pegainfer-project SEO Keyword Evaluation
Titles are keyword claims. Before choosing one, measure both sides of the market: demand (are people searching this?) and supply (who already ranks for it?). Never rank title candidates by gut feeling — every step below produces a number you can compare.
Why this works for this site
open-infer.org wins on content gaps: deep technical topics where demand exists but third-party content is near zero. Validated examples:
- green-ctx post: "green context" had NVIDIA docs and nothing else. One post → ~40 impressions/2wk from a cold site, the only non-brand traffic.
- DSpark (July 2026): 9 days after release, Google Trends showed
dsparkat parity with the years-old category termspeculative decoding, while autocomplete for "dspark" still returned unrelated brands — demand had outrun supply. That mismatch is the signal to publish.
Chasing established head terms ("llm inference optimization") loses to framework docs and review sites. Skip those.
Step 1 — Measure demand
Three free, scriptable signals. Run all three; they measure different things.
Google Trends — relative heat and trajectory. Always include one established category term as the yardstick:
uv run --with pytrends --no-project python - <<'PY'
from pytrends.request import TrendReq
pt = TrendReq(hl='en-US', tz=0)
pt.build_payload(['CANDIDATE_TERM', 'CATEGORY_BASELINE'], timeframe='today 3-m')
print(pt.interest_over_time().tail(10).to_string())
PY
Values are relative (0–100 within the compared set), not absolute volume. Read the ratio to the baseline and the direction (climbing / decaying).
Google Autocomplete — has demand solidified into query variants?
curl -s "https://suggestqueries.google.com/complete/search?client=firefox&q=TERM"
- Many topical variants → mature demand, mature competition.
- No topical variants but Trends shows volume → window open: the term exists in search boxes but no content has claimed the completions yet.
Hacker News (Algolia) — leading indicator; HN heat precedes Google search volume by days to weeks:
curl -s "https://hn.algolia.com/api/v1/search?query=TERM&tags=story"
Hundreds of points / comments on a launch story = a search wave is coming. The comment threads are also your FAQ source — the questions asked there are the queries people will type next week.
Keyword Planner / Ahrefs volume data lags 2–3 months. For terms younger than that, their "0 volume" is meaningless — ignore them.
Step 2 — Measure supply
Manual, five minutes:
- Google the exact phrase you want to rank for (e.g. "dspark benchmark"). Classify the top 10: news announcements / official docs / independent technical content. All-announcements = content-type gap → strong signal.
- Search
intitle:"exact phrase"— how many pages have claimed the phrase in their title? Near zero = unclaimed.
Step 3 — Compose the title
Rules derived from the measurements:
- Pair one emerging term with one established category phrase. The emerging term rides the wave; the category phrase is the long-term floor if the wave dies. Both must appear as complete phrases — "speculative decoder" does not match "speculative decoding".
- One H2 per long-tail variant you found in autocomplete / HN comments ("X vs Y", "what is X", "run X"). Each H2 is a future search entry point.
- Site title style:
Descriptive Statement: Subtitle(see existing posts). - Frontmatter
descriptionis the meta description — write it for the searcher, front-load the keywords.
Step 4 — Timing
Emerging-term windows are measured in weeks. The window closes when the official framework docs (vLLM / SGLang / vendor) claim the term. If Trends shows the spike already decaying, publishing this week still captures the full tail; publishing next month captures nothing.
After publishing, post to r/LocalLLaMA and HN — backlinks from those threads are what move a new domain's ranking.
Step 5 — Verify with Search Console (ground truth)
~28 days after publishing, check GSC query data:
- Impressions confirm demand estimates; position >20 with impressions means Google matched the page but the content is too thin — strengthen it.
- Queries you rank for but didn't target = free topic ideas for the next post.
Feed real numbers back into this file when they confirm or refute a call — the two examples at the top should not stay the only ones.