The Value Scales with the Cost of Redoing
Most of the conversation around AI coding assistants focuses on writing code. That makes sense — it is where these tools started. But the continuity engine underneath todo-mcp is domain-agnostic. It applies to any work that is long-running, multi-step, and interruptible.
And here is the thesis that makes non-code use cases stronger than the code ones: the further your work travels from the CPU — toward documents, physical hardware, and signed device builds — the more a write-ahead memory pays off. Because redoing a code edit costs a recompile. Redoing a hardware observation costs a walk to the bench.
The Cost-of-Redo Gradient
| Work type | Redoing one step costs |
|---|---|
| Code edit | A recompile (seconds) |
| Test suite | Minutes to re-run |
| Document audit | Re-gathering every finding by hand (easy to miss one) |
| Hardware flash and observe | Walking to the bench, re-flashing, re-watching |
| Signed device build | A long provisioning, signing, and upload ceremony |
The same breadcrumb pattern and four-system architecture that protect a code refactor also protect these higher-cost workflows. The mechanics are identical. The payoff is larger.
Document and Manual Generation
Long technical documents and manuals are authored and audited across many sessions. The discipline is: gather every finding, fix them in a safe order, then render once.
A document audit produces dozens of findings. Lose one to a compaction or a crash and you either ship a defect or waste an expensive render cycle discovering it later. The document-finalize skill owns this gather-then-resolve discipline — every finding is logged to the durable store before any fixes begin. The build-wrapup skill records what changed.
What this looks like in practice: Finalizing a 60-page reference manual. The agent reads through and logs 23 findings. Mid-audit the context window compacts — but all 23 findings survive in the store. They get fixed in a rework-safe order, and the PDF renders exactly once. Nothing re-read. Nothing missed. No wasted renders.
The key insight: the cost of a missed finding is not the finding itself — it is the re-render, the re-review, and the risk of shipping the defect. The store eliminates the gap between finding and fixing.
Testing and QA
A test run yields a long list of failures. Triage and fixes span sessions. Verification playbooks are pass/fail checklists by nature — exactly the kind of structured, sequential work that a crash or compaction scrambles.
The baseline-health skill groups failing tests by likely root cause. The test-playbook skill authors numbered pass/fail exercises. The defect-fixing skill runs a disciplined cycle: collect symptoms, form hypotheses, identify root cause, fix, then verify every original symptom is gone — not just the ones the agent happens to remember.
What this looks like in practice: 40 failing tests grouped into 6 root-cause clusters. The agent fixes them across two sessions. The store tracks which clusters are resolved and which symptoms remain. The final verification confirms every original symptom is gone — a guarantee you cannot make if the symptom list lived only in a context window that compacted between sessions.
Embedded Hardware: Download, Flash, and Observe
This is where the cost of redoing a step becomes physically real. The work leaves the computer: edit, build, flash to a board, observe the behavior, diagnose, repeat. The hardware step is slow, physical, and cannot be containerized.
Re-deriving a hardware observation is expensive. You must physically re-flash the device and re-run the test to learn what you already knew before the crash. The p2-dev-cycle skill drives this loop. Breadcrumbs record what build is on the board and what it did — so a crash never means re-flashing just to remember where you were.
What this looks like in practice: Bring-up on a microcontroller. Build #5 is flashed and hangs at smart-pin initialization. The breadcrumb records exactly that, plus the current hypothesis (clock-setup ordering). The session dies. On resume, the agent already knows build #5 is physically on the board and what it did. No re-flash. No re-watch. Continue from the hypothesis.
When "just run it again" means walking to the bench, memory matters most.
iOS and Device App Building
Apple's toolchain is long and ceremony-heavy: provisioning profiles, archive builds, code signing, simulator versus device, TestFlight submission. Each stage produces artifacts the next stage depends on. Losing your place mid-release means repeating expensive steps that may take minutes or longer to run.
The store tracks which stage you reached and what each produced. A provisioning-profile mismatch at the upload step does not mean rebuilding the archive from scratch — the breadcrumb captures the stage and the exact blocker, so tomorrow the agent resumes at the step that failed.
What this looks like in practice: A release run. Archive built, signing complete, then stuck on a provisioning-profile mismatch before upload. The breadcrumb captures the stage and the blocker. Next session, the agent picks up at provisioning — not the first step of the pipeline.
Mixed-Product Projects: One Memory Across All of It
Real projects rarely stay in one lane. A single sprint might involve refactoring a driver (code), regenerating its manual (docs), running the test suite (QA), flashing the board to verify behavior (hardware), and updating the companion app (iOS).
You do not switch memory systems when you switch domains. The same store, the same skills, and the same context_resume stitch the multi-domain thread back together across any interruption. One continuity engine. Every product type.
Why This Matters for the Future of AI Agents
As AI agents move beyond pure code generation — into documentation, testing, hardware interaction, release management, and mixed-product workflows — the assumptions built for "just write code and recompile" stop holding. Steps become expensive. Observations become physical. Ceremonies become long.
The agents that handle this well will be the ones with durable, disciplined memory underneath. Not memory that lives in a context window and hopes for the best, but memory that is written ahead, structured by purpose, and maintained by consistent procedure.
The more expensive a step is to redo, the more the memory pays off.
Getting Started
Todo-MCP works the same way regardless of what you are building. The same breadcrumb pattern that protects a code refactor protects a document audit, a test triage, a hardware bring-up, or a release pipeline. If you are working on anything that spans sessions and has steps you would rather not repeat, the four-system architecture is built for exactly that.