San Francisco can feel like a pressure cooker even when you’re only trying to write good code. One software engineer described joining a five-person startup in their third year—salary doubled, confidence didn’t.
On the first day, someone joked about Dijkstra’s algorithm. Everyone laughed. The writer smiled, then looked it up afterward so they could understand why it landed. Dijkstra’s algorithm, after all, is the shortest-path idea that sits behind a lot of navigation and shows up early in formal computer science coursework—and apparently, it had never been part of their own study.
What followed, in Misryoum newsroom reporting, reads less like a simple confidence story and more like a pattern of access: they could follow fragments of system design conversations, but not enough to contribute in a meaningful way. The description is blunt—“wide coverage, shallow roots”—and you can feel the tension in how tradeoffs and debugging were discussed. Instead of treating the gap as something actionable, they stopped speaking up, rushed toward solutions without really understanding, and hoped speed would mask the missing depth. It’s the kind of work behavior that looks productive from the outside, but doesn’t actually build.
The advice they revisit is the classic “If you’re the smartest person in the room, you’re in the wrong room.” It sounds clean on a poster. In the writer’s account, though, it’s uncomfortable in practice: it feels like nodding along to system design talk that’s only partially decodable, shipping solutions by trial and error, and worrying that someone will take a closer look. And the danger is real—fear can push you toward exactly the wrong direction, like making yourself smaller when you should be building foundations.
A turning point comes when a senior engineer leaves and delivers feedback that’s more specific than the writer had managed to name alone. Misryoum editorial desk noted that the engineer said it was difficult to work with them because they lacked foundational programming knowledge—spelling out the concepts they struggled with. Suddenly, vague inadequacy turns into a list. Not inspiring, maybe. But useful.
After that, the story shifts from feeling to planning. They define a clear picture of the engineer they want to become, compare it to where they are now, and write down what they don’t know. Then they pick bridges—books, tutorials, and small projects—and they ask for recommendations from the same engineer who gave the hard feedback. Over time, Misryoum analysis indicates, conversations get clearer. Debugging becomes more systematic. Contributions stop being just execution and start being meaningfully technical.
There’s also a twist that shows up near the end: there’s a “less-obvious version” of the same problem if you’re the strongest engineer in the room. It can feel good—less friction, more validation—but growth can stall because there’s less pressure to raise your floor. In that room, feedback loops quiet down. Some engineers, the writer says, spend years there without noticing that they’re just… fine. Both rooms carry risk: one threatens confidence, the other threatens trajectory. And maybe that’s the part that sticks. Not the slogan, but the moment you realize the room is trying to tell you something—and you have to listen, instead of just reacting.
Separate from the personal account, the piece also points to broader tech education and AI engineering themes. Misryoum newsroom reported that in the United States, early signs are emerging that Ph.D. programs in electrical engineering and related fields may be shrinking, with smaller applicant pools and graduate cohorts mentioned amid political and economic uncertainty. It also notes an “AI Café” gathering at a coffee shop hosted by three professors at Auburn University in Ala. last
November, meant to foster genuine dialogue about AI—“where scholars engage their communities in genuine dialogue about AI. Not to lecture about technical capabilities, but to listen, learn, and co-create a vision for AI that serves the public interest.” Inference—running a trained AI model on new data—is flagged as increasingly important, and the growth of open LLMs is framed as giving more engineers room to tweak models for better inference performance. Then it gestures to a
recent deep dive on inference engineering—what it is, when it’s needed, and how to do it—by “The Pragmatic Engineer,” in a way that feels like it’s meant for builders, not just readers.
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