Research & engineering notebook

Learning, with a trail.

Questions from real code, small examples to reason with, and decisions worth revisiting.

Notes & reflections

Compiled October 2026
Model learningStudy note

Model evaluation: stopping, alignment and ensembles

Use the small KAPT evaluation utilities to examine why the best epoch, sample identity and decision threshold belong in an experimental protocol.

PythonEarly stoppingEnsembleEvaluation
8 min read
Project reflectionsProject reflection

Why one order needs three consistency rules

Use a Silk Road teaching order to distinguish request retries, stock contention and state transitions, then turn those distinctions into testable decisions.

Spring BootTransactionsIdempotencyState machines
8 min read
Data qualityStudy note

From cleaning to annotation: account for every sample

Use the data preparation demo to examine IDs, duplicate conflicts and quarantine reasons, then derive boundaries for annotation records and model inputs.

Data qualityAnnotationProvenanceLeakage
8 min read
Model learningStudy note

Prompt learning starts with tensor contracts

Trace task prompts, node features and gradient paths; distinguish frozen parameters from disabled gradients, and define what component checks can establish.

PyTorchPrompt learningAutogradTensor contracts
8 min read
Project reflectionsProject reflection

From permission boundaries to reviewable delivery

Connect resource authorization, workflow states and release evidence through the archive and counselor-system cases.

AuthorizationWorkflowRelease verificationTesting
8 min read

How to read these notes

These learning summaries were compiled from public code and project documents, with AI assistance in drafting and translation. They distinguish source observations, simplified examples and proposed checks. They are not a dated lab log or evidence of new benchmark results.

More learning repositories and earlier notes