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EVERYDAY PRACTICE · STARTER PROJECT

Homework sprint planner

Turn a fictional student's task list into a realistic after-school study plan.

Beginner-friendly path · 7 guided lessons · editable JavaScript · runs in your browser · no API key

START WITH A QUESTION

Can a planner notice that your task list will not fit?

A neat-looking timetable might hide an overlap. Predict which check could expose it before accepting the plan.

Reveal what to look for

The final workspace compares durations with available blocks and reports tasks that do not fit. An honest gap is useful output.

Start from the basicsExplore the path

7 guided lessons · learn at your pace

YOUR FINISHED BUILD

Homework sprint planner

A proposed schedule plus unscheduled tasks and reasons. Keep estimates visible so the learner can change them.

WHY THIS FIELD USES THE IDEA

Everyday practice: turn time estimates into explicit constraints you can inspect and change.

SEE THE IDEAChoose a step

See the idea before you code it

Read the input

Anonymous task labels, estimated durations, deadlines, available time blocks, and explicit preferences—all supplied as fictional examples.

function solve(input, tools) { /* your workflow */ }

Click a step to inspect this project's flow. The final workspace runs your JavaScript with original, authored records and checks.

A COMPLETE PATH TO YOUR BUILD

One small step at a time.

You can begin at the first lesson, resume shared skills you already practiced, or open the project IDE when you are ready.

01 · UNDERSTAND

Start with the essentials

No coding experience needed. These common lessons stay completed across every project.

  1. 1A model is one part of the systemFirst understand a model, a fixed workflow, and an agent that chooses its next tool call.
  2. 2Your first few lines of codeStart from zero: read values, objects, functions, and the result your code returns.
  3. 3Make a rule you can testWrite a small condition, change the input, and observe how the output changes.
  4. 4Turn an idea into a clear goalTurn the project's deliverable into a specific goal, allowed actions, and a visible stopping rule.

02 · PRACTICE

Learn the tools your project needs

Short explanations, clickable diagrams, and a small coding lab for each skill.

  1. 5A tool needs a clear contractRead the exact data a task tool returns before calculating a plan.
  2. 6Return something another program can useRepresent scheduled and unresolved tasks separately so neither disappears.

03 · BUILD

Make your project work

Follow the project-specific ideas, insert the explained snippets, and inspect your real execution trace.

  1. 7Homework sprint plannerBuild and test your own version: A proposed schedule plus unscheduled tasks and reasons. Keep estimates visible so the learner can change them.

04 · TEST & REFLECT

Try to break it. Explain what holds.

3 runnable cases check the final project. Passing them demonstrates these examples, with limitations still to explore.

Change one thing

Increase a task duration and check that the revised schedule does not overlap or quietly drop an unfinished task.

Keep a useful learning record

Which time estimate would you verify with a person before relying on this plan?

Open project IDEReflect & save your notes
Inputs, checks, and project boundaries
Your starting material
Anonymous task labels, estimated durations, deadlines, available time blocks, and explicit preferences—all supplied as fictional examples.
What your agent must check
  • Use deadline order and advance the slot cursor.
  • Do not join two allowed blocks across blocked time.
  • Show unscheduled work without altering its estimate.
Keep the scope clear
No homework completion, real calendar changes, messages, grades, or private accounts. Time estimates are assumptions, not guarantees.
Research behind this project path

Original explanations and authored practice records draw on these research and engineering ideas. The source organizations do not endorse this course or supply its fictional results.

Anthropic · Building effective agents

Distinguish fixed workflows from model-directed actions; start simply, use tool observations as feedback, and set stopping conditions.

The article notes that its tooling landscape has changed. Its patterns do not establish that a more autonomous or complex system is better for every task.

Anthropic · Writing effective tools for agents — with agents

Design distinct tools with clear parameters, relevant returned information, and evaluations of how the agent actually uses them.

A description or schema does not guarantee the right action. A live tool can return different data for the same arguments as its environment changes.

Your JavaScript really runs. The model decisions and school data are authored simulations, so you can learn without an API key. Every workspace also includes a separate real SDK example to explore next. Passing the lab’s cases is practice, not proof that an agent is ready for real-world use.