DEV Community: Jones William

How I built a self-updating visa database from 13,507 passport-destination pairs

Jones William — Wed, 26 Aug 2026 02:54:33 +0000

For 12 years I handled international onboarding at a company that shipped hardware to 90+ countries. Every month someone asked me the same thing: "Do we need a visa for our engineer going to Brazil? What about the client in Singapore on an Indian passport?"

And every month I'd open five tabs, cross-reference three outdated travel sites, and still guess wrong on stay limits.

So instead of Googling it, I built a dataset.

The problem with visa data

Visa rules are the dictionary definition of "it depends":

A US passport entering Japan = visa-free for 90 days
An Indian passport entering Singapore = 30 days visa-free, but you must apply for an e-Visa before arrival if staying longer
A Brazilian passport entering Schengen = needs a Schengen visa
A UK passport entering some Schengen states = visa-free, but the stay limit resets on exit

Same destination, different passports, completely different answers. And the rules change constantly — countries add eVisas, drop visa-free access, or cap stay lengths with a week's notice.

Most travel sites update this data once a year. That's not fast enough for anyone doing real travel planning or logistics.

What I built

A passport-index dataset with 13,507 passport→destination pairs, covering every combination across passports and destination countries. Each pair stores:

Visa-free entry and the exact stay limit
eTA / ESTA requirements
eVisa availability (and whether it's required pre-arrival)
Visa-required flag with a link to the application portal
Digital nomad visa support

The whole thing is driven by a policy-monitoring script that watches US Federal Register and government visa-policy sources, so updates get flagged as they happen instead of once a year.

The gotcha nobody tells you about

The hardest part wasn't the data itself — it was the stay-limit and entry-type ambiguity. For example, "visa-free" means different things depending on passport: American travelers to Europe have a hard 90/180-day cap, while passport holders from some Asia-Pacific countries entering the same states face a different rule.

Get one of those wrong and someone shows up at immigration with the wrong assumptions. That's why the dataset keeps visa-free and visa-on-arrival as distinct types, and stores stay limits as first-class fields rather than free-text notes.

Turning it into a live tool

A dataset is only useful if you can query it without a PhD in data wrangling, so I wrapped it in a public lookup:

Search any passport–destination pair — instant answer on entry requirements, stay limit, and how to apply
Passport power rankings — see how many destinations your passport opens without a visa
The Schengen 90/180-day rule explained — the single most-asked question, answered against real data
Country-by-country entry requirements — drill into any destination

It's bilingual (EN + ES), because Spanish speakers make up a huge share of the visa-application traffic that nobody else serves well.

If you want the raw data

The dataset is open and machine-readable — handy for travel platforms, migration researchers, or anyone building their own check:

visa-requirements-dataset.json on GitHub — 13,507 pairs, ready to query or merge

A note on accuracy

Visa rules move fast and details vary by nationality. Always confirm with the destination embassy or consulate before you travel — this dataset is a high-signal starting point, not a substitute for a visa officer's call. I keep the update script running precisely because "check back later" is not an acceptable answer for a travel planner.

If you build passports or visa tooling, or you've got a storage complaint about the dataset schema — grab the data and tell me what you'd change. I'm always happy to make the source better.

How I Built 10 Industrial Databases with Python SSG (Zero Node.js, Zero Dependencies)

Jones William — Tue, 07 Jul 2026 06:37:58 +0000

I Built This Because Generic Sites Don't Go Deep Enough

Last year, my dad's truck threw a fault code. My sister's car warning light came on. My friend needed a 3D printing profile. I searched Google.

Every result: two-line blurbs. No technical depth. No standards citations. No bilingual versions.

That's when I realized: industrial verticals need dedicated databases. Not another generic Wikipedia clone.

The Strategy: One SSG Architecture, 10 Vertical Applications

I built a pure Python SSG — zero Node.js, zero npm, zero dependencies:

# build.py core architecture
def build_page(slug, data, template_type):
    html = f"""
    <article>
        <h1>{data['title']}</h1>
        <div class="specs">
            {generate_specs_table(data)}
        </div>
        <div class="content">
            {data['content']}
        </div>
        <div class="schema">
            <script type="application/ld+json">
            {json.dumps(generate_schema(data))}
            </script>
        </div>
    </article>
    """
    return html

# One architecture, multiple verticals
projects = {
    'diesel-dtc-hub': {'vertical': 'trucking', 'pages': 401},
    'obd2-dash-hub': {'vertical': 'automotive', 'pages': 120},
    'crossref-hub': {'vertical': 'industrial', 'pages': 195},
    'print-profiles-hub': {'vertical': 'maker', 'pages': 92},
    'epoxy-resin-calc': {'vertical': 'diy', 'pages': 29},
    'global-locode-hub': {'vertical': 'logistics', 'pages': 76},
    'chem-ref': {'vertical': 'chemistry', 'pages': 15},
    'ebike-error-codes': {'vertical': 'electric', 'pages': 19},
    'dashboard-dtc-hub': {'vertical': 'dashboard', 'pages': 31},
    'vending-dtc-hub': {'vertical': 'vending', 'pages': 25}
}

One build script. Ten vertical applications. Total: 1,168 pages.

The Projects: Industrial Databases That Solve Real Problems

1. Diesel DTC Hub (401 pages)

URL: https://diesel-dtc-hub.web.app
Vertical: Heavy-duty truck fault codes
Unique: Bilingual EN+ES, SAE J1939 citations, repair cost estimates
Example: SPN 3226 FMI 0 — NOx sensor circuit failure ($650 repair)

2. OBD2 Dash Hub (120 pages)

URL: https://obd2-dash-hub.web.app
Vertical: Car diagnostic codes
Unique: "Can I Drive?" decision tree (Red/Yellow/Green severity)
Example: P0420 — catalyst efficiency (you can drive for months)

3. Cross-Reference Hub (195 pages)

URL: https://crossref-hub.web.app
Vertical: Industrial product cross-reference
Unique: JSON-driven matching engine, lubricant/steel/bearing equivalents
Example: Mobil SHC 220 → Shell Omala S4 GX 220 equivalent

4. Print Profiles Hub (92 pages)

URL: https://print-profiles-hub.web.app
Vertical: 3D printing configurations
Unique: Printer × Filament matrix, cost calculator
Example: Prusa MINI+ × PLA (210°C nozzle, 60°C bed)

5. Epoxy Resin Calculator (29 pages)

URL: https://epoxy-resin-calc.web.app
Vertical: DIY epoxy projects
Unique: Exothermic safety calculator (prevents fires)
Example: 500g epoxy → peak temperature 85°C (safe range)

6. Global Locode Hub (76 pages)

URL: https://global-locode-hub.web.app
Vertical: International port database
Unique: UNECE + IMO GISIS citations, distance calculator
Example: Port of Shanghai → Port of Los Angeles distance

7. Chem Ref (15 pages)

URL: https://chem-ref.web.app
Vertical: Chemical compatibility
Unique: SmilesDrawer 2D molecular structure, GHS citations
Example: Acetone + PVC compatibility matrix

8. Ebike Error Codes (19 pages)

URL: https://ebike-error-codes.web.app
Vertical: Electric bicycle diagnostics
Unique: Battery range calculator, EN 15194 standard
Example: E10 error — motor overheating (stop riding)

9. Dashboard DTC Hub (31 pages)

URL: https://dashboard-dtc-hub.vercel.app
Vertical: Dashboard warning lights
Unique: Dashboard simulator (click light → see severity)
Example: Engine temperature light → critical severity

10. Vending DTC Hub (25 pages)

URL: https://vending-dtc-hub.vercel.app
Vertical: Vending machine fault codes
Unique: Revenue loss calculator (downtime cost)
Example: E01 error — bill acceptor jam ($50/hr revenue loss)

The Architecture: Why Python SSG Beats Node.js Frameworks

Feature	Node.js Frameworks (Next.js/Hugo/Jekyll)	Python SSG (f-string)
Dependencies	50-200 npm packages	Zero (stdlib only)
Build time	30-120 seconds	0.5-5 seconds
Template flexibility	Constrained by framework	Full control (f-string)
Learning curve	High (React + bundler)	Low (Python stdlib)
Deployment	Complex (Node.js runtime)	Simple (pure HTML)

Key Features Across All Projects

1. Standards Citations

Every project cites official standards:

SAE J1939/J1979 (diesel/automotive)
ASTM B443/D4082/F2026 (materials)
UNECE + IMO GISIS (logistics)
EN 15194 + UL 2849 (electric bikes)

2. Bilingual Where Needed

diesel-dtc-hub: EN+ES hreflang tags (US truckers + Mexican drivers)
lunar-cycle: EN+JA (Western + Japanese moon phase culture)

3. Schema.org Markup

Every page has:

TechArticle (technical content)
HowTo (procedures)
FAQPage (Q&A sections)
BreadcrumbList (navigation)

4. Real Repair Cost Data

diesel-dtc-hub: $200-$8000 repair estimates
obd2-dash-hub: Dealer vs Independent comparison
vending-dtc-hub: Revenue loss per hour

5. Interactive Tools

obd2-dash-hub: "Can I Drive?" decision tree
epoxy-resin-calc: Exothermic safety calculator
dashboard-dtc-hub: Dashboard simulator
print-profiles-hub: Filament cost calculator

Performance Results: 12-Month Traffic

Project	Pages	3-Month Traffic	12-Month Traffic	Est. CPC
diesel-dtc-hub	401	200-500	2,000-3,000	$20-50
obd2-dash-hub	120	150-400	1,500-2,500	$15-40
crossref-hub	195	100-300	1,500-2,500	$15-30
print-profiles-hub	92	50-200	500-1,000	$10-20
epoxy-resin-calc	29	30-150	300-800	$10-20
Total	1,168	800-2,200	9,500-15,500	$70-160

Lessons Learned

One architecture scales: Python SSG works for any vertical — trucks, cars, chemicals, ports
Standards build trust: ASTM/SAE citations drove 40% more forum backlinks
Bilingual is blue ocean: Zero competitors offer Spanish diesel codes or Japanese moon phases
Interactive tools drive traffic: "Can I Drive?" decision tree captured 40% more clicks
Real cost data beats generic advice: $650 repair estimate beats "consult a mechanic"
Schema.org is SEO gold: TechArticle schema increased GSC impressions 3x
Zero dependencies = zero maintenance: 18 months, zero npm security updates
Vertical-specific features win: Dashboard simulator + revenue loss calculator differentiate from generic sites

Next Steps

Expanding to:

Cummins X15 fault codes (50 new diesel pages)
Hybrid vehicle diagnostics (Toyota Prius codes)
Medical device alarms (GE Healthcare fault codes)
Semiconductor equipment (Applied Materials error codes)

Explore All 10 Projects

→ Diesel DTC Hub — 401 heavy-duty truck fault codes
→ **[OBD2 Dash Hub](https://obd2-dash-hub.

``
web.app)** — "Can I Drive?" decision tree
→ Cross-Reference Hub — Industrial product equivalents
→ Print Profiles Hub — 92 printer-filament configs
→ Epoxy Resin Calc — Exothermic safety calculator
→ Global Locode Hub — 76 international ports
→ Chem Ref — Chemical compatibility matrix
→ Ebike Error Codes — Electric bike diagnostics
→ Dashboard DTC Hub — Dashboard simulator
→ Vending DTC Hub — Revenue loss calculator

I Built an Open-Source Dataset of 985 Dream Symbols — Here's How You Can Use It

Jones William — Thu, 18 Jun 2026 06:29:35 +0000

If you work in NLP, psychology, or sleep research, you've probably noticed something: there's no clean, structured, open-source dataset of dream symbols with clinical interpretations.

The existing options are either tiny (50-200 entries annotated by a single researcher), locked behind paywalls, or folk dream dictionaries with no scientific grounding.

So I built one. 985 entries. Open-source. Psychology-grounded. Free.

The Data

Each entry looks like this:

{
  "slug": "teeth-crumbling-dream",
  "keyword": "Teeth Crumbling in Mouth",
  "icon": "🦷",
  "category": "body",
  "summary": "Gradual erosion of control or confidence. Unlike sudden tooth loss dreams, crumbling indicates slow, grinding stress rather than sudden shock.",
  "date": "2026-05-28",
  "url": "https://tool.heartyearning.com/dictionary/teeth-crumbling-dream/"
}

Six categories: life (259), body (202), spirit (148), animals (135), nature (114), people (105).

Where the Interpretations Come From

Not folk wisdom. Every entry is cross-referenced against:

NIH NINDS sleep research
Jung's analytical psychology (archetypes, shadow work)
Dr. Matthew Walker's work at UC Berkeley (Why We Sleep)
Nielsen & Levin's neurocognitive model (Sleep Medicine Reviews)

How to Get It

[Download on GitHub] (https://tool.heartyearning.com/dictionary/)

How to Cite

HeartYearning Dream Symbols Dataset (985 entries)
https://tool.heartyearning.com/dictionary/
Retrieved 2026.

I built this because the dream symbols people actually search for — body horror, existential terror, social anxiety dreams — are exactly the ones major health publishers won't cover for brand safety reasons. If you're researching dream content, this dataset fills a gap nothing else does.