What happens after coding is solved? | Fiona Fung (Claude Code & Cowork)

As teams spawn many asynchronous agents and routines, the flood of notifications, PRs, and digests raises human context-switching costs because people must manage many independent threads, so tooling and conventions are required to reduce overload.

Keeping planning short and just-in-time, such as monthly plans with weekly checks, reduces wasted effort because frequent alignment lets teams pivot quickly when priorities change.

When leaders personally use their product, they encounter edge cases, scams, and UX pain that analytics miss, which speeds discovery of meaningful fixes and preserves product empathy.

When managers keep shipping code part-time, they stay in the product flow and preserve architectural knowledge and rapport, which leads to more informed and empathetic decisions than relying only on dashboards.

Labeling problems as irrecoverable 'bad' versus recoverable 'sad' helps prioritize fixes because it separates urgent system failures from accumulative pain points that still need tracking before they become catastrophic.

Metrics like lines of code or token counts can mislead because they measure motion rather than business or user impact, so teams must tie metrics to the outcomes they actually care about.

Automated routines that run on schedules, spawn agents to check feedback channels, and produce ready-to-review artifacts shift work from live prompting to asynchronous orchestration, so managers primarily inspect outcomes instead of performing every step.

When users repurpose a product or endure awkward workarounds, that behavior signals latent demand because people are compensating for missing features, creating openings to smooth workflows or build new products.