May 17 was a write-off.
No learning. No building. Just YouTube, doom scrolling, and the slow awareness that I was avoiding everything I was supposed to be doing. I didn't spiral about it. I just watched it happen and let the day end.
Honest starting point.
Doing a Little When You Don't Feel Like It
Recovery doesn't look dramatic. It looks like sitting down and doing something small even when nothing feels like it matters yet.
I started FastAI from where I leave, got back into the course, handled some other things. Not a productive day by any measure — but motion is motion. The point wasn't output. It was direction.
The Workflow Started Making Sense
Completed FastAI Modules 1 and 2 with the book chapters, then built the classic dog-vs-cat image classifier.
Most of the code was course-guided, but something clicked about the core loop: feed labeled images in → model trains → pass a new image through → get a label and confidence score. Train, predict, evaluate. That's the foundation for everything else in this course.
Seeing it run on cats and dogs made it concrete in a way that reading about it hadn't.
The Bear Classifier
Module 3 done. Bear classifier built — classifies images as black bear, grizzly, or teddy bear.
It works. Watching it correctly label a grizzly I fed it was one of those moments that feels disproportionately satisfying. You know it's a beginner project. It's still your beginner project, and it ran.
One practical heads-up if you're following the same course: the FastAI content is a few years old, and the image collection section uses a Bing Search API free tier that no longer exists without entering card details. Hit that wall early. The fix: switch to DuckDuckGo image search via Python — fully free, works the same way for pulling training images. Worth knowing before you run into it.
What Four Days Actually Taught Me
May 17 didn't erase May 19 and May 20. Obvious in theory — easy to forget when you're in it, treating one bad day like it resets your streak to zero.
The more useful takeaway: small practical projects move understanding forward faster than rushing through theory. The dog-vs-cat and bear classifiers are simple. Neither required deep knowledge. But building them made concepts stick in a way that reading alone hadn't managed.
Consistency isn't about perfect productivity. It's about what you do after the wasted day.
May 18 wasn't impressive. It was just necessary.
What does your recovery day look like when you lose momentum — do you push through, or let yourself reset first? 👇
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