Plate 01How not to make Machine's Learn
A practical map of the places machine-learning systems break: data, sampling, features, evaluation, and assumptions.
Public Learning Trail / 07
Selected records
Plate 01A practical map of the places machine-learning systems break: data, sampling, features, evaluation, and assumptions.
Plate 02A direct introduction to securing an AWS sandbox with identities, permissions, and deliberate access boundaries.
Plate 03The build story behind a small automation that prevents free-tier databases from quietly hibernating.
Archive index

Structured notes on relational data, query construction, joins, aggregation, and the database concepts worth keeping close during implementation.

A working reference for reasoning about scale, service boundaries, data flow, reliability, caching, queues, and architectural trade-offs.

Field notes covering VPC structure, subnets, routing, gateways, security boundaries, and the network paths behind deployed AWS systems.

A practical map of the places machine-learning systems break: data, sampling, features, evaluation, and assumptions.

A direct introduction to securing an AWS sandbox with identities, permissions, and deliberate access boundaries.

The build story behind a small automation that prevents free-tier databases from quietly hibernating.

A concise explanation of how raw application data moves through collection, transformation, storage, and delivery.

A system-level look at realtime chat architecture and the engineering trade-offs behind familiar messaging products.

A field note on turning scattered startup and hackathon research into a method that produces clearer decisions.