ENGINEERING NOTES

How the projects work under the hood.

I document the decisions that affect reliability: how data is selected, how models are evaluated, what happens when an API fails, and where the current implementation falls short.

Data ingestion with provenance

SEC annual filings are normalized using specific 10-K periods and units. Demand observations preserve timestamps and balancing-authority context. GitHub responses differentiate actual public repositories from an offline synthetic test fixture.

CHECK Dates, units, missing fields, source URLs, and dataset scope.

Modeling with a baseline

GridCast uses lagged load and calendar features with time-ordered training and evaluation. On the included historical ERCOT sample, ridge regression produced 823.37 MW mean absolute error while the seasonal-naive approach produced 667.42 MW. The baseline won on the holdout.

CHECK 48-hour holdout, one-step predictions, no random train/test shuffle.

API design and graceful failures

Live integrations are optional. Local examples remain available when external services rate-limit or block traffic. Endpoints validate tickers, repository identifiers and pull request IDs. The apps avoid presenting unavailable API data as current.

CHECK Input validation, fixed upstream hosts, actionable error states.

Responsible software claims

FilingIQ's numeric research Q&A is not described as an autonomous LLM. CodeReview warnings are heuristics, not audited vulnerabilities. The electricity demand interval is a demonstrative envelope, not a statistically calibrated prediction interval.

CHECK Evidence supports each claim; unsupported certainty is removed.

SOURCE CODE & RESULTS

Explore the implementation.

The source archive includes the model-training script, automated tests, dataset notes, and API details.