Context that gathers itself
Keep your focus on the problem, not on screenshots and recordings
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
Livedisputes.pyline 17 edited
1–33lines read
loans.py
1–25lines read
tests.py
1–14lines read
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
For the work behind the answer
InterviewClue remembers the work, gathers the context and gives you answers you can discuss
1 Memory for the whole interview
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
Screenshot copilots
One frame in, one answer outdef allow(user):
count = hits[user]Knows this frameBURST = 20
WINDOW = 60Part 1 code? Not in frameclass SharedStore:
def incr(key):Limiter, burst rule? Gone1 of 1 fileof 2 filesof 3 files
Nothing to lose yetPart 1 is out of frameParts 1 and 2 are goneInterviewClue
One session, still open123 of 1 fileof 2 filesof 3 files
1 of 1 requirement2 of 2 requirements3 of 3 requirementsBuilt on 1 file and 1 requirement. Both stay for what comes next.
Built on 2 files and 2 requirements. Your Part 1 code is kept, not regenerated.
Built on 3 files and 3 requirements from one session, not on the last screenshot.
3 Clarity under pressure
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
import requestsfrom functools import lru_cache1Cache the word loader@lru_cache(maxsize=None)def load_word_list(): """Loads a file of short words.""" data = requests.get(WORDS_URL).text return data.split("\n")def decrypt(key, ciphertext): plaintexts = set() for word in load_word_list(): word = word.strip() if encrypt(key, word) == ciphertext: plaintexts.add(word) return plaintextsdef encrypt(key, plaintext): pos = ""2Encrypt by concatenation for ch in plaintext: pos += key[ch] return posWhere is the decrypt step?Step 3, on lines 11 to 16See the real screenshot
4 Precision in PR review rounds
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?”
api/refunds.py
def create_refund(payment_id, amount=None): payment = payments.get(payment_id) ledger.debit(payment.account, amount or payment.amount)Bug across files debit() now takes cents (ledger.py:17). This still passes dollars, so every refund is 100× too small.
notify_customer(payment, amount)Design Ledger write and email share one handler. If the email fails, a retry refunds twice. Split the side effects.
return {"refund_id": refund.id} return {"refund": amount}Backwards compatibility Clients read refund_id today. Add the new field and keep the old one.
services/ledger.py
def debit(account, amount):def debit(account, cents: int):API ergonomics credit() takes dollars and debit() now takes cents. Use one unit across the module and name it in the signature.
if cents > account.balance: return NoneError handling An over-refund returns None and the API still answers 200. Raise so the caller can reject it.
account.balance -= centstests/test_refunds.py
def test_full_refund(): assert create_refund("p1")["refund"] == 50Missing tests Only the full refund is covered. Add a partial refund and one larger than the payment.
Bring InterviewClue into your next interview session.
Keep your context close and your answers even closer.
From code to architecture
See the architecture, follow an individual flow, and keep the implementation details within reach
From the debit worker to the notification provider, follow the path you need to explain
Solve, discuss, and show your workspace. A few keys. One less interruption.
Try a shortcut above · actual app bindings
5 Undetectable by design
Interviewer: “Can you share your entire screen?”
How the app stays hidden Built-in behavior
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.
No Dock icon and no ⌘‑Tab entry.
No menu-bar (tray) item.
The process is listed under a neutral name with a blank icon.
During a session, clicks pass to the app below and shortcuts drive InterviewClue without it taking keyboard focus.
Shortcuts are handled natively. In the published test, no main-key event reached the page.
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 pageWhat the published test covers September 24, 2026
⌘ 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 evidenceMade to think like you In development
Choose your language and the approach that feels natural. A loop you can trace or recursion you can explain—make the answer your own.
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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Choose Question Bank (1 Year Access)FAQ
A few things to know about InterviewClue,
based on the questions we get the most.
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.
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.
The iterative and recursive switch is a design preview. User-defined solution style controls are in development.
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