DEV Community: net programhelp

Ramp OA Experience Sharing — Full 4-Level CodeSignal Breakdown

net programhelp — Mon, 25 May 2026 14:10:48 +0000

After finishing the Ramp OA, I spent hours browsing forums and realized there was almost no detailed information online.

The OA was hosted on CodeSignal: 90 minutes, 4 progressive levels. You had to fully pass the current level before unlocking the next one. This was very different from standard LeetCode-style algorithm questions. The focus was much more on system design thinking, state management, and code organization.

Here’s the full breakdown of all four levels.

Problem Background

The task was to implement an in-memory cloud storage system. No real filesystem operations — everything stayed in memory. The system mapped file paths to corresponding metadata.

Level 1 — Basic File Operations

Three APIs:

Add file
Query file size
Delete file

Rules were straightforward:

Cannot add duplicate files
Delete should return the file size if successful
Return None if the file does not exist

A simple hashmap solved the entire level in under 5 minutes.


class CloudStorage:
    def __init__(self):
        self.files = {}

    def add_file(self, name, size):
        if name in self.files:
            return False
        self.files[name] = size
        return True

    def get_file_size(self, name):
        return self.files.get(name)

    def delete_file(self, name):
        return self.files.pop(name, None)

Level 2 — Top N Largest Files

Filter files by prefix and return the largest N files in this format:


/path/file(size)

Sorting rules:

Sort by size descending
If sizes are equal, sort lexicographically by filename ascending


def get_n_largest(self, prefix, n):
    matched = [
        (name, size)
        for name, size in self.files.items()
        if name.startswith(prefix)
    ]

    matched.sort(key=lambda x: (-x[1], x[0]))

    return [
        f"{name}({size})"
        for name, size in matched[:n]
    ]

Biggest pitfall: The double-condition sorting. If you forget the negative sign for descending size order, hidden tests fail immediately.

Level 3 — User Capacity & Merge Logic

This level introduced users. Each user had:

Storage capacity
Owned files
Used storage tracking

The system also supported merging two users.

Merge rules:

Files from user_2 are merged into user_1
If filename conflicts occur, files from user_2 are discarded
Capacities are combined
user_2 is deleted afterward


def merge_user(self, user_id_1, user_id_2):
    if user_id_1 not in self.users or user_id_2 not in self.users:
        return False

    u1 = self.users[user_id_1]
    u2 = self.users[user_id_2]

    for name, size in u2["files"].items():
        if name not in u1["files"]:
            u1["files"][name] = size
            u1["used"] += size

    u1["capacity"] += u2["capacity"]

    del self.users[user_id_2]

    return True

Main pitfall: Do NOT recalculate used capacity by iterating through all files every time. Maintain a dedicated used field instead.

Level 4 — Backup & Restore

The final level added snapshot support. Users could back up their storage state and restore it later by timestamp.


import copy

def backup_user(self, user_id, timestamp):
    if user_id not in self.users:
        return False

    if user_id not in self.backups:
        self.backups[user_id] = {}

    self.backups[user_id][timestamp] = copy.deepcopy(
        self.users[user_id]
    )

    return True

def restore_user(self, user_id, timestamp):
    if (
        user_id not in self.backups or
        timestamp not in self.backups[user_id]
    ):
        return False

    for name in self.users[user_id]["files"]:
        self.files.pop(name, None)

    self.users[user_id] = copy.deepcopy(
        self.backups[user_id][timestamp]
    )

    for name, size in self.users[user_id]["files"].items():
        self.files[name] = size

    return True

Largest pitfall here: Always use deepcopy. A shallow copy causes later modifications to corrupt old snapshots.

Another easy mistake: During restore, you must first remove the user’s current files from the global storage map before restoring snapshot contents.

Overall Pattern

Across all four levels, the real difficulty was keeping three separate data structures perfectly synchronized:

Global file map
User-owned file maps
Backup snapshots

If any one of them became inconsistent, hidden tests would fail.

Strong recommendation: Before coding, draw out the full data structure relationships. Once Level 3 and Level 4 start stacking features together, knowing exactly which states need synchronization becomes extremely important.

About ProgramHelp

One thing I didn’t expect was how many “follow-up edge cases” completely blanked my brain during the OA.

Before taking the Ramp OA, I worked with ProgramHelp for targeted preparation.

They had already compiled a set of high-frequency CodeSignal OOD-style problems, including cloud storage system designs very similar to this one.

The Level 3 merge logic and the Level 4 deepcopy trap were both things they specifically warned me about beforehand, which honestly saved a lot of time during the actual assessment.

Their coaching is done by real North American CS experts instead of generic AI tools, and they focus heavily on identifying the actual hidden evaluation points behind these OA systems.

Services include:

OA assistance
Online assessment support
VO interview guidance
Resume optimization

TikTok DS Intern Two-Round VO Interview Experience Sharing

net programhelp — Sun, 24 May 2026 12:13:51 +0000

Honestly, after the first round, I thought I was done.

During the interview, I kept getting interrupted and redirected by the interviewer. A few times she had to keep digging before I finally answered what she actually wanted. After the call ended, I literally sat in my chair for ten minutes without moving.

Then three days later, I got the second-round invite.

Round 1: Getting Pressed on Every Detail

The interviewer started with questions about the dashboard project from my first internship and asked how I selected the metrics.

I started explaining a bunch of things, and she immediately cut me off:

"Why these three metrics instead of others?"

I honestly wasn't prepared for that follow-up. I tried to explain, but then she asked:

"If the business goal changes, would your metrics also change?"

That question completely threw me off at first.

Later I realized the point was actually simple: metrics should always follow the business objective. They're not fixed forever.

For the text classification discussion, she asked how to choose between Precision and Recall. I answered that if falsely banning users is costly, then Precision matters more; if missing harmful content is more dangerous, then Recall matters more. She seemed satisfied and moved on.

For my second internship, she asked about the most technical part of the ETL pipeline. I explained a deduplication optimization I worked on:

One user could generate multiple logs per day, and directly using COUNT DISTINCT was too expensive. So we first grouped records and kept the earliest event before joining downstream tables.

Then she asked:

"What if the data volume becomes 10x larger?"

I answered that approximate counting methods like HyperLogLog could help reduce computation cost.

The SQL question was calculating DAU and average online duration. I used CTEs to split the logic and then joined the results together.

She followed up with:

"How would you handle overlapping sessions from multiple logins?"

I said that if we wanted accurate session duration, we'd need to merge overlapping intervals first. But if the requirement was only a simplified average, then it wouldn't be necessary.

She nodded and said:

"The approach makes sense."

Then she mentioned time was running out and skipped the Python section.

After Round 1, I honestly felt my performance was pretty average.

Round 2: Much Better Flow

The second interviewer had a much more casual style, so the atmosphere felt less stressful.

We started with common topics like data preprocessing and Bias-Variance Tradeoff. Those were things I had already prepared well, so the conversation went smoothly.

Then came a hypothesis case:

"If a product feature is about to launch, how would you evaluate its effectiveness?"

I had practiced open-ended cases before, so I immediately structured it as an A/B testing problem:

Define the business objective
Create hypotheses
Design the experiment
Run it for two weeks
Measure the outcome metrics

He then asked:

"How do you know two weeks is enough?"

I explained that we'd estimate required sample size using historical data and back-calculate the experiment duration.

The SQL question was counting users who registered and posted on the same day.

SELECT COUNT(DISTINCT u.user_id)
FROM users u
JOIN posts p
ON u.user_id = p.user_id
WHERE DATE(u.created_at) = DATE(p.created_at);

He specifically hinted that one user could create multiple posts.

After I finished, he asked whether DISTINCT was necessary.

I answered yes — without it, users with multiple posts would be counted repeatedly.

He replied:

"Correct. A lot of people miss that."

The final discussion was a business case around improving creator engagement based on that metric.

I explained that the metric essentially reflected first-time activation for new users. The issue could come from two directions:

The onboarding flow does not encourage users to create content
New users don't receive enough exposure after posting, so they never get positive feedback

Accordingly, the solutions would be:

Improve onboarding guidance for first posts
Adjust recommendation logic to give new creators initial exposure

To measure effectiveness, I suggested tracking:

Time from registration to first post
7-day retention for new creators

He said:

"This is a very complete analysis."

Then we moved into the Q&A section.

What Changed Between Round 1 and Round 2?

In Round 1, I kept getting stuck because I focused too much on explaining what I did instead of why I made certain decisions.

Between the two rounds, I worked with ProgramHelp · FAANG Interview Coaching for VO preparation, specifically focusing on business cases and analytical thinking.

They designed several realistic TikTok-style DS interview scenarios and gave very direct feedback about where my logic broke down and how interviewers would interpret certain answers.

What helped most was that they didn't just teach generic frameworks. They focused on helping me actually understand how to communicate business reasoning clearly.

The team includes people from Oxford, Princeton, Amazon, and Google with strong DS backgrounds, so they understand how big-tech interviews are evaluated. Several of my friends also used their VO coaching and ended up landing offers.

For me, the improvement between the first and second rounds was huge.

They also help with:

Resume optimization
Behavioral interviews
VO interview coaching

ProgramHelp · FAANG Interview Coaching

Final Thoughts

TikTok DS interviews are not really about memorizing knowledge.

What they actually test is whether you can use data to clearly explain a business problem.

Ironically, almost failing the first round was exactly what helped me finally understand that.

Feel free to leave questions below.

How I Landed an Atlassian SDE Offer — Full Interview Experience Breakdown

net programhelp — Fri, 22 May 2026 16:04:58 +0000

Last year, I received an offer from Atlassian for a mid-level Software Development Engineer role on a North American backend team (remote/hybrid supported). The entire process took about two months from application to offer, and honestly, it was one of the most thoughtful interview experiences I’ve had.

What makes Atlassian different is that they don’t just evaluate your coding skills. They care deeply about communication, collaboration, engineering judgment, and cultural alignment. Compared to many “LeetCode-only” style interviews, Atlassian’s process felt much more realistic and team-oriented.

Background & Why I Chose Atlassian

I’ve been working in North America for over four years as a backend engineer, mainly building enterprise SaaS APIs and distributed data services. My previous teams used Jira and Confluence heavily, so I was already very familiar with Atlassian’s ecosystem.

When I saw the SDE opening on LinkedIn, it immediately caught my attention. The company values — “Open Company, No Bullshit”, “Play as a Team”, and “Build with Heart and Balance” — genuinely aligned with how I view engineering culture.

I applied through LinkedIn, and surprisingly, a recruiter reached out the very next day.

Preparation Phase (Probably the Most Important Part)

I started preparing about 6 weeks before the interviews.

LeetCode: Solved 300+ problems, mainly Medium and some Hard. Focused on arrays, graphs, trees, sliding windows, heaps, and hashmaps.
System Design: Studied Grokking the System Design Interview and watched several Atlassian interview breakdowns on YouTube. Practiced designs like collaborative editing systems, notification services, and tagging systems.
Behavioral / Values: Printed out Atlassian’s core values and prepared 8–10 STAR-format stories covering conflict resolution, teamwork, customer focus, ownership, and work-life balance.
English Communication: Since every round was fully in English, I practiced “thinking aloud” every day. Atlassian interviewers care a lot about how you clarify requirements and explain trade-offs.

Full Interview Timeline

1. Recruiter Call (30 Minutes)

This was a very relaxed conversation covering my background, previous projects, why I wanted to join Atlassian, and salary expectations.

The recruiter also sent me detailed interview preparation materials afterward, which honestly gave me a very positive impression of the company culture.

2. Karat Technical Screening (60 Minutes)

This round started with a short self-introduction, followed by several rapid-fire system design questions (scailing scenarios, distributed systems concepts, consistent hashing use cases, etc.).

After that, I solved two coding problems:

One graph-related problem
One hashmap optimization problem

I used Python and focused heavily on explaining my reasoning while coding. The interviewer clearly cared more about structured thinking and communication than perfect syntax.

3. Virtual Onsite (4–5 Rounds)

Coding Round

This round focused on data structures and clean code design. The problem involved hierarchical relationships and graph traversal.

I initially proposed a brute-force approach, then gradually optimized it into an LCA-style solution while discussing trade-offs.

Low-Level Design / Object Modeling

I was asked to design a Jira-like ticketing system object model. The interviewer focused heavily on extensibility, OOP principles, and maintainability.

System Design Round

This was probably my favorite round.

I designed a collaborative document editing system similar to Confluence real-time editing. We discussed:

API design
Database schema
WebSocket communication
Conflict resolution
Scalability considerations

The interviewer kept asking “why” and challenging trade-offs, which made the discussion feel very close to a real engineering brainstorming session.

Values Interview

This was the most unique part of the process.

The interviewer came from a non-engineering background and focused entirely on Atlassian values:

“Tell me about a time you played as a team.”
“How do you handle situations involving poor communication or organizational friction?”
“What does balance mean to you as an engineer?”

Because I had prepared real stories instead of memorized answers, the conversation felt natural and enjoyable.

Hiring Manager Round

This round focused on long-term growth, project impact, collaboration style, and career goals.

It honestly felt more like discussing future teamwork than a traditional interview.

Challenges I Faced

One coding round definitely didn’t start well. I got stuck initially and couldn’t immediately identify the optimal approach.

Instead of panicking, I paused and said:

“Let me clarify the edge cases first and break the problem down step by step.”

That turned out to be the right move.

Atlassian interviewers consistently seemed to value structured thinking and communication over instant problem solving.

The Offer

About two weeks later, the recruiter called and told me I had passed the Hiring Committee review.

The compensation package exceeded my expectations:

Competitive base salary
RSUs/equity
Strong benefits
Remote/hybrid flexibility
Relocation support

Signing the offer honestly felt surreal. Getting the opportunity to work on products I already loved using every day was incredibly exciting.

Advice for Future Atlassian Candidates

Technical skills alone are not enough. Cultural fit and communication matter just as much.
Practice thinking aloud. Interviewers want to understand your reasoning process.
Prepare thoroughly for Karat. Rapid-fire design questions can catch people off guard.
Use real stories for behavioral interviews. Authenticity matters more than polished scripts.
Stay calm during interviews. Atlassian interviewers are generally collaborative and supportive.

One Thing That Helped Me a Lot

One thing I realized during preparation is that modern big-tech interviews are no longer just about solving LeetCode questions quickly.

Especially for companies like Atlassian, communication quality, system design thinking, and behavioral performance can heavily impact the final result.

During my preparation process, I also reviewed materials and mock interview resources from ProgramHelp.

Their Atlassian and Karat interview question collections were surprisingly close to the actual interview style, especially the rapid-fire system design discussions and behavioral interview simulations.

What I found most useful was that they focused not only on “getting the correct answer,” but also on:

How to explain trade-offs clearly
How to structure communication during interviews
How to think aloud effectively under pressure
How to sound collaborative rather than robotic

That communication-focused preparation matched Atlassian’s interview culture extremely well.

If you’re currently preparing for Atlassian, Google, Meta, Amazon, or other North American tech companies, doing mock interviews early can genuinely save a lot of time and avoid unnecessary mistakes.

Final Thoughts

If you’re currently preparing for Atlassian interviews, I hope this breakdown gives you a clearer picture of what the process actually feels like.

The interviews are challenging, but they’re also fair and collaborative. Strong preparation combined with honest communication can absolutely get you there.

Good luck — and hopefully your dream offer is coming soon too.

Goldman Sachs Full-Time Offer Interview Experience

net programhelp — Thu, 21 May 2026 05:47:57 +0000

Complete Process Breakdown + Preparation Tips

Goldman Sachs is widely recognized as one of the most prestigious investment banks on Wall Street.
Its interview process is known for being rigorous, detail-oriented, and highly competitive.
In this post, I want to share my full journey from application to receiving a full-time offer,
hoping it can help students currently preparing for upcoming recruiting seasons.

Background & Application Timeline

Position: Analyst (applicable to IBD / Operations / Core Support divisions depending on team)

Timeline

Early August: Submitted application through official website
Mid August: Received HireVue invitation
Early September: Zoom interviews (first and second rounds)
Late September: Superday final round
Early October: Received verbal offer

Round 1: HireVue (One-Way Video Interview)

Goldman Sachs’ HireVue questions are relatively standardized.
Candidates are usually asked 3–5 behavioral questions, with limited preparation time and around 2–3 minutes to answer each question.

What They Evaluate

Motivation for applying to Goldman Sachs
Understanding of the role
Communication skills
Team collaboration and problem-solving ability

Questions I Received

Tell us about a time you faced disagreement within a team and how you resolved it.
Why Goldman Sachs? What unique value can you bring to this role?

Preparation Tips

Prepare a strong behavioral story bank using the STAR framework
(Situation, Task, Action, Result). During recording, maintain eye contact with the camera,
smile naturally, and demonstrate confidence and energy throughout the interview.

Round 2: Behavioral + Technical Video Interview

This round is usually conducted by Associates or Vice Presidents and typically lasts around 45 minutes.
It is significantly more challenging and detail-oriented.

Behavioral Questions

Interviewers often dive deeply into your resume and previous experiences.
They may ask highly specific follow-up questions such as:

“You mentioned conducting market research in your internship.
What were your exact data sources? How much did the conversion rate improve?”

Technical Questions

The technical portion varies depending on the role:

Business / Finance Roles: Financial modeling, valuation methodologies, market trends, M&A discussions, and macroeconomic topics such as inflation or interest rates.

<li>
  <strong>Tech / Data Roles:</strong> Data structures, algorithms, SQL, Python, and practical business applications.
</li>

Preparation Tips

Goldman interviewers are extremely professional and sometimes intentionally apply pressure.
If you do not know an answer, avoid bluffing. Instead, acknowledge the knowledge gap honestly
while demonstrating structured logical thinking.

“I may not know the exact figure, but based on my understanding,
my reasoning process would be...”

Showing analytical thinking under pressure is often more important than having a perfect answer.

Round 3: Superday (Final Round)

Superday is by far the most mentally and physically demanding stage of the process.
Candidates typically go through 3–4 back-to-back interviews, each lasting around 30–45 minutes.

Interviewers may include VPs, Executive Directors, and Managing Directors (MDs).

Culture Fit Evaluation

Senior leadership pays significant attention to whether candidates align with Goldman Sachs’ culture:

High performance mindset
Strong teamwork ability
Resilience under pressure
Self-driven personality

Case Study / Business Analysis

Candidates may receive a live business case, market scenario, or financial dataset
and be asked to provide structured analysis and recommendations within a short timeframe.

Reverse Questions

This is one of the most underestimated opportunities to stand out.

Instead of asking generic questions, prepare thoughtful discussions such as:

Industry trends the interviewer finds most exciting
Major projects they are most proud of at Goldman Sachs
How the firm is adapting to evolving market conditions

Strong reverse questions demonstrate curiosity, business awareness, and maturity.

Key Takeaways

Goldman Sachs interviews are not only about technical excellence.
The firm also evaluates communication, structured thinking, composure under pressure,
and long-term leadership potential.

In my experience, the most important preparation strategies were:

Consistent mock interviews
Strong networking efforts
Deep understanding of your own resume
Practicing concise and structured communication

Interview & OA Support

For candidates preparing for competitive recruiting processes,
we also provide structured support across different stages:

OA assistance and online assessment support
Mock interviews and behavioral coaching
VO (Video Online) guidance and technical preparation
Algorithm, system design, and coding support
Career consulting and interview strategy sessions

Beyond simply preparing for interviews, the focus is on helping candidates build
stronger problem-solving ability, communication skills, and confidence under pressure.

Final Thoughts

Recruiting at top firms is often a combination of preparation, execution,
information advantage, and consistency.

Hopefully this breakdown gives you a clearer picture of what the Goldman Sachs process looks like
and how to prepare effectively for each stage.

Wishing everyone the best of luck in recruiting season and hope you land your dream offer soon.

2026 Google SDE NG Virtual Onsite Deep Dive Review

net programhelp — Wed, 20 May 2026 15:52:42 +0000

In Q1 2026, competition for New Grad and entry-level roles across North American tech companies has reached an unprecedented level of intensity. Even top-tier companies like Google are now applying significantly stricter hiring standards for 2026 SDE New Grad candidates.

Recently, the ProgramHelp team successfully helped one of our full-service students secure a 2026 Google SDE New Grad Offer. In this article, we will conduct a deep dive breakdown of the candidate’s three core Virtual Onsite rounds: Behavioral, Coding, and System Design/OOD — while analyzing the newest interview trends, hidden pitfalls, and evaluation standards for 2026.

Round 1: Googliness & Leadership (Behavioral Interview)

Many technically strong candidates underestimate Google’s Behavioral round, assuming that being positive and “politically correct” is enough to pass. However, under the 2026 environment of organizational streamlining and stronger operational efficiency requirements, Google now places far more emphasis on:

Intrinsic motivation under ambiguity
Cross-team collaboration capability
Expectation management
Professional maturity under pressure

1. Core Question & Follow-Up Pressure

The candidate was asked:

“When you are assigned to a completely unfamiliar tech stack, and your mentor is unavailable due to vacation or resignation, how would you ensure the project does not get delayed?”

The interviewer immediately drilled deeper into execution-level details:

“How do you quantify your so-called fast learning ability?”
“What is your backup plan if senior engineers from other teams refuse to help?”

2. Expert-Level Breakdown Strategy

One of the biggest mistakes candidates make is relying on generic STAR-method templates filled with polished buzzwords. Google interviewers have seen thousands of rehearsed answers and can instantly identify artificial storytelling.

A strong answer must demonstrate genuine professional maturity:

Build a blocker assessment matrix: Explain how you would first spend 1–2 hours reviewing the existing codebase and internal documentation to identify core blockers before escalating for help.
Expectation management: Demonstrate how you communicate risks proactively to the Engineering Manager using milestones, dependency maps, and alternative implementation plans.
Asynchronous collaboration: Emphasize structured RFC-style documentation and concise technical questions that respect senior engineers’ time instead of requesting unnecessary meetings.

Round 2: Coding (Algorithms & Data Structures)

For 2026 Google SDE NG interviews, direct LeetCode-style originals have almost disappeared. Most coding rounds are now advanced variations built on top of classic algorithmic patterns.

Candidates are typically expected to:

Understand the pattern immediately
Design the optimal solution within minutes
Write completely bug-free production-quality code
Perform dry runs and edge-case validation

All within roughly 20–25 minutes.

1. Core Problem Reconstruction

The candidate encountered a high-level hybrid problem involving:

Sliding Window
Monotonic Queue

Problem summary:

Given an integer array nums and a positive integer k, find a contiguous subarray of length k such that:

The difference between the maximum and minimum values in the window does not exceed limit

The sum of the subarray is maximized

If no valid subarray exists, return -1.

2. Hidden Pitfalls & Complexity Analysis

Time Complexity Trap: A naive sliding window with repeated traversal results in O(N × k) or even O(N²), which immediately fails under Google’s large hidden test cases.
Space Optimization: Candidates must maintain window state within O(k) auxiliary space.
Optimal Solution: Use two monotonic deques:
- A decreasing deque for maximum values
- An increasing deque for minimum values
allowing both operations in amortized O(1) time and reducing the full algorithm to O(N).

3. High-Quality Python Reference Solution


from collections import deque

def max_sum_subarray_with_limit(nums: list[int], k: int, limit: int) -> int:
    """
    Finds the maximum sum of a contiguous subarray of fixed length k
    such that the difference between the max and min elements in this
    window does not exceed the given limit.

    Time Complexity: O(N)
    Space Complexity: O(k)
    """

    if not nums or len(nums) < k:
        return -1

    max_dq = deque()  # Monotonically decreasing
    min_dq = deque()  # Monotonically increasing

    max_sum = -1
    current_window_sum = 0

    for i, num in enumerate(nums):
        current_window_sum += num

        while max_dq and nums[max_dq[-1]] <= num:
            max_dq.pop()
        max_dq.append(i)

        while min_dq and nums[min_dq[-1]] >= num:
            min_dq.pop()
        min_dq.append(i)

        if i >= k:
            current_window_sum -= nums[i - k]

            if max_dq[0] == i - k:
                max_dq.popleft()

            if min_dq[0] == i - k:
                min_dq.popleft()

        if i >= k - 1:
            current_max = nums[max_dq[0]]
            current_min = nums[min_dq[0]]

            if current_max - current_min <= limit:
                max_sum = max(max_sum, current_window_sum)

    return max_sum

Round 3: System Design / Object-Oriented Design

For 2026 New Grad hiring, Google has significantly strengthened its evaluation of:

System fundamentals
Scalability thinking
Concurrency control
Object-oriented architecture

Although this round is labeled “System Design,” interviewers usually do not expect NG candidates to design an entire YouTube-scale platform. Instead, the focus is increasingly placed on microservice-level infrastructure components and deep implementation reasoning.

1. Core Design Question

The candidate was asked to design a distributed rate limiter capable of supporting millions of QPS.

The interviewer focused heavily on:

Algorithm selection
Concurrency handling
Latency optimization
Distributed consistency

2. Key Technical Follow-Ups

The interviewer raised several high-pressure follow-up questions:

Token Bucket vs Leaky Bucket: Which performs better under burst traffic conditions, and why?
Distributed Race Conditions: If multiple servers simultaneously refresh tokens for the same User ID in Redis, how do you prevent concurrency conflicts?
Network Latency Optimization: If every request synchronously queries centralized Redis, RT becomes unacceptable. How would you reduce latency using local cache and asynchronous batching?

During the coaching process, our technical experts guided the candidate toward a Redis + Lua scripting solution for atomic operations, while also introducing a Token Pre-allocation mechanism.

This architecture significantly reduced centralized network overhead and helped convince the interviewer that the design could lower effective latency by over 85%.

2026 Recruiting Winter: Precision Beats Luck

After reviewing these three intense Google Virtual Onsite rounds, it becomes increasingly clear: success in 2026 recruiting is no longer about simply grinding LeetCode alone.

The margin for error has become extremely small. One missed edge case in Coding, or one weak answer during Behavioral, can instantly lead to rejection and a year-long cooldown.

What Can ProgramHelp Provide?

Real-Time Big Tech Interview Assistance: Coverage across CodeSignal, HackerRank, AMCAT, and the latest FAANG Virtual Onsite processes. Our Ex-FAANG and top-tier Quant experts provide real-time behind-the-scenes support.
Safe & Professional Live Coaching: All coding solutions are handwritten in real time by experienced engineers. No AI-generated templates or high-duplication solutions.
Think-Aloud Communication Training: Beyond optimal algorithms, we guide candidates on how to communicate clearly with interviewers, control interview pacing, and demonstrate engineering maturity under pressure.

Leave uncertainty to others. Keep the offer for yourself.

2026 Google NG TL + Interview Full Review

net programhelp — Tue, 19 May 2026 14:37:30 +0000

After several months of interviews and evaluations, I finally received an offer for the 2026 Google New Grad (NG) Software Engineer role.

This post breaks down the full timeline, interview structure, and preparation strategy based on the entire process.

🚀 Full Timeline

The 2026 hiring cycle is longer and more structured than before. Here’s the full journey:

Stage	Timeline	Notes
Application / Referral	Sep 2025	Submitted via referral
Online Assessment (OA)	Oct 2025	HackerRank-style coding test
HR Pre-screen	Nov 2025	Basic background + role alignment
Virtual Onsite	Jan 2026	3 coding + 1 behavioral round
Feedback / Hiring Pool	Feb 2026	Waiting period
Team Match	Mar - Apr 2026	Two team discussions
Hiring Committee	Apr 2026	Final review stage
Offer	May 2026	Compensation + signing

💻 Interview Breakdown

1. Online Assessment (OA)

2 coding problems in 90 minutes
Common topics:
- String manipulation
- Graph traversal (BFS / DFS)
- Dynamic programming

Key focus:

Optimal time complexity
Clean and readable code
Edge case coverage

2. HR / Technical Screen

Some candidates may skip this stage depending on profile strength.

Topics:

Hash maps
Stack / Queue
Basic algorithm reasoning

Duration:

~45 minutes

3. Virtual Onsite (Main Stage)

This is the most important stage.

Coding Round 1 — Data Structure / Trees

Binary tree variations
Insert / search optimization
Follow-ups on scalability

Focus:

Improving from O(N) → O(log N)
Handling large-scale constraints

Coding Round 2 — DP + Graph

Shortest path variations
Dijkstra / BFS state expansion
Memoization-based solutions

Key skill:

Quickly identifying correct model (graph vs DP)

Coding Round 3 — System Design (Lite)

Rate limiter OR scheduling system
Object-oriented design (OOD)
API + class design

Focus:

Clean architecture
Extensibility
Robust edge-case handling

Googliness Round (Behavioral)

This round evaluates:

Team collaboration
Conflict resolution
Ambiguity handling
Ownership mindset

Common questions:

“Tell me about a disagreement with a teammate.”
“When did you take initiative beyond your role?”

🧠 Preparation Strategy

1. Algorithm Approach

Always clarify before coding
Identify edge cases first
Do a dry run after implementation

2. Complexity Awareness

Always clearly explain:

Time complexity
Space complexity

Google heavily evaluates communication clarity.

3. Behavioral (STAR Method)

Use:

Situation
Task
Action
Result

Focus on:

Leadership
Conflict handling
Ownership under ambiguity

📌 Final Thoughts

Google interviews are not just about algorithms — they evaluate how you think, communicate, and handle ambiguity.

Structured preparation makes a huge difference.

🚀 Preparation Resource

If you're preparing for Google, Meta, Amazon, or other Big Tech interviews, structured practice can significantly improve your performance.

👉 Learn more here: https://programhelp.net/en/

💬 Closing

If you're going through the same journey, stay consistent. The process is long, but the system rewards preparation and clarity of thinking.

Amazon Coding OA Experience & Pitfalls Guide (2026 Latest)

net programhelp — Mon, 18 May 2026 14:29:20 +0000

Recently, Amazon started rolling out a brand-new type of Online Assessment for some New Grad, Intern, and SDE candidates: the AI Assisted Coding OA. Unlike traditional LeetCode-style coding rounds, this assessment simulates a real engineering workflow. Instead of solving isolated algorithm problems from scratch, candidates are given an existing codebase plus an AI coding assistant, then asked to debug, optimize, and improve the system.

The overall evaluation focuses on much more than just “passing test cases.” Amazon is evaluating whether you can operate like a real software engineer in an AI-assisted development environment.

What Is Amazon AI Assisted Coding OA?

The OA usually includes:

Existing backend or service-layer code
API handlers and helper functions
Unit tests and partial integration tests
An embedded AI assistant for prompting and code generation

Your job is to understand the repository quickly, identify bugs or missing logic, and collaborate with the AI assistant to complete the required fixes.

The scoring dimensions often include:

Functional correctness
Debugging ability
Codebase comprehension
Prompt engineering quality
Edge case handling
Code maintainability and engineering judgment

In short, Amazon wants to see whether you can effectively use AI tools without blindly depending on them.

Overall OA Workflow

Part 1 — Reading the Existing Codebase

The assessment starts with a small but realistic project structure. You may see:

Service layers
Data processing modules
API routes
Utility/helper functions
Testing files

The codebase is not extremely large, but under time pressure it becomes challenging to:

Trace data flow
Understand dependencies
Identify side effects
Locate hidden failure points
Infer intended behavior from incomplete tests

This part feels very similar to onboarding into a real engineering project.

Part 2 — AI Assisted Debugging & Fixing

This is the core of the OA. You are allowed to interact with the built-in AI assistant at any time for:

Code generation
Bug fixing
Refactoring suggestions
Complexity optimization
Understanding unfamiliar code

However, the AI is far from perfect. In my experience, it frequently:

Introduced hallucinated logic
Broke existing API behavior
Ignored edge cases
Increased complexity unnecessarily
Missed concurrency or mutation issues

Amazon is not testing whether you can copy AI-generated code. They are testing whether you can validate, critique, and refine AI output like a strong engineer.

Main Pitfalls I Encountered

1. Trusting AI Too Much

At first, I relied heavily on the assistant and accepted generated code with minimal verification. Big mistake.

The visible tests passed, but hidden bugs remained everywhere. Some fixes introduced subtle side effects that only appeared under edge conditions.

The strongest candidates are usually the ones who:

Break problems down manually
Validate every AI-generated change
Add their own edge-case tests
Review time complexity carefully

AI should accelerate your workflow, not replace your engineering judgment.

2. Weak Prompting

Simple prompts like:

fix this bug

usually produce low-quality outputs.

The quality of AI output improves dramatically when prompts specify:

Expected behavior
Input constraints
Performance requirements
Modification scope
Backward compatibility

Some prompts that worked well for me:


Preserve existing API behavior
Only modify helper layer
Avoid O(n²) complexity
Handle null input safely
Do not mutate shared state
Keep current unit tests compatible

Precise prompting became one of the biggest differentiators during the OA.

3. Ignoring Hidden Tests

Amazon hidden tests are significantly stricter than sample tests. They often target:

Null or empty inputs
Duplicate values
Overflow conditions
Concurrency issues
Unexpected mutation
Performance bottlenecks

Passing visible tests means almost nothing. You must actively simulate worst-case scenarios yourself.

What Amazon Is Actually Evaluating

Engineering Thinking

This OA heavily emphasizes:

Code reading ability
Debugging methodology
System understanding
Dependency tracing

This is much closer to real software engineering work than traditional algorithm grinding.

AI Collaboration Ability

Amazon wants engineers who can:

Use AI efficiently
Filter incorrect suggestions
Improve generated solutions
Maintain code quality under AI assistance

Blindly copying AI output is actually a negative signal.

Prompt Communication Skills

Prompt engineering is now becoming part of software engineering itself.

The clearer and more structured your prompts are, the more reliable your workflow becomes. This OA strongly rewards candidates who can communicate technical constraints precisely.

My Preparation Recommendations

After experiencing this OA, I realized that pure LeetCode grinding is no longer enough. The preparation strategy needs to evolve.

I would strongly recommend focusing on:

Reading unfamiliar repositories quickly
Tracing function calls and dependencies
Reviewing AI-generated code critically
Practicing prompt engineering
Testing edge cases manually
Understanding real-world debugging workflows

A surprisingly effective method is taking small-to-medium open-source GitHub projects and practicing:

Bug localization
Flow tracing
Refactoring safely
AI-assisted debugging

About Programhelp

One of the hardest parts for me initially was simply understanding what this new AI OA even looked like. Most public interview discussions were outdated and still focused only on traditional coding rounds.

Later, a friend recommended Programhelp. They had already compiled a large amount of updated information specifically for the latest Amazon AI Assisted Coding OA, including:

Real OA workflow breakdowns
High-frequency bug patterns
Prompt templates
Hidden test pitfalls
Debugging strategies
Codebase reading techniques
Mock practice environments

For candidates attempting this new OA format for the first time, the information gap is honestly huge. Having exposure to realistic workflows beforehand can save a massive amount of time and prevent avoidable mistakes during the actual assessment.

Final Thoughts

Amazon’s AI Assisted Coding OA feels like a preview of how future software engineering interviews may evolve. It is no longer just about memorizing algorithms.

The strongest candidates are the ones who can:

Understand complex systems quickly
Collaborate effectively with AI tools
Debug under ambiguity
Think critically about generated code
Maintain engineering quality under pressure

If you are preparing for Amazon New Grad or Intern recruiting in 2026, adapting to this new interview style early will give you a major advantage.

Good luck to everyone preparing for the OA — hope you all land your Amazon offers soon.

Uber SDE Interview Experience Sharing | Full VO Breakdown + Offer Timeline

net programhelp — Sun, 17 May 2026 14:29:44 +0000

Hi everyone, I recently completed the full Uber SDE interview process and successfully received an offer.
Uber has always been one of those “dream companies” for many engineers. The interview style is relatively friendly,
but the depth of evaluation is definitely not easy. They care a lot about system thinking, real engineering experience,
and communication ability.

I wanted to share my entire experience from application to offer in detail, hoping it helps anyone currently preparing
for Uber or other large-scale tech companies.

Timeline

Application Submitted: T-4
OA Received: T-3
First Interview Invitation: T-1
First Interview: T
VO Invitation: T+2
VO Rounds: Conducted on 4/21
Offer: Received afterward

First VO Round (45 Minutes)

This round was conducted by two interviewers together. The atmosphere was actually pretty relaxed,
but the question coverage was broad.

Small Talk + Self Introduction

How familiar are you with Uber’s business lines and engineering culture?
Pros and cons of Microservices vs Monolith?

After that, they spent quite a bit of time diving deep into my resume.
The interviewers focused heavily on my high-concurrency project experience and API latency optimization work.

They basically went through the architecture descriptions on my English resume line by line,
so I strongly recommend spending time polishing your resume carefully.
Make sure you can clearly explain your architecture diagrams, system metrics, bottlenecks,
and optimization strategies.

Additional Questions

Top 3 technical challenges from your previous internship/project
Long-term career goals
Why Uber?
What do you hope to learn at Uber?

Toward the end, the interviewers also introduced their team’s tech stack and daily engineering work.
Overall, the conversation felt very professional and friendly.

Second VO Session (3 Hours 15 Minutes, Three Consecutive Interviews)

Round 1 — Coding (Graph / Interval Problems)

The coding problem was similar to a Merge Intervals or shortest-path BFS variation,
combined with ride-sharing scenarios such as driver matching or route planning.

My main strategy was prioritizing time complexity optimization.
The interviewer specifically asked whether I would prioritize time optimization or space optimization.
I chose time optimization and carefully explained my data structure choices,
complexity analysis, and edge-case handling.

This round lasted nearly one hour.
The biggest focus areas were:

Code cleanliness
Boundary condition testing
Data structure usage
Communication during implementation

Round 2 — System Design

The system design question was:

Design a driver location update system.

Main discussion topics included:

GeoHash vs QuadTree trade-offs
Optimizing high-frequency write operations
Rate limiting strategies during traffic spikes
Peak-hour traffic smoothing and scalability

I started from high-level architecture design and gradually moved into implementation details.
The interviewer asked very detailed follow-up questions,
but overall the discussion was smooth and collaborative.

Round 3 — Behavioral + Deep Dive

Describe a SEV incident you encountered in production and how you debugged it
If you had to trade off between release timeline and code quality, what would you choose?
How do you handle disagreements with PMs or teammates using logic and data?
Where do you see yourself in five years?

This round went surprisingly deep.
The interviewer kept drilling into details and decision-making logic,
but the discussion itself was very structured and professional.

Overall Thoughts

Uber’s interview process moves pretty fast,
but the interviewers are generally very experienced and respectful.

In my opinion, the two biggest factors are:

Strong resume quality
Real engineering/project experience

For system design preparation, I highly recommend practicing real-world business scenarios related to:

Ride-sharing systems
Maps and geolocation services
Dispatch systems
High-concurrency order platforms

Preparation Tips

Keep your English resume clean and professional
Practice high-concurrency and geolocation-based system design problems
Prepare behavioral questions using the STAR method
Understand Uber’s business model and engineering culture beforehand

Final Thoughts

Honestly, one of the biggest reasons I was able to perform confidently during the Uber interview process
was because of a recommendation from a friend.

He told me that when he was preparing for similar interviews,
his preparation efficiency was pretty low at first.
Later, he got help from
Programhelp,
and the improvement was significant.

I decided to give it a try as well,
and I found their mentors surprisingly professional.
They provided:

OA preparation assistance
Mock interview sessions
System design walkthroughs
Behavioral story polishing
Resume optimization
High-intensity interview simulations

What helped me the most was how targeted their feedback was.
Whether it was resume deep-dives, communication structure,
or handling difficult follow-up questions,
the preparation felt much closer to real interviews than generic online practice.

If you are currently preparing for Uber, Lyft,
or other transportation-tech companies and feel lost studying alone,
you can check out
Programhelp.

They focus on OA preparation support, interview guidance,
and full-process assistance tailored to your background and target companies.

Thanks for reading, and good luck to everyone preparing for interviews.
Hope you all get your dream Uber offer soon!

DRW Online Assessment Full Breakdown | 2026 Latest OA Experience

net programhelp — Fri, 15 May 2026 14:53:11 +0000

Over the past few years, quantitative trading company recruiting has become significantly more competitive, and
DRW remains one of the most targeted firms for SWE, Quant, Trading, and low-latency engineering candidates.
Compared to traditional big tech online assessments that heavily rely on standard LeetCode patterns, DRW’s OA feels far more
“trading-firm oriented.”

A very common reaction from candidates after finishing the assessment is:
the problems themselves may not always be harder than Meta or Google individually,
but the overall pressure level is much higher.

DRW emphasizes:

Fast system modeling ability
Stable implementation under pressure
Strong debugging instincts
Mathematical intuition
Simulation-heavy engineering thinking

After reviewing a large number of 2026 OA experiences, one thing is very clear:
DRW continues to heavily favor realistic engineering-style problems rather than pure algorithm memorization.

What Does the 2026 DRW OA Look Like?

The exact structure varies slightly across roles, but the overall process for SWE, Quant, Trading, and C++ positions
has become fairly standardized.

Most candidates receive assessments through HackerRank or CodeSignal.
The total duration is typically between 70 and 120 minutes.

Common sections include:

2-3 coding questions
Probability or math questions
Debugging tasks
Multiple-choice conceptual questions
C++ / systems fundamentals (for infrastructure roles)

SWE positions usually focus more on:

Simulation
Heap implementation
Graphs
Robust data structure handling
Edge case management

Quant and Trading roles tend to include much more:

Expected value
Conditional probability
Game theory
Mental math
Optimal strategy reasoning

Meanwhile, C++ infrastructure roles often test:

Memory management
Concurrency
Iterator invalidation
STL internals
Performance-sensitive implementation

Many candidates initially assume:
“It’s just another LeetCode OA.”
But once the DRW assessment begins, the difference becomes obvious almost immediately.

DRW’s Biggest Coding Characteristic: Real Trading System Simulation

One of the most frequently reported question categories this year involves matching engines and exchange simulations.

Typical problems provide:

Buy orders
Sell orders
Timestamps
Volumes
Cancel requests

Candidates are then asked to implement:

Price priority
Time priority
Partial fills
Order matching logic
Dynamic updates

In reality, these are simplified versions of actual exchange systems.

The difficulty usually does not come from algorithm complexity itself.
The real challenge is implementation stability.

Common failure points include:

Incorrect duplicate handling
Heap synchronization bugs after cancellation
Wrong tie-breaking logic
Volume updates after partial fills
State inconsistencies

DRW strongly cares about whether your implementation can survive complex edge cases,
not simply whether you know the “correct idea.”

Simulation + Heap Is Extremely Common

Another major 2026 trend is the heavy use of heap-based simulations.

However, these are rarely standard “median stream” style LeetCode templates.

Instead, DRW often combines:

Heap
Simulation
Dynamic updates
Lazy deletion
Rebalancing
Duplicate handling

Some candidates reported problems involving real-time price feeds with:

Add operations
Remove operations
Update operations
Top-K queries

The hardest part is usually not the data structure itself,
but maintaining consistency under continuous state changes.

Many candidates run out of time not because they cannot solve the problem,
but because debugging consumes too much time.

Graph Problems Feel More “Real World” Than LeetCode

DRW absolutely tests graph algorithms such as:

BFS
Dijkstra
Shortest path
Graph traversal

But the presentation style is very different from Google.

Google often emphasizes abstract optimization.
DRW instead prefers realistic system-oriented abstractions.

Recent examples include:

Liquidity propagation
Packet routing
Network latency spread
Arbitrage path discovery

Underneath, these may still reduce to shortest path or graph search problems,
but if candidates cannot quickly recognize the abstraction,
they lose enormous amounts of time reading and interpreting the problem statement.

Probability Is a Major SWE Weakness Area

This is one of the biggest differences between DRW and traditional internet companies.

Many candidates with extremely strong LeetCode ratings struggle badly with probability sections,
simply because large tech companies rarely emphasize probability anymore.

DRW still heavily tests:

Expected value
Conditional probability
Bayesian thinking
Dice and coin games
Optimal strategy analysis

Some questions are not even coding-based.
Instead, candidates must explain reasoning and justify strategies mathematically.

A common issue is:
candidates know formulas,
but cannot properly model the scenario once the wording changes.

The Time Pressure Is Worse Than the Difficulty

Almost every candidate mentions this point.

The biggest challenge in DRW OA is not necessarily raw difficulty,
but rapid context switching.

You may go from:

A matching engine implementation
To a probability proof
To a low-level debugging task
To a systems question

all within a very short time window.

Large simulation problems also produce extremely long code,
making time management a critical skill.

Python users especially reported performance issues this year.
Passing often required:

Fast I/O
Reduced string copying
Lower constant factors
Careful container selection

Otherwise, the final hidden test cases frequently resulted in TLE.

Debugging Questions Feel Like Real Engineering

DRW debugging sections are much more realistic than those from many other companies.

Instead of obvious syntax mistakes,
candidates often face actual production-style engineering failures.

Common examples include:

Iterator invalidation
Dangling pointers
Overflow
Race conditions
unordered_map rehash behavior
vector resize side effects

Candidates who only practiced LeetCode but lack large-scale engineering experience
often struggle badly here because the issue is not whether the code compiles —
it is whether the code survives extreme conditions safely.

Mental Math Is Another Major Filter

Especially for Trading and Quant roles,
DRW places enormous emphasis on numerical intuition.

Typical areas include:

Fast probability estimation
Expected value comparison
Card probability
Spread calculation
Mental arithmetic

Some interviews intentionally provide very limited scratch-paper time
to evaluate immediate reaction speed.

Many strong coders surprisingly struggle here because numerical intuition
is much harder to develop through short-term cramming.

How DRW Differs From Other Companies

Roughly speaking:

Amazon focuses heavily on common LeetCode patterns
Meta emphasizes optimization and coding speed
Google values abstraction and algorithmic modeling
Jane Street creates extremely high cognitive pressure
Citadel combines intense engineering and mathematics

DRW’s defining feature is that it rarely allows obvious weaknesses.

It expects candidates to simultaneously demonstrate:

Stable implementation ability
Fast coding under pressure
Mathematical intuition
Engineering robustness
System modeling skills

That is why many candidates describe the OA as feeling less like an exam
and more like a realistic trading systems simulation.

Most Effective Preparation Strategy in 2026

The most successful preparation path this year generally looked like:

Practice LeetCode Medium problems focused on heap, graph, and simulation
Move into Codeforces 1600-1900 range implementation problems
Study probability, expected value, and game theory fundamentals
Practice long-form simulation questions under strict time limits
Do mock OAs with realistic pressure conditions

The biggest issue for most candidates is not knowledge —
it is maintaining accuracy under time pressure.

A single forgotten edge case,
an incorrect comparator,
or one missed state update can destroy the entire solution.

Final Thoughts

DRW’s Online Assessment is one of the most representative modern quantitative trading interviews in North America.

It does not reward template memorization alone.
Instead, it heavily evaluates:

Engineering implementation quality
System-level thinking
Mathematical intuition
Simulation robustness
Performance under pressure

If your long-term goal is:

Quantitative trading
High-frequency trading systems
Low-latency infrastructure
Trading platform engineering

then adapting to DRW’s problem style early is extremely valuable.

Most first-time candidates walk away with the same realization:
this does not feel like normal interview prep —
it feels like solving actual trading system problems.

Need Help Preparing for DRW, Jane Street, or Citadel?

If you are preparing for top quantitative trading companies and feel stuck,
overwhelmed, or unsure about the most effective preparation strategy,
Programhelp provides personalized interview preparation support tailored to your background and target roles.

Services include:

Quant-focused mock interviews
Simulation and implementation training
Probability and trading math preparation
OA strategy guidance
System design coaching
One-on-one preparation support

Learn more here:
Programhelp

Amazon HackerRank OA | 2026 Real Questions + Coding Solutions

net programhelp — Thu, 14 May 2026 12:48:35 +0000

Amazon’s Online Assessment platform is consistently based on HackerRank. Most candidates receive 2 coding questions within 90 minutes, although a small number of OA versions contain 3 questions.

Compared with previous years, Amazon OA has become much more engineering-oriented. Instead of pure LeetCode pattern matching, candidates are increasingly expected to analyze realistic scenarios, optimize performance, and handle edge cases carefully.

Overall OA Structure

Question	Difficulty	Expected Time
Question 1	Easy-Medium	20-25 minutes
Question 2	Medium-Hard	50-60 minutes

What Amazon Actually Evaluates

Code readability and structure
Boundary condition handling
Time complexity optimization
Practical engineering thinking
Communication through clean implementation

Common failure points include:

O(n²) solutions timing out
Incorrect handling of empty arrays or single elements
Ring/circular indexing mistakes
Missing overflow or large-input considerations

Real OA Question #1 — Circular Delivery Minimum Time

Difficulty: Easy-Medium

Problem Description

Amazon's drone delivery system contains m hubs arranged in a circular topology. Hub 1 is adjacent to hub m.

You are given:

transitionTime: travel time between adjacent hubs
requestedHubs: sequence of delivery requests

The drone starts at hub 1. For each request, it may travel clockwise or counterclockwise. Return the minimum total travel time.

Example

m = 3
transitionTime = [3, 2, 1]
requestedHubs = [1, 3, 3, 2]

Output: 4

Core Idea

Any two hubs have two possible paths in a ring
Precompute prefix sums for fast clockwise distance calculation
Counterclockwise distance = total ring length - clockwise distance
Take the minimum for each transition

Python Solution


def minDeliveryTime(m, transitionTime, requestedHubs):
    prefix = [0] * (m + 1)

    for i in range(1, m + 1):
        prefix[i] = prefix[i - 1] + transitionTime[i - 1]

    ring_total = prefix[m]
    total_time = 0
    curr = 1

    for next_hub in requestedHubs:

        if next_hub == curr:
            continue

        # clockwise distance
        if next_hub > curr:
            clockwise = prefix[next_hub - 1] - prefix[curr - 1]
        else:
            clockwise = (
                prefix[m]
                - prefix[curr - 1]
                + prefix[next_hub - 1]
            )

        counterclockwise = ring_total - clockwise

        total_time += min(clockwise, counterclockwise)

        curr = next_hub

    return total_time

Important Notes

Careful with 1-based indexing
Ring traversal logic is easy to get wrong
Prefix sum preprocessing is the key optimization

Real OA Question #2 — Dynamic Task Scheduler

Difficulty: Medium-Hard

Problem Description

You are given tasks with:

arrival time
processing duration
priority

Design a scheduler in a multi-core environment that:

maximizes completed tasks
supports dynamic arrivals
supports preemptive scheduling
prioritizes high-priority tasks

Solution Strategy

Sort tasks by arrival time
Use a max-heap / priority queue
Simulate timeline progression
Handle arrival and completion events dynamically

Python Framework


import heapq

def maxTasks(tasks):

    # tasks = [
    #   [arrival, processing_time, priority]
    # ]

    tasks.sort()

    pq = []

    time = 0
    completed = 0
    i = 0

    while i < len(tasks) or pq:

        if not pq:
            time = tasks[i][0]

        while i < len(tasks) and tasks[i][0] <= time:
            heapq.heappush(
                pq,
                (-tasks[i][2], tasks[i][1] + time)
            )
            i += 1

        if pq:
            _, end_time = heapq.heappop(pq)

            time = max(time, end_time)

            completed += 1

    return completed

Common Follow-Up Questions

How would you support true preemption?
How would this scale in distributed systems?
How would you handle memory constraints?
How do you avoid starvation for low-priority tasks?

Preparation Advice

Time Management

Question 1: finish within 25 minutes
Reserve at least 50 minutes for Question 2

High-Frequency Topics

Circular arrays
Task scheduling
Greedy algorithms
Sliding window
Priority queues
Graph traversal
Dynamic programming

Practice Strategy

Simulate full HackerRank exams under strict 90-minute timing
Focus on Medium-Hard LeetCode problems
Train edge-case analysis intentionally
Review time complexity after every problem

Final Thoughts

One important realization during preparation was that blindly grinding problems was not the most efficient strategy. The biggest improvement came from targeted mock interviews, optimization reviews, and learning how to approach OA questions from an engineering perspective rather than only algorithm memorization.

For candidates preparing for Amazon, TikTok, Point72, and other major tech company OAs, structured preparation and realistic simulation can significantly improve performance under pressure.

If you're interested in additional interview preparation resources, coding guidance, or OA-focused practice support, you can explore:

ProgramHelp Interview Preparation Platform

2025-2026 latest OA question collections
Full mock OA simulations
Code optimization training
Interview preparation coaching
System design communication practice

Thanks for reading, and good luck with your Amazon OA preparation.

Anthropic SDE Interview Experience | 2026 Full 4-Round VO Breakdown

net programhelp — Wed, 13 May 2026 11:58:07 +0000

I recently completed the full interview process for the SDE (AI Infrastructure) role at Anthropic. This post shares a detailed breakdown of all four Virtual Onsite (VO) rounds while the experience is still fresh. Compared to traditional big tech companies, Anthropic places much stronger emphasis on AI safety reasoning, system design tradeoffs, and ambiguity handling.

Interview Process Overview

Stage	Content	Duration
OA (CodeSignal)	4 coding problems	70 min
Recruiter Screen	Behavioral + role alignment	30 min
VO Round 1	Coding (DSA)	60 min
VO Round 2	System Design (AI-focused)	60 min
VO Round 3	ML / AI Safety discussion	60 min
VO Round 4	Hiring Manager (Behavioral)	45 min

All VO rounds were completed within two consecutive days.

Preparation Strategy

Preparation for Anthropic requires balancing three dimensions: algorithms, system design, and AI safety understanding.

LeetCode focus: trees, graphs, union-find, sliding window, distributed systems basics
ML fundamentals: Transformer architecture, RLHF, reward modeling
System design: high-throughput logging systems, LLM inference architecture, prompt injection defense
AI safety topics: reward hacking, specification gaming, adversarial prompts

VO Round 1: Coding Interview

Problem Type: Sliding Window + Hash Map

Given a string s and an integer k, return the length of the longest substring containing at most k distinct characters. The input may include Unicode characters.

Key Ideas

Sliding window + frequency hash map
Shrink window when distinct count exceeds k
Unicode-safe iteration (language-dependent handling)

Follow-up Questions

How to handle infinite input streams?
How to enforce exactly k distinct characters?

VO Round 2: System Design

Problem: Design an LLM request audit system

The system must log all prompts and responses for safety review and compliance, supporting 100M+ daily requests with 90-day retention.

Architecture Overview

Ingestion: Kafka (partitioned by user_id hash)
Storage: Cassandra for hot data + S3 (Parquet) for cold storage
Search: Elasticsearch for full-text prompt retrieval
Alerting: async workers trigger webhook notifications

Key Discussion Topics

Async pipeline to avoid blocking API latency
Rate limiting for abusive users
Eventual consistency vs strong consistency tradeoffs
PII detection and data anonymization before storage

VO Round 3: ML / AI Safety Discussion

Reward Modeling

Discussed how to convert sparse user feedback (likes/dislikes) into a usable reward signal using ranking models such as Bradley-Terry or Elo-based systems.

Prompt Injection Defense

Explored strategies such as input/output separation, structured prompts, and secondary classifiers to detect malicious instructions.

Red Teaming Strategy

Designing automated adversarial prompt generation combined with human review and vulnerability prioritization.

VO Round 4: Hiring Manager Interview

Typical behavioral questions included:

Describe a time you identified a system risk and took ownership
Conflict resolution with engineers
Why Anthropic instead of Google or OpenAI
How you stay updated in AI safety research

Responses were structured using the STAR method, with emphasis on ownership, decision-making under uncertainty, and long-term thinking in AI systems.

Overall Reflection

Round	Performance	Improvement Area
Coding	Strong	Explain multiple approaches
System Design	Moderate	Better PII/GDPR preparation
ML Safety	Strong	Read more Anthropic papers
Behavioral	Strong	More failure-case examples

Final outcome: Offer received.

Preparation Resources

Anthropic Alignment Blog
Constitutional AI paper
LeetCode Top 100 (graphs, DP, sliding window)
System Design (Alex Xu volumes)

Additional Support

If you are preparing for Anthropic, OpenAI, or DeepMind interviews and need help with coding, system design, or AI safety preparation, you can explore structured support and resources here:

ProgramHelp Interview Preparation Platform

Latest 2025–2026 interview question bank
1-on-1 VO coaching sessions
System design speaking framework training
AI safety concept breakdowns

Anthropic SDE Interview Full Review | 2026 Four-Round VO Deep Dive

net programhelp — Wed, 13 May 2026 11:50:20 +0000

<p>
  I recently completed the full Anthropic SDE interview process. While everything is still fresh, I’m sharing a detailed breakdown
  of the four-round Virtual Onsite (VO). Compared to traditional big tech interviews, Anthropic’s style is more focused on
  AI safety awareness and reasoning-driven system design.
</p>

<h2>Interview Process Overview</h2>

<table border="1" cellpadding="8" cellspacing="0">
  <tr>
    <th>Stage</th>
    <th>Content</th>
    <th>Duration</th>
  </tr>
  <tr>
    <td>Online Assessment (CodeSignal)</td>
    <td>4 coding problems in 70 minutes</td>
    <td>1 round</td>
  </tr>
  <tr>
    <td>Recruiter Screen</td>
    <td>Behavioral + role alignment</td>
    <td>30 minutes</td>
  </tr>
  <tr>
    <td>VO Round 1</td>
    <td>Coding (DSA)</td>
    <td>60 minutes</td>
  </tr>
  <tr>
    <td>VO Round 2</td>
    <td>System Design (AI-focused)</td>
    <td>60 minutes</td>
  </tr>
  <tr>
    <td>VO Round 3</td>
    <td>ML / AI Safety Discussion</td>
    <td>60 minutes</td>
  </tr>
  <tr>
    <td>VO Round 4</td>
    <td>Hiring Manager (BQ + culture fit)</td>
    <td>45 minutes</td>
  </tr>
</table>

<p>
  I applied for the SDE, AI Infrastructure track (Spring 2026 intake). All VO rounds were completed within two consecutive days.
</p>

<h2>Preparation Strategy</h2>

<p>
  Anthropic evaluates both engineering depth and understanding of AI alignment concepts. Key preparation areas include:
</p>

<ul>
  <li>Data structures: trees, graphs, union-find, sliding window techniques</li>
  <li>System design: distributed systems, consistent hashing, rate limiting, logging pipelines</li>
  <li>ML fundamentals: transformers, RLHF, reward modeling</li>
  <li>AI safety: prompt injection, reward hacking, specification gaming</li>
</ul>

<h2>VO Round 1: Coding</h2>

<p>
  This round focused on string processing and hash map usage with edge cases.
</p>

<p>
  Problem: Given a string s and an integer k, return the length of the longest substring containing at most k distinct characters.
  The input may include Unicode characters.
</p>

<h3>Key Ideas</h3>

<ul>
  <li>Sliding window with frequency hash map</li>
  <li>Shrink window when distinct characters exceed k</li>
  <li>Unicode support is naturally handled in Python</li>
</ul>

<h3>Follow-up Questions</h3>

<ul>
  <li>How to handle infinite input streams?</li>
  <li>How to modify for exactly k distinct characters?</li>
</ul>

<p>
  Recommendation: explain approach first, then implement, then validate with examples.
</p>

<h2>VO Round 2: System Design</h2>

<p>
  Design a large-scale LLM request auditing system.
</p>

<p>
  Requirements:
</p>

<ul>
  <li>100M+ daily requests</li>
  <li>90-day data retention</li>
  <li>Query by user_id and time range</li>
  <li>Support risk tagging and alerting</li>
</ul>

<h3>High-Level Architecture</h3>

<ul>
  <li>Ingestion: Kafka with user-based partitioning</li>
  <li>Storage: Cassandra for hot data, S3 for cold storage</li>
  <li>Search: Elasticsearch for full-text prompt search</li>
  <li>Alerting: async workers + webhook triggers</li>
</ul>

<h3>Key Discussion Points</h3>

<ul>
  <li>Asynchronous ingestion to avoid blocking API latency</li>
  <li>Rate limiting abusive users via partition isolation</li>
  <li>Eventual consistency for audit tagging</li>
  <li>PII detection and anonymization before storage</li>
</ul>

<h2>VO Round 3: ML / AI Safety</h2>

<p>This round was purely conceptual, focusing on AI alignment and safety engineering.</p>

<h3>Reward Modeling</h3>

<p>
  How to convert sparse user feedback (likes/dislikes) into a reward signal?
  Common approaches include Bradley-Terry models, Elo scoring, and time-decayed weighting.
</p>

<h3>Prompt Injection Defense</h3>

<p>
  Users may attempt to override system instructions. Mitigation strategies include input/output separation,
  structured prompting, and classification-based detection.
</p>

<h3>Red Teaming</h3>

<p>
  A robust red teaming pipeline includes automated adversarial prompt generation, human review,
  and structured vulnerability tracking.
</p>

<h2>VO Round 4: Hiring Manager (Behavioral)</h2>

<p>Common questions included:</p>

<ul>
  <li>Describe a time you identified a system risk and fixed it proactively</li>
  <li>Describe a technical disagreement with a teammate</li>
  <li>Why Anthropic instead of Google or OpenAI?</li>
  <li>How do you stay updated on AI safety research?</li>
</ul>

<p>
  Answering approach: use STAR framework, emphasize ownership and long-term thinking.
  For “Why Anthropic”, focus on research-driven engineering culture and AI alignment mission.
</p>

<h2>Overall Reflection</h2>

<table border="1" cellpadding="8" cellspacing="0">
  <tr>
    <th>Round</th>
    <th>Performance</th>
    <th>Improvement Area</th>
  </tr>
  <tr>
    <td>Coding</td>
    <td>Strong</td>
    <td>Discuss multiple solution paths</td>
  </tr>
  <tr>
    <td>System Design</td>
    <td>Good</td>
    <td>Prepare compliance & PII handling deeper</td>
  </tr>
  <tr>
    <td>AI Safety</td>
    <td>Strong discussion</td>
    <td>Read more Anthropic research papers</td>
  </tr>
  <tr>
    <td>Behavioral</td>
    <td>Smooth</td>
    <td>Prepare more failure stories</td>
  </tr>
</table>

<p>
  Final result: Offer received successfully.
</p>

<h2>Recommended Preparation Resources</h2>

<ul>
  <li>Anthropic official blog (alignment research series)</li>
  <li>Paper: Constitutional AI: Harmlessness from AI Feedback</li>
  <li>LeetCode Top 100 (focus on graphs, trees, sliding window)</li>
  <li>System Design: Alex Xu Volume I & II</li>
</ul>

<h2>Need More Help?</h2>

<p>
  Preparing for Anthropic interviews requires balancing algorithmic depth, system design thinking, and AI safety awareness.
  It is a multi-dimensional preparation process.
</p>

<p>
  If you need:
</p>

<ul>
  <li>Full coding solutions and test cases</li>
  <li>Latest Anthropic / OpenAI / DeepMind interview question bank</li>
  <li>Mock interviews (coding + system design + AI safety)</li>
  <li>Resume review and referral strategy</li>
</ul>

<p>
  You can reach out to Programhelp. We provide structured interview preparation support including:
</p>

<ul>
  <li>Up-to-date 2025–2026 interview question bank</li>
  <li>1-on-1 VO simulation sessions</li>
  <li>System design communication training</li>
  <li>AI safety concept breakdown and guidance</li>
</ul>