Local Coding Models Need Smaller Loops

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-coder-local-model-agent-loop-creators

1. What does the principle 'name the job before naming the tool' recommend?

   1. Define the desired outcome and requirements before selecting how to implement it
   2. Write the tool's code before defining what problem it solves
   3. Always specify which AI model to use before describing what you want to accomplish
   4. Use the most powerful available tool regardless of the specific task

2. A developer wants an AI to add user authentication to their application. They ask the model to 'rewrite the entire backend with authentication.' Why is this too broad for a local model?

   1. The task scope is too large for the model to complete accurately in one pass
   2. Local models cannot handle authentication code
   3. Local models require exact code to be provided first
   4. The developer should ask for the frontend and backend separately

3. When testing an AI-generated feature, why should you 'run the result as a user, not as a fan of the tool'?

   1. Testing from a user perspective reveals actual usability issues rather than confirming the tool worked
   2. Developers should never celebrate successful AI outputs
   3. You should only test features you personally requested from the AI
   4. AI-generated code always works correctly when you believe in it

4. What three things should you inspect before sharing an AI-generated code change with others?

   1. The programming language, framework version, and IDE settings
   2. The diff, data access patterns, and failure paths
   3. The file size, creation date, and author
   4. The color scheme, variable names, and comments

5. What makes local model workflows different from cloud model workflows in terms of technical constraints?

   1. Cloud models cannot generate code, only local models can
   2. Cloud models require internet access while local models do not
   3. Local models are always faster than cloud models
   4. Context settings, tool-call templates, and hardware limitations matter more in local workflows

6. A local model generates code that appears to work but exposes sensitive database credentials in error messages. What did the developer fail to do?

   1. Inspect the failure path and data access before sharing
   2. Provide enough context for the model to understand security
   3. Ask the model to use a different programming language
   4. Use a more powerful local model

7. What does 'observable' mean when turning an AI demo into something safe for others to use?

   1. The code contains extensive logging statements only
   2. The code uses observable patterns from functional programming
   3. Others can see what the system is doing and verify its behavior
   4. The demo must be displayed on a screen

8. Why is 'reversible' an important property for AI-generated code that will be used by others?

   1. Others can undo changes if something goes wrong
   2. Reversible code can be run forwards and backwards
   3. AI-generated code cannot be made reversible
   4. The code must be written in a reversible language

9. What is the relationship between task scope and success when working with local coding models?

   1. Smaller, focused tasks are more likely to succeed because they fit within the model's limitations
   2. Task scope does not matter for local models
   3. Larger tasks always produce better results
   4. Only complex tasks benefit from using local models

10. A developer uses an AI to generate a data processing script. What question addresses 'what test proves the change works'?

    1. What specific input produces what expected output that validates correctness?
    2. Who will use the script once it's deployed?
    3. What programming language is the script written in?
    4. What happens if the script runs out of memory?

11. What does the lesson mean by 'AI can make a working demo quickly, but real skill is making it safe for others'?

    1. Only professional developers can use AI-generated code
    2. The difficult part is not generating code but turning that code into something secure, reliable, and usable by others
    3. AI demos should never be used in production
    4. AI demos are never safe to share

12. What does 'context window' refer to in local models?

    1. The memory allocated to store conversation history
    2. The time period the model has been running
    3. The amount of text the model can consider at once when generating a response
    4. The visible area of the code editor

13. When using Ollama or similar local models, what should you verify before relying on specific UI elements or plan limits?

    1. If other developers have used the same exact approach
    2. Whether the model supports Python code generation
    3. The current date is April 2026 or later
    4. The latest model documentation, since features and capabilities change frequently

14. What is an 'agent loop' in the context of local coding models?

    1. The code structure used for handling user input
    2. The version control commit history
    3. The infinite loop that can crash a program
    4. The repeated cycle of the AI receiving context, generating a response, and taking action based on that response

15. A developer gives a local model a vague request: 'make the app better.' What is the primary problem with this approach?

    1. The request is too vague—the model needs specific, bounded objectives to work effectively
    2. The developer should use a cloud model instead
    3. Local models cannot improve existing code
    4. Local models cannot understand the word 'better'