Uploaded March 2026 | Updated September 2026, 1 hour ago
Most AI interview prep is backwards.
People memorize questions, grind LeetCode, and hope for the best.
But 24-hour take-homes usually test something else: how you think when the spec is messy, how you choose a baseline, how you measure results, and how clearly you explain tradeoffs.
A much better way to prepare? Build one tiny project end to end. An OCR pipeline is perfect. Pick 10 docs, extract a few fields, compare approaches, track accuracy, save results, and write the README.
Use AI tools, sure. Just don’t let them do the thinking for you.
In the interview, you’ll need to explain every choice. What was the hardest take-home you got?
I’m Louis-François, PhD dropout, now CTO & co-founder at Towards AI. Follow me for tomorrow’s no-BS AI roundup 🚀 #short
Most AI interview prep is backwards.
People memorize questions, grind LeetCode, and hope for the best.
But 24-hour take-homes usually test something else: how you think when the spec is messy, how you choose a baseline, how you measure results, and how clearly you explain tradeoffs.
A much better way to prepare? Build one tiny project end to end. An OCR pipeline is perfect. Pick 10 docs, extract a few fields, compare approaches, track accuracy, save results, and write the README.
Use AI tools, sure. Just don’t let them do the thinking for you.
In the interview, you’ll need to explain every choice. What was the hardest take-home you got?
I’m Louis-François, PhD dropout, now CTO & co-founder at Towards AI. Follow me for tomorrow’s no-BS AI roundup 🚀 #short
