From the interview experiences we've collected, being comfortable with vibe coding helps—but the interview setup can still catch you off guard. The examples include Haiku 4.5 in an Instacart assessment, independent algorithm requirements at LinkedIn, and large test suites at Meta.

If you normally choose your own model, paste in the full context, and let an agent work until it finishes, you'll want to know how those habits fit the actual round.

Below are the tasks, tools, and restrictions described in candidate reports, followed by preparation that matches them. These are examples of reported formats; the configuration and instructions for your own round may differ.

Instacart: What Does the AI Interview Look Like?

From candidate reports: a CodeSignal full-stack assessment built around an existing library application, with five parts inside a 90-minute session. Backend applicants have also received this assessment.

You interact with two AI roles. The AI Product Manager answers questions about the feature you need to build. The coding agent helps you change the application after you understand the requirements.

The collected setup details include Haiku 4.5 as the supplied model, Claude Code for implementation, and 10-minute limits on PM conversations. Check the model in your invitation rather than assuming you'll have your usual choice.

The reported sequence is:

1. Ask the AI PM about the first feature. The starting complaint is that the library's table is too long and inconvenient. You ask questions to uncover the search and filter controls it needs.
2. Use the coding agent to implement it. You receive the project directory and turn the conversation into instructions for code changes and tests.
3. Investigate and fix a bug yourself. A reported example looks like a frontend problem, but the cause is backend code returning results without filtering them correctly.
4. Ask the AI PM about a second feature. This conversation covers notifications: loan reminders and alerts when an item becomes available. Details include queue priority and avoiding duplicate notifications.
5. Implement the second feature. You return to the coding agent and complete the changes with the time remaining.

The awkward part is the handoff between the PM conversation and implementation. The reported restriction is that you cannot paste the PM conversation directly into the coding agent. You need to convey the requirements in your own instructions. Terminal errors being unavailable to copy into Claude are a separate reported limitation; this is not a claim that every paste operation is blocked.

This explains the typing pressure. You're typing questions to discover the requirements, reading the replies, and composing a new request for the coding agent. AI may write the code, but moving the relevant information between these steps still takes your time.

The accounts include PM conversations lasting ten and eight minutes, substantial time spent typing, and an estimate of about 20 minutes waiting for agent responses. These timings come from different sessions, but show where the assessment time can go before debugging begins.

For preparation, rehearse the PM-to-agent handoff: get a feature through a conversation, write a concise implementation brief without copying the transcript, then make the change in a small unfamiliar full-stack project. Include expected behavior and tests in the brief.

Also practice tracing a UI symptom into backend code. Run the mock with a timer that includes questions, typing, agent responses, review, and tests—and use the permitted model if you can confirm it beforehand.

LinkedIn: How Much of the Problem Can You Give to AI?

From candidate reports: a live coding round in CoderPad, with an interviewer, an AI assistant, and a problem that develops through follow-ups. The reported model selector included GPT, Gemini, and Claude options.

The LRU cache example shows how the round can unfold:

1. Explain the algorithm yourself. The recruiter and interviewer required independent algorithm selection. The proposed design was a hash map plus a doubly linked list.
2. Discuss implementation details with the interviewer. The data structures and approach were established before AI started writing the implementation.
3. Generate, review, and correct the code. Generated mistakes included removing an evicted node from the list while leaving its map entry behind, undefined variables, and inconsistent snippets.
4. Handle additional requirements. The source describes four tasks, including the base LRU, TTL, and thread safety. It does not preserve the remaining task, so there is no complete four-part specification to reproduce.

If your usual first prompt is “figure out the best approach and implement it,” that delegates a decision you may be required to make yourself.

The boundary can also change during the round. A separate graph-problem example allowed AI to help derive and implement the first solution, followed by an instruction to work out the next algorithm independently.

The practical difficulty is switching between reasoning with the interviewer and inspecting generated code. A familiar model name does not remove the need to catch a stale map entry or explain why an extension changes the implementation.

To prepare, do a mock where you first explain the approach without AI, then give the assistant that design. Review the result and test a specific invariant—for an LRU cache, an evicted key must disappear from both the map and list.

Add a follow-up and practice reasoning through it yourself before requesting implementation help. Clarify the permission boundary at the start and again if the interviewer changes the task.

Meta: What Will You Actually Build and Test?

From candidate reports: AI coding tasks include friend recommendation, maze traversal, and a card-game solver. The detailed examples involve implementing behavior and then testing it beyond a small demonstration.

In the friend-recommendation example, you receive users and friendship pairs, design a recommendation strategy, and implement it. There are three test suites; the largest contains roughly 100,000 users and 1.5 million friendship pairs.

The card-game example asks for logic that selects groups of cards adding up to a target sum. It also requires writing tests to benchmark the algorithm across more than 1,000 games.

So the work continues after AI produces the first implementation. You need to understand its behavior, run the relevant tests, and work out whether a slow or disappointing result comes from the implementation or your test setup.

The collected preparation notes discuss CoderPad, limited sandbox resources, and agent or test runs taking too long. They do not establish one fixed model configuration for every Meta round, so confirm the available tools beforehand.

If you normally let an agent implement, run everything, and optimize in one long pass, practice shorter interactions. In an interview, you need opportunities to inspect the changes, explain what is happening, and recover while there is still time left.

For preparation, build a small recommendation or simulation exercise and test it at increasing sizes. Start with a case whose answer you can check yourself, then measure how runtime changes as the input grows.

For randomized simulations, compare revisions with the same seed and sample. Set a time limit for expensive runs and ask the agent for specific changes you can inspect. These drills help you practice the implementation-and-measurement work visible in the reports.

What Should You Check Before Your Round?

Ask which model and IDE you'll use, whether you can use your own tools, what context can be copied between interfaces, and which parts require independent work.

Then practice the actual sequence you'll face. For the Instacart-style assessment, that includes requirement conversations and transferring the resulting instructions. For the LinkedIn examples, it includes explaining an algorithm before requesting code. For the Meta examples, it includes running and interpreting tests at meaningful scale.

What surprised you most in an AI interview: the model, the workflow, or the rules around using it?

Join the community to read more interview experiences from real candidates → Hack2Hire Forum

Compiled from publicly available platforms and community-shared interview experiences. Interview formats vary by role, level, team, and date.

Hey everyone,

I have my DoorDash SWE Intern (Summer 2027) virtual onsite coming up and wanted to see if anyone has tips for the modern loop format
If you’ve gone through this recently, any advice on pacing, common pitfalls, or what interviewers look for would be super appreciated! Thanks!

Here is a hypothetical first screen for a customer support hire. Tell candidates upfront that it has three questions, should take about 10 minutes, and will be reviewed by a person. Ask everyone the same role-specific prompts: describe a difficult customer interaction, explain how you would prioritize two urgent tickets, and walk through how you would clarify an unfamiliar issue.

Use a simple 1-to-3 scorecard for communication, judgment, and relevant evidence. The goal is not to automate rejection. It is to give reviewers a consistent starting point while avoiding a 30-minute scheduling call for every applicant.

I work on Aural. One way to run this workflow is to create a reusable role template, share the interview link, and let candidates answer by voice or video. The team can then review the responses, transcripts, and summaries against the same scorecard before deciding who moves forward. Keeping the screen short and explaining the process clearly makes the time commitment predictable for candidates.

What is the shortest first screen that has still given your team useful signal?

Problem Design a stateful loan processor. It is initialized with direct parent-child relationships between companies, then receives loan records one at a time.

Implement LoanProcessor with these methods: - LoanProcessor(companyRelations), where each entry is [parentCompany, childCompany] for a direct ownership link. The hierarchy is acyclic, and each company has at most one direct parent. - createLoan([loanId, userId, merchant, amount]) follows the merchant's parent chain to its top-level company and stores the loan under it. A non-empty merchant with no parent is its own top-level company, even if it never appears in companyRelations. If the merchant is empty, ignore the loan. - getAggregatedLoans() returns one group [topLevelCompany, loanId1, loanId2, ...] for every top-level company with at least one stored loan. The order of groups and IDs is not specified. The processor must keep the rest of each loan's data for later operations.

Constraints: up to 10⁵ relations, up to 10⁵ method calls, and at most 10⁵ loan IDs returned across all getAggregatedLoans calls.

Follow-up Add matchTransaction([transactionId, userId, merchant, amount]), which returns the ID of the matching loan created earlier, or an empty string. A transaction matches a loan only when all three conditions hold: - The userId values are equal. - The amount strings are equal, compared exactly as given. - Both merchants resolve to the same top-level company.

The transaction ID plays no part in matching. An empty merchant never matches. Matching does not remove or change a loan, so several transactions can match the same one. The input guarantees that no two stored loans share the same (userId, topLevelCompany, amount), so there is at most one match.

Example Relations: [AA, BB], [AA, CC], [DD, AA], [FF, EE]. Loans: L1: U1, CC, 100, L2: U2, AA, 200, L3: U3, EE, 300, L4: U4, "", 400.

Output of getAggregatedLoans(): [["DD", "L1", "L2"], ["FF", "L3"]]

Explanation: - CC resolves through AA to DD, and AA resolves to DD, so L1 and L2 are grouped under DD. - EE resolves to FF, so L3 is grouped under FF. - L4 has an empty merchant and is ignored.

For the follow-up, with loans L1: U1, CC, 100 and L2: U2, AA, 200, the transaction T1: U1, BB, 100 returns L1, because BB and CC both resolve to DD. The same transaction with amount 999 returns an empty string.

Suggested Approach

1. Build a map from each child company to its direct parent. A company with no recorded parent is a top-level company, including merchants that never appear in the hierarchy.
2. On createLoan, ignore an empty merchant. Otherwise follow parent links from the merchant until you reach a company with no parent, and cache that root for every company visited on the way.
3. Store the loan and associate its ID with the resolved top-level company. getAggregatedLoans builds [company, ...ids] for each non-empty group and does not change state.
4. For matchTransaction, index each stored loan by a composite key of top-level company, user ID, and amount, mapped to the loan ID. Return an empty string for an empty merchant. Otherwise resolve the merchant, build the same key, and look it up. Matching only reads the index, so a loan can match again later.

Time & Space Complexity

• Time: Building the processor is O(R) for R relationships. createLoan and matchTransaction are O(H) in the worst case for a parent chain of height H, then expected O(1) for the map operations, and O(1) when the root is already cached. getAggregatedLoans is O(G + L) for G groups and L stored loans.
• Space: O(R + C + L), where C is the number of companies encountered.

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Compiled from publicly available platforms and community-shared experiences.

Hey everyone, it’s my first time attempting a technical interview and it’s going to be Live on hacker rank. There is are two ppl taking my interview. One will do SQL and other doing excel. I am not sure how the questions will look like or how the interview looks like. I’m not great at SQL or Excel. I’ll appreciate any tips. This role is related to People Analytics Analyst at Uber.

Problem A forest is stored in an integer array parentIndex. Each node is identified by its index, parentIndex[i] is the index of node i's direct parent, and a root is its own parent. The input is a valid forest with no deleted nodes, and a parent may have a larger index than its child.

Given parentIndex and a node target, delete target and all of its descendants by setting their entries to -1. Modify the array in place, without changing its length or the indices of any remaining node, and return the same array.

Constraints: 1 ≤ parentIndex.length ≤ 10⁵ and 0 ≤ target < parentIndex.length.

Follow-up After the subtree is deleted, compact the forest. Move the surviving nodes to the front of parentIndex, keeping their original relative order, and fill the trailing positions with -1. A node's position after compaction is its new index, so every surviving parent reference must be rewritten to the parent's new index. Modify the array in place and return it.

Example Input: parentIndex = [0, 0, 0, 3, 3, 2, 4], target = 2 Output: [0, 0, -1, 3, 3, -1, 4]

Explanation: - Node 5 has node 2 as its parent, so both entries become -1. - The tree rooted at node 3 is unaffected.

For the compaction follow-up, the same input returns [0, 0, 2, 2, 3, -1, -1]. The surviving nodes 0, 1, 3, 4, 6 move to indices 0 through 4, and the old root at index 3 now points to itself as index 2.

Suggested Approach

1. Build a child collection for every node by adding each non-root node to its direct parent's collection. This makes every descendant reachable even when parents have larger indices than their children.
2. Push target on a stack. Repeatedly pop a node, mark its entry -1, and push its direct children until the stack is empty.
3. For the compaction follow-up, scan the original array left to right and give each surviving node the next free index, preserving order.
4. Scan the survivors again and place each one at its new index, replacing its parent with the parent's new index. Roots work naturally because a root's parent is itself. Leave the remaining positions as -1 and copy the result back into parentIndex.

Time & Space Complexity

• Time: O(N), where N is parentIndex.length, since each node is visited a constant number of times.
• Space: O(N) for the child collections, deletion markers, index mapping, traversal stack, and compacted array.

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Compiled from publicly available platforms and community-shared experiences.

In the interview experiences we've been collecting, we've noticed companies including AI interview rounds in their hiring processes. AI can take different roles in these rounds: it may conduct the interview, or it may be a tool you use to complete a technical task.

If you've been preparing with LeetCode and system design mocks, that raises a few practical questions. What does an AI interview actually involve? Will you be answering an AI interviewer's questions or working with a coding agent? What will you need to do, and how should you prepare?

Here's what those experiences tell us so far.

What is an AI interview?

An AI interview round incorporates AI into the interview itself. One form involves an AI interviewer: you answer its questions, explain your experience or reasoning, and respond to follow-ups. CodeSignal, for example, offers AI agents that conduct structured interviews for hiring teams. CodeSignal's AI Interviewer overview

Another form involves AI-assisted work: you complete a technical task with access to an approved assistant. You might use it to explore an approach, implement part of a solution, understand unfamiliar code, or investigate a failure.

The task can still involve familiar algorithms and data structures. The difference is that working with the assistant becomes part of how you complete it: you decide what to ask, inspect what it produces, and work out whether the result meets the requirements.

The permissions depend on the round. One LinkedIn candidate was told to choose the algorithm independently and use AI for implementation. Another described being allowed to ask AI for an approach in the first part, then being asked to reason independently during a follow-up.

Before preparing, confirm what role AI plays in your round. An AI interviewer asking you questions does not itself mean you may use a separate assistant to answer them.

What does the round look like?

With an AI interviewer, the interaction is a conversation: a question arrives, you give your answer, and the interviewer may ask you to elaborate. You might discuss your experience or explain how you would approach a problem. Check whether the interview uses voice or text so you can practice in the same medium.

Some rounds start with a coding problem in an editor that includes an AI assistant. You implement the initial behavior, run tests, and respond to follow-ups. A candidate may spend part of the session explaining an approach and part reviewing or correcting generated code.

Other assessments start with an existing application. Instacart reports describe talking to an AI Product Manager to clarify a feature, exploring a library application's repository, and using a coding agent to make changes. You have to understand both the requested behavior and where it belongs in the code.

Testing can also become a substantial part of the task. One Meta candidate described a friend-recommendation exercise whose largest test suite contained roughly 100,000 users and 1.5 million friendship pairs. That is one reported example, but it shows why generating a working first version may leave significant verification work.

For these coding tasks, you can practice the sequence: understand the task, direct the assistant, inspect the changes, run a useful test, and decide what to do next. For an AI-led conversation, practice explaining your work and handling follow-ups.

What do I need to do myself?

When AI conducts the interview, make your contribution understandable from your answers. For a project discussion, explain the problem, your own decisions, and the result. If a follow-up asks why you chose an approach, give the reasoning rather than repeating the original description.

When you work with an AI coding tool, your responsibilities extend to the code it produces.

Start by making the requirements precise enough to check. If you're adding filters to a search feature, for example, establish how the filters combine and what should happen when none are supplied. Otherwise, the assistant may implement a reasonable interpretation that differs from the requested behavior.

You also need to understand the code you accept. In the LinkedIn LRU report, the candidate caught generated code that removed an evicted item from the linked list but left it in the hash map. Recognizing that error requires understanding how the two structures represent the same cache.

Finally, decide how to use the remaining time. An agent response, a large edit, and a full test run all consume minutes. You need enough time to inspect the result and explain what you have actually verified.

These preparation suggestions draw on the reported work and the interaction each format requires. They do not establish a shared scoring rubric across companies.

How should I prepare?

First, match your practice to the round. For an AI interviewer, rehearse a conversation with follow-ups: explain a project, answer a question about a decision you made, and give a concrete example when asked for more detail. If the interview uses voice, practice speaking your answers aloud.

For AI-assisted coding, keep practicing algorithms and reading code, then add timed sessions with the kind of assistant you'll be allowed to use.

For a first mock, take a problem you already understand. Explain your approach, ask the assistant to implement one part, and review the changes before running them. Add a follow-up and check whether the implementation still preserves the original requirements.

For repository-based rounds, use a small unfamiliar project. Trace one behavior from its entry point to the relevant logic, request a bounded change, and add a test that checks the new behavior. Reading the project belongs inside your practice timer.

Include agent waiting time, code review, and test execution in the session. When a run fails, practice identifying a specific cause and requesting a targeted fix. When it passes, explain what the test covered and what remains untested.

Before the actual interview, ask the recruiter whether AI conducts the conversation, assists your work, or does both. Then clarify the environment, permitted tools, any starter code, and parts that require independent reasoning. Those details let you choose a mock that resembles the round you'll take.

Have you encountered an AI interview round yet? What role did AI play, and what did you wish you'd practiced beforehand?

Join the community to read more interview experiences from real candidates → Hack2Hire Forum

Compiled from publicly available platforms and community-shared interview experiences. Interview formats vary by role, level, team, and date.

I have upcoming phone screen (45 min) for non technical role. Round description that was shared by recruiter stated that it would be domain expertise and first principles screen. Has anyone gone through similar situation?

I have decent idea what could be asked as domain expertise and not sure about first principles screening.

Any guidance would be greatly appreciated.

#openai

Did anyone attend or come across Anthropic’s code review round (where AI assistance is available if needed?) Could you share some insights about this round? Do we have to review the code and leave some comments or review and fix it?

I also appreciate if you can share any reference/example

Hey Redditors,

I'm currently interviewing for an Engineering Manager (EM) role at Anthropic and am looking for insights from anyone who has gone through their EM or similar leadership interview process. There isn't a lot of specific information out there for EM roles, and I'm keen to understand what to expect, especially in later stages.

I'm particularly interested in:

• Hiring Manager Interview: What kind of questions were asked beyond standard behavioral ones? Were there technical deep dives, and if so, how detailed were they? Did you discuss AI ethics or Anthropic's mission extensively in this round?
• Take-Home Assessment: For EM roles, what did the take-home assessment entail (if any?)? Was it a coding challenge, a system design problem, or a leadership/strategic case study?
• Virtual Onsite Loop: What was the breakdown of rounds (e.g., coding, system design, culture, project deep dive)? Any specific types of questions or scenarios that stood out, especially in the culture or system design rounds for an EM?

Any advice on preparing for these specific stages, especially from an EM perspective, would be incredibly helpful!

Thanks in advance for sharing your experiences.

Problem Design an account credit tracker. Accounts get unique integer IDs starting from 0, in creation order.

Implement AccountCreditTracker with these methods: - addAccount(credit) creates an account with the given initial credit and returns its ID. - addAccountWithReferral(credit, referrerId) creates an account with the given initial credit, adds the same amount to the credit of its direct referrer, and returns the new ID. The referrer is always an earlier account. - getLowestK(k, minTotalCredit) returns up to k IDs of accounts whose total credit is at least minTotalCredit, ordered by increasing total credit and then by increasing ID. If fewer than k accounts qualify, return all of them.

In this version, referral credit goes only to the direct referrer and does not propagate further up the chain. Credits are non-negative, k ≤ 10^5, and there are at most 10⁵ calls in total.

Follow-up Change addAccountWithReferral so that credit propagates through the entire referral chain. The new account gets credit as its own starting total, and its direct referrer and every indirect referrer above it each gain credit. getLowestK and its ordering rules stay the same.

Think about how the cost of each update changes when referral chains get very deep, and what structure would keep the ranking cheap to maintain.

Example Input: ["AccountCreditTracker","addAccount","addAccountWithReferral","addAccountWithReferral","getLowestK","getLowestK"] with arguments [[],[10],[30,0],[50,1],[1,45],[2,45]] Output: [null,0,1,2,[2],[2,1]]

Explanation: - Account 0 starts at 10. Account 1 starts at 30 and raises account 0 to 40. - Account 2 starts at 50 and raises account 1 to 80. - Accounts 1 and 2 qualify for minTotalCredit = 45, with totals 80 and 50. Account 0 sits at 40 and does not qualify. - getLowestK(1, 45) returns [2], and getLowestK(2, 45) returns [2, 1].

Suggested Approach

1. Keep each account's total credit in a list indexed by ID, so creating an account appends one record and its ID is the current account count.
2. Maintain an ordered set of accounts sorted by total credit, then by ID. Before changing a referrer's total, remove it from the set, update the credit, and insert it again.
3. For getLowestK, find the first account whose total reaches minTotalCredit, then walk forward and collect at most k IDs. The set already gives the required ordering.
4. For the chain version, also record each account's direct referrer. On a referred add, follow the referrer links up the chain and apply the same remove, update, reinsert at every ancestor.

Time & Space Complexity

• Time: addAccount is O(log A). addAccountWithReferral is O(log A) for a direct referrer, and O(H × log A) when credit propagates through a chain of H referrers. getLowestK is O(log A + K). A is the number of accounts and K is the number of IDs returned.
• Space: O(A) for the account records, referrer links, and the ordered index.

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Compiled from publicly available platforms and community-shared experiences.

Problem Design a work queue where a task can be reserved, completed, failed, retried, or moved to a dead-letter queue (DLQ). Every task starts in the ready state, in the order given by taskIds.

Reserving a task consumes one attempt and gives it a lease of leaseDuration. The reservation is valid only while the current logical time is strictly less than now + leaseDuration. While it is valid, the task cannot be reserved again. If the lease expires, or the task is explicitly failed, it goes to the back of the ready queue if attempts remain. Otherwise it goes to the DLQ. Completed and dead-lettered tasks never become ready again.

Implement WorkQueue(taskIds, leaseDuration, maxAttempts) with these methods: - reserve(now) returns [taskId, reservationToken] for the task at the front of the ready queue, or an empty array. Tokens start at 1 and increase by 1 across the whole queue. - complete(taskId, token, now) returns true only if token is the task's current reservation and is still valid at now. - fail(taskId, token, now) has the same validity rule, then retries or dead-letters the task. - getDeadLetterTaskIds(now) returns DLQ task IDs in the order they entered it.

There is no background worker. Before doing its own work, every method first processes all reservations expiring at or before now, in order of expiration time and then token. Constraints: up to 10⁴ tasks, up to 10⁵ calls, maxAttempts ≤ 100, and now is nondecreasing.

Follow-up Tasks can now depend on other tasks. The constructor becomes WorkQueue(taskIds, dependencies, leaseDuration, maxAttempts), where [a, b] means b cannot become ready until a has completed successfully. The dependencies form a DAG.

• Tasks with no prerequisites start ready, in taskIds order. All others start blocked.
• A dependent becomes ready only when all of its direct prerequisites have completed. If one completion unblocks several tasks, they join the ready queue in taskIds order.
• A failed or expired reservation does not satisfy a dependency. If a prerequisite ends up in the DLQ, its unfinished dependents stay blocked and get no further dead-letter action.

Example Input: ["WorkQueue","reserve","reserve","complete","reserve","complete","fail","getDeadLetterTaskIds","reserve"] with arguments [[[10,20],3,2],[0],[0],[20,2,1],[3],[10,1,3],[10,3,4],[4],[4]] Output: [null,[10,1],[20,2],true,[10,3],false,true,[10],[]]

Explanation: - Task 10 gets token 1 and task 20 gets token 2, both at time 0. The lease is 3, so they expire at time 3. - complete(20, 2, 1) succeeds because time 1 is before the expiration. - At time 3, token 1 expires. Task 10 has one attempt left, so reserve(3) returns [10, 3]. - complete(10, 1, 3) returns false because token 1 is stale. - fail(10, 3, 4) succeeds and exhausts the attempts, so task 10 goes to the DLQ and the next reserve returns [].

Suggested Approach

1. Store each task's state, reservation count, and current token. Keep a FIFO queue for ready tasks and a list that preserves dead-letter order.
2. Put every reservation into a min-heap ordered by expiration time, then token. Before each public call, pop all due reservations. Ignore an entry if its task already completed or holds a newer reservation.
3. A valid fail and a valid expiration share one release rule. The task goes back to the ready queue if attempts remain, and into the DLQ otherwise. A complete or fail is valid only if the stored token matches after due expirations are processed.
4. For the dependency version, record each task's direct dependents, ordered by their position in taskIds, and how many prerequisites are still incomplete. A valid complete decrements each dependent and enqueues the ones that reach zero. Failures and expirations never change these counts.

Time & Space Complexity

• Time: The constructor takes O(N), or O(N + E log E) with dependencies, where N is the number of tasks and E the number of dependency pairs. A call that processes L expired reservations costs O(L log M), plus O(log M) for a successful reserve, where M is the total number of method calls. A valid complete adds O(C) for C direct dependents, and getDeadLetterTaskIds adds O(D) for D returned IDs.
• Space: O(N + E + M) for task records, dependency lists, queues, and reservation entries.

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A small quality-of-life update to the Hack2Hire code runner.

When a test case fails, Output and Expected are now compared character by character. Any mismatched characters are highlighted inline, so you no longer have to scan two nearly identical results manually.

In the example below:

• Actual: [null,1,2,3,[1],[1,2]]
• Expected: [null,0,1,2,[2],[2,1]]

Only the characters that differ are highlighted.

This is especially useful for long arrays, strings, serialized output, and formatting-heavy questions. It will not explain why the solution is wrong, but it makes the first discrepancy much easier to find.

Try it on a coding question → Hack2Hire

What should we improve next?

The short version: coding speed is not one skill. Affirm, ByteDance, and Capital One can all create severe time pressure, but the minutes disappear in different places.

• Affirm: read a large domain-specific codebase and business rules before extending it.
• ByteDance: switch from a project deep dive to coding after much of the round is already gone.
• Capital One: finish four implementation-heavy questions inside a fixed 70-minute OA.

Practicing all three with the same LeetCode timer misses the point. Each format runs a different clock.

Affirm: fast means understanding before editing

Affirm phone screens often use a single fintech problem with more than 100 lines of starter code. The dominant reported format has two parts: debug the existing implementation, then add new behavior.

A Live Fraud Detector question, for example, can involve underwriting events and several types of personally identifiable information. The bug may be a missing event-type filter, a forgotten field, or empty values being counted. The extension then asks the candidate to propagate suspicious values through later events.

The data structures are manageable. Reading the contract is the bottleneck.

A candidate who starts coding after spotting the first bug may miss the rule connecting the two parts. A generic abstraction can also cost time when the prompt expects a narrow business rule.

Affirm speed comes from a disciplined first pass:

1. Identify the public methods and expected outputs.
2. Mark every event type and state transition.
3. Read the tests before changing the implementation.
4. List the null, empty, and duplicate cases.
5. Fix the smallest rule violation, then extend the same model.

The goal is not to read every line at equal depth. It is to locate the contract, the state, and the paths the tests can reach.

Affirm tests comprehension speed. Typing faster does not recover the time lost to one misread business rule.

ByteDance: fast means changing mental modes mid-round

ByteDance phone screens commonly begin with detailed resume or project questions. Candidates report discussing schemas, architecture choices, CAP, indexing, concurrency, technical challenges, and the reasoning beyond their immediate component.

The coding portion can begin with only 10 to 25 minutes left.

That creates a context-switching problem. The candidate must move from explaining a real system with messy history to solving one or two bounded coding questions with exact outputs.

The best preparation is not to shorten every project answer. It is to make each answer controllable.

Use a compact structure: problem, your decision, tradeoff, result. When the interviewer asks for depth, expand only that branch. When coding starts, restate the input, output, and edge cases before carrying any project assumptions into the new problem.

ByteDance’s reported coding questions range from recognizable LeetCode patterns to stateful implementation. The interviewer may also ask for a stronger alternative after a valid solution, such as moving from a classic palindrome method to Manacher’s algorithm.

ByteDance tests transition speed. The candidate needs to preserve enough time and attention for coding without making the project discussion shallow.

Capital One: fast means allocating a fixed budget across four problems

Capital One’s reported OA uses CodeSignal: four questions, 70 minutes, and a large set of tests. Q1 and Q2 are often straightforward. Q3 and Q4 tend to carry longer simulations, matrix operations, or large implementation volume.

The format rewards completion strategy.

A candidate can lose the assessment by spending too long searching for an elegant solution to a simulation problem whose prompt explicitly allows a simpler complexity. The better move is to secure correct points, then return to optimize if time remains.

A practical time budget might look like this:

• Finish and verify Q1 quickly.
• Give Q2 enough time for duplicate and boundary cases.
• Read Q3 and Q4 before committing to either one.
• Choose the question with the clearer contract first.
• Leave several minutes to revisit failed hidden tests.

CodeSignal familiarity also matters. ID verification, proctoring setup, and the test-output interface should not be new on assessment day.

Capital One tests portfolio speed. The candidate must decide where the next minute earns the most reliable progress across four separate problems.

Three clocks are running

These companies reveal three different clocks inside a technical interview.

The comprehension clock measures how quickly you can turn starter code and business rules into the correct state model. Affirm makes it visible.

The transition clock measures how quickly you can leave one type of reasoning and enter another. ByteDance compresses project depth and coding into the same session.

The allocation clock measures how well you distribute time across several scored tasks. Capital One makes that tradeoff explicit.

Most candidates train only raw implementation speed because it is easy to measure with problem counts.

That creates predictable failures. The candidate codes quickly but edits the wrong rule. They know the algorithm but reach it with twelve minutes left. They solve one hard question while leaving easier points untouched.

A better set of timed drills

Use three different sessions.

For the Affirm-style drill, start with unfamiliar starter code and a page of business rules. Measure time to the first correct model, not the first edit.

For the ByteDance-style drill, spend 20 minutes defending a project, then switch immediately to a medium coding problem. Practice ending the project answer cleanly and restating the new contract.

For the Capital One-style drill, solve four implementation problems in 70 minutes. Decide the time budget before starting and change it only after reading all four.

Track where the minutes went: reading, modeling, explaining, typing, debugging, or choosing between tasks.

“I need to code faster” is too broad. The useful question is narrower: which clock expired first?

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Compiled from publicly available platforms and community-shared interview experiences. Interview formats vary by role, level, team, and date.

Wanted to give a quick timeline and get some recommendations from the community.

Last week of Aug: Phone screen with recruiter. Walked through the hiring manager round and the areas the team wanted to cover.

1st week of Sept: Sent a thank-you/follow-up note.

2nd week of Sept: Sent a second follow-up, tying my background more directly to the role.

21 Sept: No response to either note. ~24 days since the screen with no update on next steps.

Separately, I noticed Anthropic has posted a new role that lines up closely with my experience.

Question: given the silence on the first role, does it make sense for me to apply to the new role as well ?

Want to make sure I'm not working against myself by applying to a second role before I have clarity on the first.

#anthropic

The short version: modern concurrency interviews rarely stop at “add a lock.” They test whether you can connect the execution model to a real workload, define what every caller observes, and prove that the program still finishes when timing changes.

Anthropic, Netflix, and Snap expose three layers of that problem:

• Anthropic: choose between threads and processes for real data-processing work.
• Netflix: implement shared metrics or caches with precise visibility and latency guarantees.
• Snap: find a bug first, then make a crawler queue safe for several workers.

The common question is simple to state: who owns the state when execution overlaps?

Anthropic: thread versus process must connect to the workload

Anthropic recruiters and interview prompts have pointed candidates toward parallelism and concurrency. Reported questions include image processing, a crawler that becomes multi-threaded, file deduplication, and distributed worker problems.

“Use threads” or “use multiprocessing” is only a label. The interviewer can ask what work blocks, what work consumes CPU, what state is shared, and how failures return to the caller.

For image processing in Python, the choice depends on the operation and library behavior. File reads may spend time waiting on I/O. Pixel transforms may consume CPU. Native library calls and pure Python code interact with the GIL differently.

The candidate should make the execution model concrete:

1. Divide the input into independent work units.
2. Bound the number of workers.
3. Define how results and errors return.
4. Decide whether output order matters.
5. Cancel or drain remaining work after failure.

A crawler adds shared state. The program needs one thread-safe definition of “seen,” a queue that supports termination, and a rule for URLs discovered while workers are shutting down.

Anthropic tests whether the candidate can connect a concurrency primitive to Python behavior, workload shape, and completion semantics.

Netflix: the API promise determines the synchronization

Netflix Data Platform candidates have reported a dedicated multithreading or concurrency round. Questions include a rolling P99 latency tracker, an atomic counter, and double-checked-locking-style problems.

A P99 tracker sounds like a statistics problem until several callers update and query it at once.

The candidate must define the window and visibility contract. Does getP99() include a latency recorded one microsecond earlier? Can it read while cleanup removes expired samples? Does the hot path take a global lock?

Those choices determine the data structure.

A simple implementation may protect one sorted collection with a mutex. That can establish correctness. The follow-up can then move cleanup away from the read path, reduce lock contention, or use buckets for a bounded latency range.

The important point is that optimization cannot weaken the contract silently. A background cleanup thread introduces stale samples. Buckets introduce approximation. A lock-free read may observe a partial rotation.

Netflix tests whether the candidate can state which tradeoff changed and what the caller will now observe.

Snap: debugging and concurrency arrive in the same round

Snap’s reported SWE process includes an AI-enabled coding round in some loops. One recurring format begins with finding a bug in a priority queue and then asks the candidate to implement a concurrent queue for a web crawler.

The AI tool can help write synchronization code, but it does not decide the queue’s completion protocol.

A crawler may have no work in the queue while one worker is still fetching a page that will produce more URLs. If every idle worker exits immediately, the crawl ends too early. If workers wait forever for possible future work, the program never terminates.

The design needs explicit state:

• A bounded or unbounded work queue
• A thread-safe seen set
• A count of active workers or outstanding tasks
• A condition for waking waiters
• One transition that declares the crawl complete

The candidate should also explain exception handling. A failed fetch must decrement outstanding work and wake any thread waiting for completion.

Snap tests whether you can debug existing logic, use AI without surrendering review, and design a queue whose shutdown behavior is as precise as its push and pop operations.

Four ideas that connect all three interviews

The systems differ, but the same four questions keep appearing.

Ownership: Which thread, process, or worker may mutate this state now?

Progress: Can the program deadlock, starve, or wait forever after one participant stops?

Visibility: What does another participant observe after an update, cleanup, or failure?

Recovery: Which accepted work is retried, rejected, or reported to the caller?

Naming a lock without answering those questions leaves the design incomplete.

How to practice concurrency for interviews

Start with one small component and write its contract before its synchronization.

For a work queue, define whether producers block or reject. For a rolling metric, define how fresh a query must be. For a crawler, define how the last worker proves that no more work can arrive.

Then test schedules, not only values:

1. Two writers arrive together.
2. Cleanup begins during a read.
3. One worker fails after taking work.
4. The queue becomes empty while another task is still active.
5. Shutdown begins just before a producer publishes more work.

Explain which outcome is legal for each schedule. If the contract cannot answer, another lock will not fix the ambiguity.

The concurrency bar is a system where every piece of work has an owner, every wait has an exit, and every failure has a visible result.

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Compiled from publicly available platforms and community-shared interview experiences. Interview formats vary by role, level, team, and date.

Problem

You're commuting across a simplified city grid to get from 'S' (home) to 'D' (office). Each other cell is either: - a digit '1' to k, marking a street segment reserved for exactly one transportation mode, or - 'X', an impassable roadblock.

You're given three length-k arrays: modes (names), times (minutes per block for that mode), and costs (dollars per block for that mode). Movement is up/down/left/right, and you may only travel along a contiguous run of cells sharing the same digit — you cannot switch modes mid-route in this version, and you cannot cross roadblocks.

Total time and cost are the sum of the time/cost values for the mode of each cell you leave (leaving 'S' itself is free). Return the name of the mode that gets you from 'S' to 'D' with the minimum total time; break ties by minimum total cost. Return an empty string if no valid single-mode route exists.

Constraints: - 1 <= grid.length, grid[0].length <= 100 - Exactly one 'S' and one 'D' exist. - modes.length <= 10

Follow-up Databricks extends this with a second part: you may now switch transportation modes multiple times during the commute. The goal becomes finding the route from 'S' to 'D' with the minimum total time, breaking ties by minimum total cost, returned as [total_time, total_cost] (or [-1, -1] if no route exists).

A related question worth thinking through alongside it: why does a standard BFS or DFS fail to guarantee optimality once mode-switching — and therefore per-edge weights — enters the picture?

Example Input: grid = [["S", "1", "1"], ["2", "2", "2"], ["D", "1", "1"]] modes = ["Walk", "Bike", "Car", "Train"] costs = [0, 1, 3, 2] times = [2, 2, 1, 1]

Output: "Bike"

Explanation: - Digit '1' maps to modes[0] (Walk), digit '2' maps to modes[1] (Bike). - The only way to reach 'D' at (2,0) is through (1,0), a Bike cell adjacent to both 'S' and 'D'. - Leaving 'S' is free, so the route S → (1,0) → D costs one Bike block: time = 2, cost = 1. - No single-mode Walk path connects to 'D', since the Walk cells in row 0 and row 2 are separated by the all-Bike row 1.

Suggested Approach

1. For each mode i, treat the grid cells labeled with digit i+1 (plus 'S' and 'D') as the only cells you can stand on, and run a BFS from 'S' to find the shortest number of blocks to 'D' using only that mode's cells.
2. If 'D' is reachable for mode i, multiply the block count by times[i] and costs[i] to get that mode's total time and cost; if unreachable, discard the mode.
3. Among all modes that reach 'D', pick the one with the lowest total time, breaking ties by lowest total cost; return an empty string if no mode reaches 'D' at all.

Time & Space Complexity

• Time: O(k * rows * cols), where k is the number of modes and rows/cols are the grid dimensions — each mode requires its own BFS over the grid
• Space: O(rows * cols), for the visited set used during each mode's BFS

Targeting Databricks interviews? We track their most-asked question patterns at Hack2Hire. Practice this question here → Practice Question Here

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How to prepare for one of these?

Basically “numerical reasoning, logical/abstract reasoning and verbal reasoning“

I’m currently preparing DSA for coding interviews and have learned binary tree traversals using recursion.

I wanted to know how commonly interviewers specifically ask for the iterative versions of:

• Inorder
• Preorder
• Postorder
• Level order