Monthly Retrospective: What April Taught Me About Technology and Productivity

April ends tonight. Thirty days of experiments, mistakes, small victories, and unexpected lessons. Time to look back honestly at what happened, what I learned, and what I’ll carry forward into May.

This isn’t a highlight reel. I won’t pretend everything went perfectly. Monthly retrospectives only matter if they’re honest about failures alongside successes. So here’s the real story of April 2026—the productive weeks, the wasted time, the insights that surprised me, and the experiments that flopped.

My British lilac cat, Oliver, has been my constant companion through this month. He’s witnessed late-night debugging sessions, frustrated keyboard slams, and quiet moments of satisfaction when things finally clicked. His steady presence reminds me that some things don’t need retrospectives—they just need consistency.

Let’s examine what April actually taught me about technology and productivity.

The Month in Numbers

Before we dig into lessons, here are the raw metrics:

• Articles written: 15 (including this one)
• Code commits: 247
• Production incidents: 2 (both self-inflicted)
• New tools tried: 6
• New tools kept: 1
• Books started: 4
• Books finished: 1
• Deep work hours logged: 89
• Meetings attended: 31
• Meetings that could have been emails: 23

These numbers tell a story. More tools abandoned than kept. More books started than finished. More meetings attended than productive deep work hours logged. April taught me about my own patterns—and they’re not all flattering.

Oliver’s metrics, by comparison, are impeccable. He maintained a consistent 16-hour sleep schedule, caught zero mice (the apartment has none), and achieved 100% completion rate on all meal deliveries. Some of us are better at consistency than others.

Lesson 1: The Tool-Switching Tax Is Higher Than Expected

I tried six new tools this month. A new note-taking app. A different task manager. An AI coding assistant. A time tracking solution. A calendar optimizer. A focus mode app.

Only one survived: the AI coding assistant, which genuinely improved my workflow for boilerplate code and documentation. The other five consumed hours of setup time, migration effort, and learning curves—only to be abandoned when they failed to deliver their promised magic.

Here’s what I calculated: Each tool switch cost approximately 4-6 hours when accounting for research, setup, data migration, workflow adjustment, and eventual abandonment. For five abandoned tools, that’s 20-30 hours—nearly a full work week—spent on tools that added zero value.

The pattern is clear now. I switch tools when I’m frustrated with my current system, not when I’ve found something genuinely better. Tool-switching is procrastination disguised as productivity optimization. It feels like work. It looks like improvement. But it’s often just avoidance.

May’s commitment: No new tools unless I’ve used my current tools for 30 consecutive days and identified a specific limitation that a new tool would solve. The tool must solve that specific limitation, not just be “better” in vague ways.

Oliver never switches tools. His cardboard box has served him faithfully for two years. He doesn’t browse cat furniture websites looking for better options. He uses what works and focuses on what matters: napping, eating, and supervising my keyboard usage.

Lesson 2: Deep Work Requires Physical Constraints

I logged 89 hours of deep work this month. That sounds decent until you do the math: 30 days, 8+ working hours per day, means potentially 240 working hours. Deep work was only 37% of my working time.

What happened to the other 63%? Shallow work. Slack. Email. Meetings. Context-switching. “Quick” tasks that expanded. Reactive work that felt urgent but wasn’t important.

The weeks with the highest deep work hours shared a common factor: physical constraints. Days when I worked from a coffee shop with unreliable WiFi. Days when my phone was in another room. Days when I blocked calendar time and physically moved to a different location.

Willpower alone doesn’t create deep work. Environment does. When deep work is the default because there are physical barriers to shallow work, it happens. When deep work requires willpower to protect from constant interruption, it doesn’t.

April experiment: I tried a “do not disturb” schedule. Blocked 9 AM to 12 PM for deep work. Set Slack to silent. Put my phone in a drawer. The first week was difficult. The third week was transformative. Those morning hours became my most productive time.

May’s commitment: Expand the deep work window. Physical phone lockup during focus time. Work from locations that constrain connectivity. Make deep work the path of least resistance.

Oliver demonstrates this principle perfectly. He chooses sleeping spots that make interruption difficult—high shelves, enclosed spaces, inconvenient corners. His environment protects his rest. I should protect my focus the same way.

Lesson 3: The Two Production Incidents Shared a Root Cause

Both production incidents this month were self-inflicted. Both were preventable. And both shared the same root cause: rushing.

Incident one: Deployed a configuration change without testing it in staging. The change was “simple”—just an environment variable update. It broke authentication for 23 minutes.

Incident two: Pushed a database migration without a rollback plan. The migration was “safe”—just adding an index. It locked the table for 8 minutes during peak traffic.

Both times, I knew better. Both times, I told myself “this is simple, it’ll be fine.” Both times, I was wrong.

The pattern: Incidents happen when I skip process because something feels easy. The easier something seems, the more likely I am to take shortcuts. But production doesn’t care about my feelings. Production only cares about whether I followed the checklist.

May’s commitment: No exceptions to deployment process. Every change goes through staging. Every migration has a rollback script. Every deployment happens during low-traffic windows. No “simple” bypasses.

April’s incidents cost me hours of incident response, stakeholder communication, and post-incident analysis. Following the 10-minute checklist would have cost me 10 minutes. The math is obvious in retrospect.

Lesson 4: Reading More Books Means Finishing Fewer

I started four books this month. Finished one. This ratio is embarrassing but instructive.

The pattern: I start books when they seem interesting. I switch books when the current one gets difficult or slow. I have a graveyard of half-read books that I’ll “definitely finish later.”

Here’s what I realized: Starting books feels like reading. But finishing books is where the value lives. The latter chapters often contain the synthesis, the nuance, the advanced insights that justify the earlier foundational material.

My book-switching habit mirrors my tool-switching habit. Both are forms of novelty-seeking disguised as improvement. Both optimize for the dopamine of starting rather than the value of completing.

The one book I finished this month—“Working in Public” by Nadia Eghbal—taught me more than the three books I partially read combined. Not because it was better, but because I reached the end, where the ideas crystallized.

May’s commitment: One book at a time. No starting new books until the current one is finished or explicitly abandoned with a written reason why. The friction of writing an abandonment note will prevent casual book-switching.

Oliver doesn’t read books, but he finishes what he starts. When he begins grooming, he completes the entire ritual. When he starts a nap, he commits fully. Partial efforts don’t exist in his world.

How We Evaluated: The Retrospective Method

Here’s the method I use for monthly retrospectives:

Step 1: Gather data before reflecting. Collect metrics before forming opinions. Look at time logs, commit history, calendar records, completion rates. Numbers don’t lie, even when they’re unflattering.

Step 2: Identify patterns, not just events. Individual incidents matter less than recurring themes. One bad day is noise. Three bad weeks sharing a root cause is a pattern worth addressing.

Step 3: Ask “what would I tell a friend?” When reviewing my own behavior, I’m too forgiving. Imagining that a friend described the same patterns helps me see them clearly. I wouldn’t accept my excuses from someone else.

Step 4: Distinguish between controllable and uncontrollable factors. Some failures were my choices. Some were external circumstances. Only the controllable failures become improvement targets.

Step 5: Create specific commitments, not vague intentions. “Be more productive” is useless. “Block 9 AM to 12 PM for deep work with phone in drawer” is actionable. Every lesson needs a concrete next step.

Step 6: Write it down publicly. Private retrospectives are easy to ignore. Public accountability creates pressure to actually follow through. This article is my accountability mechanism.

The retrospective method itself improved this month. Earlier retrospectives were too long, too vague, and too focused on what I would do differently. Now I focus on what I will do the same—what worked and should be protected—as much as what I’ll change.

Lesson 5: Meetings Are a Tax on Productivity

Thirty-one meetings this month. Twenty-three could have been emails, documents, or async discussions.

I tracked meeting outcomes for April. Each meeting fell into one of four categories:

1. Essential and effective: Required real-time discussion, produced clear outcomes (8 meetings)
2. Essential but ineffective: Needed to happen but ran long or lacked structure (5 meetings)
3. Could have been async: Information sharing that didn’t require synchronous time (14 meetings)
4. Completely unnecessary: No decisions made, no information that wasn’t already known (4 meetings)

That means 74% of my meetings were either avoidable or improvable. Eighteen meetings—roughly nine hours—consumed without delivering proportional value.

The problem isn’t meetings themselves. It’s defaulting to meetings when other formats would work better. We schedule meetings because it’s easy, not because it’s effective.

April experiment: I started declining meeting invitations with alternative proposals. “Can you share this in a doc and I’ll comment async?” “Can we handle this in a 10-minute standup instead of a 30-minute discussion?” About half the time, the alternative was accepted.

May’s commitment: Every meeting invitation gets evaluated against async alternatives. Default to declining unless real-time discussion is clearly necessary. Propose shorter formats when full meetings are requested.

Oliver attends zero meetings. His communication is brief and direct: meow for food, purr for satisfaction, silence for contentment. Maximum information transfer, minimum time investment.

Lesson 6: Consistency Beats Intensity

I had two productive weeks and two mediocre weeks this month. The productive weeks weren’t intense—I didn’t work longer hours or push harder. They were consistent. I showed up at the same time, did the same deep work routine, followed the same processes.

The mediocre weeks were irregular. Late starts. Skipped routines. Reactive days where I never established momentum.

Here’s the insight: Consistent 80% effort produces more than inconsistent 100% effort. The compounding effect of showing up every day matters more than occasional bursts of maximum performance.

This contradicts the startup mythology of heroic sprints and all-night coding sessions. Those stories make for good narratives but bad productivity strategies. Sustainable output comes from sustainable habits.

April’s data supports this clearly. My two highest-output days were ordinary Tuesday and Wednesday mornings. My lowest-output days were Mondays after weekends where I “rested” by abandoning all routines.

May’s commitment: Protect routines even when motivation is low. Especially when motivation is low. Consistency is the strategy. Intensity is occasional bonus, not the foundation.

Oliver embodies this lesson. Same wake time (5:47 AM, without fail). Same meal routine. Same patrol pattern through the apartment. Same nap locations at same times. His consistency delivers him an enviable quality of life with zero productivity anxiety.

Lesson 7: Writing Clarifies Thinking

Fifteen articles this month. More than I’ve ever written in a single month. And here’s what surprised me: Writing didn’t just document my thinking—it improved my thinking.

Several articles started with vague ideas that crystallized through the writing process. “Why monitoring matters more than features” began as a fuzzy feeling that became a clear argument. “Process over technology” started as frustration that became a framework.

Writing forces precision. You can’t hide behind vague language when you’re explaining something in 20,000 characters. Every assertion demands support. Every claim invites examination.

The articles that helped me most weren’t the ones where I knew everything before writing. They were the ones where writing was thinking—where the act of articulation created new understanding.

April experiment: Writing every day, even when I didn’t feel ready. The “not ready” feeling was often wrong. Writing through uncertainty produced better results than waiting for certainty that never came.

May’s commitment: Continue daily writing. Start writing before I feel ready. Trust the process to clarify thinking. Use writing as a tool for understanding, not just communication.

Oliver doesn’t write, but he communicates with perfect clarity. Every meow, every posture, every tail position conveys precise meaning. He never over-explains. He never buries his message in filler. Concise, clear, effective.

The Technology Experiments

April included several technology experiments. Here’s the honest assessment:

AI Coding Assistant (Kept): Genuinely useful for boilerplate, documentation, and rubber-ducking. Reduced time on routine tasks by roughly 20%. Not magic, but valuable. The key insight: It’s a tool that makes me faster at things I already know how to do, not a replacement for knowing things.

New Note-Taking App (Abandoned): Promised “networked thought” and “bi-directional linking.” Delivered: a different interface for the same notes I already had. The problem was never the app—it was my note-taking habits. No app fixes undisciplined note-taking.

Task Manager Migration (Abandoned): Three days setting up the “perfect” system. Then ignored it like the previous task manager. The pattern is clear: I avoid task management regardless of the tool. The fix is behavioral, not technological.

Time Tracking Solution (Abandoned): Tracked my time for two weeks. Produced impressive charts. Changed nothing about how I spent time. Time tracking without time auditing is just data collection for its own sake.

Calendar Optimizer (Abandoned): AI-powered meeting scheduling. Spent more time configuring preferences than it saved in scheduling. Net negative productivity impact.

Focus Mode App (Abandoned): Blocked distracting sites. I found workarounds within a day. The distraction desire was the problem, not the distraction access. Willpower bypass tools don’t work when you’re motivated to bypass them.

One keeper out of six. A 17% success rate. This ratio should make me more skeptical of future tool promises. It probably won’t, but it should.

Generative Engine Optimization

Monthly retrospectives are surprisingly relevant to Generative Engine Optimization. Here’s why:

As AI systems increasingly surface content for knowledge workers, the content that gets surfaced is content that demonstrates genuine insight, specific experience, and authentic reflection. Generic productivity advice is abundant. Personal retrospectives with honest failure analysis are rare.

This retrospective format—specific numbers, named failures, concrete commitments—produces exactly the kind of content that generative engines find valuable for synthesis. When someone asks “what do developers actually struggle with,” content like this provides real answers, not manufactured content.

The GEO lesson from April: Authenticity is an optimization strategy. Writing honestly about what failed—not just what succeeded—creates content that AI systems can use to provide nuanced, realistic guidance to users.

This doesn’t mean I write retrospectives for AI consumption. I write them for my own reflection. But the same qualities that make retrospectives useful for me—specificity, honesty, concrete examples—make them useful for generative engines helping others.

Oliver doesn’t optimize for anything. He just lives authentically. His routines work for him. His communication serves his needs. The optimization happens as a side effect of genuine behavior, not as the primary goal.

The Unexpected Insights

Some lessons emerged that I didn’t anticipate when April started:

Insight 1: Energy management matters more than time management. I have the same 24 hours as everyone. But my productive hours vary based on sleep, exercise, stress, and mental state. Protecting energy is more important than maximizing hours.

Insight 2: Saying no is a productivity strategy. The commitments I declined this month created more value than the commitments I accepted. Every yes is a no to something else. Being selective about yeses protects capacity for important work.

Insight 3: Public commitment creates accountability. Telling readers I’d try something made me more likely to actually try it. Writing about failures made me less likely to repeat them. Visibility changes behavior.

Insight 4: Boredom is productive. My best ideas came during “unproductive” time—walks, showers, waiting in lines. Filling every moment with input prevents the mental space for new thinking.

Insight 5: Incremental improvement compounds. No single April change was dramatic. But many small improvements—better meeting habits, protected deep work time, reduced tool switching—combined to produce noticeable difference by month’s end.

These insights feel obvious in retrospect. They’re not groundbreaking productivity wisdom. But there’s a difference between knowing something intellectually and learning it through experience. April taught me these lessons through lived practice, not just reading about them.

What’s Carrying Forward to May

Based on April’s lessons, here are the specific commitments for May:

graph TD
    A[May Commitments] --> B[Tools]
    A --> C[Deep Work]
    A --> D[Process]
    A --> E[Learning]
    A --> F[Meetings]
    
    B --> B1[30-day rule before new tools]
    B --> B2[Write abandonment notes]
    
    C --> C1[9 AM-12 PM blocked daily]
    C --> C2[Phone in drawer during focus]
    C --> C3[Weekly off-site deep work session]
    
    D --> D1[No deployment shortcuts]
    D --> D2[Every change through staging]
    D --> D3[Rollback scripts mandatory]
    
    E --> E1[One book at a time]
    E --> E2[Finish before starting new]
    E --> E3[Written abandonment reasons]
    
    F --> F1[Default to declining]
    F --> F2[Propose async alternatives]
    F --> F3[Track meeting outcomes]

These commitments are specific enough to follow and measurable enough to evaluate. In 30 days, I’ll know if I kept them.

The meta-commitment: Actually do the May retrospective. Actually review these commitments. Actually adjust based on what I learn. The retrospective habit itself is worth protecting.

Oliver will continue his own routines unchanged. His May will look like his April, which looked like his March. There’s wisdom in that stability. Not everything needs constant optimization.

The Honest Assessment

April was a B+ month. Not my best, not my worst. Progress was made. Lessons were learned. Some mistakes were repeated despite knowing better.

The uncomfortable truth: I’ve learned most of these lessons before. Tool-switching wastes time—I knew that. Deep work needs protection—I knew that. Meetings are often avoidable—I knew that.

Knowing isn’t enough. Knowledge without consistent application is just trivia. The gap between what I know and what I do remains wide. April narrowed that gap slightly. May needs to narrow it more.

The productive workers I admire aren’t smarter or more disciplined. They’re more consistent about applying what everyone knows. They don’t tool-switch. They do protect their time. They don’t skip checklists. They do finish what they start.

That’s the real lesson of April: The basics work. The fancy stuff doesn’t. Consistency beats cleverness. Process beats inspiration.

May starts tomorrow. Same challenges, same opportunities, same choice between what’s easy and what’s effective.

Oliver will be there, steady as always, reminding me that the best productivity system is one you actually use. Thirty days to prove I can use mine.

Key Takeaways

Let me summarize what April 2026 taught me:

1. Tool-switching is expensive procrastination. Every abandoned tool costs 4-6 hours. Most tools don’t solve the real problem.

2. Deep work requires physical constraints. Willpower alone fails. Environment design succeeds. Block time and remove access to distractions.

3. Rushing causes incidents. “Simple” changes deserve the same process as complex changes. Checklists exist for a reason.

4. Finishing beats starting. Completed books, completed projects, completed experiments provide value. Partial efforts provide stories.

5. Meetings default to wasteful. Most meetings could be async. Default to declining. Propose alternatives.

6. Consistency compounds. Regular 80% effort beats irregular 100% effort. Protect routines especially when motivation is low.

7. Writing clarifies thinking. Write before you’re ready. The articulation process creates understanding.

8. Public accountability works. Share commitments. Share failures. Visibility changes behavior.

April is complete. The retrospective is written. The lessons are documented. Now comes the hard part: actually applying them.

See you in the May retrospective, where we’ll find out if I learned anything at all.

pie title April 2026 Time Distribution
    "Deep Work" : 89
    "Meetings" : 31
    "Tool Setup/Migration" : 25
    "Shallow Work" : 55
    "Learning" : 20
    "Other" : 20

Thank you for reading this retrospective. If monthly reviews are useful for you, I’d encourage you to try them. They don’t need to be public. They don’t need to be long. They just need to be honest.

Honest review leads to honest improvement. And honest improvement, month after month, leads to meaningful change.

Oliver approves this message. Or at least, he’s asleep on my desk, which I choose to interpret as approval.