The article contrasts “smart” engineers who rely on rapid intuition with “strong” engineers who use a steady, methodical process when they hit a tough problem. It argues that identifying true system invariants and forcing yourself to sit with raw data leads to clearer thinking and more reliable solutions.

• "Smart" engineers rely on fast intuition and fall apart when it fails, flailing through fixes and defending wrong turns as "close enough"
• "Strong" engineers use a repeatable process instead: list the invariants you trust unconditionally, then test weaker assumptions one by one until you find the false one
• Grinding through raw data methodically feels frustrating but catches issues that intuition alone would miss, especially outside familiar patterns
• Training yourself to think clearly regardless of speed—not raw talent—is what lets engineers handle unfamiliar problems without collapsing into panic

The author uses experience at startups and leading AI companies to pinpoint which skills will matter as AI handles routine tasks. He argues you should focus on scarce resources—time, relationships, reputation—learn to spot high-impact problems, and polish the last mile of work. He also explains how to boost your career by choosing ambitious challenges, improving efficiency in front of goal, and even breaking into research on your own.

• As AI masters gradeable tasks, career value shifts to work that can't be reduced to a loss function—prioritize scarce assets like time, relationships, and reputation over pure compensation.
• Problem-finding beats problem-solving in agent-native environments; spotting high-impact gaps matters more than LeetCode-style skills.
• Choose the most ambitious version of a problem (per the "bitter lesson") and invest heavily in the last 10% of polish—architecture, UX, edge cases—since agents handle the initial draft.
• Career progress depends on both opportunity volume (xG) and conversion rate: reputation opens doors, but decision-making efficiency and culture fit turn offers into the right fit.

The tweet tells engineers to forward their toughest technical challenges to Fable, implying the platform can tackle complex problems. It pitches Fable as a go-to resource for difficult engineering questions.

• Elvis Sun tells engineers to send their hardest technical problems to Fable
• He claims Fable can solve them, capped with a "thank me later"
• The tweet gives no specifics on Fable's features, pricing, or proof of results

This MIT course focuses on estimating answers and tackling problems through intuitive shortcuts instead of formal proofs or exact calculations. It covers guesswork techniques and heuristic strategies to quickly arrive at useful results.

• MIT's Street Fighting Mathematics (IAP 2008, taught by Scott Aaronson, J. Michael Steele, Steven G. Johnson) teaches estimation and heuristic problem-solving instead of rigorous proofs.
• Key techniques include dimensional analysis, bounding arguments, perturbation methods, and picking simpler functions to bound nasty integrals.
• The course derives results like the √N law for random walks and Stirling's formula through physical intuition and rough approximation rather than formal derivation.
• These heuristics extend beyond physics to algorithm analysis, offering quick mental shortcuts for estimating runtime or complexity (e.g., spotting O(N log N) behavior without formal analysis).