Jeremiah Alalade@jeremiqque
1
Events attended
1
Submissions made
Nigeria
2+ years of experience
About me
Most AI-generated interfaces look fine and feel wrong. Competent, polished, and completely forgettable. Closing that gap is my job. I'm a design engineer. I design in Figma and ship the same thing as production code: React, TypeScript, Next.js, Tailwind, and Framer Motion. No handoff, no "that's not what the mock said," and no six-week round trip between design and dev. I work with product teams who ship fast but refuse to ship ugly. Usually that means: Design systems and component libraries that actually survive contact with a real codebase Figma file to deployed, responsive, accessible front end one person, one loop Motion that carries information instead of decorating it. If you delete the animation and lose nothing, it shouldn't have been there AI-native workflows (Claude Code, Figma-to-code pipelines) that compress ideas to shipped UI Here's my actual bet: AI made generating interfaces cheap. Deciding which interface is right didn't get cheaper. That part still takes taste, and taste is what I'm selling.
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AFRI-EDGE
AFRI-EDGE is an intelligent infrastructure routing platform designed to optimize where AI and compute workloads run across Local, Edge, and Cloud environments. Instead of sending every workload to the cloud, AFRI-EDGE continuously evaluates available infrastructure conditions and workload requirements, then uses a Decision Engine to select the most appropriate execution target. The platform combines workload profiling, infrastructure monitoring, constraint-based candidate evaluation, and intelligent routing into a unified system. For each workload, the Decision Engine evaluates the available targets, ranks eligible candidates, and produces a transparent routing decision with a score and reasons behind the selection. AFRI-EDGE is designed with a practical African infrastructure context in mind, where connectivity, bandwidth, compute availability, latency, and resource constraints can vary significantly. By making infrastructure placement more adaptive, the project aims to provide a foundation for more efficient and resilient AI infrastructure. The current MVP includes an end-to-end routing flow, infrastructure state collection for LOCAL, EDGE, and CLOUD, workload profiling, candidate evaluation, and a dashboard for visualizing routing decisions and candidate rankings. Core principle: don't assume the cloud is always the right destination — evaluate the workload and the infrastructure, then route accordingly.
16 Sep 2026
