Product features
From preparation to the live interview, AskCc turns your materials into perfect answers.
Build a preparation workspace for each target role, turn project experience into reusable skill cards, and use that context during interviews, written tests, and screenshot-based questions.
New scenario
▷ Start interview◎ Language settings
Confirm▤ Import interview materials
ContinueResume context
resume.pdf
Job description context
job-description.md
▤ Select retrievable context
ConfirmPersonal knowledge base
Official question banks
Retrieve supporting passages only when the question benefits from context.
✦ Review project skill
ConfirmAI agent project: RAG, inference, planning, and boundaries.
Capability map
Cover every high-pressure interview moment.
One workflow for preparation spaces, project skill cards, live teleprompter help, and screenshot-based questions, each shown as an actual product surface.
New scenario
▷ Start interview◎ Language settings
Confirm▤ Import interview materials
ContinueResume context
resume.pdf
Job description context
job-description.md
▤ Select retrievable context
ConfirmPersonal knowledge base
Official question banks
Retrieve supporting passages only when the question benefits from context.
✦ Review project skill
ConfirmAI agent project: RAG, inference, planning, and boundaries.
Preparation workspace
Create one focused workspace for each interview target.
A preparation keeps the resume, job description, languages, personal knowledge base, official question banks, and approved skill cards together, so the live interview starts from a stable snapshot instead of scattered files.
Project skill cards
▷ Start interviewCurrent preparation
AI algorithm engineer
AI Agent implementation
Project · Enabled
Connect planning, retrieval, tool calls, and replay into an observable workflow.
Recommendation latency
Project · Enabled
Explain tradeoffs between realtime features, cache refresh, and ranking quality.
Skill detail preview
AI Agent implementation
Generated skill content
ReviewedWhen the interviewer asks about the AI agent project, start with my ownership of the RAG retrieval, planning, and tool execution pipeline; emphasize observability and replay instead of only adding model capability.
We did not rely on pure vector search. We combined structured tags, semantic recall, and reranking, trading some engineering complexity for steadier evidence hits and lower hallucination risk.
Early agents mixed unrelated material into answers. I added scope checks, source previews, and replay cases so failures can be traced to retrieval, planning, or generation.
Cognitive depth tree
L1-L5 builtOwned orchestration, RAG hit strategy, tool timeline, and failure replay.
Q: Which layer did you own?
Slice user materials into stable project snapshots, then retrieve live answers from that snapshot.
Q: Why not assemble context live?
Structured fields constrain scope, semantic recall covers variants, and reranking surfaces speakable evidence.
Q: How do you recover from weak recall?
Prioritized evidence stability and explainability, accepting a more involved preparation flow.
Q: How did you balance latency, cost, and quality?
Avoid claiming model training; stay with retrieval, orchestration, and evaluation loops.
Q: What if they press on model training?
Project skill cards
Turn vague project stories into reusable skill cards.
AskCc researches the project and target role first: company context, key angles, and a cognitive depth tree that turns role, decisions, tradeoffs, failures, and defensible follow-ups into a skill card.
◇ Interview dialogue
Live Interview Assistant
Listen to the question, match your context, and draft a spoken answer.
During a live interview, AskCc processes the interviewer question, classifies the intent, retrieves relevant material when needed, matches approved skill cards, and formats the response into short lines you can adapt out loud.
124. Binary Tree Maximum Path Sum
HardGiven a binary tree, a path may start and end at any node, but it must follow parent-child links.
Return the maximum sum of values on such a path. The path contains at least one node and values may be negative.
Input: root = [-10,9,20,null,null,15,7]
Output: 42
Path: 15 → 20 → 7
1. -1000 <= Node.val <= 1000
2. 1 <= nodes <= 3 * 10^4
3. The path does not have to pass the root
1class Solution:
2 def maxPathSum(self, root):
3 best = -10**18
4 def gain(node):
5 nonlocal best
6 if not node:
7 return 0
8 left = max(gain(node.left), 0)
9 right = max(gain(node.right), 0)
10 best = max(best, node.val + left + right)
11 return node.val + max(left, right)
12 gain(root)
13 return best
◇ Screenshot solver
def gain(node):
if not node: return 0
left = max(gain(node.left), 0)
right = max(gain(node.right), 0)
best = max(best, node.val + left + right)
return node.val + max(left, right)
Screenshot Problem Solver
Capture written tests, coding prompts, and case questions from the screen.
For exam-style moments, AskCc can analyze a screenshot, identify the task type, and produce a solution path for coding, multiple choice, and written-response questions.
Preparation workspace
Create one focused workspace for each interview target.
A preparation keeps the resume, job description, languages, personal knowledge base, official question banks, and approved skill cards together, so the live interview starts from a stable snapshot instead of scattered files.
- Upload or paste resume, JD, project notes, and reference material before the interview.
- Bind a personal knowledge base and selected official question banks to the same target role.
- When an interview starts, AskCc uses a frozen preparation snapshot for consistent answers.
New scenario
▷ Start interview◎ Language settings
Confirm▤ Import interview materials
ContinueResume context
resume.pdf
Job description context
job-description.md
▤ Select retrievable context
ConfirmPersonal knowledge base
Official question banks
Retrieve supporting passages only when the question benefits from context.
✦ Review project skill
ConfirmAI agent project: RAG, inference, planning, and boundaries.
Project skill cards
Turn vague project stories into reusable skill cards.
AskCc researches the project and target role first: company context, key angles, and a cognitive depth tree that turns role, decisions, tradeoffs, failures, and defensible follow-ups into a skill card.
- Ask guided questions around project role, technical decisions, hard parts, and outcomes.
- Study the company, JD, uploaded material, and question banks to flag angles interviewers may press on.
- Organize L1 to L5 depth: facts, solution, principles, assumptions, and boundaries.
- Send cards to the user for review before any unsupported claim becomes interview context.
Project skill cards
▷ Start interviewCurrent preparation
AI algorithm engineer
AI Agent implementation
Project · Enabled
Connect planning, retrieval, tool calls, and replay into an observable workflow.
Recommendation latency
Project · Enabled
Explain tradeoffs between realtime features, cache refresh, and ranking quality.
Skill detail preview
AI Agent implementation
Generated skill content
ReviewedWhen the interviewer asks about the AI agent project, start with my ownership of the RAG retrieval, planning, and tool execution pipeline; emphasize observability and replay instead of only adding model capability.
We did not rely on pure vector search. We combined structured tags, semantic recall, and reranking, trading some engineering complexity for steadier evidence hits and lower hallucination risk.
Early agents mixed unrelated material into answers. I added scope checks, source previews, and replay cases so failures can be traced to retrieval, planning, or generation.
Cognitive depth tree
L1-L5 builtOwned orchestration, RAG hit strategy, tool timeline, and failure replay.
Q: Which layer did you own?
Slice user materials into stable project snapshots, then retrieve live answers from that snapshot.
Q: Why not assemble context live?
Structured fields constrain scope, semantic recall covers variants, and reranking surfaces speakable evidence.
Q: How do you recover from weak recall?
Prioritized evidence stability and explainability, accepting a more involved preparation flow.
Q: How did you balance latency, cost, and quality?
Avoid claiming model training; stay with retrieval, orchestration, and evaluation loops.
Q: What if they press on model training?
Live Interview Assistant
Listen to the question, match your context, and draft a spoken answer.
During a live interview, AskCc processes the interviewer question, classifies the intent, retrieves relevant material when needed, matches approved skill cards, and formats the response into short lines you can adapt out loud.
- Use real-time audio input to follow interviewer questions and follow-ups.
- Recognize self-introduction, project deep dive, behavioral, technical, situational, and clarification questions.
- Pull from resume, JD, personal knowledge base, official question banks, and approved skill cards when relevant.
- Prefer concise, natural, teleprompter-friendly wording instead of written-style paragraphs.
◇ Interview dialogue
Overlay
Keep guidance available without interrupting the interview flow.
The desktop overlay is built for live pressure: it can stay on top, show subtitles, surface answer progress, trigger actions by shortcut, and protect its content from ordinary screen sharing workflows where supported.
- Floating, draggable, resizable panel for interview and exam assistance.
- Real-time subtitles help you track what the system heard before acting.
- Keyboard shortcuts can ask from the latest question, capture a screenshot, or hide the panel.
- Content protection and click-through behavior reduce disruption during calls.
◇ Interview dialogue
Knowledge base and question banks
Ground answers in your materials, not generic templates.
Personal documents and curated question banks give AskCc searchable context for role-specific explanations, project details, and likely interview angles.
- Create personal knowledge bases for project docs, notes, and reference files.
- Select official question banks by role, domain, or interview focus.
- Retrieve supporting passages only when the question benefits from context.
Knowledge base and question banks
▷ Start interviewPersonal documents and curated question banks give AskCc searchable context for role-specific explanations, project details, and likely interview angles.
▤ Knowledge base and question banks
ConfirmPersonal knowledge base
Official question banks
Retrieve supporting passages only when the question benefits from context.
Selected 3 / 5
Screenshot Problem Solver
Capture written tests, coding prompts, and case questions from the screen.
For exam-style moments, AskCc can analyze a screenshot, identify the task type, and produce a solution path for coding, multiple choice, and written-response questions.
- Trigger a screenshot flow from the overlay instead of copying the prompt by hand.
- Classify coding, choice, and short-answer tasks before solving.
- Return code, reasoning steps, option analysis, or answer structure according to the question type.
124. Binary Tree Maximum Path Sum
HardGiven a binary tree, a path may start and end at any node, but it must follow parent-child links.
Return the maximum sum of values on such a path. The path contains at least one node and values may be negative.
Input: root = [-10,9,20,null,null,15,7]
Output: 42
Path: 15 → 20 → 7
1. -1000 <= Node.val <= 1000
2. 1 <= nodes <= 3 * 10^4
3. The path does not have to pass the root
1class Solution:
2 def maxPathSum(self, root):
3 best = -10**18
4 def gain(node):
5 nonlocal best
6 if not node:
7 return 0
8 left = max(gain(node.left), 0)
9 right = max(gain(node.right), 0)
10 best = max(best, node.val + left + right)
11 return node.val + max(left, right)
12 gain(root)
13 return best
◇ Screenshot solver
def gain(node):
if not node: return 0
left = max(gain(node.left), 0)
right = max(gain(node.right), 0)
best = max(best, node.val + left + right)
return node.val + max(left, right)
Feature FAQ
The 4 questions candidates ask before using AskCc live.
What should I prepare before the interview? +
AskCc keeps your resume, JD, language preferences, personal knowledge base, official question banks, and approved project skill cards in one preparation workspace. When the interview starts, the assistant works from that stable snapshot instead of searching scattered notes.
How does AskCc answer during a live interview? +
The desktop app receives interviewer audio. When you press Ask in the floating overlay, AskCc sends the latest question, conversation history, resume/JD, knowledge base, and skill cards to the interview agent, then returns short lines you can say out loud.
What kinds of problems does screenshot solving handle? +
Press Shot in the overlay and AskCc captures the current screen for the exam agent. It first classifies the prompt as coding, choice, or written response, then returns the approach, complexity, option analysis, or code skeleton.
Will it invent experience or get in the way of a call? +
No. AskCc is designed to reuse the materials you provide and approve, not fabricate facts. The overlay supports hide, click-through, and content protection so it causes less friction during screen sharing and meeting controls.
AskCc is an AI interview assistant for campus recruiting and experienced hires, helping candidates prepare and respond in real time across tech, finance, and other industries.
Upload your resume and JD, generate focused preparation materials, and get real-time support for interviews, written tests, and screenshot-based questions in one desktop app.
Download desktop app