The Knowledge Flywheel: Don't Wait for Perfect to Start

Every iteration's artifacts become the next stage's input; knowledge accrues as a byproduct of real iteration. Fix anti-patterns by build only when they repeat.

At this point you've run the whole vibe coding chain end to end. The last thing is to let this practice grow itself: recycle each round's knowledge back into the next round's foundation, so you don't keep "re-selling the start"—you keep getting smoother.

After reading, you should be able to answer:

How does the knowledge flywheel "turn"?
Why is "don't wait for perfect to start" right?
How do you keep the anti-patterns from piling up?

How the flywheel turns

Capture each round: record what problem this round solved, what conclusion, what pit it hit anthropic-sdlc
Recycle: write it back into project context / rules / scripts so the next round takes effect automatically
Reuse rolling: the next round stands directly on the last round's ground, no need to re-trip

Once the wheel turns, AI understands you better every time than the last.

Don't wait for perfect

Don't build a "perfect knowledge base" before you start—that usually never starts
Knowledge is a byproduct of real iteration: capture the conclusions you reached while working; don't treat it as a separate project
Get it turning first, patch as it goes—far more effective than hoarding a big reveal

Fix anti-patterns, don't build them

One-off problem: fix it; don't rush to write a rule
Recurring: then crystallize into a rule / script
Never happened: don't pre-build; that's not guidance, it's baggage

Put a bar on "building": only when the same thing repeats is it worth hardening into a rule. Otherwise the rulebase bloats and nobody trusts it.

Every manual intervention is a knowledge gap

The flywheel's input does not only come from retrospectives; it comes from every moment you had to take over from the AI. When the model gets stuck or does it wrong, ask yourself first: what do I know that it does not? Each intervention exposes a knowledge or data gap; add that piece of context to the knowledge base and, ideally, next time there is nothing to take over. Harvest interventions as signals instead of enduring them as tax.

Keep the flywheel clean

Rules that grow forever need periodic deletion; like code, they rot
Keep only "information outside the code that shapes judgment"; don't copy what the code can express
Tag entries with status/source; judge applicability before reading
Pick the carrier by its update path: files plus Git first — knowledge, code, tests, and indexes enter the same MR together, get reviewed together, share one version after merge, and take effect on merge; RAG or knowledge graphs serve only as retrieval supplements, because after code changes they run the long chain of sync, chunking, and re-indexing, and update lag is the window of staleness taotian-price360

Get the flywheel turning and turning clean, and vibe coding becomes a "sustainable collaboration," not just a trick. Next, make it survive changing people and sessions: someone else can pick it up.