makes cross-source searching difficult, and can reduce trust in AI-generated answers.
𝐓𝐡𝐞 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧
The platform combines RAG, AI agents, natural-language-to-SQL, structured and unstructured retrieval, source attribution, confidence scoring, and evaluation pipelines.
Users can ask a business question in natural language, and the system decides whether to search documents, query databases, run SQL, or combine multiple sources before returning an evidence-backed answer.
𝐀𝐜𝐜𝐮𝐫𝐚𝐜𝐲 & 𝐏𝐫𝐨𝐝𝐮𝐜𝐭 𝐄𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞
The system evaluates answer correctness, retrieval quality, SQL accuracy, source relevance, hallucination risk, and response performance. Low-confidence results are captured for review and continuous improvement.
The frontend is built with React + TypeScript and includes AI chat, dataset exploration, source references, analytics, evaluation dashboards, and real-time updates.
𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐕𝐚𝐥𝐮𝐞
The platform helps teams analyze data faster, reduce manual SQL work, improve trust in AI outputs, and uncover insights across multiple data sources.