Context that gathers itself

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Animated diagram. The prompt, every file and line you view or edit, and what is said in the discussion flow into one memory as you work. An arrow connects that memory directly to premium models, and the answer comes back as the kind of work the interview asked for.

Every file you view

Every line of code

Every requirement

Context gathers as you work

Persistent Contextual Memory

Live

Prompt

  • Part 1Fix an outdated dispute status
  • Part 2Fold disputes into one loan status

Files

  • py

    disputes.pyline 17 edited

    1–33lines read

  • py

    loans.py

    1–25lines read

  • py

    tests.py

    1–14lines read

Voice notes

  • Newest event must win
  • Fraud beats an open dispute

3 files · 72 lines · 2 notes

PREMIUM LLM MODELSClaude Opus 5.5GPT-5.6 Sol

Coding · part 2 of 2Range 1 is the part 1 fix, range 2 builds on it · product screenshot

Multi-file debugging · cause and fixStep 1 names the helper that returns rows out of order · product screenshot

PR review · 6 findings in 3 filesIllustration in the product’s numbered-range format

System design · architecture diagramWith the data model, critical flows, APIs and scaling behind it · product screenshot

Discussion · spoken questions answeredAnswered from the code already in memory · product screenshot

InterviewClue 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 InterviewClue 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
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Prepare for your next chapter.

For the work behind the answer

Put your attention
back on the problem

InterviewClue remembers the work, gathers the context and gives you answers you can discuss

What InterviewClue handles compared with three other interview copilots
What the interview demandsInterview
capability
InterviewClueSensFinal RLocke
1Live coding across many filesEach file you open is read and kept, not only the one on screenYesNoNoNo
Context that survives every file switchCode, edits and requirements stay for the whole sessionYesNoNoNo
2Multi-part problems that keep evolvingEach new part builds on the code and rules before itYesNoNoNo
3Structured workspace, not a chat threadApproach, code, complexity and tests in fixed placesYesNoNoNo
4Live Debugging across filesThe failure traced to the file that causes itYesNoNoNo
Multi-file PR review with inline commentsEach finding pinned to its line, across six checksYesNoNoNo
Bugs that span filesYesNoNoNo
Design issuesYesNoNoNo
Missing tests and coverageYesNoNoNo
API ergonomicsYesNoNoNo
Backwards compatibilityYesNoNoNo
Error handlingYesNoNoNo
5Undetectable while you share your screenLeft out of screen share and recordings by a macOS window settingYesNoYesYes
Undetectable by the browser shortcuts never reach the pageYesNoNot statedYes
Invisible in the Dock no icon, no ⌘‑Tab entryYesNoNot statedYes
Invisible to screen share the window is left out of the captureYesNoYesYes
Invisible to the tray no menu-bar itemYesNoNot statedYes
Unidentifiable in Activity Monitor a neutral name and a blank iconYesNoNot statedYes
Click-through clicks pass through the windowYesNoNot statedYes
Published, signed test evidence a report you can read yourselfYesNoNoNo

1 Memory for the whole interview

Answer every follow-up
Without starting over

Real interviews hand you a repository, then add a Part 2 and a Part 3. InterviewClue reads each file as you open it and keeps the code, your edits and every requirement for the whole session, so each answer extends your work instead of restarting it.

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

One interviewThree parts

Still in playAt Part 1At Part 2At Part 3

Screenshot copilots

One frame in, one answer out
limiter.pydef allow(user):
  count = hits[user]
Knows this frame
config.pyBURST = 20
WINDOW = 60
Part 1 code? Not in frame
store.pyclass SharedStore:
  def incr(key):
Limiter, burst rule? Gone

1 of 1 fileof 2 filesof 3 files

Nothing to lose yetPart 1 is out of frameParts 1 and 2 are gone

InterviewClue

One session, still open
Files
limiter.py
config.py
store.py
Requirements
100 requests a minute per user
Bursts of 20 allowed
One shared limit

123 of 1 fileof 2 filesof 3 files

1 of 1 requirement2 of 2 requirements3 of 3 requirements
The Part 1 answerA working limiter

Built on 1 file and 1 requirement. Both stay for what comes next.

The Part 2 answerAdds bursts to the limiter you wrote

Built on 2 files and 2 requirements. Your Part 1 code is kept, not regenerated.

The Part 3 answerExtends your limiter. Keeps the burst rule

Built on 3 files and 3 requirements from one session, not on the last screenshot.

Illustrated three-part session · select a part to rewind · files and requirements stay available after they leave the screen

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. InterviewClue numbers every step and pins its reasoning to the lines it explains.

Spend the moment explaining, not searching

Interviewer: “Walk me through the decrypt step”

Literally every other AI interview copilot

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

Where is the decrypt step?Paragraph 4 of 6, after the code

InterviewClue

ImplementationPython
  1. 1import requests
  2. 2from functools import lru_cache1Cache the word loader
  3. 3
  4. 4@lru_cache(maxsize=None)
  5. 5def load_word_list():
  6. 6 """Loads a file of short words."""
  7. 7 data = requests.get(WORDS_URL).text
  8. 8 return data.split("\n")
  9. 9
  10. 10def decrypt(key, ciphertext):
  11. 11 plaintexts = set()
  12. 12 for word in load_word_list():
  13. 13 word = word.strip()
  14. 14 if encrypt(key, word) == ciphertext:
  15. 15 plaintexts.add(word)
  16. 16 return plaintexts
  17. 17
  18. 18def encrypt(key, plaintext):
  19. 19 pos = ""2Encrypt by concatenation
  20. 20 for ch in plaintext:
  21. 21 pos += key[ch]
  22. 22 return pos
Time complexityO(W·L)encrypt each of W words of length L
Space complexityO(M)the M matching words kept in the set

Where is the decrypt step?Step 3, on lines 11 to 16See the real screenshot

Left: an illustrative reply in the chat-overlay form other copilots share · Right: the InterviewClue implementation view, redrawn and shortened for this page

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?”

InterviewClue Review workspace

3 files · 6 findings pinned to lines

api/refunds.py

  1. 41def create_refund(payment_id, amount=None):
  2. 42 payment = payments.get(payment_id)
  3. 43 ledger.debit(payment.account, amount or payment.amount)
  4. 1

    Bug across files debit() now takes cents (ledger.py:17). This still passes dollars, so every refund is 100× too small.

  5. 44 notify_customer(payment, amount)
  6. 2

    Design Ledger write and email share one handler. If the email fails, a retry refunds twice. Split the side effects.

  7. 45 return {"refund_id": refund.id}
  8. 45 return {"refund": amount}
  9. 3

    Backwards compatibility Clients read refund_id today. Add the new field and keep the old one.

services/ledger.py

  1. 17def debit(account, amount):
  2. 17def debit(account, cents: int):
  3. 4

    API ergonomics credit() takes dollars and debit() now takes cents. Use one unit across the module and name it in the signature.

  4. 18 if cents > account.balance:
  5. 19 return None
  6. 5

    Error handling An over-refund returns None and the API still answers 200. Raise so the caller can reject it.

  7. 20 account.balance -= cents

tests/test_refunds.py

  1. 9def test_full_refund():
  2. 10 assert create_refund("p1")["refund"] == 50
  3. 6

    Missing tests Only the full refund is covered. Add a partial refund and one larger than the payment.

Your work stays with you
Your attention stays on the interview

Bring InterviewClue into your next interview session.
Keep your context close and your answers even closer.

Get started with InterviewClue

From code to architecture

Make the system
easy to explain

See the architecture, follow an individual flow, and keep the implementation details within reach

Trace a flow through the system

From the debit worker to the notification provider, follow the path you need to explain

InterviewClue payment-system architecture with the payment-notification flow highlighted through the debit worker, event stream, notification worker, and notification provider
Actual product view · Payment notification selectedExplore the full screenshot

Keep your hands in the flow.

Solve, discuss, and show your workspace. A few keys. One less interruption.

⌘command
↵return

Try a shortcut above · actual app bindings

5 Undetectable by design

Invisible on screen share
Undetectable by browsers

Interviewer: “Can you share your entire screen?”

Covered view · what the call capturesYour Mac · what you see

How the app stays hidden Built-in behavior

  • Screen share and recording

    The window is left out of the capture. The setting is applied at every launch, and if macOS can’t confirm it the app closes instead of opening.

  • Dock and app switcher

    No Dock icon and no ⌘‑Tab entry.

  • Menu bar

    No menu-bar (tray) item.

  • Activity Monitor

    The process is listed under a neutral name with a blank icon.

  • Click-through

    During a session, clicks pass to the app below and shortcuts drive InterviewClue without it taking keyboard focus.

  • Browser

    Shortcuts are handled natively. In the published test, no main-key event reached the page.

100% Undetectable on calls and platforms

Video calls

  • Zoom
  • Google Meet
  • Microsoft Teams

Coding test platforms

  • CodeSignal
  • HackerRank
  • Codility
  • CoderPad

It works the same wherever you interview. macOS leaves InterviewClue’s window out of screen share and recordings for sharing apps that honor its window setting, the app has no Dock icon, menu-bar item or ⌘‑Tab entry, and its shortcuts never reach the browser page.

See how it is verified on the verification page

What the published test covers September 24, 2026

Global Shortcut Isolation Test
23 / 23
shortcuts activated with Chrome in front
0
main-key events reached the browser

⌘ and Shift stay visible to the page. This report covers shortcuts, not screen capture. It is an AI-assisted review with signed evidence, not an independent certification.

Read the report and signed evidence

Models that don’t meet our standard
don’t make the cut for all tiers

Coding

Claude Opus 5.5

Systems Design

Claude Opus 5.5

Voice Transcription

GPT-4o Transcribe

Live Assistance

Claude Haiku

Made to think like you In development

Customize solutions to your preferences
Code how you’re comfortable.

Choose your language and the approach that feels natural. A loop you can trace or recursion you can explain—make the answer your own.

An explicit stack. Every step in sight.
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

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FAQ

Frequently asked questions

A few things to know about InterviewClue,
based on the questions we get the most.

Does it remember work that leaves my screen?

The session retains previously observed context as you change files and continue through follow-ups. The demo illustrates memory within one session, not unlimited memory across future sessions.

Am I in control of screen capture?

You start and stop observation. Selected evidence for a solve is sent to the model provider. See the product’s privacy and verification pages for current behavior and tested configurations.

Is solution customization available now?

The iterative and recursive switch is a design preview. User-defined solution style controls are in development.

What’s included in the free plan?

Explore our features at no-cost with free coding and system design live AI copilot demos included. No payment required. Every new account receives 5 Solve Tokens once, to use for screenshot solution solves or interview question unlocks. Live copilot sessions require Lite or Pro.

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