Problem Solving Across Domains

How the loop enters DSA, databases, backend, frontend, system design, ML and agentic engineering — the same questions, different next lessons.

Problem Solving and DSA

Problem → Constraints → Pattern → Algorithm. The algorithm is the last step, and the step people start from. The constraints — how much data, how often it changes, what "fast enough" means — are what pick the pattern, and the pattern is what picks the algorithm.

Q · A piece of the store needs an algorithm and you do not know which. How does the problem-solving loop get you from the requirement to the right one, and what does it hand off to the DSA domain?
Problem Solving and Databases

Data → Access Patterns → Model → Index → Query. The database domain teaches models, indexes and query plans; this lesson is the step before — discovering that there is data, how it will be read and written, and only then which model and which index.

Q · The store has data and you need a schema. How does the loop get from plain-English requirements to a model you can hand to the database domain, and what decides the indexes before any query is slow?
Problem Solving and Backend

User Workflow → Request → Business Rules → State → Dependencies. The backend is where the workflow becomes a request, the request meets the rules, the rules change state, and the state depends on things outside the process. Each arrow is a question; the backend domain answers them.

Q · A workflow in the store has to become an endpoint. How does the loop get from "the customer checks out" to a request whose rules, state changes and external dependencies are all named, and what does it hand to the backend domain?
Problem Solving and Frontend

User Goal → Interaction → State → UI. The screen is the last step. What the user is trying to do, the interactions that get them there, and the state each interaction reads and changes come first — and they decide the UI far better than a component library does.

Q · A screen in the store has to be built. How does the loop get from what the user wants to the state the screen holds and the interface that shows it, and what does it hand to the frontend domain?
Problem Solving and System Design
▶ lab

Requirements → Scale → Bottlenecks → Architecture. Architecture is the last step, and the reflex starts there. The requirements say what must hold; the scale says how much load; the bottleneck is what actually breaks first under that load; the architecture is the smallest change that relieves it.

Q · The store has to "scale". How does the loop get from that word to an architecture decision with evidence behind it, and what does it hand to the architecture and distributed-systems domains?
Problem Solving and ML

Business Problem → Target → Data → Metric → Model. The model is the last step, and the one the word "AI" makes people start from. The target is what you are actually predicting, the data is whether you can, the metric is what "good" means to the business — and the ML domain teaches each once they are named.

Q · Someone wants to "use ML" in the store. How does the loop get from that request to a formulated problem with a target, data, and a metric — or to the finding that ML is not the answer — and what does it hand to the ML domain?
Problem Solving and Agentic Systems

Agentic architecture should emerge from requirements, the way any architecture should. Do not start with a multi-agent system; start with one deterministic workflow and ask, at each step, why it is insufficient. The answer names the one place an agent is needed — or finds that none is.

Q · Someone wants the store's support assistant to be "agentic". How does the loop get from that word to a workflow with a reason for every non-deterministic step, and what does it hand to the agentic-engineering domain?