Grady Algire

01
Database Website Mental Health

Self-Improvement
Through Music

A curated platform using intentionally selected music to promote emotional regulation, discipline, and forward growth — not escapism.

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// tech stack

  • Frontend HTML, CSS, Javascript
  • Backend Netlify, Supabase
  • Database PostgreSQL
  • Other Spotify Embed API, Github

The Problem

Music strongly influences emotion and thought patterns, but during difficult periods people often default to content that reinforces avoidance, fixation, or emotional regression. I couldn't find music focused on genuine self-improvement — so I built a platform to share what I discovered and created a space where contributions can grow the playlist.

The Solution

A manually curated, structured platform where songs are reviewed for emotional impact and long-term growth alignment before being added. The playlist dynamically pulls song data from Supabase with structured descriptions and triggers, while the recommendations page allows users to submit suggestions for intentional review — not automatic approval.

Challenges

Designing a system that enforces intentional curation instead of automatic aggregation required careful planning of song metadata — descriptions, triggers, visual theming, and sort order — within a relational PostgreSQL schema. Dynamically rendering from Supabase while maintaining security, balancing emotional depth with clear scope boundaries, and building a calm, cohesive responsive design all added meaningful complexity.

02
AI APIs Website

Stock
Analysis

An AI-powered stock analysis tool that combines financial data APIs with language model insights to help users make more informed investment decisions.

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// tech stack

  • Frontend HTML, CSS, Javascript
  • Backend Python, Flask
  • APIs Financial data, OpenAI
  • Other Github

The Problem

Retail investors are overwhelmed by financial data without clear context. Raw numbers from APIs are meaningless without analysis — and most tools are either too simple or too complex for someone learning to invest.

The Solution

A web application that fetches real-time stock data and passes it through an AI layer to generate plain-English analysis, highlight key signals, and surface relevant context — making financial data accessible without oversimplifying it.

Challenges

Coordinating multiple external APIs with rate limits and inconsistent response structures required robust error handling. Prompting the AI to give useful, accurate, and appropriately hedged financial commentary — without hallucinating data — was an ongoing design challenge.

03

Project coming soon

04

Project coming soon

05

Project coming soon