Expert guides, tutorials, and insights on mastering technical interviews and landing your dream job.
You've spent months grinding LeetCode. You know your data structures. You can explain Big O notation in your sleep.
Most candidates now use some form of AI interview prep-ChatGPT, custom GPTs, coding copilots, or specialized platforms. The results are mixed: some people accelerate dramatically, others burn months “practicing” without actually getting better.
Most engineers prepare intensely for system design and coding rounds-and then get blindsided by the behavioral interview. The questions sound deceptively simple (“Tell me about a time you disagreed with your manager”), yet they often determine the final hiring decision. Behavioral interviews are whe
You've scheduled mock interviews with human coaches. You've paid $200+ per session. You've waited days for feedback.
You've solved 200 LeetCode problems. You can tackle Medium questions. You understand time complexity.
Technical interviews are designed to filter out unprepared candidates. Unfortunately, they often filter out strong ones too.
Most engineers eventually ask the same question: *what actually changes between a junior and senior engineer in an interview?* The job descriptions sound similar-write code, review PRs, ship features-but interview expectations are very different.
Preparing for product company interviews can feel like chasing a moving target: some people say “do 500 LeetCode problems,” others say “just know the basics.” If you’re serious about software engineering roles at product-based companies, you’ve probably asked yourself:
Most candidates know they should “think out loud” in coding interviews. Very few know how to do it well.
Most candidates focus on getting the right answer in a coding interview. The stronger signal, though, is *how* you get there. Your thought process-how you understand the problem, explore options, reason about trade-offs, and react to hints-is what separates a passable solution from a strong hire.
Most people underestimate how much focused technical interview preparation they can do in 30 days-and overestimate how much they need to “know everything.” A good 30 day interview plan is not about cramming every data structure; it’s about deliberate practice on the right patterns, under realistic c
You’re in the middle of a problem-solving interview. You’ve restated the question, maybe sketched a few ideas-and then your mind goes blank. The interviewer is waiting. The silence feels louder every second. You are, very clearly, stuck in an interview.
Switching into software engineering is one of the few career moves where the interview itself feels like a second job: algorithms, system design, behavioral stories, portfolios, take-home projects. For many career switchers, the real question isn’t “How do I prepare?” but “What should I focus on fir
You've opened two browser tabs. One has LeetCode's stark interface. The other shows HackerRank's gamified dashboard with badges and leaderboards. You need to prepare for upcoming technical interviews, and everyone seems to have a different opinion about which platform is better.
Most engineers discover the same thing the hard way: you can grind LeetCode for months, hit 500+ problems, and still feel blindsided in real software interviews.
Most candidates walk out of a live coding interview thinking, “If only I’d written a more optimal solution, I’d have passed.” In reality, many rejections happen *before* the code is even complete-because of how the candidate approaches the problem, communicates, and collaborates.
Most engineers know they “should” do mock interviews, but few use them in a way that actually accelerates improvement. Many treat mock interviews like one-off practice exams: show up, struggle through a problem, get a vague “you did fine” at the end, and move on. Then they’re surprised when real tec
Remote interviews and onsite interviews look similar on paper: same company, same role, often the same question bank. Yet candidates who perform confidently in one format can struggle in the other. The difference is rarely about algorithms or system design knowledge-it’s about environment, constrain
Most engineers prepare for interviews as if there are only “coding rounds.” Then a recruiter mentions a “system design interview,” and the preparation playbook suddenly feels incomplete-especially as you move into senior engineer interviews and beyond.
Most data science interview guides feel like laundry lists: “learn SQL, brush up stats, revise ML algorithms.” Helpful, but not enough. A real data science interview is a sequence of different conversations-product sense, modeling, experimentation, analytics, and sometimes systems design-compressed
You've built dashboards. You've run A/B tests. You've even deployed ML models to production.
Most data science candidates list “Built an ML model to predict X” on their resume. Interviewers have seen hundreds of these. What actually stands out are end-to-end data science projects: work that starts from a vague problem, touches raw data, applies modeling thoughtfully, and ends with a decisio
If you’re preparing for a machine learning interview and feel torn between memorizing equations and building end‑to‑end projects, you’re not alone. Many candidates discover too late that the interview they studied for (heavy theory, lots of ML concepts) is not the interview they actually get (messy
Most candidates preparing for data science interviews ask the same question: *Should I focus more on SQL or Python?*
Most data science interviews are lost not on deep learning, but on basic statistics. You’ll be building a model, debugging an A/B test, or interpreting a metric-and suddenly the interviewer asks a “simple” question about p-values, confidence intervals, or conditional probability. This post walks thr
Most product managers can talk about strategy, metrics, and roadmaps. But in a PM interview, all of that compresses into a single pressure test: the product case study.
Landing a Product Manager role at top tech companies is harder than ever. With acceptance rates below 1% at companies like Google and Meta, PM candidates face increasingly rigorous interviews testing everything from technical product sense to stakeholder management.
Preparing for a product manager interview can feel strangely unstructured for a role that lives and dies by structure. You’re told to “think like a CEO,” “be data-driven,” “tell great stories,” and “prioritize ruthlessly”-all in 45-minute conversations with strangers.
Most PM candidates walk into product metrics interviews thinking they’re about memorizing KPIs. Interviewers, however, are testing something deeper: how you think about *causality*, *behavior*, and *tradeoffs* using data.
Most product management candidates collapse when the interviewer switches from “What would you build?” to “How would you run this?” The content feels similar, but the evaluation suddenly changes. That’s the core confusion behind product sense vs execution interviews in PM interviews.
Most product management job descriptions look similar until you reach one crucial phrase: “technical PM preferred” or “non-technical PM role.” The difference isn’t just semantics-it changes what your interviewers expect, how you’ll be evaluated, and even how you should tell your stories.
If you've ever submitted dozens of applications and heard nothing back, there's a good chance your resume never made it past an Applicant Tracking System (ATS). Before a human reads your resume, software parses it, scores it, and often filters it out. The good news: you don't need tricks-just solid,
Most engineers assume they’re rejected because their skills aren’t strong enough. In reality, a large share of tech hiring rejections happen before anyone evaluates your skills at all-your resume is filtered out due to avoidable resume mistakes and subtle resume red flags.
Most engineers treat their resume as a static document: write it once, tweak a few bullets, and keep reusing it. That works-until you move from “fresher” to “experienced” and suddenly the rules change.
Most software engineers spend hours polishing their resume-yet a recruiter may decide in under 10 seconds whether it moves forward. That gap between effort and attention is where many strong candidates quietly get filtered out.
Most developer resumes read like job descriptions: “Built APIs”, “Worked on microservices”, “Improved performance.” None of that tells a hiring manager what actually changed because you were there.
Most software engineers obsess over their resume. A few obsess over their portfolio. Very few think deeply about how the two work together in modern tech hiring.