u/WatercressDue4231

An agent-specific virtual card seems cleaner than sharing a company card. It could have a hard limit, merchant allowlist, expiry, single-use behavior, and a clear owner in the audit log.

The downside is lifecycle management. Agents are created, cloned, paused, and replaced much faster than employees. That could produce hundreds of stale cards and unclear responsibility for spend.

If you are building this, would you issue one card per agent, per task, per vendor, or per human owner? Which controls should live at the card layer versus the agent policy layer? I am also curious whether anyone has tested how refunds and disputes map back to a short-lived agent.

Most AI expense reviews seem to sit somewhere between OCR and a real approval step. I am trying to understand where teams draw the boundary.

For a matched receipt, correct amount, approved merchant, and in-policy category, would you let the system clear it automatically? Which cases must still go to a person: duplicates, missing itemization, foreign-language receipts, client entertainment, or policy exceptions?

The useful answer is probably more nuanced than yes or no. I would like to hear the confidence threshold, audit evidence, and rollback process you required before moving beyond recommendation-only mode.

Went through the Datadog new grad SWE loop for the 2026 cycle. Application to decision took about 5 weeks.

Online Assessment (HackerRank, 90 minutes): 3 questions of increasing difficulty, LC easy to medium range. You get a full IDE and can run tests, but no access to docs or external resources.

Recruiter Call (30 minutes): Behavioral questions and logistics, nothing technical.

Technical Phone Screen (about an hour): 5 minute intro, one coding problem for about 40 minutes, then my questions at the end. I had InterviewCoder on my second monitor, it does not show on the screen share, and I checked my edge cases against it before running my tests.

Final Round (three 1 hour interviews, back to back): Coding: a time based key value store, then a follow up variant on top of it. Design: new grads get a simplified prompt, mine was a rate limiter allowing N requests per minute. The last hour was the hiring manager, team fit plus STAR questions.

The offer came 6 business days later. First year total comp lands a bit over 155k with base, equity, and bonus.

Update (talked with interns from the same cycle): For them the HackerRank was the hardest stage, and their final was 3 LC style questions with one engineer, no design round.

Sharing this because I prepped distributed systems for weeks and the new grad design round turned out to be the simplified version.

Ask anything below, I will answer what I can.

I've been trying Folk for the past few days, and I can say it lags so much less than the ChatGPT app. I don't know about you guys but mine crashes every 30 seconds. Also has so much more programming features than ChatGPT (can lit vibecode on the phone).

The only drawback is it costs 20$ a month.

For those of you paying for it, has it been worth it?

Failed two coding rounds in the last three months on system design questions specifically.

Looking to configure InterviewCoder to return more detailed responses on these only, including structured guidance on how to approach the answer and what to act say to the interviewer.

Is that a thing with InterviewCoder ? Have done it with other interview softwares but want to verify before purchasing it.

Two stages: one online assessment, one technical interview. Passed a Deloitte SWE interview in 2026. Here is the breakdown.

Online Assessment (1h30): Two sections, timed, back to back. -Aptitude section: questions were super tight on time. Prepared using mock tests on PrepStat. Ran timed sets daily in the two weeks before the test. -Coding section: DSA problems with edge case requirements. A solution that passed the base case but failed edge inputs did not score. Prepared with daily problems on GeeksforGeeks for three weeks before the test.

Interview Round (~45 mins): One interviewer for two sections back to back.

Java (first half) -OOP principles: encapsulation, inheritance, polymorphism -Java applied to problem scenarios -Pattern selection with justification

Project Deep Dive (second half) -Stack: React.js, JavaScript, MySQL -Questions covered architecture decisions and data flow between frontend and backend -SQL query optimization: written on the spot during the interview -Code snippet debugging: live, during the interview

UPDATE: Received the offer one week after the interview. No additional rounds beyond what is listed above.

Takeaways: PrepStat for aptitude, GeeksforGeeks for coding. For the project discussion, prepare to explain data flow, technology selection rationale, and specific debugging decisions for each project on your resume.

Drop your questions in the comments.