How Should I Practice For These Interviews?

In this lesson we talk about how AI is making previously time-prohibitive advanced data structures and algorithms newly relevant in coding interviews, and what that means for how you should prepare.

- **AI unlocks harder concepts by handling the boilerplate** — complex structures like tries, heaps, and LRU caches were rarely testable in a 45–60 minute interview because implementation alone could eat up 15–20 minutes; with AI generating that scaffolding in seconds, interviewers can now reasonably include them, making them worth studying once you've solidified the fundamentals.
- **What you actually need to demonstrate has shifted** — since AI handles much of the implementation, your value lies in recognizing when a given data structure is the right tool, clearly articulating *why* it helps (e.g. "a trie eliminates the factor of M by checking all patterns simultaneously through shared prefix traversal"), and writing precise prompts that reflect that understanding.
- **Master the basics first, and don't over-rotate on this** — the vast majority of interviews (roughly 95%) will not require these advanced concepts, so treat this as the final 5–10% polish layer, not the foundation; verification of AI output remains just as critical here as everywhere else in the interview.

How Do I Talk Without Sounding Chaotic?

In this lesson we talk about how to communicate effectively during an AI-assisted coding interview without sounding scattered, frantic, or going completely silent.

- **Avoid the two extremes** — narrating every single keystroke comes across as junior, while going silent for several minutes leaves the interviewer in the dark; the goal is to sound controlled and intentional somewhere in between.
- **Choose the communication model that fits your thinking style** — Model A is light, continuous commentary as you work ("I'm pulling the date out of each event to populate the per-day set"), while Model B is stating your high-level intent upfront, working silently for 60–90 seconds, then summarizing what you did before repeating the cycle.
- **Practice talking while you work before the interview** — like touch typing, verbalizing your thought process while solving a problem is a skill that needs to be rehearsed, not figured out on the spot; a few mock sessions with a friend or mentor can make a significant difference.

How To Debug Effectively with AI As My Partner?

In this lesson we talk about how to debug effectively with AI as a partner during a coding interview, and when to use it versus when to rely on your own judgment.

- **Use AI to generate more test cases, but step through code yourself** — AI can rapidly produce test cases that would take much longer to write manually, but since it predicts words rather than executing code, it can reproduce the same bugs it originally introduced, making it unreliable for tracing logic line by line.
- **Avoid leaning on AI for debugging, but use it strategically when stuck** — rather than asking AI to find or fix a bug directly, prompt it to brainstorm multiple possible causes, which keeps you in the driver's seat, surfaces hypotheses for you to validate with your own judgment, and reduces the risk of confidently wrong AI suggestions.
- **Always rerun all tests after a fix, not just the failing one** — thorough test coverage lets passing tests serve as reliable verification; if your test suite is thin, compensate with more manual code inspection to avoid introducing regressions you won't have time to track down later.

What Should I Do In The First 10 Minutes?

In this lesson we talk about how to strategically approach the first 10 minutes of an AI-assisted coding interview to set yourself up for success.

- **Resist the urge to dive in immediately** — instead of jumping straight into coding or prompting the AI, start by restating the problem in your own words and doing a strategic skim of the codebase to understand existing functionality and function signatures.
- **Uncover hidden requirements** — as you explore the code, actively look for things like bot user filtering, non-active event handling, or data normalization that may not have been spelled out in the prompt, and clarify these with your interviewer.
- **Turn requirements into tests early** — unlike traditional interviews, convert each discovered requirement into an executable test case upfront (using AI to help generate them if needed), since verifying AI-generated code is a core part of the assessment.

Leverage Your Unfair Advantage

We explain how to accelerate your job search after a layoff by intentionally leveraging the advantages already on your résumé. Here are the core points from the lesson:

- We should leverage company brand as an unfair advantage, since recognizable names (Big Tech, well-known startups, or respected niche companies) carry trust and credibility that immediately raise our odds versus the average applicant.
- We can increase our chances by targeting adjacent companies, customers, or competitors where our background naturally fits, instead of applying randomly to roles with no contextual advantage.
- We should confidently lean into domain and product association—using the halo effect of teams, products, or problem spaces we worked on—because perceived relevance often matters more than perfect technical alignment.

Verify Your Severance Package

We explain what to do immediately after a layoff to protect yourself financially and legally, and to reduce unnecessary stress. Here are the core points from the lesson:

- Most large tech companies provide severance due to legal requirements and long-term reputational incentives, but layoffs are messy, so you must carefully review all paperwork and verify dates, vesting, and severance amounts.
- Never sign termination documents on the spot; take time to review them, ask questions, and involve a lawyer if possible, especially for severance disputes or visa-related considerations.
- After a layoff, it’s critical to understand your personal burn rate and runway by reviewing your finances, since clarity around how long you can sustain yourself reduces anxiety and helps you plan your next steps.

There Is Nothing Wrong With You

We close by reframing layoffs as a survivable, even formative, career event rather than a personal failure. Here are the core points from the lesson:

- Being laid off is not a reflection of your ability or worth, since layoffs are driven by executives making decisions with incomplete information and often feel effectively random from an individual’s perspective.
- There is no value in self-blame or overanalyzing what you could have done differently, because once a layoff happens, the only productive move is to look forward.
- With severance as temporary financial runway, layoffs can be treated as a reset point to reflect, recover, and intentionally choose your next step, knowing this is a normal part of a long tech career.

Build Up Relationships

We highlight why building strong relationships and social capital is essential for career resilience, especially during layoffs. Here are the core points from the lesson:

- Relationships are a form of social capital, and having people who know you, like you, and will advocate for you can directly influence whether you survive layoffs or get new opportunities.
- Engineers often underinvest in relationship-building by staying heads-down, but proactively connecting with managers, skips, and peers across teams is increasingly important in today’s economy.
- Building relationships doesn’t require forced networking—it can happen naturally through code reviews, coffee chats, offsites, and one-on-ones if you are intentional about seeking opportunities.
- No matter how senior or skilled you are, everyone is expendable, so you should never rely on company loyalty alone and should continuously invest in relationships that outlast any single job.

Grow Fundamental Skills

We explain why growing fundamental, transferable skills is one of the most powerful ways to reduce layoff risk and strengthen long-term career resilience. Here are the core points from the lesson:

- There is far more to software engineering than coding, especially at scale, where communication, project management, leadership, and relationship-building determine whether work actually ships.
- Over-indexing on a specific technology, language, or product is risky, because teams, systems, and entire products can disappear, while transferable skills allow you to move across teams or companies.
- Engineers who can clearly design systems, think through edge cases, write thoughtful plans, and communicate tradeoffs create outsized impact compared to those who jump straight into code.
- So-called “soft skills” are actually the highest-leverage skills, because they compound across roles, companies, and industries, while technical tools and frameworks inevitably change.
- Focusing your time on high-leverage problems and influence, rather than low-level tooling details, is what drives career growth, adaptability, and durability during periods of layoffs.

Optimize Your Performance

We focus on the most practical actions within your control to reduce layoff risk by becoming a consistently high-performing, high-impact contributor.

- Optimizing performance should be a constant priority, starting with deeply understanding your company’s performance review system and knowing exactly what ratings mean and how they affect your risk and growth.
- Staying calm and avoiding gossip or obsession with layoff rumors is critical, since anxiety and distraction directly undermine performance and effectiveness.
- Maintaining a clear, transparent, and concise dialogue with your manager is essential, because managers advocate for you using only a few high-level impact points.
- Expanding scope and proactively tackling important, ambiguous problems is one of the strongest career multipliers and a key trait of top performers.
- Building a clear professional “brand” on your team—where others can easily articulate what you’re known for and the value you deliver—makes you more visible, promotable, and resilient during layoffs.

Be Deliberate With Team Selection

We explain how team selection within a company can influence both career growth and layoff risk, while stressing the importance of balance and personal fit.

- Team choice is one of the remaining factors within your control, and teams that are important to leadership or closely tied to core revenue are generally less likely to be cut.
- Importance is relative to executive priorities, not just revenue, so understanding what leadership truly cares about is key, especially at companies with strong founder influence.
- Choosing a high-priority team is usually good career advice anyway, since those teams tend to have more scope, visibility, and growth opportunities.
- You should not sacrifice genuine interest or passion just to chase perceived safety, because lack of engagement hurts performance and long-term career growth.
- This guidance is clearer at companies with product-market fit, while earlier-stage startups carry more inherent uncertainty, making stability harder to assess regardless of team choice.

Don't Freak Out

We emphasize the mindset shift that matters most when facing the reality of layoffs and uncertainty in tech.

- The most important first step is not to freak out, even though layoffs can feel shocking and destabilizing, especially for people who assumed big tech jobs were inherently safe.
- Acceptance is key: layoffs are a normal part of professional life, often driven by factors far outside an individual contributor’s control, including executive decisions and market conditions.
- Obsessing over layoffs or drastically changing your behavior can actually backfire, while staying steady and focused on your work is usually the healthier and more effective approach.
- If a layoff is going to happen, the decision is typically already made, so recognizing what you cannot control can be freeing and allows you to focus your energy on what you actually can control.

Signs A Layoff Is Coming

We outline the practical signals that can help you tell when layoffs may be coming, especially in tech companies.

- For public companies, a prolonged and severe stock price decline is a strong warning sign, as companies generally can’t sustain operations when shares remain far below their highs.
- For startups and private companies, stagnant growth, hiring freezes, and an indefinite pause on recruiting often indicate belt-tightening ahead of potential layoffs.
- Companies preparing for layoffs frequently cut perks first, and leadership demeanor may shift noticeably through more guarded communication and changes that trickle down to managers.
- Gossip can surface through internal networks or platforms like Blind, but it’s unreliable and stressful, so the most consistent signal is the broader move into an “age of austerity,” where profitability replaces growth and visible reductions in spending become the norm.

The Effect of Interest Rates

We walk through how macroeconomic conditions—especially interest rates—directly affect tech companies and tech workers.

- The federal funds rate guides how expensive it is for banks (and ultimately companies) to borrow money, and while near-zero rates felt normal recently, historically higher interest rates are actually the norm.
- When interest rates rise, the cost of borrowing increases, which makes companies far less willing to invest in long-term bets that may not pay off for several years.
- Because the tech industry is heavily driven by growth funded through borrowing and investment, higher rates trigger spending pullbacks, reduced hiring, and ultimately the contraction and layoffs we’re seeing across the market.

Why Layoffs Happen

We explain why layoffs happen and why they’re a recurring reality in the tech industry.

Here are the core points from the lesson:

- Layoffs occur when companies face bad economics, often triggered by unforeseen macroeconomic shifts like the post-COVID slowdown after years of stimulus-fueled growth.
- Overambitious hiring is a major contributor, especially when overly positive economic conditions lead companies to scale headcount faster than the business can sustainably support.
- Fundamentally unsound business models and poor strategic direction also drive layoffs, and in practice, these factors usually overlap—making layoffs inevitable since executives can’t make perfect decisions all the time.

What Are Layoffs?

We break down what layoffs are, how they differ from firings, and why terminology matters in the tech industry.

Here are the core points from the lesson:

- Layoffs are ad hoc, unpredictable events that cut a large number of employees for economic reasons, unlike regular performance-based cuts where low performers are ranked out.
- Being laid off is generally not the employee’s fault and is different from being fired for incompetence, which is why layoffs—especially in tech—typically come with generous severance packages.
- Most recent “layoffs” in tech are technically RIFs (reductions in force), meaning the company has no intention of rehiring those employees, even though the industry commonly uses the term layoff.

Often Forgotten Team Considerations

We wrap up with two often-overlooked but highly consequential factors when selecting a team: on-call health and code review speed.

Here are the core points from the lesson:

- We explain that on-call quality can make or break your work-life balance, and many early-career engineers don’t evaluate it; the two key questions are how *frequently* you go on call and how *painful* it is (night pings, fire drills, number of incidents).
- We highlight that not all on-call is equal—middleware teams or teams responsible for triaging other teams’ outages often have disproportionately stressful rotations unless strong runbooks exist.
- We emphasize that code review speed is critical for learning and momentum: slow iterations (1–3 days per review) compound quickly, turning a small change into a week- or multiweek-long process and severely limiting growth for junior and mid-level engineers.
- We define healthy expectations: reviews returned within a day are ideal, &gt;1 day is a yellow flag, and &gt;2 days per revision is a red flag because it throttles how often you ship and how fast you improve.

Finding Scope To Grow

We walk through how to evaluate whether a team offers real *scope for growth*—one of the most important factors for long-term career progression.

Here are the core points from the lesson:

- We suggest assessing a team’s track record of major launches: frequent, meaningful wins signal room for impactful work, while long dry spells often indicate a team that is either too new (unstable) or too mature (no hard problems left to solve).
- We point out that very mature orgs like core Facebook Feed may be so optimized that it’s nearly impossible to produce meaningful results, making promotions extremely difficult; meanwhile, very new orgs offer big scope but come with chaos and uncertainty.
- We recommend examining the level distribution on the team: a healthy mix of engineers more senior *and* more junior than you enables both mentorship (learning) and leadership (scope creation), whereas being the most junior or most senior can severely limit growth.
- We advise deeply evaluating the engineering manager’s track record—whether they have successfully grown people from junior → mid-level → senior → staff—because a strong EM accelerates your development, while a newly minted or unproven EM introduces career risk.

Talk To Engineers

We explain how to get a real, unbiased understanding of a team by talking directly to engineers—not just the hiring manager.

Here are the core points from the lesson:

- We stress that managers have strong incentives to “sell” their team—they’re evaluated on hiring success and growth—so their perspective is inherently biased and overly positive.

- We recommend explicitly asking to meet 1-2 engineers on the team, ideally ICs who have been there for years and are leaving on good terms, as they provide the most honest, nuanced picture of day-to-day culture, challenges, and growth.

- We emphasize that speaking with the tech lead is especially valuable, since alongside the manager, the TL will influence our experience more than anyone else after joining.

Team Importance

We explain how to evaluate whether a specific *team* within a company is a strong place for growth, impact, and stability.

Here are the core points from the lesson:

- We show how to assess whether a team is core to the business by asking whether it’s a profit center or cost center; teams tied to revenue (like Google Ads) offer stability and long-term opportunity, while peripheral or internal-tool teams often limit growth.
- We illustrate how to categorize teams: mature core teams (stable, slower growth), big bets (high upside but real risk of shutdown, like Stadia), and low-impact internal teams (comfortable but slow-growth).
- We recommend questions that reveal real importance: What does success look like? What growth targets matter? Does the CEO mention this product in earnings calls? These signals show whether leadership actually prioritizes the work.
- We show how to “backdoor” the truth by asking leaders their top three priorities; if your team isn’t on that list, it’s unlikely to be a strong engine for your career unless you explicitly want a lower-pressure role.

Tech Career Growth Videos

Our instructional videos provide software engineers with insights needed for career growth. Masterclasses go incredibly in-depth on a specific tech topic while case studies are real examples from established engineering leaders.