Everything Talqo does for your interviews.
Four AI modes, real-time transcription, system design diagrams, memory-driven STAR answers, and solo practice - all running locally on your Mac.
AI Interview Modes
Screenshot. Analyze. Solve.
Press Cmd+1 to capture your screen. Talqo analyzes the coding problem, whiteboard diagram, or system design prompt with vision - and delivers a structured solution in seconds.
> Analyzing screenshot...
The problem asks for the maximum subarray sum.
Use Kadane's algorithm - O(n) time, O(1) space:
def max_subarray(nums):
current = best = nums[0]
for n in nums[1:]:
current = max(n, current + n)
best = max(best, current)
return best
Full conversation context.
The entire transcript feeds into the AI. Talqo knows what the interviewer asked, what you said, and what was discussed 20 minutes ago. Responses reference the actual conversation, not generic templates.
> Based on your transcript + memory files
Situation: At my previous company, our payment
processing service was hitting 3s p99 latency during
peak hours - directly impacting checkout conversion.
Task: I was responsible for redesigning the service
to handle 10x traffic within the existing infra budget.
Action: I introduced a write-behind cache with Redis,
batched database writes, and moved to async processing.
Result: p99 dropped to 180ms. Checkout conversion
increased 12% in the following quarter.
The questions they don't expect.
Generate insightful follow-up questions based on the full conversation context. Show the interviewer you were listening - with questions that prove deep understanding of their architecture, constraints, and trade-offs.
> 3 follow-up questions based on transcript
1. You mentioned the migration from monolith to
microservices took 18 months - what was the
strategy for handling shared state during the
transition period?
2. Given the team uses event sourcing for the order
pipeline, how do you handle schema evolution
without breaking downstream consumers?
3. You said the on-call rotation covers 40 services -
what does the runbook structure look like, and how
do you keep them current as services evolve?
Ask anything. Full context included.
Type any question or instruction. Talqo sends it with the full transcript, memory files, and session context. Use it to explore tangents, get explanations, or prepare for curveball questions on the fly.
> You: "Explain the CAP theorem trade-offs for their
architecture - they use Cassandra + Redis"
Their stack suggests AP over CP:
Cassandra: partition-tolerant, eventually consistent.
Redis: in-memory, single-leader replication.
Key trade-off to mention:
They likely accept stale reads during partitions
(AP) but use Redis for hot-path consistency where
strong reads matter (e.g., session state).
Good follow-up: "How do you handle the consistency
gap between Cassandra and Redis during failover?"
The Only AI Copilot That Generates System Design Diagrams
When the interviewer says "design WhatsApp," every other copilot gives you a wall of text. Talqo generates architecture diagrams you can actually present.
Generate From Scratch
Interviewer asks 'design WhatsApp.' Talqo detects the system design question from the transcript and generates a complete architecture diagram with components, data flow, and storage layers.
System architecture showing L4 Load Balancer, Chat Server, DynamoDB, Redis Pub/Sub, Blob Storage S3
Detect and Augment
You're 20 minutes in. The interviewer has a whiteboard. You press Cmd+1 to screenshot it. Talqo identifies every existing component and proposes additions - rendered in a different color so you know exactly what's new.
System architecture showing Chat Server, Database
System architecture showing L4 Load Balancer, Chat Server, DynamoDB, Redis Pub/Sub, Blob Storage
Evolving Diagrams
As the conversation progresses, Talqo evolves the diagram. Ask about 10x traffic and Talqo proposes sharding, read replicas, CDN cache invalidation - each in a third color so the progression is visible.
System architecture showing L4 Load Balancer, Chat Server, DynamoDB, Redis Pub/Sub, Blob Storage, LastDisconnect Table, Chat Server x N
Memory Files: Your Prep, Your Words, Verbatim
Every other copilot generates generic AI answers. Talqo recalls the exact stories you prepared - your words, your numbers, your outcomes - and delivers them character for character when they match. All memory files stay on your Mac for complete privacy.
Before each interview, attach a folder of preparation files. LP stories with pre-written STAR answers. System design notes. Company research. The interviewer's LinkedIn summary. Salary negotiation boundaries. Coding patterns you've practiced.
Talqo loads every file into the AI context at session start. When the interviewer asks a question and a memory file matches, Talqo outputs your prepared answer EXACTLY as you wrote it. No summarizing. No AI "improvements." No restructuring.
You spent hours crafting those answers. Talqo delivers them.
[AI Response] You could talk about a time when you disagreed with a manager about a technical decision. Consider using the STAR method: - Situation: Describe the context - Task: What was your role - Action: What steps did you take - Result: What was the outcome Try to include specific metrics...
[Memory Match: lp-stories.md > "Disagree and Commit"] At my previous company, I was building the spam classification pipeline and the team lead wanted to use a single monolithic model. I disagreed - I'd benchmarked multi-agent decomposition against the monolith on our 200-question evaluation suite and the multi-agent approach hit 98% accuracy vs 84%. I presented the data in a 15-minute technical review. The lead initially pushed back on operational complexity. I proposed a 2-week trial with rollback criteria... Result: Multi-agent system shipped to production, processing 2M+ documents/day at 98% accuracy.
The left is what every AI copilot gives you. The right is YOUR story, YOUR numbers, delivered the moment the question matches.
Talqo matches questions to your stories by content relevance, not by labels. A story tagged "Ownership" about building an evaluation suite is also a "Dive Deep" story and a "Highest Standards" story. Talqo reads every story in your memory bank, scores them all against the live question, and surfaces the best match - regardless of what section header it sits under.
Solo Practice Mode: Test Before the Real Interview
Solo Mode runs a full Talqo session with no audio, no recording, and no one on the other end. Practice with your memory files, test your presets, and see exactly what the AI will say - before the real interview. Full privacy, nothing leaves your machine.
System Design Interview Coaching, the Way Staff Engineers Do It
Other copilots give you a wall of text. Talqo walks you through it step by step.
SCOPE IT
3 requirements above the line. 2 below. Check in with your interviewer.
NAME IT
Core entities first. No fields yet - just what exists in the system.
WIRE IT
One API endpoint per requirement. Simple to start, evolve as you go.
BUILD IT
Add components one requirement at a time. Walk through each request flow.
STRESS IT
Deep dives with a Bad, Good, Great ladder. Name the trade-offs out loud.
DIAGRAM IT
Your final architecture, updated live. Not a brain dump - a story.
The same structured framework used by staff engineers at top companies. Built into every Talqo session.
Coding Interview AI Assistance, Optimized
Talqo leads with the best solution. Always.
CLARIFY
Restate the problem. Ask about constraints, edge cases, expected output.
APPROACH
Name the optimal solution and its complexity. Offer a simpler fallback.
CODE
Clean code in whatever language your interviewer expects. Detected from your screen.
TRACE
Dry-run with a small input. Show state at each step.
COMPLEXITY
Time and space analysis with one-line justification.
EDGE CASES
Confirm the code handles empty input, duplicates, overflow.
FOLLOW-UP
Anticipate the interviewer's next question before they ask it.
Optimal solution first. Simpler alternative if the interviewer wants it. Never starts with brute force.
AI Interview Copilot FAQ
Talqo is a native macOS app that acts as a real-time AI copilot during interviews and meetings. It captures audio from your microphone and system, transcribes it live, and provides on-demand AI assistance through four modes: screenshot analysis, suggested responses, follow-up questions, and freeform queries.
No. Talqo has zero backend servers. Audio, transcripts, and session data stay on your Mac in ~/Documents/Talqo. The only outbound calls are to Anthropic (AI, using your own API key) and optionally AssemblyAI (cloud transcription). No analytics, no telemetry, no accounts.
Talqo itself is free. You bring your own Anthropic API key and pay Anthropic directly for usage - typically $1-2 per interview session. There are no subscriptions, no monthly fees, and no hidden costs.
No. Talqo uses macOS native window.sharingType = .none, which makes the overlay completely invisible to Zoom, Google Meet, Microsoft Teams, and any other screen sharing app. This is an OS-level feature, not a workaround.
Talqo supports coding interviews (with screenshot-based problem analysis), system design rounds (with architecture diagram generation), behavioral interviews (with STAR-format answers matched from your prep files), and general meetings (with contradiction detection and decision logging).
Talqo captures two separate audio streams: your microphone (you) and system audio (them). Each stream is transcribed independently via your choice of provider - local on-device STT or AssemblyAI cloud STT. Speaker identity is guaranteed by stream origin, not AI diarization.
Talqo supports multiple providers - Anthropic, OpenAI, and local servers like Ollama and LM Studio. You can assign a different provider and model to each AI mode. Route heavy analysis to Claude or GPT-4o, and fast transcript-based responses to a local model running on your Mac - instant, free, nothing leaves your machine. You control the split.
Talqo is macOS only (macOS 15+). It uses native Apple frameworks (ScreenCaptureKit, AVAudioEngine, SwiftUI) that have no cross-platform equivalent. This native approach gives lower memory usage and better OS integration than Electron-based alternatives.
Memory files are markdown documents you attach to a session - your STAR stories, job descriptions, system design notes, company research. Talqo includes them in the AI context so responses reference your actual preparation, not generic templates.
Yes. Solo Mode runs a full Talqo session with no audio capture, no recording, and no one on the other end. Practice with your memory files, test your presets, and see exactly what the AI will say before the real interview.