ENGINEERING NOTES
Notes from the build.
A few technical observations from the projects, including where simple methods outperform more complicated ones and where data semantics matter more than the chart.
01 / MACHINE LEARNINGWhy the forecasting baseline matters
GridCast · historical ERCOT data
On a 48-hour holdout from the 2015 ERCOT sample, ridge regression recorded an MAE of 823.37 MW. Predicting demand from the same hour one day earlier returned a lower MAE of 667.42 MW. The simpler model performed better on this evaluation.
This does not mean ridge regression is generally worse. It means one small, time-local experiment is not sufficient evidence of improvement. More convincing work would use rolling-origin backtests, longer history, weather covariates and tests across distinct seasons.
See GridCast evaluation ↗02 / DATA ENGINEERINGHow annual SEC numbers become misleading
FilingIQ · public XBRL company facts
Company Facts responses may include multiple taxonomies, filed dates and observations for the same period. A flow metric such as net income measures a duration, while assets describe a balance at one instant. Mixing the two produces incorrect comparisons.
FilingIQ selects 10-K records, checks annual durations where appropriate, normalizes units and keeps the source filing visible. The derived research output should remain verifiable from the report itself.
Read the FilingIQ case study ↗03 / DEVELOPER EXPERIENCEHeuristic review signals are not defect proofs
CodeReview Intelligence · public GitHub pull requests
A suspicious API call or large patch may deserve closer review, but matching a regular expression is not equivalent to proving exploitability. GitHub may also omit patches, especially when files are large, and a missing patch should not silently count as clean.
CodeReview turns scan output into explained reviewer prompts, tracks missing patch coverage and avoids claims that require deeper execution or expert assessment.
Explore CodeReview Intelligence ↗