Hikari Builds
AI SaaS Engineer | MVPs, Agents, Automation, Next.js, Python
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Hikari is ready for their next project!
Gribbit – Real-Time Crypto Community Platform with On-Chain Analytics
I built Gribbit, a full-stack crypto community platform that unifies on-chain data, real-time interaction, and social features into a single dashboard.
The goal was to reduce fragmentation in crypto communities by combining price tracking, discussions, and content discovery into one cohesive experience.
I developed the platform end-to-end using Next.js, TypeScript, and Supabase. It includes live token metrics, real-time chat, a community board, and curated content in a responsive, high-performance app.
For on-chain data, I integrated Moralis Streams to process live swap activity into structured data powering trade feeds, wallet tracking, and Pulse dashboards. I also used DexScreener for real-time price, liquidity, and volume data.
On the community side, I built real-time chat with Supabase Realtime and a Reddit-style board with posts, comments, voting, and moderation. I also created a “lore” system for curating and preserving important content.
The platform emphasizes user experience with a responsive dual-layout dashboard, smooth animations, and performance optimizations for handling live updates.
This project demonstrates my ability to build complex, real-time, full-stack applications that integrate external data and deliver a polished user experience.
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Enterprise AI Search System with Embeddings, Pipelines & Widget Builder
I worked as a Full Stack Engineer at Gloo, contributing to the architecture and development of an AI-powered discovery widget designed for large-scale content platforms.
The product is an embeddable search system that enables semantic content discovery across podcasts, sermons, articles, and other media, allowing organizations to surface relevant content through intent-based search rather than traditional keyword matching.
I helped design and build the system end-to-end, working across frontend, backend, and data infrastructure. On the frontend, I developed a modular widget and configuration system using Next.js, TypeScript, and modern UI tooling, enabling partners to integrate the search experience with a single script tag.
A key part of the project was the widget builder interface. I built a multi-step configuration system with live preview, allowing non-technical users to customize layout, data sources, API keys, and embedding behavior in a simple and intuitive workflow.
On the backend and data layer, I implemented ingestion pipelines to process large volumes of publisher content via RSS feeds. This included generating embeddings, enriching metadata, and preparing data for semantic retrieval, enabling domain-specific AI search across diverse content types.
The search system itself was built using a hybrid approach. I combined vector-based semantic search using Weaviate with high-speed autocomplete and indexing via Typesense, resulting in accurate intent-based results alongside instant query suggestions.
I also contributed to system reliability and observability by integrating analytics and monitoring tools, enabling detailed tracking, debugging, and performance optimization across deployments.
The result is a scalable, production-grade AI search platform that can be embedded into external sites with minimal effort, providing powerful discovery capabilities backed by modern AI infrastructure.
This project highlights my ability to design and build complex AI systems that combine frontend experience, backend architecture, and data pipelines into a cohesive, high-performance product.
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I designed and developed a full-scale SaaS platform for legal professionals to automate deadline tracking, case management, and compliance workflows.
The goal was to replace manual processes and reduce the risk of missed deadlines by building a system that could dynamically calculate and manage complex legal timelines. I built the entire product end-to-end as the sole developer, taking it from initial concept through to a production-ready platform.
The application was built using Next.js, TypeScript, Tailwind CSS, and PostgreSQL, with a modular architecture that allows reusable logic templates to power complex deadline calculations across different use cases. This made the system flexible, scalable, and easy to extend as new requirements emerged.
On the backend, I implemented secure authentication using NextAuth, including support for two-factor authentication, along with a complete Stripe integration for subscription billing. This included webhook-driven renewals, plan management, and a reliable billing workflow designed for SaaS scalability.
The result is a robust, secure, and highly functional platform that streamlines legal workflows, reduces manual overhead, and provides a strong foundation for future growth. 🚀
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I worked as the Founding Frontend Engineer on Umynd, an AI-powered web platform designed to help teams generate, manage, and operate AI-driven content and workflows at scale.
I led the design and development of the entire frontend architecture, building a production-grade application using Next.js, TypeScript, Tailwind CSS, and shadcn/ui. The system was designed around a scalable, component-driven design system to support rapid iteration and long-term maintainability as the product evolved.
Beyond the frontend, I contributed to shaping the overall platform architecture by integrating a Python-based backend powered by LangChain and ComfyUI. This enabled dynamic generation and management of AI-driven assets, effectively turning the platform into a no-code CMS for non-technical users and internal operators.
A key part of my work involved building reliable engineering workflows. I implemented CI/CD pipelines using GitHub Actions, along with automated testing using Playwright and Pytest, ensuring stable releases and strong integration between frontend and backend systems.
I also introduced AI-first development workflows across the team by configuring advanced Cursor setups, rule-based automation, MCP servers, and intelligent code review loops. This significantly improved development speed, collaboration, and consistency across the codebase.
The result was a robust, scalable AI platform that combined strong frontend architecture, powerful backend automation, and modern development practices to support rapid product growth.
I was also consistently trusted to navigate complex technical challenges, adapt to evolving requirements, and deliver high-quality solutions with clear and reliable communication throughout the project.
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I led the development of MyOfLink as founding engineer, building a full-scale SaaS platform designed to help creators and businesses manage their online presence through advanced link infrastructure, analytics, and monetization.
The goal was to go beyond traditional “link-in-bio” tools by creating a high-performance platform that improves engagement, tracks user behavior, and enables scalable monetization.
I architected and built the platform end-to-end using Next.js, TypeScript, and Tailwind CSS, leveraging server-side rendering and React Server Components to significantly improve performance. The result was a fast, responsive product achieving a 95–98+ Lighthouse score and noticeably reduced load times.
A core innovation was the deep-linking system. I engineered a solution to bypass in-app browser limitations on platforms like Instagram, Reddit, and TikTok using intelligent user-agent detection and platform-specific routing. This improved link conversion rates and overall user engagement by approximately 40 percent.
I also built a real-time analytics dashboard to give users actionable insights into their traffic and performance. This included click-through rates, conversion metrics, and geographic data, enabling more informed decision-making for creators and businesses.
On the monetization side, I implemented a full Stripe-based billing system with subscription tiers, usage-based pricing, and webhook-driven automation. The checkout experience was designed to be seamless, secure, and optimized for conversion.
The final product is a high-performance SaaS platform that combines advanced linking technology, real-time analytics, and scalable monetization into a single cohesive system.
This project highlights my ability to design and deliver complex SaaS products from the ground up, with a strong focus on performance, user experience, and real business outcomes.
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AI Chrome Extension for Instant Cover Letter Generation (Job Jolt)
I built Job Jolt, an AI-powered cover letter generator designed to help users create tailored job applications directly from real job listings in seconds.
The product combines a Chrome extension with a backend web application to streamline the entire workflow. Users can visit a job listing on platforms like LinkedIn, extract relevant job data with a single click, and instantly generate a customized cover letter powered by AI.
On the frontend, I developed a React-based Chrome extension that injects seamlessly into job listing pages. The extension handles data extraction, sanitization, and formatting before securely sending structured requests to the backend.
On the backend, I built a Flask-based system integrated with OpenAI to generate high-quality, context-aware cover letters in under 20 seconds. The system was designed for speed and reliability, making it one of the fastest solutions in its category.
A strong focus was placed on security and robustness. I implemented API key validation, token-based authentication, rate limiting, and secure password handling to ensure the platform could scale safely while protecting user data.
The result is a smooth, end-to-end user experience that removes friction from the job application process and demonstrates how AI can be integrated directly into real-world workflows.
This project highlights my ability to combine frontend UX, browser extensions, and AI-powered backend systems into a cohesive and high-performance product.
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