2 Commits

Author	SHA1	Message	Date
Víctor Falcón	8056ede636	feat(ai): suggest automation rules during onboarding (#523) ... Suggests transaction categorization rules during onboarding. After a sync or import, it groups the uncategorized transactions, asks Gemini (via laravel/ai) to map the common merchants to categories, and shows the results for review. The user edits or drops any and creates the ones they want. During onboarding the accepted rules also categorize existing transactions right away. Off by default: it needs the `AiRuleSuggestions` Pennant flag and a per-user AI consent. The model and thresholds are config-driven. `ai:suggest-rules {user}` prints what a user would get. The settings-page surface and monthly regeneration are a follow-up.	2026-06-13 22:51:15 +02:00
Víctor Falcón	e36d6f3e16	Speed up PR CI browser path (#365) ... ## Summary - rebalance Browser tests with explicit class-filter shards - build PR browser assets inside shards, removing the build-assets dependency gate - run Browser shards with Pest parallelism () ## Autoresearch metric - baseline modeled PR critical path: 406.50s - final modeled PR critical path: 234.27s - modeled improvement: 172.23s (42.4%) ## Verification - ran METRIC ci_total_s=234.270 METRIC actual_recent_pr_total_s=422.000 METRIC build_assets_s=0.000 METRIC tests_s=170.000 METRIC browser_matrix_s=232.270 METRIC linter_s=60.000 METRIC static_analysis_s=26.500 METRIC performance_tests_s=63.000 METRIC job_count=13.000 METRIC browser_shards=6.000 for each experiment - coverage guard in autoresearch script checks Browser filters cover all recent Browser classes exactly once ## Notes - wall-clock faster; runner minutes likely higher due extra shards and duplicate asset builds - real CI should validate Browser parallelism flake risk	2026-05-07 20:40:13 +01:00