1) The “Expert Logic” Loop (use this every time)

1. Frame the problem

   • What’s the exact goal? Success criteria? Constraints (time, memory, SLAs, security, compliance)?

   • Inputs → Outputs (types, ranges).

2. Model the domain

   • Name the core entities and their relationships. Draw a 5-min sketch.

3. Work small examples

   • 2–3 typical cases + 3 edge cases (empty, huge, invalid, concurrent).

4. Start with a brute-force baseline

   • Make it correct first.

5. Prove/defend correctness

   • Invariants, pre/postconditions.

6. Measure, then optimize

   • Identify the hot path; change one thing at a time.

7. Automate guardrails

   • Tests, linters, type checks, CI.

8. Document the decision

   • “We chose X over Y because Z.”

Copy-paste template for tickets/notes:

Goal:
Constraints:
Inputs/Outputs:
Data model:
Examples & edge cases:
Algorithm (baseline):
Correctness argument:
Complexity:
Risks & mitigations:
Tests to write:

2) Core skills to train (with minimal fluff)

• Data structures & algorithms: arrays, hash maps/sets, stacks/queues, heaps, trees/graphs, sorting, two-pointer, sliding window, DP.

• Decomposition & interfaces: split into small pure functions with clear contracts.

• State & invariants: what must always be true? (e.g., balances never negative, totals consistent).

• Error handling strategy: fail fast vs graceful, retries, idempotency.

• Persistence logic: SQL schemas, indexes, transactions, isolation.

• Concurrency & async: race conditions, locks, queues, backpressure.

• Testing pyramid: unit → integration → contract → e2e; generate fixtures; property-based tests for core logic.

• Observability: logs with context, metrics, traces, feature flags.

• Security & compliance basics: input validation, authn/authz, secrets, audit logs.

3) Deliberate practice plan (30/60/90)

Next 30 days (Foundations)

• 30–45 min/day: one focused DSA pattern (e.g., “sliding window week”).

• Implement 10 utility functions from scratch (parsers, validators, cache LRU, rate limiter).

• Start a kata repo: each function with tests, docstring, complexity note.

Days 31–60 (Systems & correctness)

• Build one small service end-to-end: REST API + DB + tests + CI.

• Add property-based tests (e.g., Hypothesis in Python) for core logic.

• Introduce observability: structured logs, a few metrics, error budgets.

Days 61–90 (Scale & architecture)

• Add queues (retry, DLQ), idempotency keys, pagination, indexing strategy.

• Load test & profile; write a short decision record after each change.

• Perform and receive two code reviews/week with a checklist (below).

4) Code review checklist (quick but sharp)

• Clarity: names, function length, comments explain why, not what.

• Correctness: tests reflect edge cases; invariants visible.

• Complexity: simplest thing that works? unnecessary generalization?

• Safety: errors, timeouts, retries, idempotency, input validation.

• Data: indexes match queries; migrations are safe; N+1 avoided.

• Perf: measured evidence for changes.

• Observability: actionable logs/metrics for each critical path.

5) Thinking tools you can reuse

• Specification by example: write 5 examples before coding.

• Design at two levels: (a) pure core logic (framework-free) (b) thin I/O layer.

• Invariant board: keep a running list: “must always hold”.

• Pre-mortem: “It failed in prod—why?” design mitigations upfront.

• Complexity guard: if a function > 25 lines or > 2 branches, split.

6) Practice menu (pick a few)

• Implement:

  • LRU cache, circuit breaker, token bucket rate limiter, top-K with heap, interval scheduler, consistent hashing, CRDT counter (if collaborative features), pagination with keyset.

• Data/AI tilt (fits your background):

  • A deduplication service for documents (shingling + MinHash).

  • Feature store: validate schemas, backfills, drift checks.

  • EMR mini-system: patients, meds, prescriptions with audit log, dosage checks, and conflict alerts (your pharmacy expertise = superpower).

7) “Expert logic” coding style (Python-flavored)

• Prefer pure functions for core rules; keep side effects at the edges.

• Use type hints and pydantic/dataclasses for contracts.

• Encode rules as data when possible (tables, config) so logic is testable and auditable.

• Write property tests for invariants; fuzz invalid inputs.

• Add a profiling path (cProfile, time.perf_counter) around hot logic.

8) Weekly rhythm (lightweight)

• Mon: select 1 pattern, write examples + tests first.

• Tue–Wed: implement baseline, prove correctness, measure.

• Thu: optimize only if metrics demand it.

• Fri: write a 10-line decision record + do one code review.

• Weekend: one real-world bug hunt in your own or open-source code.