Capstone: Mock Loop
Run the whole loop against yourself
Live debugging dry run
After this you can perform a timed, narrated troubleshooting of a Cursor-style issue.
The technical screen is the round most people underestimate. They think it grades whether you know the answer. It grades how you move when you don't - whether you clarify, reproduce and reason out loud like someone who debugs for a living.
You can't fake this on the day. The only honest prep is to run the round on yourself, with a clock, on a problem you didn't pre-solve. Pick a scenario with the texture of a real Cursor ticket and give yourself roughly 20 minutes start to finish.
Interactive diagram. Tab through its regions; each focused region shows its detail in the panel below.
Step through each stage - this capstone runs all six against yourself.
Pick a scenarioDrill setup
Choose one issue and commit to it. These four cover the most common shapes a Cursor TSE sees and each one tests a slightly different muscle.
Codebase won't index or @-references return nothing.
Tests: do you check repo size, .gitignore, ignore files and the indexing status before blaming the model?
Suggestions lag by seconds or stall entirely.
Tests: do you separate network latency from model latency from a local extension hogging the event loop?
Sign-in bounces, token never sticks, enterprise SAMLAn enterprise standard that powers single sign-on. stalls.
Tests: do you ask about proxy, corporate network, browser vs in-app auth before escalating?
Requests fail, time out or return capacity errors.
Tests: can you read the error, check status and tell a transient outage from a config or billing problem?
Run the clockDrill protocol
Force yourself through five phases in order. The discipline is refusing to skip ahead to a fix the moment you have a guess.
Interactive diagram. Tab through its regions; each focused region shows its detail in the panel below.
Resolve/escalate is the quality gate - you don't pass it until you can name the cause.
- 10–3 min · Clarify. Restate the problem and pull the environment up front: OS and version, Cursor version, network shape (VPN, proxy, corporate), account type and whether it's reproducible or intermittent. “When did this start? Did anything change - an update, a new network, a new repo?”
- 23–8 min · Reproduce. State the exact steps you'd run to see it yourself. If you can't reproduce, that's a finding, not a dead end - narrow the conditions until it happens or until you've proven it's environment-specific.
- 38–13 min · Isolate. Cut the surface in half. Does it happen in a fresh repo? With extensions disabled? On a different network? Off privacy mode? Each toggle removes a suspect.
- 413–17 min · Root-cause. Form a hypothesis, then name the one observation that would confirm or kill it. Read the logs, the diagnostics, the network tab. Separate the symptom (slow Tab) from the cause (a proxy stripping the streaming response).
- 517–20 min · Resolve or escalate. Either give the fix with the reason or write the escalation: what you tried, what you ruled out, the minimal repro and the logs. A clean handoff is a pass even when you didn't solve it.
Narrate the why behind every step, not just the step. “I'm disabling extensions first because a conflicting language server is the cheapest thing to rule out and it explains both the lag and the CPU spike.” Interviewers can't grade silent thinking. The narration is the deliverable.
What good narration sounds like
Symptom: Tab suggestions lag ~4s, only on the work laptop.
Hypothesis 1: network - corporate proxy buffering the stream.
Check: does it lag off-VPN? -> No lag off-VPN. Strong signal.
Check: is the proxy stripping chunked transfer? -> HAR shows
the response arriving in one block, not streamed.
Root cause (not symptom): proxy is buffering the streamed
completion, so nothing renders until the full response lands.
"Slow Tab" was the symptom; proxy buffering is the cause.
Resolve: allowlist the API host / disable buffering for it,
or escalate to their network team with the HAR as evidence.The fastest way to fail is to fix the symptom. If you tell the user to restart and the lag returns tomorrow, you treated a cause you never found. Restarting clears state, it doesn't explain anything. Always be able to say what was actually wrong.
Score your own tapeSelf-rubric
- Dimension
- Asked for environment
- Pass looks like
- Pulled OS, version, network, account in the first two minutes
- Red flag
- Started guessing fixes with zero context
- Dimension
- Method
- Pass looks like
- Clarify → reproduce → isolate → root-cause → resolve, in order
- Red flag
- Jumped straight to a fix on the first hunch
- Dimension
- Symptom vs cause
- Pass looks like
- Named the underlying cause, not just the visible behavior
- Red flag
- “Tell them to reinstall” with no explanation of why
- Dimension
- Communication
- Pass looks like
- Narrated hypotheses and what would confirm each
- Red flag
- Long silences or jargon with no plain-language anchor
- Dimension
- Clean handoff
- Pass looks like
- Escalation has repro, environment, logs and what was ruled out
- Red flag
- “Couldn't figure it out, sending to Eng” with no notes
| Dimension | Pass looks like | Red flag |
|---|---|---|
| Asked for environment | Pulled OS, version, network, account in the first two minutes | Started guessing fixes with zero context |
| Method | Clarify → reproduce → isolate → root-cause → resolve, in order | Jumped straight to a fix on the first hunch |
| Symptom vs cause | Named the underlying cause, not just the visible behavior | “Tell them to reinstall” with no explanation of why |
| Communication | Narrated hypotheses and what would confirm each | Long silences or jargon with no plain-language anchor |
| Clean handoff | Escalation has repro, environment, logs and what was ruled out | “Couldn't figure it out, sending to Eng” with no notes |
Watch the recording with this open. Mark the timestamp of every red flag.
Then redo it once. Same scenario or a fresh one, but this time fix your single weakest step on purpose. Most people find their gap is the same every run: they skip environment or they narrate the what and never the why.
Takeaway. The live debug grades method and narration, not trivia. Pull the environment first, work the phases in order and always separate the symptom from the root cause you can name out loud.
Self-check
QIn a live troubleshooting round for slow Tab suggestions, what is your highest-value first move and why?
Written reply & bug report
After this you can produce a publishable support reply and an engineering-ready bug report from one prompt.
The async exercise is where two audiences collide. The same incident becomes a warm reply to a furious user and a cold, precise report for an engineer who has never seen the ticket. Both come from one prompt and you write them fast.
Take a messy, angry ticket - the kind with caps lock, three unrelated complaints and no version number - and produce both artifacts. The reply ships to the customer. The report goes to Eng. Grading is partly volume done well under time pressure, so set a clock.
Two artifacts, two jobsThe deliverables
Acknowledge the frustration in the first sentence, specifically.
Give exact steps, not vague reassurance.
Be honest about timeline - no promises you can't keep.
Plain language; a senior dev shouldn't feel talked down to.
Minimal reproduction someone else can run cold.
Environment: OS, version, account type, network.
Expected vs actual, stated as one line each.
Logs attached, plus a severity and why.
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One ticket, two readers - mixing the registers weakens both artifacts.
The reply self-check
- Acknowledgement - does the first line name what this user lost (a day of work, a broken demo), not a generic “sorry for the inconvenience”?
- Accuracy - is every claim true? Don't say it's fixed if you mean it's escalated.
- Exact steps - could the user follow your instructions without a single follow-up question?
- Honest timeline - did you give a real next-update time instead of a fake ETA?
Hi Sam - losing a morning to a sign-in loop right before your team's rollout is genuinely bad and I'm sorry you hit it. Here's what I think is happening and how to confirm it: 1. Open Help > Toggle Developer Tools > Network. 2. Reproduce the sign-in. Look for a 4xx on the callback URL. 3. Send me that request's URL and status code. My current read: your corporate proxy is dropping the auth callback. If the network tab confirms it, the fix is an allowlist on your side and I'll send the exact hosts. I'm treating this as high priority. I'll update you by 3pm ET today either way, even if it's just progress.
The bug-report self-check
Title: SSO callback fails behind forward proxy (enterprise) Severity: S2 - blocks sign-in for one enterprise team (~40 seats) Environment: - Cursor 0.4x.x, macOS 14.5 - Okta SAML, forward proxy (Zscaler) on corp network - Reproduces only on-network; clean off-VPN Repro (minimal): 1. On corp network, sign in via SSO. 2. Auth completes in browser, redirect to app callback. 3. App never receives token; returns to sign-in screen. Expected: token reaches app, session established. Actual: callback request 407 at proxy; token never delivered. Evidence: HAR attached. 407 Proxy Authentication Required on the callback host. No retry with proxy creds. Notes: ruled out - browser, account, Cursor version (repros on two machines). Isolated to proxy auth on the callback host.
When you hand this off live, frame the split: “The reply gives Sam something to do in the next five minutes and an honest update time. The report gives Eng a repro they can run without me in the loop and the one fact that matters - it's a 407 on the callback host, not a Cursor auth bug.”
Don't let the reply leak internal uncertainty or the report inherit the reply's warmth. The user doesn't need your S2 label; Eng doesn't need an apology. Each artifact serves exactly one reader and mixing them weakens both.
Time-box the whole thing to 15 minutes for both artifacts, then compare against the writing rubric from module 5. The async round rewards someone who produces a lot, cleanly, fast - not someone who polishes one paragraph for an hour.
Takeaway. One incident, two audiences: the reply needs acknowledgement, exact steps and an honest timeline; the report needs a minimal repro, environment, expected-vs-actual, logs and a justified severity.
Self-check
Automation pitch
After this you can pitch one support automation with problem, design and impact in three minutes.
Cursor says it plainly: this is an engineering-flavored support role and they want people who build the automations that scale support. The tooling round is where you prove you think in impact, not in tickets closed.
Pick one real, recurring piece of toil and pitch the automation that kills it. Three minutes, four beats: the problem, the design, the toolkit, the measurable impact. Then the trade-offs, because an honest pitch names its own failure modes.
Pick toil worth automatingCandidates
Auto-labels and routes incoming tickets by type and severity.
Flags likely outages by clustering similar reports.
Drafts the first-response for human review.
Extracts version, OS and error signatures from pasted logs.
Matches the signature against known issues.
Surfaces the likely root cause and a linked runbook.
Drafts replies grounded in the KB and the user's exact ticket.
Cites the doc it pulled from so the human can verify.
Human always edits and sends; never auto-fires.
The four-beat pitchStructure
- 1Problem. Name the toil with a number. “Roughly 30% of tickets are the same five setup issues and each one costs an agent ten minutes of copy-paste triage.”
- 2Design. Describe the flow in one breath. “A bot reads the incoming ticket, classifies it against the top issue signatures and either drafts a grounded reply or routes a likely-novel one to a human.”
- 3Toolkit. Be concrete. The ticketing API for ingest, a small Python or TS service, an LLM call for classification and drafting, the KB as the grounding source, posted back via the Slack workflow.
- 4Impact. Tie it to a metric. “Cuts first-response time on the common 30%, frees agent hours for the hard tickets and the drafts get better as the KB grows.”
Name your failure modes before they ask. “The risk is a confidently wrong auto-draft, so a human edits and sends every reply - the bot drafts, it never fires. And I'd watch the misclassification rate weekly; if novel tickets get auto-labeled as common ones, I'd lower the confidence threshold for routing.” Naming the downside is what separates an engineer's pitch from a demo.
Trade-offs to keep on a card
- Hallucinated answers
- Ground every draft in the KB and cite the source; human reviews before send.
- Silent misrouting
- Log every classification; review the confusion matrix weekly and tune the threshold.
- Drift as product changes
- Cursor ships weekly, so the KB and signatures must be refreshed, not set-and-forget.
- Over-automation
- Keep a human in the loop on anything customer-facing; automate the toil, not the judgment.
Have these ready - the round rewards the candidate who volunteers the risks.
Practice saying the whole thing out loud until it lands in three minutes without notes. The content is half the grade; the other half is whether you can pitch it crisply, the way you'd pitch a teammate in a standup.
Every automation should ladder up to one sentence: this lets the same team support more users without the quality dropping. That is the explicit goal Cursor states for the role. If your pitch can't connect to scaling support, pick a different automation.
Takeaway. Pitch one automation in four beats - problem with a number, design in a breath, concrete toolkit, measurable impact - then volunteer the failure modes and tie it to scaling support.
Self-check
QWhy should an LLM-assisted reply bot draft rather than auto-send and how would you defend that choice in the tooling round?
Product & values lightning round
After this you can deliver sharp answers on Cursor vs competitors and on Anysphere's values.
Interviewers can tell within minutes whether you actually use Cursor for real work. The lightning round is built to surface that fast: short prompts, no notes and a low tolerance for buzzwords from a truth-seeking org.
Run it like flashcards out loud. Each prompt gets 60 seconds, said cleanly, with a concrete example instead of an adjective. If your answer would survive a senior engineer pushing back, it's ready.
Product takes - 60 seconds eachSay these cold
- Where Cursor wins. Pick one real strength and ground it: agent mode and codebase-wide context in a fast, familiar VS-Code-fork editor. Cite a task it changed for you, not a feature list.
- Where a competitor wins. Be honest. Maybe Copilot's deep GitHub-native integration or Claude Code's terminal-first agent flow for certain workflows. Refusing to name a competitor's strength reads as either dishonest or unaware.
- Your favorite feature. One you actually reach for daily - Tab, agent mode, @-symbol context - and why it changed how you work.
- Your most-wanted fix. A real friction you've hit. This is the truth-seeking test: a candidate who loves everything has used nothing seriously.
“Cursor is just better” is a fail. So is reciting the marketing site. The screen punishes hype precisely because the role demands authentic fluency - you'll be in technical conversations with senior developers who'll spot a script in one question. Specifics over superlatives, always.
Behavioral - two STAR stories coldRehearse without the prompt in front of you
Situation + Task: the gnarly issue and why it mattered.
Action: your method - how you isolated and root-caused it.
Result: the fix and what you changed so it didn't recur.
Lands the truth-seeking and ownership themes.
Situation + Task: an angry or churning user.
Action: how you de-escalated while staying technically honest.
Result: the outcome and the relationship after.
Lands customer empathy under pressure.
Why Cursor, why support, no notes: “I use Cursor daily and I like debugging the messy edge of a fast-moving product more than I like closing tickets. This role is engineering-flavored support - I get to build the automations and feed real signal back into a roadmap that changes weekly. That's the part of support I actually want.”
Three smart questions, readyYou're being graded on these too
- How does customer signal from support actually reach the roadmap - what's the loop between TSE and Product?
- What does the first 90 days look like and what would a strong start look like to you?
- Where is support tooling today and what would you most want a new TSE to automate first?
Substance over hype is the through-line of every answer here. Anysphere is talent-dense and truth-seeking; a confident, specific, occasionally critical take beats a polished one every time. Saying what's actually true is the value they're screening for.
Takeaway. Win the lightning round with specifics, not superlatives: name where a competitor wins, name a real fix you want and have two cold STAR stories plus three smart questions ready.
Self-check
QAn interviewer asks where a competitor beats Cursor. Why is naming a genuine competitor strength the right move?
Self-scorecard & gap plan
After this you can rate your readiness and build a focused final-week plan.
You've now run every stage against yourself. The last move is to turn that into an honest number and a plan that fixes your two weakest stages, not the ones you already enjoy practicing.
Treat the rubric as a gap finder, not a score to admire. Mark the weakest proof, turn it into one practice rep and keep the artifact you would show an interviewer.
Score each loop stage 1–5 against the rubrics from modules 2 through 6. Be the harsh grader - the loop is reputationally tough and a generous self-score is the most expensive mistake you can make this week.
Interactive diagram. Tab through its regions; each focused region shows its detail in the panel below.
Weight your prep here - these are the signals Anysphere is actually grading.
Score the loop1 = shaky, 5 = ready
- Loop stage
- Recruiter / HM screen
- What a 5 looks like
- Crisp why-Cursor, why-support, logistics handled, no rambling
- Your score (1–5)
- __
- Loop stage
- Technical screen (live debug)
- What a 5 looks like
- Environment first, method in order, symptom vs cause named
- Your score (1–5)
- __
- Loop stage
- Written / async exercise
- What a 5 looks like
- Reply and report both pass their self-checks, done fast
- Your score (1–5)
- __
- Loop stage
- Automation / tooling round
- What a 5 looks like
- Four-beat pitch, named trade-offs, tied to scaling support
- Your score (1–5)
- __
- Loop stage
- Cross-functional / product
- What a 5 looks like
- Escalation path clear, customer-voice framing, authentic usage
- Your score (1–5)
- __
- Loop stage
- Values / behavioral
- What a 5 looks like
- Two cold STAR stories, truth-seeking under push-back
- Your score (1–5)
- __
| Loop stage | What a 5 looks like | Your score (1–5) |
|---|---|---|
| Recruiter / HM screen | Crisp why-Cursor, why-support, logistics handled, no rambling | __ |
| Technical screen (live debug) | Environment first, method in order, symptom vs cause named | __ |
| Written / async exercise | Reply and report both pass their self-checks, done fast | __ |
| Automation / tooling round | Four-beat pitch, named trade-offs, tied to scaling support | __ |
| Cross-functional / product | Escalation path clear, customer-voice framing, authentic usage | __ |
| Values / behavioral | Two cold STAR stories, truth-seeking under push-back | __ |
Anything below a 4 is a target. Two-plus below 4 means you're not done.
Target your two weakest stagesSpecific drills, not vibes
- Live debug is weak
- Run two more recorded 20-min scenarios; force environment-first and out-loud root-cause every time.
- Written round is weak
- Do three messy-ticket drills at 15 min each; check both artifacts against the module 5 rubric.
- Automation pitch is weak
- Rehearse one pitch to three minutes flat, then have someone push on the failure modes.
- Product takes are weak
- Do a real side-by-side with one competitor on one task this week; write the verdict in two sentences.
- Behavioral is weak
- Write and time two STAR stories; deliver them cold to a friend who'll interrupt.
Confirm the non-negotiablesReadiness gates
- Daily Cursor usage for at least the last 2–4 weeks on real work, not a toy repo. This is the thing they detect in minutes.
- One real competitor comparison done and written down - Copilot, Windsurf or Claude Code on a task you actually ran.
- A story bank of at least three polished STAR stories covering a hard debug, a turned-around customer and ownership in ambiguity.
- One automation example you can pitch in three minutes with its trade-offs.
- Three smart questions ready for the cross-functional and values rounds.
Set the bar before you read your scores, so you can't move it. A reasonable gate: no stage below 3, the two technical stages (live debug, written) at 4+, daily usage confirmed and the story bank and automation example ready. Miss the bar and the plan is to drill, not to wing it.
A final-week schedule that works
- 1Days 1–2. Two recorded live-debug drills and two written drills; score both against their rubrics.
- 2Day 3. The competitor comparison on one real task; write the two-sentence verdict.
- 3Day 4. Lock the story bank and rehearse the automation pitch to three minutes.
- 4Day 5. Full mock loop end to end, in order, with the scorecard open; re-drill only the stage that scored lowest.
- 5Day before. Light review of your questions list and STAR stories. No new material - rest beats cramming for a high-pace loop.
A 14/30 on this scorecard a week out is not a verdict, it's a map. The candidates who pass aren't the ones who started ready; they're the ones who scored themselves honestly and drilled the two stages they wanted to avoid.
Takeaway. Score every stage honestly against the module rubrics, drill your two weakest, confirm daily Cursor usage and a real competitor comparison and set a go/no-go bar before you look at your numbers.