Frequently Asked Questions About Lock-in
What platforms does Lock-in support?
Lock-in is purpose-built for macOS—leveraging native frameworks like AppKit, Core Analytics, and on-device ML accelerators (Apple Neural Engine) for ultra-low latency and battery-efficient monitoring. This deep OS integration enables granular, permission-respectful observation—no screen recording, no keylogging—just intelligent, privacy-preserving pattern analysis. A Windows version is under active development, with early access expected Q4 2024.
How does Lock-in help improve focus and prevent distractions?
Lock-in operates on a predictive, not reactive, model. Using lightweight behavioral fingerprinting, it establishes your baseline for focused work—then flags deviations tied to *cognitive cost*, not just activity type. For example, it recognizes that reviewing 20-line PR diffs may demand higher sustained attention than drafting 200-word emails—and adjusts nudge sensitivity accordingly. Its nudges are designed to be frictionless: a soft chime, a brief text overlay, or a subtle desktop tint shift—never full-screen interruptions. And because it learns continuously, its accuracy improves with every session.
What kind of insights does Lock-in provide after work sessions?
Each session ends with a distilled Insight Card: a single-page visual summary highlighting your strongest focus window, top three distraction sources (with timestamps and contextual tags), your Recovery Efficiency Ratio (how quickly focus rebounded post-break), and one high-leverage recommendation—backed by behavioral science and personalized to your data. Weekly reports surface longitudinal trends: “Your average deep-focus duration increased from 28 to 41 minutes over 3 weeks,” or “You achieve peak clarity 92 minutes after waking—schedule your most demanding tasks there.” All data remains on-device unless explicitly exported.
Is Lock-in suitable for different types of work and professions?
Absolutely. Lock-in’s architecture is role-agnostic but context-aware. It doesn’t classify you as “developer” or “designer”—it classifies your *current workflow* as “high-cognitive-load sequential reasoning,” “creative ideation with open-ended exploration,” or “multi-source information synthesis”—then applies the appropriate intervention logic. From academic researchers managing literature reviews to UX designers conducting user interviews to legal professionals drafting contracts, Lock-in adapts its nudges, break suggestions, and insights to match the cognitive signature of the task—not the job title.
How does Lock-in prevent burnout while increasing productivity?
Burnout isn’t caused by too much work—it’s caused by too little recovery *in the right way, at the right time*. Lock-in combats this by embedding recovery into the workflow itself. Its adaptive break system doesn’t wait for exhaustion—it watches for early neurophysiological proxies (e.g., declining cursor precision, increased scroll depth without content engagement) and invites micro-recovery *before* stress accumulates. Over time, users develop heightened interoceptive awareness—the ability to recognize their own fatigue signals earlier—turning Lock-in from an external coach into an internal compass. The outcome isn’t just more hours worked—it’s more resilience built, and more energy preserved.