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// Stack

Tools I compile against in prod,
not just star on GitHub.

Same live radar as home: content-fed tiers, SVG in the browser, zero “hero image generated by vibes.” If the shape looks wrong, it’s a data bug — the renderer is just doing math in public.

FrontendBackendDevOpsAIFrontendBackendDevOpsAI

// What this radar is showing

Each spoke is a skill area; the outline is how strong that area is.
Same chart as my homepage — one dataset, so you’re never seeing two different stories.

Tags and tiers come from my CMS. Core skills move the shape the most; Pro and Project add smaller bumps. Learning tags add a little, but with diminishing returns — piling them on can’t inflate the chart into a fake “expert everywhere” silhouette.

// Inventory

Browse by domain

Compact lists grouped by tier — hover a chip for the CMS description. Core and Pro read first; Learning stays de-emphasized.

Core

8 skills
  • Vue
  • TypeScript
  • Tailwind CSS
  • Pinia
  • NuxtJS
  • HTML
  • CSS
  • JavaScript

Pro

4 skills
  • Astro
  • Sanity CMS
  • Ruby On Rails
  • Ruby

Project

5 skills
  • React
  • Next.js
  • DrawIO
  • Python
  • SVELTE

How skills are counted

A simple 3-step scoring model

This chart does not reward long keyword lists. It rewards demonstrated usage depth, then adjusts for quality consistency.

The process at a glance

1

Assign each skill to a usage tier

Every skill gets one evidence-based tier first, so scoring starts with proof instead of keyword volume.

2

Calculate category depth

Tier points are summed per category with saturation, so early strong evidence matters most and spam has diminishing impact.

3

Blend depth and quality

Final category score uses a transparent mix: 80% depth + 20% quality consistency across Core/Pro vs lower tiers.

What this prevents

Keyword inflation

Listing many low-evidence tools cannot dominate the score. The model rewards sustained production usage over breadth without proof.

  • Long shallow lists are capped by tier weights and saturation.
  • Proven Core and Pro usage raises score faster and more honestly.

Score formula

Final score = (Depth × 0.80) + (Quality × 0.20)

Depth comes from tier points plus saturation logic. Quality checks if evidence is concentrated in stronger tiers (Core/Pro) rather than mostly lower tiers.

Depth effect

Strong early evidence increases confidence quickly.

Quality effect

Consistent Core/Pro distribution improves trust in the score.

Why the radar chart matches this explanation

The visual shape on the main chart is generated from the exact same tier points and formula shown here, so the explanation and chart always stay aligned.