I like the rejection log because it makes asking feel less like a verdict on you and more like a way to test the edges of your options.

This is increasingly needed as we use AI to write a lot. I like the shift from writing as a promotion skill to a way of making thoughts, tradeoffs, and decisions durable across teams; it ties the advice to everyday engineering work, from code reviews to postmortems. The focus on editing, feedback, and building a writing habit gives the piece more to offer than another reminder that senior engineers should write well.

I like the startup-equity analysis because it follows the “lottery ticket” past the headline valuation to dilution, exercise costs, taxes, and the trouble of selling private shares. The comparison is assumption-heavy, and the compensation figures are dated, so I’d read it for the mechanics of the tradeoff rather than as current salary advice.

The recruiter example stood out: talks for recruiters surface complaints about hiring managers, including the blunt line, “Hiring managers are required by law to be unrealistic,” that a polite coffee chat might never reach. I like the case for reading content native to the roles you work with; it can reveal friction that cross-functional collaboration depends on understanding, though the career advantage is argued rather than demonstrated.

I like the advice to “play the ball as it lies” while recognizing that some interview processes are better than others. The funnel of 25 applications, 17 phone interviews, 7 on-sites and 5 offers makes the case for applying broadly, though it’s one person’s result, shaped by a London-to-San Francisco move and a work visa, not a conversion benchmark.

I’m wary of “always, always negotiate” as universal advice, especially since the author notes that race, gender, and power can change the dynamics. Still, I like the shift from “getting a job” to “selling your labor,” and the advice to write down offer details and avoid answering “what do you think?” in a way that closes the door before you’re ready.

If you’re aiming for a broader engineering role, I like the example of bringing an RFC, an estimate, and a plan to reduce service incidents instead of just flagging the problem. The case for showing that kind of team-level judgment consistently is sound; I’m less sold on the 3-6 month promotion window without more detail on the research behind it.

If you’re choosing what to work on, I like Fischbach’s framing of problem choice as a skill to practice, not just a matter of taste. The “altitude dance” names the shift between focused work and stepping back to update the plan; I especially like the advice to stay clear on the goal while keeping the route flexible, though the article gives a framework rather than evidence that it improves outcomes.

I trust this framing because it treats advisor choice as a risk-management problem too: the advice to identify a second possible advisor avoids betting a PhD on one relationship. The four lenses, career, identity, stability, and variability, counterbalance prestige and paper-count optimization; they make the tradeoffs easier to see, though they don’t tell you how to weigh them.

Richard Hamming’s legendary Bell Labs seminar on doing first-class research.

• Why working on important problems, cultivating emotional courage, and communicating clearly define impactful scientific careers.

Yong Zheng-Xin’s candid breakdown of interviewing across industry AI research labs.

• Covers job talk preparation, system design rounds, and choosing research teams.

John Schulman’s personal reflections on problem selection, experimental iteration speed, and developing technical taste in machine learning.

• Highly practical advice on research notebook discipline and avoiding premature optimization.

Michael Nielsen on developing scientific intuition, active note taking, spaced repetition for research mastery, and sustained deep exploration.

Andrej Karpathy’s timeless reflection on navigating graduate school, handling imposter syndrome, running fast experiments, and writing papers.

Max Liani’s meditation on why software craftsmanship, engineering taste, and deep systems understanding matter more than ever in the era of code generators.

Vlad Feinberg’s concrete roadmap for preparing for systems and infrastructure roles at OpenAI, Anthropic, and DeepMind.

• Focuses on CUDA profiling, distributed networking primitives, and hardware failure recovery.