Idea taken from TheNetsky/Microsoft-Rewards-Script, which builds search terms
from public feeds rather than a model. No code from it: that project is
GPL-3.0 and this one is MIT, so only the approach crosses over.
The LLM has exactly two call sites here, both producing a short string to type
into Bing. Everything the dependency costs, an Ollama account, cloud usage and
the provider work in #15, is paid for search strings. Three keyless sources
answer the same question:
Google Trends RSS queries people are actually typing right now
Wikipedia most-read topic seeds when trends is unavailable
Bing autosuggest expands a seed into related queries
Autosuggest is what makes the chaining work. Asking Bing what follows a term
returns queries Bing already expects, which is nearer to what the prompt in
llm_utils was reaching for than a model guessing unaided.
Selected with QUERY_SOURCE=trends. The default stays llm, so no existing setup
changes. stdlib only, no new dependencies.
Measured against the LLM on the same cards from a live account:
card llm trends
airport parking best rates airport parking reservations reserve airport parking best rates
checking vs savings compare checking vs savings accounts compare checking savings account options
cruise deals best cruise deals and destinations cruise deals destinations
Verified live with OLLAMA_HOST pointed at a dead port, so nothing could reach
a model: five queries generated from feeds and three typed into Bing, each
landing on a real results page.
Every source degrades to an empty list rather than raising, and both entry
points fall back, to the trimmed task description and to nouns.txt. A search
that does not happen costs points; a run that dies costs the rest of the day.
