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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsThere is no universal winner between driving and delegating work to an AI coding agent. The practical choice is whether you can verify the resulting change more cheaply than you could create it yourself. Driving keeps implementation close to your mental model; delegating can produce a large, reviewable diff quickly, but shifts time into checking assumptions, testing behavior, and repairing integration problems.
That distinction comes from Simple Memo’s first-person account in “Driving vs. delegating: I timed two ways of pairing with AI”, published on DEV Community on July 31, 2026. The account covers a few dozen tasks in one solo iOS codebase, not a controlled benchmark. Its value is a decision framework, not a universal speed ranking.
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Driving and delegating are different working postures
The terms describe where the developer keeps implementation control and how much work is handed to the model.
Driving: you author the change and ask for bounded help
In a driving posture, you write most of the code. The AI assistant may complete a line, suggest a function, explain an API, generate a small test, or point out a likely error. You decide the design and maintain a running understanding of the surrounding code while the change is being built.
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This does not mean refusing assistance. It means keeping the work in small enough increments that each suggestion is judged against context you still hold in your head. A driver can reject a locally plausible completion before it becomes part of a larger design.
Delegating: you describe a task and inspect an agent-authored diff
In a delegating posture, you describe an outcome, constraints, and acceptance checks, then let an agent make a broader change. The agent may edit several files, add tests, rename symbols, or restructure a module. You return later to read the diff, run checks, investigate assumptions, and integrate the result.
Delegation moves effort rather than eliminating it. Typing and navigation may fall, while review, correction, and reconstruction of context become more important.
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Generation speed and time to trusted, integrated code are separate measurements. A model can produce code in seconds while the developer still needs substantial time to understand whether it is correct, safe in the application’s state model, and compatible with neighboring components.
| Clock | What it measures | How the posture tends to affect it |
|---|---|---|
| Time to first output | When a visible implementation or diff appears | Delegation often wins for broad mechanical edits; driving can be slower because the developer types and steers each increment. |
| Time to understand | Reading the change and recovering the assumptions behind it | Driving can preserve context as the code evolves. Delegation can require a deliberate review pass, especially across unfamiliar files. |
| Time to verify | Running tests, inspecting behavior, and checking invariants | The result depends on the available checks. A visible UI or compiler error is cheaper to verify than a hidden concurrency or synchronization invariant. |
| Time to repair and integrate | Fixing defects, resolving conflicts, and fitting the change into the rest of the system | A large delegated diff can create more back-loaded work if its assumptions do not match the codebase. |
| Time to trust | When you can reasonably rely on the change in normal use | This is the meaningful finish line. It may occur long after code first appears. |
Simple Memo reports a vivid example: an agent rewrote a 300-line module in about a minute, followed by about two hours of cleanup. Those are the author’s timings from the solo iOS project, not independently verified measurements, and they should not be converted into a general productivity rate.
The decision rule: compare production cost with verification cost
Simple Memo’s central claim is that task size is not the deciding axis. In the author’s words, “The axis that actually decides which one is faster is not task size. It is verifiability.” The practical paraphrase is:
Delegate when checking the result is cheaper than building it. Drive when checking approaches the cost of authoring.
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The author states the rule more directly: “I delegate when I can check the result faster than I could have written it, and I drive when checking is as expensive as authoring.” This is a judgment about a particular codebase and your available checks, not a claim that an agent is generally faster.
A pre-handoff worksheet
Before assigning a real task, answer these questions in writing. If several answers are uncertain, keep the work in a driving posture or split it into smaller delegated units.
- What observable result proves success? Name a screen state, compiler result, test fixture, log sequence, or other concrete check.
- Can the check exercise the important behavior? A style review cannot establish a lock, transaction, cache, or synchronization invariant.
- How much context must the reviewer reconstruct? Count the modules, actors, persistence layers, external services, and historical decisions that affect correctness.
- What is the consequence of a missed defect? A harmless label mismatch and a lost queued write do not justify the same review burden.
- Can the work be rolled back cleanly? A small, isolated commit is easier to compare, test, and discard than a cross-cutting rewrite.
Delegation works best when the output has a cheap check
The strongest candidates are tasks where correctness is visible, mechanically testable, or bounded by explicit examples. The following cases are the author’s examples, not a guarantee for every project.
An isolated SwiftUI settings screen
A self-contained settings screen can be delegated when the acceptance criteria are concrete: the expected controls exist, labels and defaults are correct, navigation reaches the screen, and interactions produce the specified state changes. You can run the app, inspect the layout at the supported device sizes, exercise each control, and compare the result with a short checklist.
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Keep the assignment narrow. State the target view, the source of truth for each setting, accessibility labels, persistence behavior, and the exact states to inspect. Ask the agent to avoid unrelated refactors. Review the diff for accidental changes to shared styling or environment objects before accepting it.
Parser scaffolding with table-driven examples
Parser scaffolding is easier to delegate when the input and expected output can be expressed as a table. Give the agent representative valid inputs, malformed inputs, empty values, boundary cases, and the expected errors. Then run the table-driven tests and inspect any case that passes for the wrong reason.
The examples are valuable because they make the check cheaper than reconstructing the parser’s intent from implementation details. They do not replace tests for limits you have not described; an agent cannot infer every grammar rule or security constraint from a vague request.
A mechanical rename
A symbol or file rename can be a good delegated task when the compiler, search, and tests can reveal missed references. Define the old and new names, the scope, and any strings or serialized keys that must remain unchanged. After the edit, compile all relevant targets, search for the old identifier, and inspect generated or persisted representations separately.
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A compiler-clean rename is not automatically behaviorally safe. Names that cross a storage boundary, public API, or migration format need an explicit compatibility check.
Driving is safer when correctness depends on hidden context
Some changes look small but are governed by timing, ownership, ordering, or integration rules that are expensive to recreate during review. Simple Memo uses two examples from the solo iOS codebase.
Retry logic in an offline-first queue
In the author’s account, a retry-path change moved a state write outside a lock. The resulting code could look tidy and plausible in a diff while violating the queue’s synchronization invariant. Detecting that problem requires understanding which state is protected, which thread or actor can access it, and what interleavings are possible.
For this class of task, driving keeps the invariant visible while the code changes. Write down the protected state, allowed transitions, lock or actor boundaries, and failure sequence first. Make one conceptual change at a time, then exercise retries, cancellation, process termination, and concurrent enqueue/dequeue behavior. If you do delegate a narrow portion, retain ownership of the synchronization design and review every read and write against it.
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The author also cites a coordinated write into another app’s iCloud folder. Correctness there depends on integration behavior: file coordination, ownership, timing, conflict handling, and the other app’s expectations. A local unit test may not reproduce the conditions that matter.
Driving is appropriate when the check requires a live system context that is difficult to set up repeatedly. Define the handoff contract, observe reads and writes from both participants, test conflicts and interruption, and preserve a small change history so a regression can be isolated. Delegation may still help with a helper or test harness, but the integration boundary should remain under close human control.
How to delegate without turning review into archaeology
A broad request such as “refactor this subsystem” creates a large search space for both the agent and the reviewer. A bounded handoff makes verification cheaper.
- State the outcome. Describe the user-visible or system-level behavior that must change, not merely the files you expect to be edited.
- List invariants. Include thread or actor isolation, persistence rules, API compatibility, error semantics, performance limits, and anything that must not change.
- Define the allowed scope. Name directories, targets, modules, or symbols the agent may touch. Prohibit unrelated formatting and opportunistic refactors.
- Supply acceptance checks. Give commands or test targets that can run in the project, plus manual scenarios when automation is insufficient. Do not claim a check exists if you have not verified that it does.
- Require a concise change summary. Ask the agent to identify files changed, assumptions made, tests run, and known uncertainties. Treat this as a map for review, not as evidence of correctness.
- Inspect the diff in layers. Start with the file list and public interfaces, then examine state transitions, error paths, concurrency boundaries, and data-format changes. Only after that review stylistic details.
- Run independent checks. Use the project’s compiler, unit tests, UI tests, static analysis, and manual scenarios. A test generated by the same agent is useful but should not be the sole basis for trust.
- Integrate incrementally. Land isolated changes separately when possible. If the diff is too broad to explain, ask for a smaller patch rather than spending unlimited time reverse-engineering it.
- Record the decision. Note which checks established confidence and which risks remain. This makes a later regression easier to investigate.
How to drive a context-heavy change
- Write the invariant before the implementation. For a queue, state which transitions are atomic and what must be true after a retry. For a file handoff, state the ownership and conflict contract.
- Trace the existing path. Follow callers, state storage, error propagation, and lifecycle events before editing. The goal is to keep the relevant context active rather than reconstructing it later.
- Ask for local assistance. Use the assistant to explain a function, suggest a test case, or identify call sites, while retaining the design decision yourself.
- Make the smallest coherent edit. Avoid mixing a concurrency fix with renaming, formatting, and unrelated cleanup. Small diffs make causal review possible.
- Test the failure path deliberately. Trigger retries, cancellation, crashes, conflicts, offline transitions, and restart recovery when those events govern correctness.
- Review the final state against the invariant. Do not stop at passing tests; confirm that every state write and external operation still obeys the rule you wrote first.
Isn’t this just “use AI for boilerplate”?
Not exactly. “Boilerplate” is a rough proxy for cheap verification, not a definition of safe delegation. A large amount of generated code can be easy to accept if its behavior is isolated and mechanically checked. Conversely, a ten-line change can be dangerous when it alters a hidden invariant.
The better classification is based on the cost of knowing that the result is right:
| Task characteristic | Likely posture | Reason |
|---|---|---|
| Visible output with a short manual checklist | Delegation is plausible | The reviewer can inspect the important behavior directly. |
| Deterministic input/output with strong fixtures | Delegation is plausible | Examples provide an inexpensive acceptance test. |
| Compiler-verifiable symbol movement | Delegation is plausible | Builds and searches expose many omissions. |
| Shared mutable state or lock ordering | Drive or delegate only a tightly bounded helper | The central risk is an invariant that style review may miss. |
| Cross-process, cloud, or third-party integration | Drive the boundary | Correctness depends on context that local generation cannot establish. |
| High-consequence behavior with weak tests | Drive until checks exist | Without a cheap check, delegation only increases review debt. |
Does delegating more make you worse at the hard parts?
The source does not establish an empirical answer. It reports a work-practice comparison, not a longitudinal study of skill, and provides no evidence that delegation either improves or degrades long-term ability. The prudent concern is context loss: if you repeatedly accept changes without understanding the model, you may become less familiar with the system that you later need to debug.
Valentin Monteiro raises a related question in a DEV Community comment: does verification become cheaper when you have recently worked in that part of the system, and more expensive as familiarity decays? The comment is a question, not a measured finding. It suggests a useful operational response: treat familiarity as a variable in your worksheet.
Preserve familiarity deliberately
- Read the relevant design and data-flow notes before delegating a change in a subsystem you have not touched recently.
- Ask for a short explanation of existing invariants, then verify it against the code instead of accepting it as fact.
- Keep delegated diffs small enough that you can explain each changed branch.
- Periodically implement or drive a representative change yourself so that your mental model is refreshed.
- Leave tests, comments, and decision records that make the next verification pass cheaper for you or another maintainer.
These are risk controls, not proof that one posture produces better developers. The author’s own conclusion is narrower: the bottleneck was not merely how well a task could be specified; it was whether the result could be checked economically.
Measure your workflow without pretending it is a benchmark
You can learn which posture fits your project by recording task-level observations, while avoiding false precision. Track the same categories for a few weeks:
- Posture used: driving, delegating, or a split approach.
- Time until the first usable output.
- Time spent reading and understanding the change.
- Time spent on tests, manual checks, and debugging.
- Number and severity of revisions after the first review.
- Whether a later integration or production issue required reopening the work.
- Confidence in the acceptance checks, separately from confidence in the implementation.
Do not combine these observations into a universal generation rate. The author’s “few dozen tasks,” 300-line rewrite, one-minute generation, and two-hour cleanup describe one person, one solo iOS codebase, and one period in spring 2026. They are useful prompts for what to measure, not a controlled comparison of AI tools or developers.
Common failure modes and recovery steps
The diff is too large to understand
Stop reviewing line by line. Ask for a smaller patch divided by behavior, revert unrelated formatting, and inspect the public interfaces first. If you cannot explain the change’s control flow, do not merge it merely because tests pass.
Tests pass but the important invariant is untested
Name the missing invariant explicitly and add a check that exercises it, or switch to driving while you make the change. Passing tests establish only the behavior they cover; they do not validate an unmodeled lock, ownership, or integration contract.
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The agent changed assumptions silently
Compare the implementation with the handoff’s constraints and inspect call sites, persistence formats, error handling, and lifecycle boundaries. Require the agent to list assumptions in the next iteration, then confirm each one in the codebase.
Cleanup takes longer than expected
Use the cleanup time as evidence about verification cost, not as a reason to abandon delegation categorically. Identify which missing acceptance check caused the extra work. Add that check for future tasks or keep similar work in a driving posture.
Generated tests merely restate the implementation
Write expected outcomes independently from the agent’s code. Include edge cases and failure sequences selected from the domain, not only examples copied from the implementation. A test that would pass after reproducing the same mistake is weak evidence.
A compact decision checklist
Use this immediately before handing work to an agent:
- Can I state success in observable terms?
- Is there a fast, independent check for the behavior that matters?
- Are the relevant assumptions visible in the task description or tests?
- Can I isolate and roll back the change?
- Do I understand the subsystem well enough to recognize a plausible but wrong result?
- What is the worst consequence of a missed defect?
- Will reviewing this diff cost less than authoring the same result?
If the final answer is no or uncertain, drive the work or delegate only a preparatory slice such as call-site discovery, fixture generation, or a narrowly scoped helper. If the answers are yes, delegation is a rational experiment—but still finish at the trusted, integrated state rather than the first generated diff.
The practical conclusion
Driving and delegating are complementary ways to spend attention. Driving is valuable when correctness lives in context you must keep active. Delegating is valuable when the result is bounded and cheap to verify. The useful question is not whether the task is large, repetitive, or impressive to automate. It is whether your verification method can expose the important mistakes for less effort than writing the change yourself.
That is the limited but useful lesson of Simple Memo’s account: generation speed is only one clock. Choose the posture that minimizes the cost of reaching code you can explain, test, integrate, and trust.
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