Reputo

Positioning

Reputo is a multilingual career exploration platform that turns vague job aspiration into a comprehensible preparation path for students and early career explorers. Not an encyclopedia, not a counseling service, an exploration site where role-model narratives, step-by-step roadmaps, and reality checks live in a single flow.

Market and Problem

Students can name jobs they admire but rarely understand what those roles require or where preparation begins. Existing career content tends to be (1) abstract like a job dictionary, (2) success-story-heavy without reality, or (3) gated behind expensive admissions consulting.

Reputo reduces that uncertainty by linking role-model narrative + execution roadmap + reality check within a single page. See “the person you want to become,” follow “the stages of their growth,” and decide “the next action you can actually start”, all in one flow.

Core Audience and Personas

• Middle / high school and university students, Users who want both inspiration and actionable signals beyond vagueness
• Early career explorers, People interested in a role but unsure of preparation order
• Parents, teachers, mentors, Adults who need clear, structured material to discuss with students
• Multilingual student audiences, ko / en / ja / zh-cn / zh-tw, each version emphasizing region-relevant companies and figures

Value Proposition and Differentiation

• Role model + roadmap + reality on one page, Other career sites split person stories / job descriptions / preparation guides into separate pages. Reputo connects “how this person reached the role → what stages along that path look like → what challenges to expect realistically” on a single page
• Five languages, fully equivalent, Korean / English / Japanese / Simplified Chinese / Traditional Chinese. Each version is not just a translation, region-specific companies and figures are emphasized (e.g., Korean = Samsung / Naver / Kakao, Japanese = Sony / Toyota, zh-cn = Alibaba / Tencent)
• AI pipeline for content scale, a runner.py orchestrator makes a single Anthropic Claude API call that carries research → 5-language body → recommendations in one flow, letting a single operator maintain a 5 languages × N roles × M people content matrix
• Free, no-friction entry, Browsing requires no signup; lightweight authentication only for interactive features (quiz / mentoring)
• A browsing experience that accumulates without login, Saved careers (bookmarks), recently-viewed roles, and roadmap exploration progress persist in the browser and carry across visits. Discovery is stronger too: per-career at-a-glance signals (growth · demand) with source links, face strips of real people who walked the path, same-field semantic recommendations, and people / field filters, all with no signup

Core User Flow

• Field exploration: Start at a category, Technology / Business / Healthcare / Finance / Design, and narrow
• Role model discovery: Pages for real people in roles of interest (growth story, decisive moments, failure and recovery)
• Roadmap study: Sequence of preparation → learning → experience accumulation, plus recommended materials
• Reality check: Rewards, challenges, common misconceptions, what year 1–3 actually looks like
• Decide next step: Career quiz to narrow interest / recommended reading / 1:1 mentoring (live professional connection)

User flow: from aspiration to action

flowchart LR
    Browse["Field exploration
(Tech · Business · ...)"]
    Person["Role model discovery
growth story · decisive moments"]
    Road["Step-by-step roadmap
prep → learn → experience"]
    Real["Reality check
rewards · challenges · misconceptions"]
    Next["Next step
quiz · books · mentoring"]

    Browse --> Person
    Person --> Road
    Road --> Real
    Real --> Next
    Next -.->|narrowed interest| Browse

A closed loop of explore → validate → act → re-explore. A single visit doesn’t end the relationship, the product accompanies the entire decision process.

Business Model Hypothesis

Pricing is being validated in stages.

1. 1:1 mentoring matching, Connect students directly with working professionals on an hourly basis. Free content builds conviction; mentoring carries the action-stage fee
2. School / institution licensing, Schools curate Reputo content for their students under their own white-label or integration
3. Career book / program affiliate, Recommended books, bootcamps, and certification programs as partner placements

System Architecture (planning decisions become system structure)

Two planning decisions drove the architecture.

1. “A 5-language content matrix must be sustainable by a single operator.” → Direct authoring is impossible at this scale, so a runner.py orchestrator carries the load: a single Anthropic Claude API call (api_client.py’s _call_claude) performs research, the 5-language body, and recommendations via staged prompts, while content_checker.py validates, image_downloader.py collects images, and persistence.py writes to disk. The operator handles only inputs (job / person candidates) and review of outputs. The only path to keeping all five languages at equivalent quality.

2. “An exploration site should be static for SEO and speed.” → Hugo static build + Tailwind. Content lives in markdown + JSON, and a single build generates the entire 5-language site. pagefind handles search via a client-side index, no search server required for 5-language search.

flowchart TD
    Source["Input candidates
(roles · people · fields)"]
    Runner["runner.py
orchestrator (CLI)"]
    Claude["api_client.py
_call_claude → api.anthropic.com
single Anthropic Claude call
(research · 5-language body · recommend)"]
    Check["content_checker.py
5-language & link validation"]
    Img["image_downloader.py
external public-source images"]
    Persist["persistence.py
disk output"]
    Content[("content/
markdown + JSON")]
    Hugo["Hugo build
(5 languages together)"]
    PF["pagefind
(client-side search index)"]
    CDN["Firebase Hosting
reputo.net"]

    Source --> Runner
    Runner --> Claude
    Runner --> Check
    Runner --> Img
    Claude --> Persist
    Img --> Persist
    Persist --> Content
    Content --> Hugo
    Hugo --> PF
    Hugo --> CDN
    PF --> CDN

Content production via AI pipeline, site delivery via static build, search via client-side index, operating cost is compressed at every layer.

Technology Choices and Trade-offs

• Site: Hugo + Tailwind + PostCSS. Five languages built simultaneously. Different from the workspace’s Vite + React standard, content sites win on SEO, speed, and multilingual operations with static builds
• Content pipeline: Python runner.py orchestrator (api_client / content_checker / image_downloader / persistence). A single Anthropic Claude API call (api_client.py’s _call_claude → api.anthropic.com/v1/messages) performs research, the 5-language body, and recommendations via staged prompts. The layer that makes 5 langs × N roles × M people maintainable solo
• Search: pagefind (client-side index). Five-language search with no server
• Data: Structured data (jobs.json etc.) for role / field mapping; person pages live as content/people/<slug>/index.{ko,en,ja,zh-cn,zh-tw}.md (5 files per person)
• Hosting: Firebase Hosting (site reputo, project mrlatte), workspace standard
• What was dropped: User signup (browsing is anonymous; only mentoring requires auth), realtime recommendations / algorithmic feed (preserves static-site advantages), self-hosted LLM (external APIs win on cost and quality)

Operational Automation

• Content pipeline, Input candidate queue → runner.py (single Anthropic Claude call) → markdown output. Operator handles review only
• 5-language completeness lint, Every page must have all five language files; missed translations fail the build (the multilingual rule from CLAUDE.md as a hard guardrail)
• Image auto-download, image_downloader.py collects person / company / role images and verifies licensing
• Site deployment, main push → Hugo build → Firebase Hosting deploy, automated
• Content checks, content_checker.py pre-validates language completeness, broken links, and missing metadata

Current State and Operational Signals

• Status: Live. Five languages running at parity, content pipeline stabilized
• Started: 2025-09 (content pipeline buildout); launched 2026-01
• Infrastructure: Firebase Hosting (static, site reputo); Python content pipeline
• Verification signals: Per-language traffic ratios (Korea vs global vs Japan vs Chinese-speaking regions), career quiz completion rate, and average dwell time on person pages
• 2026-07 UX round: unified dark design tokens across all pages + discovery upgrades (career signal cards, sources, role-model strips, semantic recommendations, field / people filters) + login-free personalization (save, recently-viewed, roadmap progress), shipped. Per-career research sources and last-updated dates are surfaced for credibility

Retrospective and Next Hypotheses

• What worked: AI pipeline + 5 languages + static build combined into a structure where a single operator can maintain the content matrix, what would take a year of manual writing fits into a week. The “role model + roadmap + reality on one page” structure is validated by student dwell time
• What I would redo: Launching all five languages at once stretched review capacity (one operator can’t simultaneously vet five languages). Stabilizing ko / en first, then sequentially adding ja / zh would have been safer
• Next hypotheses: (1) 1:1 mentoring matching (live professional connection), (2) School / institution licensing (curated white-label), (3) Career book / bootcamp affiliate, (4) Mobile PWA / app (after usage pattern validation)

Comparable Engagements

The capabilities developed solo on Reputo transfer cleanly to other domains.

• AI-pipeline multilingual content sites, Maintaining N × M × L content matrices solo (encyclopedias, guides, comparison sites, catalogs)
• Role model / case / roadmap content UX, Translating abstract information into person stories + ordered actions for any domain (career, self-development, learning, health)
• Single-LLM-call content automation orchestrator, From a single input to reviewable output via staged prompts, with five-language parity guaranteed
• Hugo multilingual static site + pagefind search, Server-less five-language search site operations
• JSON data + markdown content separation model, Decoupling structured data (role / field mapping) from narrative content (people, roadmaps) so operations stay simple

I prefer engagements where one person carries the work end to end. Reach me via /work-with-me or /contact.