Data Analysis Projects in LortonData Analysis Projects in LortonSelf-initiated internal product. Odds Desk uses public data only. It is not client work, a trading tool, or financial advice.
I built this because a research brief is only as trustworthy as the source snapshot behind it. Catalog freshness, derived summaries, file identity, and editorial status need to stay consistent across every generated artifact.
The workflow synchronizes a public catalog, exports one canonical bundle, generates and validates a summary, regenerates a manifest, checks declared SHA-256 values against disk, and produces a dashboard and Markdown brief from the same saved files.
The workflow fails closed. If a required validation fails, the bundle stops, the evidence is preserved, and no buyer-ready issue is produced.
The gallery shows a July 30, 2026 snapshot with 675 saved markets, 36 unverified research leads, zero verified cards, and four of four declared hashes matching disk. The state remains RESEARCH_PREVIEW.
Best fit: public API ingestion, reproducible research bundles, validation pipelines, provenance checks, fail-closed editorial workflows, and automated briefing systems. As a designer, I focused on crafting an experience that brings clarity to complexity. This project embodies the future of intelligent stadium management and sets a new standard in how we interact with data at scale.
This bird’s-eye view gives an effortless preview of how data flows across the venue, highlighting availability, network performance, and traffic patterns, helping managers ensure smooth operations for thousands of spectators.
Through motion-driven design, complex data is turned into meaningful insights. Dynamic, color-coded charts and intuitive visuals reduce cognitive load, offering clear, actionable information, like network status, crowd accessibility, and engagement - at a glance.
The real power of this animation lies in its ability to provide contextual insights at scale, enabling proactive decision-making. A self-initiated Brief Factory internal system. The gallery shows public post titles only. No names, handles, contact details, or private messages are shown.
I built this to make public automation research more consistent. Instead of manually checking communities for scattered requests, the monitor reads selected RSS and Atom feeds, normalizes source URLs, rejects stale or generic items, scores observable workflow pain, deduplicates accepted rows, and records source health.
The output is a structured review queue, not permission to contact anyone. The system does not post, bid, message, or send.
The gallery shows a dated run from July 30, 2026: seven configured feeds, seven review rows, two HTTP 429 rate limits, zero high-intent rows, and zero external actions authorized.
Best fit: public-source monitoring, qualification queues, source-health reporting, research triage, and review-first opportunity workflows. I’m excited to share EyeViz - a data-driven dashboard suite for exploring mutual funds through an artistic, human lens. ✨
👁️ Data You Can See (Eye Chart)
Meet the hero screen: a living iris where each colored blade maps an asset class, orbiting a bold KPI. It’s data-as-vision - instant clarity on allocation, balance, and momentum - with a slim Interactions stream and a built-in AI sidekick for quick explanations.
💡 Research Workspace
Search, compare, and understand — fast. Clean tables, smart chips, and a compact chart show stability, income focus, diversification, and risk side-by-side. The chat panel flags Unverified insights so trust stays front and center.
📱 Personal Workspace
Your everyday hub: activity tiles, pinned studies, and ongoing team chats - so you can jump back into work in seconds and keep insights reusable. 
