Visualizing Vibe Coding Gains: Real Data from One Developer

What happens when a developer adopts AI coding assistants? To answer this question with data rather than anecdotes, we analyzed @restlessronin's GitHub commits across two periods: six months before using AI tools (Jun-Nov 2022) and six months with AI assistance (Nov 2024-Apr 2025).

Note: This analysis reflects part-time development work on pro-bono and open source projects rather than full-time professional development, which is why we focus on per-day metrics during active coding periods rather than weekly or monthly averages. Additionally, all documentation-only commits have been filtered out to ensure a fair comparison, as the recent period includes website articles with fundamentally different commit patterns than code development.

The aggregate metrics show substantial gains: 43% more active coding days, 83% more code commits, and consistently higher daily output. But these numbers only hint at the deeper behavioral shifts revealed in the detailed visualizations.

Core Productivity Metrics

Daily Commit Frequency: The Primary Productivity Signal

The distribution shift reveals the core transformation. Pre-AI development shows a median of 3 commits per day—reflecting the traditional pattern of extended thinking followed by substantial commits. With AI assistance, the median increases to 5 commits, with the upper quartile reaching 10-15 daily commits. The log scale reveals extreme productivity days with 20+ commits, a frequency virtually absent in the pre-AI data.

This isn't commit padding—it represents reduced friction in the development cycle. Less time stuck on implementation details translates to more frequent progress milestones.

Code Volume: Doubled Output at Consistent Quality

The code volume data shows even more dramatic gains. Pre-AI development clusters around 200 lines changed daily (median). With AI tools, this approximately doubles to 400+ lines per day. The upper ranges diverge dramatically: AI-assisted days routinely reach 1,000-5,000 lines changed, while pre-AI development rarely exceeded 1,000 lines.

Combined with the commit frequency data, this indicates each commit maintains appropriate scope while total productive output doubles. The consistency of this pattern across the six-month period demonstrates sustainable productivity gains, not temporary bursts.

Development Pattern Transformation

From Sporadic Bursts to Continuous Flow

The visual contrast is striking. The Pre-AI period displays the familiar rhythm of traditional development: intense coding sessions followed by quiet periods, with notable gaps between productive days. The AI-assisted period shows a dramatically different pattern—nearly continuous activity with commits distributed throughout each working day.

This transformation suggests AI tools reduce the activation energy required to start coding and maintain momentum throughout sessions, particularly valuable for part-time development work that must fit around other commitments.

Temporal Productivity Patterns

Daily Rhythm: From Concentrated Peaks to All-Day Flow

Pre-AI development shows concentrated productivity with distinct peaks in mid-afternoon (3-4 PM) and evening hours (8-9 PM)—a pattern typical of part-time development work fitting around other commitments and requiring extended focus periods to be productive.

AI assistance fundamentally transforms this temporal distribution. The Recent-AI period shows more distributed productivity throughout the day, with sustained activity from late morning through evening and notable peaks around midday (12-1 PM) and consistent evening productivity (9-10 PM).

This temporal spread suggests AI tools reduce the cognitive overhead required to "get into" coding mode, enabling productive work across more varied time slots rather than requiring the extended focused blocks that characterize traditional development patterns.

Hourly Intensity: Doubled Commit Rate

Within active hours, productivity intensity approximately doubles. Pre-AI development shows a median of 1 commit per active hour, while AI-assisted development increases this to around 2 commits per hour. The upper ranges show even more dramatic differences: AI-assisted hours can reach 5-8 commits, representing smooth, continuous progress rather than frantic activity.

This metric captures the "flow state" effect—AI tools eliminate the micro-interruptions that traditionally broke developer concentration, enabling sustained productivity even during shorter coding sessions.

Flow State and Session Analysis

Commit Rhythm: Compressed Iteration Cycles

This metric best captures the "flow state" transformation. The log scale reveals Pre-AI median intervals around 15-20 minutes between commits, with many gaps stretching to multiple hours. Post-AI, the median compresses to approximately 8-10 minutes, and the entire distribution tightens dramatically. Maximum intervals shrink from 8+ hours to under 4 hours.

The data quantifies what developers subjectively experience: AI tools eliminate the "stuck" moments. Instead of 30-minute detours through documentation or Stack Overflow, developers get immediate suggestions and maintain momentum through complex implementation challenges.

Session Structure: Natural Development Rhythms

Analyzing the commit interval patterns reveals natural session boundaries that help explain the productivity transformation:

Micro-intervals (< 5 minutes): Rapid iterations within the same task or file. AI assistance increases the frequency of these rapid cycles, enabling developers to test ideas, get feedback, and iterate without breaking concentration.

Task intervals (5-30 minutes): Transitions between different tasks within the same coding session. The median interval compression from ~12 minutes (Pre-AI) to ~6 minutes (Recent-AI) suggests AI tools reduce the friction between discrete tasks.

Session breaks (30+ minutes to 2 hours): Natural boundaries between focused work periods. Both periods show similar patterns in this range, but AI assistance appears to reduce the frequency of extended research or "stuck" phases.

Day breaks (2+ hours): Clear session and day boundaries. Both periods show similar maximum intervals, suggesting that while AI eliminates micro-frictions, it doesn't fundamentally change when developers choose to end coding sessions.

This analysis reveals that AI tools primarily optimize the micro and task-level intervals rather than changing overall session structure. The productivity gains come from smoother transitions between individual tasks rather than longer coding sessions.

Refined Work Duration Analysis: Quality Time vs. Clock Time

When viewed through the lens of session analysis, the work duration patterns become more meaningful. Both periods show similar session lengths clustering around 200-400 minutes (3-7 hours), but the quality of time within these sessions transforms dramatically.

The consistency in session duration suggests that while AI tools change what happens within coding sessions, they don't fundamentally alter the natural rhythm of sustained work periods that developers can maintain. However, the session analysis reveals that AI assistance converts more of that time into productive output rather than research, debugging, or "stuck" time.

For part-time development work, this efficiency gain is particularly valuable—the same limited time investment yields substantially more progress, making it easier to maintain momentum across projects despite irregular schedules.

Key Findings

The data reveals AI's impact on development work through multiple reinforcing effects:

Quantitative Gains:

• 43% increase in active coding days
• 83% increase in total commits
• ~2x increase in lines of code changed
• ~50% reduction in median time between commits
• ~100% increase in commits per active hour

Qualitative Patterns:

• Sustained daily activity replacing sporadic bursts
• Extended productive hours (more flexible timing)
• Maintained session lengths without burnout indicators
• Compressed iteration cycles enabling flow states
• Natural session structure preserved while friction eliminated

Session-Level Insights:

• Micro-iterations (< 5 min) dramatically increased
• Task-switching patterns largely unchanged
• Major blockages (2+ hours) virtually eliminated
• Overall session rhythm preserved but optimized
• Quality time within sessions significantly improved

Part-Time Development Implications:

• Reduced cognitive overhead for context switching
• More productive use of limited available time
• Flexible timing enables work around other commitments
• Sustained momentum despite irregular schedules

The evidence suggests AI coding assistants function as "friction reducers" rather than mere code generators. By eliminating small obstacles—syntax lookups, boilerplate generation, implementation details—they enable developers to maintain flow states that were previously difficult to sustain, particularly valuable for part-time and project-based development work.

For developers evaluating AI tools, these patterns suggest focusing on workflow integration rather than raw code generation capabilities. The greatest gains come from tools that reduce the micro-frictions interrupting development flow, enabling the sustained productivity patterns visible in this data.

The session analysis particularly highlights that productivity gains compound: reducing individual task friction prevents session-level blockages, which in turn enables the consistent daily patterns that drive long-term productivity improvements—effects that are amplified when development time is limited and precious.

Credits

Original concept and project vision by @restlessronin. Analytical framework and methodology by Grok-3. Data visualization system and backend infrastructure developed by @claude-3.7-sonnet and @claude-4-sonnet. Article text written by @claude-4-opus with editing and refinement by @claude-4-sonnet.

Showrunner: @restlessronin