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Programming foundations
Validate JavaScript inputs at the boundary
Turn unknown input into a small explicit contract before it reaches application logic.
Handle async failures without losing user work
Design pending, success, and failure states for a small asynchronous workflow.
Model UI state before adding features
Use an explicit state model to avoid contradictory screens and confusing retry behavior.
Use SQL constraints to prevent duplicate work
Let the database enforce uniqueness when concurrent requests can reach the same operation.
Write tests around meaningful user outcomes
Choose tests that catch failures a real user or operator would care about.
Build a form that works with keyboard and errors
Make labels, validation, and status messages part of the first implementation.
Validate a SaaS idea
Find a recurring problem worth testing
Look for recent repeated work and costly workarounds rather than broad enthusiasm for AI.
Ask customer questions that reveal actual work
Use a short interview to understand triggers, workarounds, and purchasing constraints.
Score SaaS demand without inventing certainty
Use a scorecard to expose missing evidence and select the next experiment.
Test an AI product manually before automating
Deliver a small result by hand to learn what quality and turnaround actually require.
Write a one-page MVP scope that excludes work
Define the smallest complete workflow with acceptance criteria and clear limits.
Set a price for the first self-serve test
Connect price to the job, costs, and a concrete purchasing decision.
Build a focused AI MVP
Define the contract around an AI feature
Specify inputs, evidence, output shape, and rejection rules before choosing prompts.
Keep model API keys on the server
Separate browser requests from credentialed provider calls and apply usage limits.
Make AI drafts reviewable with source evidence
Design output that helps a person check claims against the supplied material.
Set boundaries for untrusted model input
Treat source documents as data and limit the actions a model-driven workflow can take.
Handle model timeouts and retries deliberately
Keep expensive AI work bounded and avoid duplicate jobs when the network is uncertain.
Test data permissions with two accounts
Verify that every read, write, and download uses trusted ownership checks.
Test AI quality and cost
Build a small AI evaluation set
Collect representative and difficult examples before judging a prompt by a single demo.
Write an output rubric people can apply consistently
Separate factual support, completeness, usability, and style in your evaluation.
Test prompt changes for regressions
Compare candidate prompts against a stable baseline with failure cases and cost measurements.
Calculate AI unit economics from observed usage
Include retries, failed work, fees, and support when estimating margin.
Set a latency budget for an AI workflow
Measure the whole user-visible path and choose synchronous or queued processing deliberately.
Add spend guardrails before opening access
Limit input, output, concurrency, and aggregate spend at the application boundary.
Payments and digital delivery
Separate checkout redirects from payment proof
Use server-verified payment state before showing paid access or delivery.
Make webhook fulfillment durable and idempotent
Use unique order and job keys so retries do not create repeated fulfillment work.
Protect paid downloads with verified entitlements
Keep paid archives private and serve them only after checking access on the server.
Send delivery emails with a durable retry queue
Track intended email work, retry transient failures, and suppress obsolete messages.
Make refunds update access and revenue reporting
Handle cumulative refund state without counting the same amount repeatedly.
Create a sandbox payment and delivery test plan
Cover the whole purchase lifecycle without charging real cards or sending production mail.
Launch and grow organically
Write a landing page around one clear outcome
Connect the buyer’s task, product contents, evidence, price, and next action.
Build an optional email funnel around a useful sample
Give visitors value first and make consent, confirmation, and unsubscribe explicit.
Design a topic cluster around distinct user tasks
Connect a learning path to useful guides instead of producing many near-identical pages.
Check technical SEO for a small content site
Make useful public pages discoverable with coherent canonical URLs, metadata, and links.
Measure a funnel with explicit definitions
Separate visits, qualified actions, checkout starts, verified purchases, and refunds.
Run a weekly organic growth routine
Improve useful pages and acquisition based on observed demand rather than publishing volume alone.