The character check allowed "." and "..", which are made entirely of allowed
characters and still walk out of the directory, so the traversal guard only
stopped the cases containing a separator. Reject both by name and check the
resolved path against the profile root as well, since the character set
constrains the characters rather than where they point.
Also drop the MouseUtils and KeyboardUtils built in run_account and never
used, RewardsTaskUtils builds its own, along with the two imports that
leaves unused, and say "lowercased" in the no-source warning, which is what
the code returns.
Both change main.py and README.md, and three separate pull requests touching
the same entry point is worse for review than one. Resolved by keeping every
section of the README and folding the logging setup into the new multi-account
main, so the profile-in-use message from #35 is now a logger.error and uses
log_utils.exception_summary rather than repeating the truncation inline.
queries.py moves to logging with the rest of the runtime.
Both taken from TheNetsky/Microsoft-Rewards-Script, which packages a container
and handles several accounts. Approach only: that project is GPL-3.0 and this
one MIT, so no code crosses over.
**Accounts.** Rewards is per Microsoft account and the browser profile holds
the sign-in, so an account here is a profile directory. REWARDS_ACCOUNTS takes
a comma separated list and gives each its own directory under the configured
one. They run in sequence, and a profile that will not start is reported and
skipped rather than ending the run. Left unset, a run uses the single profile
exactly as before.
Names are validated rather than trusted: they become directory names, so
"../escape" is refused instead of quietly writing outside data-dir.
**Docker.** The image carries only what main.py actually reaches, selenium and
numpy. pygetwindow, keyboard, matplotlib and pygame are used solely by the
recording and visualisation scripts, and two of those are Windows-only, so
none of them belong in a container. msedgedriver is pinned at build time to
the Edge the image installed rather than to latest, which drifts from it
between releases.
QUERY_SOURCE defaults to trends in the image, so a container needs no Ollama
account and no model download at all.
That default turned out to require a fix. queries.py imported llm_utils at
module scope, which imports ollama, so a trends-only install still needed the
ollama package: exactly what running in a minimal image is good at exposing.
The import is now made inside the llm branch, and the wordlist fallback reads
nouns.txt directly rather than borrowing llm_utils.get_random_noun.
REWARDS_HEADLESS drives the headless flags. The window size is set explicitly
because the pointer code works in viewport coordinates and the default
headless window is small enough to put cards out of reach, which is the
MoveTargetOutOfBoundsException from #19. Verified on the host that
move_to_element and human_like_click both work headless before relying on it.
Verified in the built image: Edge 151.0.4129.107 with a driver of exactly the
same build, the trends feed reachable from inside, Edge driven to bing.com and
rewards.bing.com at 1920x1080, and REWARDS_ACCOUNTS producing separate profile
directories with traversal refused.
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.
Ethan Stoner