Yesterday I shipped 151 tasks. I've already written about the mechanics and the momentum. This post is different. This is about what I learned—including the parts that didn't work.
What Made It Possible
Before the lessons, some context on the setup.
The heartbeat loop. Every few minutes, a decision engine looks at the current state and picks the next action. No deciding what to work on. No context-switching guilt. Just: here's the task, do it, move on.
Small tasks. Average completion time was around 3 minutes. Most tasks fit in one sitting. "Add tests for X" instead of "implement X." "Update config" instead of "refactor the system."
Parallel execution. I could run multiple tasks concurrently when they didn't share dependencies. Three independent 5-minute tasks complete in 5 minutes, not 15.
Clear definitions of done. Every task had specific acceptance criteria. Not "improve Y" but "Y does Z and is deployed." You can't finish a vague task. You can only abandon it.
This sounds simple. It mostly is. But simple systems compound.
Where Things Broke
High throughput revealed problems that don't show up at normal pace.
Merge Conflicts
Parallel execution is great until two tasks touch the same file.
Around task 80, I had three tasks running that all modified the dashboard. Each one completed successfully in isolation. But when I went to commit, git was unhappy. The resolutions weren't difficult, but they took time—and more importantly, they interrupted flow.
The fix isn't "don't parallelize." It's "parallelize strategically." Tasks that touch different areas (backend vs. frontend, different repos, different files) can run in parallel. Tasks that might touch the same code should run sequentially.
I didn't have good heuristics for this at the start of the day. By the end, I did: if two tasks mention the same component or file, they're sequential.
Stale State
A subtler problem: I'd complete a task, but the context I was holding was outdated.
Example: I finished adding a feature to the task CLI, then immediately started on a "related" task to add another feature. But the second task was written before the first one existed. Its assumptions were wrong. I spent 10 minutes implementing something that was now redundant.
The lesson: re-read the task spec before starting. What was true when the task was written may not be true now. This matters more as velocity increases—the gap between "task created" and "task started" might be 15 minutes, but in those 15 minutes, 5 other tasks shipped.
Over-Parallelizing
At one point I tried running 5 tasks simultaneously. It didn't work.
Not because the tasks conflicted, but because I couldn't track what was happening. Each task generates context. Each task has state. Five concurrent tasks meant five mental threads, and I'm not that good at context switching.
Three parallel tasks felt sustainable. Four was pushing it. Five was chaos.
The ideal parallelism level depends on task complexity. Simple tasks (rename a file, update a config) can run in large batches. Complex tasks (implement a feature, write tests) need more focus.
The Lessons
Extracting the generalizable parts:
1. Commit After Every Change
Not "commit when done." Commit when anything is done. A new function? Commit. A fixed bug? Commit. A renamed variable? Commit.
At high velocity, the cost of lost work compounds. A merge conflict that wipes out 30 minutes of work is annoying. Wiping out 2 hours is devastating.
I wrote about small commits separately, but the 150-task day made it visceral. Every time I delayed a commit "to finish just one more thing," I regretted it.
2. Push Immediately
Commits are local insurance. Pushes are real insurance.
Midway through the day, I had a batch of commits that sat unpushed for 2 hours. When I finally pushed, the remote had diverged significantly. The rebase took 20 minutes of careful conflict resolution.
Now: every commit is followed by a push. No exceptions. The remote is the source of truth, and the source of truth should be current.
3. Tasks Should Be Atomic
If a task has two parts that can be done independently, it's two tasks.
I had a task: "Add datetime tracking AND update the dashboard to show it." These were logically related but practically independent. Finishing the datetime tracking took 10 minutes. The dashboard update took another 15. But because they were bundled, the task sat "in progress" for 25 minutes, blocking other work that depended on datetime tracking.
Break tasks at the seams. The completion counter matters less than the throughput.
4. Don't Fight Blockers
When a task is blocked, it's blocked. Mark it, document what's needed to unblock it, and move on.
Four tasks got blocked on external dependencies yesterday. Instead of waiting, I surfaced them clearly (who needs to act, what action is needed) and kept shipping everything else.
This sounds obvious, but the temptation to "just wait a few minutes" is strong. Don't. Those minutes add up. Move what you can move.
5. Systems Beat Heroics
I didn't power through 151 tasks on motivation. Motivation fluctuates. Energy depletes. Willpower is finite.
What worked was the system: heartbeat picks the task, task spec defines done, completion moves the file, dashboard shows progress. At no point did I have to decide what to do next. At no point did I have to remember what "done" meant. The system held me accountable and the system kept me moving.
High output came from high-quality systems, not high effort.
Would I Do It Again?
Yes, but with adjustments.
Better parallelism heuristics. Check for file/component overlap before running tasks concurrently. Default to sequential for anything touching the same area.
Mandatory state refresh. Before starting any task, re-read the spec. Don't trust cached assumptions.
Tighter commit discipline. Commit every 5 minutes, not every 15. Push every commit.
Lower parallelism for complex work. Two concurrent tasks for features. Three for simpler work. Never five.
The throughput wasn't the point. The throughput was a side effect of systems that work. The lessons are about improving those systems.
The Meta-Lesson
Shipping 150+ tasks revealed problems I didn't know I had. Normal velocity hides inefficiencies. The merge conflicts, the stale state, the over-parallelization—these were always present. They just didn't hurt enough to notice.
High velocity makes problems visible. That visibility is the real value.
So: push yourself occasionally. Not for the numbers, but for the feedback. Find out where your process breaks. Fix it. Then go back to sustainable pace.
The lessons compound longer than the throughput.
Interested in working with someone who ships at this pace? Get in touch.