REAL-TIME AI STEALTH COPILOT FOR TECHNICAL INTERVIEWSREAL-TIME AI STEALTH INTERVIEW COPILOT

Ace Any Technical Interview
Undetectable AI with Context

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HOW IT WORKS

One continuous session context
Every file, line and requirement

Build on every file you opened, not just the last screenshot

Problem1 of 2
Dispute status
Find and fix the bug. A dispute’s status is its latest event
- Events arrive out of order
- Timestamps are unique
Fraud outranks open
Fold every dispute of a loan into one status
- Keep your part 1 fix
- Fraud beats open beats clean
Every file you view
disputes.pyloans.py
1. 1from db import rows_for
2. 2from models import Event
3. 3
4. 4OPEN = "opened"
5. 5FRAUD = "closed_fraud"
6. 6CLEAR = "closed_not_fraud"
7. 7
8. 8def events_for(dispute_id):
9. 9 rows = rows_for(dispute_id)
10. 10 return [Event(*r) for r in rows]
11. 11
12. 12def by_time(event):
13. 13 return event.created_at
14. 14
15. 15def status(dispute_id):
16. 16 events = events_for(dispute_id)
17. 17 if not events:
18. 18 return CLEAR
19. 19 # rows arrive in insert order
20. 20 return events[-1].kind
21. 21
22. 22def all_statuses(loan_id):
23. 23 ids = disputes_of(loan_id)
24. 24 return [status(i) for i in ids]
25. 25
26. 26def is_open(dispute_id):
27. 27 return status(dispute_id) == OPEN
28. 28
29. 29def is_fraud(dispute_id):
30. 30 return status(dispute_id) == FRAUD
1. 1from disputes import all_statuses
2. 2from disputes import OPEN, FRAUD
3. 3
4. 4FRAUDULENT = "fraudulent"
5. 5REVIEW = "under_investigation"
6. 6CLEAN = "not_fraudulent"
7. 7
8. 8def loan_status(loan_id):
9. 9 states = all_statuses(loan_id)
10. 10 if not states:
11. 11 return CLEAN
12. 12 # TODO part 2: fraud outranks open
13. 13 return states[0]
14. 14
15. 15def worst(states):
16. 16 if FRAUD in states:
17. 17 return FRAUDULENT
18. 18 if OPEN in states:
19. 19 return REVIEW
20. 20 return CLEAN
21. 21
22. 22def review_all(loan_ids):
23. 23 return {i: loan_status(i)
24. 24 for i in loan_ids}
Every line of code
Every requirement

Persistent contextual memoryContext builds as the session progresses

Live

Prompt

Part 1Fix the status bug
Part 2Fraud outranks open

Files

py
disputes.pyline 20 edited
1–30lines read
py
loans.pyline 13 edited
1–24lines read
The next file you open lands here

Voice notes

Listening for notes and requirements
created_at is when each event was recorded
RequirementDon’t rely on row order

2 parts · 2 files · 54 lines · 2 edits · 2 notes

Premium LLM models Anthropic logo

Claude Opus 5.5 OpenAI logo

GPT-5.6 Sol

Coding · part 2 of 2

HeapSeek solution steps: 1, Fix dispute state selection, pick the event maximizing created_at. 2, Fold dispute states into loan status, fraud first, then open, otherwise not fraudulent

HeapSeek implementation view: line 3 is highlighted as the part 1 fix and lines 8 to 12 as the part 2 loan status logic, in one solution

4 bugs in 4 files2 fixed · 2 to go

disputes.pyline 20
loans.pyline 13
ledger.pyline 41
api.pyline 88

Cause

Dispute events are read in insert order, so the latest event is not the last row. Every status, the loan roll-up and the API response inherit the same wrong pick

Fix · disputes.py

20−return events[-1].kind20+return max(events, key=by_time).kind

Fix · loans.py

13−return states[0]13+return worst(states)

Next · ledger.py

The ledger write reads status() before the fix lands, so it posts the stale kind. Same cause, one-line fix

5 findings · 3 filesblocker high low

Blockerpayments/refund.py · 45
Removing refund_id breaks every client that stores it. Keep the field and add refund beside it
Highpayments/refund.py · 44
The ledger write and the email share one handler, so a failed email retries the refund twice. Commit the ledger, then queue the email
Highapi/refunds.py · 88
The route trusts amount from the request body. Clamp it to the original charge before calling refund()
Lowpayments/ledger.py · 12
LedgerWriter persists and notifies. Move the notifier into its own class so each has one reason to change
Lowpayments/refund.py · 51
refundAmount breaks the module's snake_case, and the json import is unused

ListeningAuto-Transcribing

InterviewerSo walk me through the runtime for decrypt

Decrypt is O(W × L): each of the W candidate words is checked once against the L-character pattern, and nothing is rescanned

Why it holds · the pattern signature is computed once per word

Quick note · design

Point out that load_word_list sits behind lru_cache: the remote list is fetched once per session, so every later decrypt reads from memory and the design holds up under many calls

InterviewerAnd how is the LRU cache helping in our scenario?

It memoizes the word list, so the network call happens once and the rest is O(1) lookups

WE'RE BUILT DIFFERENT

Not all AI tools are built the same
Only one survives the actual interview

The real software engineer interview is no longer a single leetcode prompt — it contains rounds with multiple files or parts, changing requirements, AI code craft sessions, and code reviews all while watching your screen.

See how AI changed modern software interviews

What HeapSeek handles compared with three other interview copilots
What the interview demandsInterview capability		Ultracode AI	Final Round AI	LockedIn AI
1Single-part LeetCode-style questionsAlgorithms and data structures, one focused problem at a time	Yes	Yes, documented	Yes, documented	Yes, documented
Live coding across many filesEach file you open is read and kept, not only the one on screen	Yes	No	No	No
Context that survives every file switchCode, edits and requirements stay for the whole session	Yes	No	No	No
Multi-part problems that keep evolvingEach new part builds on the code and rules before it	Yes	No	No	No
2Structured workspace, not a chat threadApproach, code, complexity and tests in fixed places	Yes	No	No	No
System-design diagrams & flowsExplore the architecture and trace each flow	Yes	No	Not confirmed: Screen reading and design guidance documented; generated diagrams and interactive flows not confirmed	Not confirmed: System-design frameworks documented; generated diagrams and interactive flows not confirmed
Live AI suggestionsAnswers as the conversation unfolds	Yes	Yes, documented	Yes, documented	Yes, documented
3Completely 100% UndetectableVerified stealth features	Yes	Yes, documented	Yes	Yes
Undetectable by the browser	Yes	Yes, documented	Not stated	Yes
Invisible in the Dock	Yes	No	Not stated	Yes
Invisible to screen share	Yes	Yes, documented	Yes	Yes
Invisible to the tray	Yes	No	Not stated	Yes
Unidentifiable in Activity Monitor	Yes	No	Not stated	Yes
Click-through	Yes	No	Not stated	Yes
Published, signed test evidence	Yes	No	No	No
4Live Debugging across filesThe failure traced to the file that causes it	Yes	No	No	No
Multi-file PR review with inline commentsEach finding pinned to its line, across six checks	Yes	No	No	No
Bugs that span files	Yes	No	No	No
Design issues	Yes	No	No	No
Missing tests and coverage	Yes	No	No	No
API ergonomics	Yes	No	No	No
Backwards compatibility	Yes	No	No	No
Error handling	Yes	No	No	No
Price USD	View plans	$1,799	$150/month	$1,999

Comparison sources and review dates

✓ Documented · × Unsupported or not confirmed · – Not confirmed in the sources checked

Ultracode AI · reviewed November 2, 2026. Advertises LeetCode, spoken follow-ups, system design, live answers, full-screen sharing invisibility and browser stealth. Its guarantee excludes monitoring software with full system access. A linked third-party test article does not establish a signed report. Capabilities not confirmed on its site are marked × in this comparison.
Final Round AI documents live suggested answers and a separate results panel. Screen Help reads diagrams; generated diagrams and interactive flows are not confirmed.
LockedIn AI documents real-time suggestions and system-design frameworks; generated diagrams and interactive flows are not confirmed.

Final Round AI and LockedIn AI item 2 sources were reviewed October 9, 2026. This is a public-documentation comparison, not hands-on testing.

1 Memory for the whole interview

The question moves on
Your context comes with you

You build the first part. Open another file. Add a new rule. Then the interviewer asks about an edge case that touches all three. HeapSeek keeps the code, edits and requirements you’ve already shown it, so the next answer builds on your work.

The hard follow-up needs the whole interview

One interview · three connected filesBuild a ticket booking service

Select a part to follow the interview

Other AI copilots

Screenshot-only context

Current frame

Available to this answer

booking.py On screen

booking.py Out of frameholds.py On screen

booking.py Out of frameholds.py Out of framepayments.py On screen

Enough context for nowThe opening problem fits on screen

The reservation code and its one-seat rule are both visible.

Missing the earlier implementationHow does your reservation work?

The current frame doesn’t include the ownership check you built in Part 1.

The earlier rules are out of viewIs this seat still theirs to confirm?

The payment handler alone doesn’t show how holds expire or who owns the seat now.

HeapSeek AI

Persistent session memory

Context retained

Available to this answer

booking.py ✓ One seat, one owner

booking.py ✓ One seat, one ownerholds.py ✓ Expires after 5 minutes

booking.py ✓ One seat, one ownerholds.py ✓ Expires after 5 minutespayments.py ✓ Late payment received

Part 1 · the foundationOne seat. One successful reservation

Claim the seat atomically. Keep the winning reservation ID for the next part.

Part 2 · builds on your codeExpire the hold. Preserve ownership

Release only the seat still owned by that expired reservation.

Part 3 · connects all three filesRefund the late payment. Keep the new booking

Use the reservation ID and expiry check already built. An expired hold must never confirm someone else’s seat.

Same follow-up. The difference is everything that came before it.

2 Undetectable by design

Invisible on screen share
Undetectable by browsers

Loan repayment system architecture with the payment notification flow highlighted

3 Clarity under pressure

Find what you need
Exactly when you need it

The interviewer points at one part of the code and waits. A chat reply makes you reread it to find that part. HeapSeek numbers every step and pins its reasoning to the lines it explains.

Spend the moment explaining, not searching

Other AI copilots

Sure! Let’s walk through the solution step by step. First, to make sure we’re aligned on the problem: we’re given an encryption key that maps each character to a short code, and we need to implement both encrypt and decrypt, where decrypt returns every word from the supplied word list whose encryption equals the given ciphertext.

Before the code, a quick note on loading the words. The list comes from a remote file, so fetching it on every call would be wasteful. A common approach is to wrap the loader in functools.lru_cache, so the request happens once and later calls reuse the result. This matters because decrypt may be called many times in a row.

For encryption, we iterate over the plaintext and look up each character in the key. If a character is missing we return None, otherwise we concatenate the mapped values and return the resulting string. Here is one way to put it all together:

def encrypt(key, plaintext):
    pos = ""
    for ch in plaintext:
        if ch not in key:
            return None
        pos += key[ch]
    return pos

def decrypt(key, ciphertext):
    plaintexts = set()
    for word in load_word_list():
        word = word.strip()
        if encrypt(key, word) == ciphertext:
            plaintexts.add(word)
    return plaintexts

Now, for decryption, it might be tempting to invert the mapping, but that gets ambiguous because several plaintexts can produce the same ciphertext. A simpler idea is to go forward instead: take each candidate word from the cached list, strip the trailing newline, encrypt it with the key and compare the result with the ciphertext. If they match we add the word to a set, since more than one word can match and we don’t want duplicates.

In terms of complexity, encrypt is O(L) for a word of length L, and decrypt runs encrypt once per word, so it is O(W·L) overall for W words, plus O(M) extra space for the M matches we keep. The cache adds O(W) memory for the list itself, which is usually fine.

A few edge cases worth mentioning to your interviewer: an empty ciphertext, characters that are not in the key, words with trailing whitespace, and an empty word list, in which case we simply return an empty set. You may also want to point out that the loader could fail, so in production code

Word encryption & decryptionFind every word that produces the ciphertext

Encrypt each candidate word and keep the exact matches

Forward matching handles shared encodings
A set keeps every distinct match
Alternative · inverting the key is ambiguous, several plaintexts share a ciphertext
Edge case · an empty word list returns an empty set

1
Cache the word list
Load once and reuse it for every lookup
2
Encrypt each word
Join the character codes from the key
3
Decrypt by forward matching
Collect matching words in a set

ImplementationPython

1def decrypt(key, ciphertext):
3Decrypt by forward matching
Encrypt each word, compare it with the ciphertext, and collect every match in a set
2 matches = set()
3 for word in load_word_list():
4 word = word.strip()
5 value = encrypt(key, word)
6 if value == ciphertext:
7 matches.add(word)
8 return matches
9
10def encrypt(key, plaintext):
11 if any(ch not in key for ch in plaintext):
12 return None
13 return "".join(key[ch] for ch in plaintext)

Time complexity

O(W·L)

Encrypt each of W words of length L

Space complexity

O(M)

The M matching words kept in the set

Test cases3 cases

Key: a → x, b → x, c → y · Words: ac, bc, ca

Ciphertext	Expected matches
`xy`	`{ac, bc}`Shared encoding
`yx`	`{ca}`Single match
`zz`	`∅`No match

Drag to compare

4 Precision in PR review rounds

Know exactly what to flag
And the line to point at

Every finding pinned to its file and line

Debugging and review rounds grade what you catch. The costly bugs live between files, where one screenshot can’t look.

The interviewer asks “What would you flag before this merges?”

HeapSeek Review workspace

3 files · 6 findings pinned to lines

api/refunds.py

41def create_refund(payment_id, amount=None):
42 payment = payments.get(payment_id)
43 ledger.debit(payment.account, amount or payment.amount)
1
BlockerBug across files debit() now takes cents (ledger.py:17). This still passes dollars, so every refund is 100× too small.
44 notify_customer(payment, amount)
2
HighDesign Ledger write and email share one handler. If the email fails, a retry refunds twice. Split the side effects.
45 return {"refund_id": refund.id}
45 return {"refund": amount}
3
BlockerBackwards compatibility Clients read refund_id today. Add the new field and keep the old one.

services/ledger.py

17def debit(account, amount):
17def debit(account, cents: int):
4
LowAPI ergonomics credit() takes dollars and debit() now takes cents. Use one unit across the module and name it in the signature.
18 if cents > account.balance:
19 return None
5
HighError handling An over-refund returns None and the API still answers 200. Raise so the caller can reject it.
20 account.balance -= cents

tests/test_refunds.py

9def test_full_refund():
10 assert create_refund("p1")["refund"] == 50
6
LowMissing tests Only the full refund is covered. Add a partial refund and one larger than the payment.

Made to think like you

Customize solutions to your preferences
Code how you’re comfortable

Choose from a wide range of languages, coding styles, and solution preferences to tailor every answer to your needs

max_depth.pyPython

def max_depth(root):
    stack = [(root, 1)]
    depth = 0
    while stack:
        node, level = stack.pop()
        if node:
            depth = max(depth, level)
            stack += [(node.left, level + 1),
                      (node.right, level + 1)]
    return depth

Your next chapter awaits

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$95 / month

Live AI Technical Interview Copilot
20+ verified stealth features
100 Solve Tokens every month
Question Bank Access (5 questions/week)
Supports Coding (leetcode, multi-file, multi-part, debugging), Systems Design
Use across unlimited sessions
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Serious interview season

$175 / month

Live AI Technical Interview Copilot
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250 Solve Tokens every month
Unlimited Question Bank Access
Supports Coding (leetcode, multi-file, multi-part, debugging), Systems Design, AI Code Craft, Code Review
Personal notes for each interview type
Use across unlimited sessions
24/7 Customer Support

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Company-specific interview questions

$20 / month

Access thousands of company interview questions sourced from original candidate reports on 1Point3Acres, Blind, LeetCode Premium, and more

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FAQ

Frequently Asked
Questions

What sets HeapSeek apart from other AI interview tools?

Persistent session context
No one else offers it. Without it a copilot is useless for long code that was never captured in a single snapshot, for the multiple files you need to investigate, and for multi-part problems that build on your previous solutions.
The entire technical interview, every category
No other platform offers more than LeetCode screenshot solving, and a long chat thread about a systems design helps no one. HeapSeek covers five rounds: coding, systems design, AI-assisted coding, debugging and code review.
High-quality, structured, efficient solutions
Every answer comes from our agentic harness, which undergoes rigorous quality testing weekly against rubrics crafted by our team of UC Berkeley EECS graduates and ex-FAANG senior engineers, with combined decades of experience interviewing and understanding scoring guidelines.
100% undetectable
We don’t just claim it. Every layer of evidence behind the testing is published on our verification page.

How does HeapSeek work?

Open the Mac app, choose your interview type, and start a session. HeapSeek builds context from the screen and audio sources you enable. Use a global shortcut to solve or discuss a follow-up, with answers grounded in what’s been seen and said.

Can HeapSeek handle more than LeetCode questions?

Yes. Lite covers algorithms, multi-file coding, multi-part problems, debugging, and system design. Pro adds AI Code Craft and Code Review rounds. Get the output each round needs: implementation steps, architecture and flows, or actionable findings beside the code.

Does it remember work that leaves my screen?

Yes. Code, edits, and requirements already observed stay in the session’s context as you switch files. When Part 2 builds on Part 1, HeapSeek can build on that work too. You don’t need to recapture every earlier file for each follow-up. Before starting your next problem, you can easily clear the context memory from the Solve button’s options.

Which Macs and interview platforms does HeapSeek support?

HeapSeek runs on Apple Silicon Macs with macOS 14 or later and an internet connection. It reads what you show on screen, so you can work in browser editors or desktop IDEs alongside your call. See our verification page for tested configurations.

Is HeapSeek invisible during screen sharing?

HeapSeek uses macOS capture exclusion to keep its window out of supported screen shares and recordings. It also stays out of the Dock, menu bar, and app switcher, with native shortcuts that keep your editor in focus. Check the published tests and signed evidence for coverage.

What programming languages do you support?

Every programming language. By default HeapSeek dynamically uses the language your IDE is set to, read straight off your screen, so there is no configuration to worry about. If no language can be detected for a coding assessment, your default preference applies: choose from 11 backend languages (Python, Java, C++, C#, Go, TypeScript, JavaScript, Ruby, Rust, Kotlin and Scala) and 2 frontend languages (TypeScript and JavaScript) in Settings.

Can I choose my programming language and coding style?

Yes. Set your preferred backend and frontend languages, then choose iterative or recursive approaches, comment detail, naming style, and comprehensions or explicit loops. Pro also includes personal notes for each interview type. Get solutions that are easier for you to read, explain, and extend.

What’s included in the free plan?

Every new account gets 3 Solve Tokens once, with no credit card required. Use them for screenshot solves or interview-question unlocks, and explore the coding and system-design demos for free. Live copilot sessions require Lite or Pro. Try HeapSeek free.

Do you offer monthly plans, and which should I choose?

Yes. Lite is for coding and system-design preparation. Pro adds AI Code Craft, Code Review, personal notes, longer sessions, and more Solve Tokens. Both offer monthly or three-month billing, so you can choose the term that fits your interview season. Compare plans.

Can I prepare with real company interview questions?

Yes. The Question Bank organizes company-specific questions sourced from candidate reports. Lite includes five new question unlocks per week; Pro includes unlimited access throughout your plan’s duration. You can also subscribe directly to the Question Bank.

Am I in control of screen capture and my data?

You choose the screen and audio sources and when to start or stop capture. Enabled sources may be sent to HeapSeek and its AI providers before you request an answer. Session content can be stored for history and service operation; our Privacy Policy explains retention and your choices.

What is your refund policy?

All sales are final. Like every tool in this space, we have had accounts run a couple of interviews, land the job and ask for the money back the same week, so fees are nonrefundable, subject to the exceptions in our Terms and applicable law. That is also why every account starts with 3 free Solve Tokens and the full set of demos, so you can experience the entire live session flow before making a decision. Cancel renewal from your account at any time; paid access continues through the current billing period, or contact us with a billing question.

Ace Any Technical Interview
Undetectable AI with Context