Engineering is fundamentally built on trust — the moment people stop checking your work. This piece explores how trust operates across five dimensions: with teammates (built through reliability and honesty, lost instantly with one lie), with managers (those trusted with ambiguity get high-leverage work and promotions), with tools and AI (trust as far as you can cheaply verify and survive being wrong), with third-party systems (dependency trust is transitive and you control none of it — use timeouts, circuit breakers, pinned versions), and with data (a dashboard nobody believes is worse than no dashboard). The core insight: trust is slow to earn, fast to lose, and worth more than almost any technical decision.
Table of contents
Trust with Your TeamTrust with your managerTrust in Your ToolsTrust in SystemsTrust in Your DataSee the pattern?Questions this post answers
Why do senior engineers get their PRs approved faster even for large changes?
Senior engineers get faster PR approvals because reviewers have already built a mental model of them through past interactions. A reviewer skims the code and checks only the parts that worry them, because they've already 'reviewed the person' — they know this engineer tests edge cases, flags risky changes proactively, and doesn't claim something is done when it isn't. The first PR from anyone gets a full, slow, suspicious review. Engineers navigating code review dynamics and team reputation find the broader conversation on daily.dev.
How should I decide how much to trust an LLM to write production code?
Trust an LLM exactly as far as you can cheaply verify its output and survive it being wrong. An LLM writing a regex or a throwaway test is low-risk — you can read the output in seconds and a mistake causes no lasting damage. Letting an LLM quietly rewrite a financial calculation you won't check is high-risk — a wrong answer can sit undetected for months doing real damage. The same framework applies to compilers, ORMs, and autoscalers. Developers calibrating AI tool use in production codebases track these trade-offs on daily.dev.
What are the real risks of transitive dependencies in software projects and how do I mitigate them?
Transitive dependency trust is inherited but unverified — you audit almost none of the tree. Left-pad's removal broke half the internet's builds from eleven lines of code; Log4Shell forced every team to prove a negative over a weekend; the xz backdoor was inserted by an attacker who spent years earning maintainer trust through clean commits. Mitigations include timeouts, circuit breakers, pinned versions, and vendoring only the specific lines you actually use rather than importing an entire library. Supply chain incidents like these surface quickly for developers watching the space on daily.dev.