Last Words
A language dies every two weeks. We write the obituaries — with maps, family trees, and the untranslatable words the world just lost.
Channel: Last Words Tagline: A language dies every two weeks. We write the obituaries — with maps, family trees, and the untranslatable words the world just lost. Niche: Consumer-facing endangered language death tracking — automated language obituaries, regional endangerment dashboards, linguistic uniqueness scorecards, “what dies with this language” feature spotlights, revitalization progress reports, and weekly dispatches that make you feel what it means when a language with no word for “ownership” but twelve words for types of snow vanishes from the Earth. Powered by Glottolog, WALS, OpenAlex, and Wikidata — all free, all open, all machine-readable. Target audience: Linguistics-curious adults (22-55) who share “did you know?” content, polyglots, language learners, diaspora communities mourning heritage languages, indigenous rights advocates, educators, documentary lovers, r/linguistics lurkers (2M+), the “cognitive science life hacks” crowd, travel writers, anthropology students, and anyone who’s ever wondered why some languages have click consonants and others don’t. Also: the growing “cultural preservation” movement that overlaps heavily with the climate/conservation crowd. Why now: UNESCO’s Atlas of World’s Languages in Danger — the only major consumer resource — was last updated in 2010 and UNESCO publicly admits it’s stale. Meanwhile, AI language tools are accelerating shift to dominant languages. The UN Decade of Indigenous Languages (2022-2032) is at its midpoint with zero consumer-facing dashboards tracking progress. r/linguistics has 2M members. Language revival movements (Welsh, Irish, Māori, Hawaiian) are generating viral social content. And Glottolog just released v5.3 with updated endangerment classifications — but nobody is translating this data into beautiful storytelling.
Content Example
🪦 Obituary: Siletz Dee-ni — A Language That Counted Stars Differently
Published April 6, 2026 · Last Words Weekly
Language: Siletz Dee-ni (also known as Siletz Dee-ni Athabaskan) Glottolog: sile1254 | ISO 639-3: sil | Family: Na-Dené → Athabaskan → Pacific Coast Location: Siletz Reservation, Lincoln County, Oregon, USA Status: Nearly extinct — fewer than 3 fluent elders remain Speakers (peak): ~2,000 (1850s) → ~2 (2026)
The Siletz Reservation in coastal Oregon is one of the most linguistically diverse places in North American history. When the US government forcibly relocated members of 27 distinct tribal nations onto a single reservation in 1856, the result was a pressure cooker of at least a dozen mutually unintelligible languages. Out of that crucible emerged Siletz Dee-ni — an Athabaskan language that became the community’s lingua franca, absorbing loanwords from Tutuni, Chetco, Coquille, and the surrounding Chinook Jargon trade pidgin.
What makes Siletz Dee-ni linguistically remarkable isn’t just its survival story — it’s what it does with numbers. Like most Athabaskan languages, Siletz Dee-ni uses a base-10 counting system, but its numeral classifiers — the grammatical machinery that tells you what kind of thing you’re counting — distinguish between long rigid objects, long flexible objects, flat objects, round objects, and animate beings. You don’t just count “five” of something. You count five-long-rigid (sticks), five-flat (blankets), or five-alive (elk). The number itself changes shape depending on what exists in your hand.
This isn’t a quirk. It’s a worldview. Siletz Dee-ni grammar forces speakers to constantly classify the physical nature of everything they touch, see, or imagine. Cognitive linguists call this “obligatory categorization” — and research suggests it creates measurably different patterns of spatial reasoning and object recognition. When Siletz Dee-ni falls silent, an entire cognitive framework for understanding the physical world goes with it.
What dies with Siletz Dee-ni:
- 🧮 Numeral classifier system — one of the most elaborate in North America, with 9+ shape-based categories
- 🌲 Ecological vocabulary — 40+ terms for types of rain, fog, and coastal weather that distinguish conditions relevant to fishing and foraging
- 🗣️ Ejective consonants — a series of sounds (t’, k’, ts’) produced with a glottalic airstream that exist in only ~18% of the world’s languages
- 📖 Oral history encoding — evidentiality markers that grammatically distinguish “I saw it happen” from “someone told me” from “it is generally known” — a built-in fact-checking system
Revitalization effort: The Confederated Tribes of Siletz Indians run a language program with classes, an online dictionary, and recordings of elder speakers. But with fewer than 3 fluent speakers remaining, the window is measured in years, not decades.
The ugly math: If the revitalization program produces 50 conversational speakers by 2030 (its stated goal), Siletz Dee-ni joins Welsh and Hebrew as a language partially pulled back from the edge. If it doesn’t, everything described above enters the archive — and exits the living world.
📊 Linguistic uniqueness score: 87/100 — based on WALS feature rarity analysis across 2,679 languages 📍 Map: [interactive map showing Siletz Reservation, with overlay of the original 27 tribal territories] 🌳 Family tree: [Na-Dené → Athabaskan → Pacific Coast → Siletz Dee-ni, with sister languages highlighted]
Data Sources
- Glottolog CLDF Dataset (https://github.com/glottolog/glottolog-cldf) — Complete languoid catalog with Agglomerated Endangerment Status (AES), geographic coordinates, genealogical classification, and bibliography. CC-BY. Updated regularly. ~8,000+ entries. This is the backbone — automated weekly sync fetches the latest endangerment statuses and new language entries.
- WALS Online (https://wals.info) — 192 typological features across 2,679 languages. CC-BY. Powers the “linguistic uniqueness score” — how many rare grammatical features a language has compared to the global sample. If a language is one of only 12 that uses base-20 counting, that’s a high uniqueness score.
- Wikidata SPARQL (https://query.wikidata.org) — Query languages by ISO 639-3 code, get speaker counts, country, writing system, endangerment status, notable features. Free, no auth.
- OpenAlex (https://openalex.org) — Track recent linguistics papers mentioning specific languages. Detect whether a language is being actively studied or academically forgotten. Free API, 250M+ works indexed.
- Endangered Languages Project (https://endangeredlanguages.com) — Community-contributed language profiles, audio samples, revitalization program listings. Downloadable dataset.
- Wikipedia API — Language articles, historical context, cultural background for AI synthesis.
- Wikimedia Commons — CC-licensed photos of communities, landscapes, cultural artifacts.
Automation Pipeline
- Schedule: Weekly (every Sunday midnight UTC) for the main “obituary” dispatch + monthly deep-dive on a language family
- Collect:
- Sync Glottolog CLDF dataset (git pull from GitHub)
- Query WALS for typological features of featured language
- Query Wikidata SPARQL for speaker counts, countries, metadata
- Query OpenAlex for recent papers mentioning the language (detect research momentum)
- Fetch ELP data for revitalization programs and audio samples
- Fetch Wikipedia article for cultural/historical context
- Process:
- AI synthesizes data into narrative obituary format — NOT a Wikipedia summary, but opinionated storytelling that highlights what’s linguistically unique and what’s lost
- Compute “linguistic uniqueness score” from WALS feature data (% of features that are rare globally)
- Generate “what dies with this language” section from WALS + Wikidata features
- Generate evidentiality/grammar explainer for the featured language
- Fact-check speaker counts against multiple sources (Glottolog, Ethnologue summary, Wikidata)
- Generate:
- Regional endangerment heatmap (Mapbox GL JS / Leaflet with Glottolog coordinates)
- Language family tree visualization (D3.js dendrogram)
- Speaker decline timeline chart (historical data from multiple sources)
- “Unique features” infographic card (AI-generated via DALL-E/Midjourney API showing the linguistic concept visually)
- Obituary memorial card (styled template with language name, dates, family, speaker count arc)
- Publish: Astro static site → GitHub Pages. Weekly RSS feed. Newsletter via Buttondown.
Tech Stack
- Static site: TypeScript + Astro (content collections for each language, excellent SSR/SSG, built-in image optimization)
- Image generation: D3.js + canvas for maps/charts/family trees (deterministic, beautiful). DALL-E/Flux for conceptual illustrations (“visualize a numeral classifier system”). Satori + sharp for social cards.
- Data collection: Node.js scripts — git clone Glottolog, SPARQL queries, OpenAlex REST API, Wikipedia API
- CI/CD: GitHub Actions (weekly cron)
- Hosting: GitHub Pages (free) or Cloudflare Pages
- Maps: Mapbox GL JS (free tier: 50K loads/mo) or Leaflet + OpenStreetMap tiles (fully free)
- Newsletter: Buttondown (free tier: 100 subscribers) or Listmonk (self-hosted, free)
Monetization Model
- Primary — Donations/Tips: Buy Me a Coffee, Ko-fi, GitHub Sponsors. Endangered language content triggers strong emotional responses → donation-friendly. “Help us write one more obituary before it’s too late.”
- Secondary — Newsletter premium tier: $5/mo for monthly “deep dive” language family profiles, exclusive audio recordings analysis, and “language family tree” poster PDFs.
- Tertiary — Print/Merch: Language family tree posters, “last words” art prints with untranslatable words, memorial cards. Print-on-demand via Printful.
- Quaternary — Affiliate: Language learning platforms (Duolingo, iTalki, Pimsleur), linguistics books, documentary streaming (PBS, CuriosityStream). Contextual — “want to learn a related language that’s still alive?”
- Sponsorship potential: Language preservation orgs (Endangered Languages Project, FPCC), university linguistics departments, cultural foundations. Not high-value individually, but highly aligned = easy yes.
- Projected month-1 revenue: $50-150 (donations from early linguistics community shares)
- Projected month-6 revenue: $800-1,500 (newsletter growth, merch, donation base, SEO traffic on “endangered languages in [country]” queries)
Channel Soul & Identity
Name: Last Words — blunt, emotional, impossible to ignore. The double meaning (final words of a dying language + the last recorded words in that language) is deliberate.
Mascot/Visual Identity: A stylized owl — the linguist’s night watchman, staying awake while languages die in their sleep. The owl holds a quill in one talon and a microphone in the other (documentation). Illustrated in a woodcut/linoprint style — black and cream, with one accent color per language family (warm gold for Afro-Asiatic, deep teal for Austronesian, burnt orange for Na-Dené, etc.). The visual style echoes old naturalist field guides — because this IS a field guide to disappearing human biodiversity.
Voice: A melancholy archivist who refuses to be boring. Think Oliver Sacks writing about languages instead of neurological conditions — deeply empathetic, scientifically precise, occasionally devastated, and always finding the astonishing detail that makes you lean forward. Uses first person (“I spent three hours in the Glottolog data this week and found something that ruined my morning”). Not neutral. Takes a stance: every language death is a preventable loss, and the data proves what we’re losing.
Opinion/Stance: Language death is not natural selection — it’s the result of specific, documentable policy choices (forced assimilation, education policy, economic pressure). We name the causes. We track the culprits. We celebrate the revitalization wins. We don’t both-sides linguistic genocide.
Running Jokes & Traditions:
- 🪦 “Obituary of the Week” — the flagship format. Memorial card, linguistic autopsy, what died with it.
- 🏆 “Revitalization Win of the Month” — celebrating a language that gained speakers, got a new dictionary, or landed government recognition.
- 🧮 “Feature Friday” — a linguistic feature so weird/wonderful it stops you scrolling (base-27 counting! sentences that change meaning based on whether you witnessed the event! a language with no word for “left” or “right”!)
- 📊 “The Countdown” — running tally in the header: “Languages lost this year: X | Languages with <10 speakers: Y | Revitalization programs active: Z”
- 🗺️ “Language Graveyard” — interactive map of languages that went extinct in the last 50 years, with date of last speaker’s death
Visual Style: Woodcut/field guide aesthetic. Cream backgrounds, black ink typography (Crimson Pro for body, Cabinet Grotesk for headers), botanical illustration style for decorative elements. Each language family gets a signature color. Maps use muted earth tones with bright endangerment markers. Every page feels like opening a naturalist’s journal — beautiful, precise, and slightly heartbreaking.
Shareability: The “what dies with this language” cards are designed to be screenshot-and-shared — beautiful typography, one astonishing fact, the language name and speaker count. Like those “Word of the Day” posts but with existential stakes.
Scores
Launch Complexity: 3/5 — Glottolog data is well-structured and open. WALS is clean CSV. The hard part is writing the AI prompt templates that produce genuinely moving obituaries (not generic summaries). Map/chart generation is straightforward with D3.js.
Content Quality Score: 5/5 — This is inherently emotional, intellectually rich, visually stunning, and genuinely useful content. Linguistics is endlessly fascinating to the curious public. The obituary format gives narrative structure. The data gives authority. The uniqueness scores give novelty. This is the kind of content that makes people share and say “I had no idea.”
Automation Score: 4/5 — Data collection is fully automated (git sync, API queries). AI synthesis needs carefully tuned prompts but is automatable. Map/chart generation is deterministic. The only semi-manual piece: occasional quality review of AI-written obituaries to catch factual errors about specific languages (mitigated by multi-source cross-referencing).
Revenue Potential: 5/5 — Donation-trigger content (emotional stakes are real), clear newsletter premium path, merch potential (language tree posters sell), growing audience (linguistics is trending on social media), and the topic has strong SEO opportunity with stale competition (UNESCO’s 2010 data still ranks #1).
Total: 17/20
Why This Will Work
Psychology: People are fascinated by what they don’t know they’re losing. The “untranslatable word” genre has been viral for years (The Dictionary of Obscure Sorrows, Lost in Translation, Ikigai). But those focus on cute vocabulary. Nobody is doing the hard, data-driven version: here’s a complete cognitive framework that’s vanishing, here’s the science of what that means, and here’s a beautiful memorial for it. Loss aversion + intellectual curiosity + visual beauty = donation + share behavior.
Market logic: The linguistics-curious audience is massive (r/linguistics 2M, r/languagelearning 1.2M), underserved by content that’s either too academic (Glottolog) or too paywalled (Ethnologue), and increasingly activated by the UN Decade of Indigenous Languages. The SEO opportunity is wide open — UNESCO’s stale data still dominates search results. First mover with beautiful, automated, weekly content on this topic can own the niche within 6 months.
Scaling: The template works for any language. With ~3,000 endangered languages, that’s 57 years of weekly content — effectively infinite. Language family deep-dives, regional spotlights, feature comparisons, and historical extinction retrospectives add more content vectors. Adjacent channels: endangered writing systems, dying musical traditions, vanishing culinary vocabulary.
Risk & Mitigation
- Risk: Sensitivity — Writing about indigenous language death can be culturally fraught. Mitigation: Always center the community’s own framing, link to their revitalization programs, invite community review, never treat language death as “natural.”
- Risk: Data staleness — Glottolog updates lag real-world changes. Mitigation: Cross-reference with ELP community data, OpenAlex paper activity, and Wikidata. Flag uncertainty explicitly.
- Risk: AI quality — GPT-written obituaries could feel generic. Mitigation: Heavily templated prompts that force specific data points (speaker count, WALS features, unique vocabulary, historical cause of decline). Human-quality-review cadence: monthly spot-check.
- Risk: Ethnologue paywalled competition — If Ethnologue releases a free consumer product. Mitigation: Unlikely — SIL’s business model depends on subscriptions. And our differentiator is storytelling, not data.