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08-11

What Is an AI Engineer?

A concise role introduction to AI engineering, drawing on Latent Space's 'The Rise of the AI Engineer.' It draws the line at the API boundary: AI Engineers orchestrate models to build applications, while ML Engineers build the model APIs themselves. The post argues newcomers don't need linear algebra or pretraining experience; instead they need strong software fundamentals, evaluation frameworks, and feedback loops. It also distinguishes AI Engineers from AI-assisted developers who merely use tools like Copilot. Web developers are presented as well suited for the transition, and TypeScript is called a fast-growing fit. The article is accessible but conceptual, with a promotional block for the author's AI Hero skills system.

www.aihero.dev · 4 min · AI Engineering · Career Advice · LLM
06-29

how to be good at research

A thread by @itsreallyvivek arguing that research skill is a stack of trainable sub-skills, not a gift. Core moves: pick problems you genuinely want to exist (Schulman), upgrade inputs by reading old papers and skipping summaries, write everything down to expose hidden gaps (Graham, Feynman, Darwin), tighten the experimental loop with scripted tooling (Karpathy), stare directly at failure cases instead of loss curves (Andrew Ng), deliberately wander across subfields to find your unfair advantage, and cultivate collaborators who will tell you an idea is bad. The post synthesizes concrete tactics from Hamming, Sutton, Shannon, and others, emphasizing falsifiable forecasts, reproducible tooling, and reading raw data over third-hand threads. Actionable for research engineers and PhD students tired of surface imitation.