Contents
The Problem
Every execution cycle adds weight. Each feature adds lines of code. Each fix adds conditional logic. Each decision becomes precedent that constrains the next decision. Each document joins a growing corpus where old versions sit alongside new ones, contradictions multiplying as context changes. Nothing leaves without deliberate removal, so systems grow monotonically -- they only get heavier.
I've watched this play out firsthand. The standard framing calls it "technical debt" and treats it as a maintenance problem: we took shortcuts, now we pay them down. But that framing carries a hidden assumption -- that everything accumulated was intentional and still relevant. Most of the time it's not. Code was written for requirements that changed. Decisions were made under assumptions that proved false. Patterns were adopted for contexts that no longer apply. It's not debt. It's irrelevant accumulation masquerading as something that needs careful remediation when it really just needs to be released.
The deeper problem is familiarity blindness. Long engagement with a system makes you stop seeing it. Components that should be questioned become invisible. "That's just how it works" replaces "should it work that way?" Sunk cost attachment locks in past decisions -- "we already built it this way" blocks the better question: "should we rebuild it differently?" And the original reasons for decisions fade while the decisions themselves persist, making them impossible to question because the context is gone. High-output execution forces a dilemma: pause for evaluation and break momentum, or keep running and let accumulated constraint drag you down. I needed a third option.
What Regroup Actually Is
Regroup is a scheduled interval mechanism where I deliberately cut off the past and view all accumulated work as if encountering it for the first time. It operates at two scales: regular regroups every two weeks for quick directional checks, and major regroups every 30-45 days for deep architectural evaluation. The action is simple -- look at everything as new, question what exists, release what no longer serves.
What it provides:
- Structured fresh perspective -- scheduled intervals that force me to see my own system with new eyes, breaking through familiarity blindness
- Safe release through retrieval confidence -- the Foundation guarantees that anything truly critical either persists in protected storage or can be found if needed later
What it does not provide:
- Permission to skip evaluation when things are going well -- smooth execution is precisely when accumulation hides, making regroups more important, not less
- A substitute for the Foundation -- without retrieval confidence, release becomes too risky and the mechanism collapses into hoarding
The 80/20 principle applies here. Roughly 80% of accumulated artifacts remain relevant -- core functionality, current patterns, active documentation. The other 20% are release candidates: edge case handlers for abandoned cases, documentation for deprecated features, code for changed requirements. I don't need to evaluate every artifact. I focus on the likely 20%.
The Cutting-Off Operation
"Cutting off the past" is the core operation, and it follows a specific pattern with four moves.
Questioning assumptions. I ask "why does this work this way?" and answer from current requirements, not historical decisions. If the historical reason doesn't justify current continuation, the artifact becomes a release candidate.
Evaluating relevance. I ask "do I still need this?" and answer honestly. Features built for abandoned use cases. Documentation for deprecated patterns. Code for changed requirements. If it's no longer needed, it goes.
Identifying constraints. I ask "what's limiting me?" Often the answer is something accumulated -- a decision made early that now constrains options, an early architecture choice preventing current evolution. I identify these and evaluate them for release.
Accepting release risk. Some releases might prove wrong. The artifact might be needed later. Regroup accepts this risk because the Foundation enables retrieval. If something was truly critical, it either resurfaces naturally through execution or I can find it when the need appears. Released artifacts that never come back were genuinely unnecessary. The release was correct.
When these four moves reveal that incremental release isn't enough -- when accumulated constraint exceeds what piece-by-piece removal can handle, or when foundational decisions need revision -- the regroup escalates to a clean-slate rebuild. I acknowledge that accumulated state is constraint and rebuild from current capability rather than continue patching around historical limitation.
What the Data Shows
The headline number: zero accumulated backlog across 596,903 lines of code and 2,561 raw commits. No formal backlog items persisting across regroups. No technical debt tickets logged. No "future consideration" lists accumulating. That absence is the regroup effect -- without these intervals, some portion of work inevitably piles up as deferred decisions, noted constraints, and identified-but-unexecuted improvements.
The PRJ-03 Guide clean-slate rebuild on January 28 is the clearest single demonstration. I reached a regroup, evaluated accumulated state, determined that rebuilding exceeded patching in efficiency, and executed a complete architectural restart: 24 commits in 4.2 hours, producing cleaner architecture than incremental work would have achieved.
PRJ-01 shows a different regroup pattern -- architecture evolution rather than rebuild. Phase 1 (October-November) used an outsourced foundation architecture. At a regroup transition, I evaluated that accumulated architecture against my own growing capability, then drove Phase 2 (December-January) with operator-driven evolution adapted to my understanding.
Velocity data confirms regroups prevented accumulated drag rather than imposing overhead:
| Month | Daily Commit Average | Pattern |
|---|---|---|
| October | 6.8 | Foundation building |
| November | 4.8 | Regroup/consolidation |
| December | 10.0 | Accelerating |
| January | 31.1 | Full velocity |
If accumulation created drag, later months would show decreasing averages. Instead, velocity increased by 4.6x from October to January. Rework patterns tell the same story from another angle: git/infra learning rework sat at 1.6%, integration calibration at 1.9%, and design polish at 7.2% -- each category representing regroup behavior where early patterns were evaluated and evolved rather than carried forward unchanged.
How to Apply It
1. Schedule the Intervals and Never Skip Them Every two weeks, run a brief evaluation: is the current direction still correct? Are recent additions still relevant? Any obvious accumulated constraints to release? Every 30-45 days, go deeper: should architecture change? Are foundational assumptions still valid? Do not skip because execution is going well. Smooth velocity is when accumulation hides best.
2. Practice the Fresh Perspective Look at your system as if seeing it for the first time. For every component, ask "why is this here?" If you can't answer from current requirements -- only from historical decisions -- it's a release candidate. Train yourself to see what familiarity has made invisible. AI tools can help here because they don't carry your familiarity blindness.
3. Release Confidently Using the Foundation Your Foundation and stash provide retrieval. If you release something that proves critical, you can find it. If you never need it again, you were right to release it. The key shift: release is the default posture during a regroup, not remediation. You're not "paying down debt." You're letting go of weight.
4. Consider the Clean-Slate Rebuild When a regroup reveals heavy accumulated constraint, don't reflexively patch. Evaluate whether rebuilding from current capability would be faster and produce better architecture than refactoring from historical limitation. Current capability -- especially with AI acceleration -- often makes rebuilds faster than past capability did. PRJ-03 Guide's January 28 rebuild proved this: a single 4.2-hour session produced cleaner architecture than months of incremental patching would have.
References
- Koch, R. (1998). The 80/20 Principle. Currency. Based on Vilfredo Pareto's observation of wealth distribution.
- Keating, M.G. (2026). "Foundation." Stealth Labz CEM Papers. Read paper
- Keating, M.G. (2026). "Pendulum." Stealth Labz CEM Papers. Read paper
- Keating, M.G. (2026). "Nested Cycles." Stealth Labz CEM Papers. Read paper
- Keating, M.G. (2026). "Sweeps." Stealth Labz CEM Papers. Read paper
- Keating, M.G. (2026). "Governor." Stealth Labz CEM Papers. Read paper